Sensitivity to Social Contingency in Adults with High-Functioning Autism during Computer-Mediated Embodied Interaction.

PubMed

Zapata-Fonseca, Leonardo; Froese, Tom; Schilbach, Leonhard; Vogeley, Kai; Timmermans, Bert

2018-02-08

Autism Spectrum Disorder (ASD) can be understood as a social interaction disorder. This makes the emerging "second-person approach" to social cognition a more promising framework for studying ASD than classical approaches focusing on mindreading capacities in detached, observer-based arrangements. According to the second-person approach, embodied, perceptual, and embedded or interactive capabilities are also required for understanding others, and these are hypothesized to be compromised in ASD. We therefore recorded the dynamics of real-time sensorimotor interaction in pairs of control participants and participants with High-Functioning Autism (HFA), using the minimalistic human-computer interface paradigm known as "perceptual crossing" (PC). We investigated whether HFA is associated with impaired detection of social contingency, i.e., a reduced sensitivity to the other's responsiveness to one's own behavior. Surprisingly, our analysis reveals that, at least under the conditions of this highly simplified, computer-mediated, embodied form of social interaction, people with HFA perform equally well as controls. This finding supports the increasing use of virtual reality interfaces for helping people with ASD to better compensate for their social disabilities. Further dynamical analyses are necessary for a better understanding of the mechanisms that are leading to the somewhat surprising results here obtained.

Simple deterministic models and applications. Comment on "Coupled disease-behavior dynamics on complex networks: A review" by Z. Wang et al.

NASA Astrophysics Data System (ADS)

Yang, Hyun Mo

2015-12-01

Currently, discrete modellings are largely accepted due to the access to computers with huge storage capacity and high performance processors and easy implementation of algorithms, allowing to develop and simulate increasingly sophisticated models. Wang et al. [7] present a review of dynamics in complex networks, focusing on the interaction between disease dynamics and human behavioral and social dynamics. By doing an extensive review regarding to the human behavior responding to disease dynamics, the authors briefly describe the complex dynamics found in the literature: well-mixed populations networks, where spatial structure can be neglected, and other networks considering heterogeneity on spatially distributed populations. As controlling mechanisms are implemented, such as social distancing due 'social contagion', quarantine, non-pharmaceutical interventions and vaccination, adaptive behavior can occur in human population, which can be easily taken into account in the dynamics formulated by networked populations.

Human systems dynamics: Toward a computational model

NASA Astrophysics Data System (ADS)

Eoyang, Glenda H.

2012-09-01

A robust and reliable computational model of complex human systems dynamics could support advancements in theory and practice for social systems at all levels, from intrapersonal experience to global politics and economics. Models of human interactions have evolved from traditional, Newtonian systems assumptions, which served a variety of practical and theoretical needs of the past. Another class of models has been inspired and informed by models and methods from nonlinear dynamics, chaos, and complexity science. None of the existing models, however, is able to represent the open, high dimension, and nonlinear self-organizing dynamics of social systems. An effective model will represent interactions at multiple levels to generate emergent patterns of social and political life of individuals and groups. Existing models and modeling methods are considered and assessed against characteristic pattern-forming processes in observed and experienced phenomena of human systems. A conceptual model, CDE Model, based on the conditions for self-organizing in human systems, is explored as an alternative to existing models and methods. While the new model overcomes the limitations of previous models, it also provides an explanatory base and foundation for prospective analysis to inform real-time meaning making and action taking in response to complex conditions in the real world. An invitation is extended to readers to engage in developing a computational model that incorporates the assumptions, meta-variables, and relationships of this open, high dimension, and nonlinear conceptual model of the complex dynamics of human systems.

Analyzing the Dynamics of Communication in Online Social Networks

NASA Astrophysics Data System (ADS)

de Choudhury, Munmun; Sundaram, Hari; John, Ajita; Seligmann, Doree Duncan

This chapter deals with the analysis of interpersonal communication dynamics in online social networks and social media. Communication is central to the evolution of social systems. Today, the different online social sites feature variegated interactional affordances, ranging from blogging, micro-blogging, sharing media elements (i.e., image, video) as well as a rich set of social actions such as tagging, voting, commenting and so on. Consequently, these communication tools have begun to redefine the ways in which we exchange information or concepts, and how the media channels impact our online interactional behavior. Our central hypothesis is that such communication dynamics between individuals manifest themselves via two key aspects: the information or concept that is the content of communication, and the channel i.e., the media via which communication takes place. We present computational models and discuss large-scale quantitative observational studies for both these organizing ideas. First, we develop a computational framework to determine the "interestingness" property of conversations cented around rich media. Second, we present user models of diffusion of social actions and study the impact of homophily on the diffusion process. The outcome of this research is twofold. First, extensive empirical studies on datasets from YouTube have indicated that on rich media sites, the conversations that are deemed "interesting" appear to have consequential impact on the properties of the social network they are associated with: in terms of degree of participation of the individuals in future conversations, thematic diffusion as well as emergent cohesiveness in activity among the concerned participants in the network. Second, observational and computational studies on large social media datasets such as Twitter have indicated that diffusion of social actions in a network can be indicative of future information cascades. Besides, given a topic, these cascades are often a function of attribute homophily existent among the participants. We believe that this chapter can make significant contribution into a better understanding of how we communicate online and how it is redefining our collective sociological behavior.

Will a category cue attract you? Motor output reveals dynamic competition across person construal.

PubMed

Freeman, Jonathan B; Ambady, Nalini; Rule, Nicholas O; Johnson, Kerri L

2008-11-01

People use social categories to perceive others, extracting category cues to glean membership. Growing evidence for continuous dynamics in real-time cognition suggests, contrary to prevailing social psychological accounts, that person construal may involve dynamic competition between simultaneously active representations. To test this, the authors examined social categorization in real-time by streaming the x, y coordinates of hand movements as participants categorized typical and atypical faces by sex. Though judgments of atypical targets were largely accurate, online motor output exhibited a continuous spatial attraction toward the opposite sex category, indicating dynamic competition between multiple social category alternatives. The authors offer a dynamic continuity account of social categorization and provide converging evidence across categorizations of real male and female faces (containing a typical or an atypical sex-specifying cue) and categorizations of computer-generated male and female faces (with subtly morphed sex-typical or sex-atypical features). In 3 studies, online motor output revealed continuous dynamics underlying person construal, in which multiple simultaneously and partially active category representations gradually cascade into social categorical judgments. Such evidence is challenging for discrete stage-based accounts. (c) 2008 APA, all rights reserved

Identifying and tracking dynamic processes in social networks

NASA Astrophysics Data System (ADS)

Chung, Wayne; Savell, Robert; Schütt, Jan-Peter; Cybenko, George

2006-05-01

The detection and tracking of embedded malicious subnets in an active social network can be computationally daunting due to the quantity of transactional data generated in the natural interaction of large numbers of actors comprising a network. In addition, detection of illicit behavior may be further complicated by evasive strategies designed to camouflage the activities of the covert subnet. In this work, we move beyond traditional static methods of social network analysis to develop a set of dynamic process models which encode various modes of behavior in active social networks. These models will serve as the basis for a new application of the Process Query System (PQS) to the identification and tracking of covert dynamic processes in social networks. We present a preliminary result from application of our technique in a real-world data stream-- the Enron email corpus.

Modelling the spread of innovation in wild birds.

PubMed

Shultz, Thomas R; Montrey, Marcel; Aplin, Lucy M

2017-06-01

We apply three plausible algorithms in agent-based computer simulations to recent experiments on social learning in wild birds. Although some of the phenomena are simulated by all three learning algorithms, several manifestations of social conformity bias are simulated by only the approximate majority (AM) algorithm, which has roots in chemistry, molecular biology and theoretical computer science. The simulations generate testable predictions and provide several explanatory insights into the diffusion of innovation through a population. The AM algorithm's success raises the possibility of its usefulness in studying group dynamics more generally, in several different scientific domains. Our differential-equation model matches simulation results and provides mathematical insights into the dynamics of these algorithms. © 2017 The Author(s).

Neural dynamics of social tie formation in economic decision-making.

PubMed

Bault, Nadège; Pelloux, Benjamin; Fahrenfort, Johannes J; Ridderinkhof, K Richard; van Winden, Frans

2015-06-01

The disposition for prosocial conduct, which contributes to cooperation as arising during social interaction, requires cortical network dynamics responsive to the development of social ties, or care about the interests of specific interaction partners. Here, we formulate a dynamic computational model that accurately predicted how tie formation, driven by the interaction history, influences decisions to contribute in a public good game. We used model-driven functional MRI to test the hypothesis that brain regions key to social interactions keep track of dynamics in tie strength. Activation in the medial prefrontal cortex (mPFC) and posterior cingulate cortex tracked the individual's public good contributions. Activation in the bilateral posterior superior temporal sulcus (pSTS), and temporo-parietal junction was modulated parametrically by the dynamically developing social tie-as estimated by our model-supporting a role of these regions in social tie formation. Activity in these two regions further reflected inter-individual differences in tie persistence and sensitivity to behavior of the interaction partner. Functional connectivity between pSTS and mPFC activations indicated that the representation of social ties is integrated in the decision process. These data reveal the brain mechanisms underlying the integration of interaction dynamics into a social tie representation which in turn influenced the individual's prosocial decisions. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Trends in Social Science: The Impact of Computational and Simulative Models

NASA Astrophysics Data System (ADS)

Conte, Rosaria; Paolucci, Mario; Cecconi, Federico

This paper discusses current progress in the computational social sciences. Specifically, it examines the following questions: Are the computational social sciences exhibiting positive or negative developments? What are the roles of agent-based models and simulation (ABM), network analysis, and other "computational" methods within this dynamic? (Conte, The necessity of intelligent agents in social simulation, Advances in Complex Systems, 3(01n04), 19-38, 2000; Conte 2010; Macy, Annual Review of Sociology, 143-166, 2002). Are there objective indicators of scientific growth that can be applied to different scientific areas, allowing for comparison among them? In this paper, some answers to these questions are presented and discussed. In particular, comparisons among different disciplines in the social and computational sciences are shown, taking into account their respective growth trends in the number of publication citations over the last few decades (culled from Google Scholar). After a short discussion of the methodology adopted, results of keyword-based queries are presented, unveiling some unexpected local impacts of simulation on the takeoff of traditionally poorly productive disciplines.

Dynamics of organizational culture: Individual beliefs vs. social conformity.

PubMed

Ellinas, Christos; Allan, Neil; Johansson, Anders

2017-01-01

The complex nature of organizational culture challenges our ability to infer its underlying dynamics from observational studies. Recent computational studies have adopted a distinctly different view, where plausible mechanisms are proposed to describe a wide range of social phenomena, including the onset and evolution of organizational culture. In this spirit, this work introduces an empirically-grounded, agent-based model which relaxes a set of assumptions that describes past work-(a) omittance of an individual's strive for achieving cognitive coherence; (b) limited integration of important contextual factors-by utilizing networks of beliefs and incorporating social rank into the dynamics. As a result, we illustrate that: (i) an organization may appear to be increasingly coherent in terms of its organizational culture, yet be composed of individuals with reduced levels of coherence; (ii) the components of social conformity-peer-pressure and social rank-are influential at different aggregation levels.

Dynamics of organizational culture: Individual beliefs vs. social conformity

PubMed Central

Allan, Neil; Johansson, Anders

2017-01-01

The complex nature of organizational culture challenges our ability to infer its underlying dynamics from observational studies. Recent computational studies have adopted a distinctly different view, where plausible mechanisms are proposed to describe a wide range of social phenomena, including the onset and evolution of organizational culture. In this spirit, this work introduces an empirically-grounded, agent-based model which relaxes a set of assumptions that describes past work–(a) omittance of an individual’s strive for achieving cognitive coherence; (b) limited integration of important contextual factors—by utilizing networks of beliefs and incorporating social rank into the dynamics. As a result, we illustrate that: (i) an organization may appear to be increasingly coherent in terms of its organizational culture, yet be composed of individuals with reduced levels of coherence; (ii) the components of social conformity—peer-pressure and social rank—are influential at different aggregation levels. PMID:28665960

Theoretical and Experimental Investigation of Opinion Dynamics in Small Social Networks

DTIC Science & Technology

2016-07-01

Sciences, Social Informatics and Telecommunications Engineering 2013 96 M. Gabbay described. Section 4 illustrates the application of the methodology...group of cyber terrorists has already gained access to multiple computers. The attack will attempt to disrupt and destroy a large oil refinery; at

Identity in agent-based models : modeling dynamic multiscale social processes.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ozik, J.; Sallach, D. L.; Macal, C. M.

Identity-related issues play central roles in many current events, including those involving factional politics, sectarianism, and tribal conflicts. Two popular models from the computational-social-science (CSS) literature - the Threat Anticipation Program and SharedID models - incorporate notions of identity (individual and collective) and processes of identity formation. A multiscale conceptual framework that extends some ideas presented in these models and draws other capabilities from the broader CSS literature is useful in modeling the formation of political identities. The dynamic, multiscale processes that constitute and transform social identities can be mapped to expressive structures of the framework

Using Social Network Graphs as Visualization Tools to Influence Peer Selection Decision-Making Strategies to Access Information about Complex Socioscientific Issues

ERIC Educational Resources Information Center

Yoon, Susan A.

2011-01-01

This study extends previous research that explores how visualization affordances that computational tools provide and social network analyses that account for individual- and group-level dynamic processes can work in conjunction to improve learning outcomes. The study's main hypothesis is that when social network graphs are used in instruction,…

Dynamic search and working memory in social recall.

PubMed

Hills, Thomas T; Pachur, Thorsten

2012-01-01

What are the mechanisms underlying search in social memory (e.g., remembering the people one knows)? Do the search mechanisms involve dynamic local-to-global transitions similar to semantic search, and are these transitions governed by the general control of attention, associated with working memory span? To find out, we asked participants to recall individuals from their personal social networks and measured each participant's working memory capacity. Additionally, participants provided social-category and contact-frequency information about the recalled individuals as well as information about the social proximity among the recalled individuals. On the basis of these data, we tested various computational models of memory search regarding their ability to account for the patterns in which participants recalled from social memory. Although recall patterns showed clustering based on social categories, models assuming dynamic transitions between representations cued by social proximity and frequency information predicted participants' recall patterns best-no additional explanatory power was gained from social-category information. Moreover, individual differences in the time between transitions were positively correlated with differences in working memory capacity. These results highlight the role of social proximity in structuring social memory and elucidate the role of working memory for maintaining search criteria during search within that structure.

People Power--Computer Games in the Classroom

ERIC Educational Resources Information Center

Hilliard, Ivan

2014-01-01

This article presents a case study in the use of the computer simulation game "People Power," developed by the International Center on Nonviolent Conflict. The principal objective of the activity was to offer students an opportunity to understand the dynamics of social conflicts, in a format not possible in a traditional classroom…

Combining Computational and Social Effort for Collaborative Problem Solving

PubMed Central

Wagy, Mark D.; Bongard, Josh C.

2015-01-01

Rather than replacing human labor, there is growing evidence that networked computers create opportunities for collaborations of people and algorithms to solve problems beyond either of them. In this study, we demonstrate the conditions under which such synergy can arise. We show that, for a design task, three elements are sufficient: humans apply intuitions to the problem, algorithms automatically determine and report back on the quality of designs, and humans observe and innovate on others’ designs to focus creative and computational effort on good designs. This study suggests how such collaborations should be composed for other domains, as well as how social and computational dynamics mutually influence one another during collaborative problem solving. PMID:26544199

The Immuno-Dynamics of Conflict Intervention in Social Systems

PubMed Central

Krakauer, David C.; Page, Karen; Flack, Jessica

2011-01-01

We present statistical evidence and dynamical models for the management of conflict and a division of labor (task specialization) in a primate society. Two broad intervention strategy classes are observed– a dyadic strategy – pacifying interventions, and a triadic strategy –policing interventions. These strategies, their respective degrees of specialization, and their consequences for conflict dynamics can be captured through empirically-grounded mathematical models inspired by immuno-dynamics. The spread of aggression, analogous to the proliferation of pathogens, is an epidemiological problem. We show analytically and computationally that policing is an efficient strategy as it requires only a small proportion of a population to police to reduce conflict contagion. Policing, but not pacifying, is capable of effectively eliminating conflict. These results suggest that despite implementation differences there might be universal features of conflict management mechanisms for reducing contagion-like dynamics that apply across biological and social levels. Our analyses further suggest that it can be profitable to conceive of conflict management strategies at the behavioral level as mechanisms of social immunity. PMID:21887221

The immuno-dynamics of conflict intervention in social systems.

PubMed

Krakauer, David C; Page, Karen; Flack, Jessica

2011-01-01

We present statistical evidence and dynamical models for the management of conflict and a division of labor (task specialization) in a primate society. Two broad intervention strategy classes are observed--a dyadic strategy--pacifying interventions, and a triadic strategy--policing interventions. These strategies, their respective degrees of specialization, and their consequences for conflict dynamics can be captured through empirically-grounded mathematical models inspired by immuno-dynamics. The spread of aggression, analogous to the proliferation of pathogens, is an epidemiological problem. We show analytically and computationally that policing is an efficient strategy as it requires only a small proportion of a population to police to reduce conflict contagion. Policing, but not pacifying, is capable of effectively eliminating conflict. These results suggest that despite implementation differences there might be universal features of conflict management mechanisms for reducing contagion-like dynamics that apply across biological and social levels. Our analyses further suggest that it can be profitable to conceive of conflict management strategies at the behavioral level as mechanisms of social immunity.

Designing Collaborative Learning Environments Mediated by Computer Conferencing: Issues and Challenges in the Asian Socio-Cultural Context.

ERIC Educational Resources Information Center

Gunawardena, Charlotte N.

1998-01-01

Explores issues related to the design of collaborative-learning environments mediated by computer conferencing from the perspective of challenges faced in the sociocultural context of the Indian sub-continent. Examines the impact of online features on social cohesiveness, group dynamics, interaction, communication anxiety, and participation.…

Real-time path planning in dynamic virtual environments using multiagent navigation graphs.

PubMed

Sud, Avneesh; Andersen, Erik; Curtis, Sean; Lin, Ming C; Manocha, Dinesh

2008-01-01

We present a novel approach for efficient path planning and navigation of multiple virtual agents in complex dynamic scenes. We introduce a new data structure, Multi-agent Navigation Graph (MaNG), which is constructed using first- and second-order Voronoi diagrams. The MaNG is used to perform route planning and proximity computations for each agent in real time. Moreover, we use the path information and proximity relationships for local dynamics computation of each agent by extending a social force model [Helbing05]. We compute the MaNG using graphics hardware and present culling techniques to accelerate the computation. We also address undersampling issues and present techniques to improve the accuracy of our algorithm. Our algorithm is used for real-time multi-agent planning in pursuit-evasion, terrain exploration and crowd simulation scenarios consisting of hundreds of moving agents, each with a distinct goal.

A spread willingness computing-based information dissemination model.

PubMed

Huang, Haojing; Cui, Zhiming; Zhang, Shukui

2014-01-01

This paper constructs a kind of spread willingness computing based on information dissemination model for social network. The model takes into account the impact of node degree and dissemination mechanism, combined with the complex network theory and dynamics of infectious diseases, and further establishes the dynamical evolution equations. Equations characterize the evolutionary relationship between different types of nodes with time. The spread willingness computing contains three factors which have impact on user's spread behavior: strength of the relationship between the nodes, views identity, and frequency of contact. Simulation results show that different degrees of nodes show the same trend in the network, and even if the degree of node is very small, there is likelihood of a large area of information dissemination. The weaker the relationship between nodes, the higher probability of views selection and the higher the frequency of contact with information so that information spreads rapidly and leads to a wide range of dissemination. As the dissemination probability and immune probability change, the speed of information dissemination is also changing accordingly. The studies meet social networking features and can help to master the behavior of users and understand and analyze characteristics of information dissemination in social network.

A Spread Willingness Computing-Based Information Dissemination Model

PubMed Central

Cui, Zhiming; Zhang, Shukui

2014-01-01

This paper constructs a kind of spread willingness computing based on information dissemination model for social network. The model takes into account the impact of node degree and dissemination mechanism, combined with the complex network theory and dynamics of infectious diseases, and further establishes the dynamical evolution equations. Equations characterize the evolutionary relationship between different types of nodes with time. The spread willingness computing contains three factors which have impact on user's spread behavior: strength of the relationship between the nodes, views identity, and frequency of contact. Simulation results show that different degrees of nodes show the same trend in the network, and even if the degree of node is very small, there is likelihood of a large area of information dissemination. The weaker the relationship between nodes, the higher probability of views selection and the higher the frequency of contact with information so that information spreads rapidly and leads to a wide range of dissemination. As the dissemination probability and immune probability change, the speed of information dissemination is also changing accordingly. The studies meet social networking features and can help to master the behavior of users and understand and analyze characteristics of information dissemination in social network. PMID:25110738

Dynamic social community detection and its applications.

PubMed

Nguyen, Nam P; Dinh, Thang N; Shen, Yilin; Thai, My T

2014-01-01

Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods.

Dynamic Social Community Detection and Its Applications

PubMed Central

Nguyen, Nam P.; Dinh, Thang N.; Shen, Yilin; Thai, My T.

2014-01-01

Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods. PMID:24722164

The Impact of Time Delay on the Content of Discussions at a Computer-Mediated Conference

NASA Astrophysics Data System (ADS)

Huntley, Byron C.; Thatcher, Andrew

2008-11-01

This study investigates the relationship between the content of computer-mediated discussions and the time delay between online postings. The study aims to broaden understanding of the dynamics of computer-mediated discussion regarding the time delay and the actual content of computer-mediated discussions (knowledge construction, social aspects, amount of words and number of postings) which has barely been researched. The computer-mediated discussions of the CybErg 2005 virtual conference served as the sample for this study. The Interaction Analysis Model [1] was utilized to analyze the level of knowledge construction in the content of the computer-mediated discussions. Correlations have been computed for all combinations of the variables. The results demonstrate that knowledge construction, social aspects and amount of words generated within postings were independent of, and not affected by, the time delay between the postings and the posting from which the reply was formulated. When greater numbers of words were utilized within postings, this was typically associated with a greater level of knowledge construction. Social aspects in the discussion were found to neither advantage nor disadvantage the overall effectiveness of the computer-mediated discussion.

The ultimatum game: Discrete vs. continuous offers

NASA Astrophysics Data System (ADS)

Dishon-Berkovits, Miriam; Berkovits, Richard

2014-09-01

In many experimental setups in social-sciences, psychology and economy the subjects are requested to accept or dispense monetary compensation which is usually given in discrete units. Using computer and mathematical modeling we show that in the framework of studying the dynamics of acceptance of proposals in the ultimatum game, the long time dynamics of acceptance of offers in the game are completely different for discrete vs. continuous offers. For discrete values the dynamics follow an exponential behavior. However, for continuous offers the dynamics are described by a power-law. This is shown using an agent based computer simulation as well as by utilizing an analytical solution of a mean-field equation describing the model. These findings have implications to the design and interpretation of socio-economical experiments beyond the ultimatum game.

Neural and computational processes underlying dynamic changes in self-esteem

PubMed Central

Rutledge, Robb B; Moutoussis, Michael; Dolan, Raymond J

2017-01-01

Self-esteem is shaped by the appraisals we receive from others. Here, we characterize neural and computational mechanisms underlying this form of social influence. We introduce a computational model that captures fluctuations in self-esteem engendered by prediction errors that quantify the difference between expected and received social feedback. Using functional MRI, we show these social prediction errors correlate with activity in ventral striatum/subgenual anterior cingulate cortex, while updates in self-esteem resulting from these errors co-varied with activity in ventromedial prefrontal cortex (vmPFC). We linked computational parameters to psychiatric symptoms using canonical correlation analysis to identify an ‘interpersonal vulnerability’ dimension. Vulnerability modulated the expression of prediction error responses in anterior insula and insula-vmPFC connectivity during self-esteem updates. Our findings indicate that updating of self-evaluative beliefs relies on learning mechanisms akin to those used in learning about others. Enhanced insula-vmPFC connectivity during updating of those beliefs may represent a marker for psychiatric vulnerability. PMID:29061228

Neural and computational processes underlying dynamic changes in self-esteem.

PubMed

Will, Geert-Jan; Rutledge, Robb B; Moutoussis, Michael; Dolan, Raymond J

2017-10-24

Self-esteem is shaped by the appraisals we receive from others. Here, we characterize neural and computational mechanisms underlying this form of social influence. We introduce a computational model that captures fluctuations in self-esteem engendered by prediction errors that quantify the difference between expected and received social feedback. Using functional MRI, we show these social prediction errors correlate with activity in ventral striatum/subgenual anterior cingulate cortex, while updates in self-esteem resulting from these errors co-varied with activity in ventromedial prefrontal cortex (vmPFC). We linked computational parameters to psychiatric symptoms using canonical correlation analysis to identify an 'interpersonal vulnerability' dimension. Vulnerability modulated the expression of prediction error responses in anterior insula and insula-vmPFC connectivity during self-esteem updates. Our findings indicate that updating of self-evaluative beliefs relies on learning mechanisms akin to those used in learning about others. Enhanced insula-vmPFC connectivity during updating of those beliefs may represent a marker for psychiatric vulnerability.

Bipartite graphs as models of population structures in evolutionary multiplayer games.

PubMed

Peña, Jorge; Rochat, Yannick

2012-01-01

By combining evolutionary game theory and graph theory, "games on graphs" study the evolutionary dynamics of frequency-dependent selection in population structures modeled as geographical or social networks. Networks are usually represented by means of unipartite graphs, and social interactions by two-person games such as the famous prisoner's dilemma. Unipartite graphs have also been used for modeling interactions going beyond pairwise interactions. In this paper, we argue that bipartite graphs are a better alternative to unipartite graphs for describing population structures in evolutionary multiplayer games. To illustrate this point, we make use of bipartite graphs to investigate, by means of computer simulations, the evolution of cooperation under the conventional and the distributed N-person prisoner's dilemma. We show that several implicit assumptions arising from the standard approach based on unipartite graphs (such as the definition of replacement neighborhoods, the intertwining of individual and group diversity, and the large overlap of interaction neighborhoods) can have a large impact on the resulting evolutionary dynamics. Our work provides a clear example of the importance of construction procedures in games on graphs, of the suitability of bigraphs and hypergraphs for computational modeling, and of the importance of concepts from social network analysis such as centrality, centralization and bipartite clustering for the understanding of dynamical processes occurring on networked population structures.

P-HS-SFM: a parallel harmony search algorithm for the reproduction of experimental data in the continuous microscopic crowd dynamic models

NASA Astrophysics Data System (ADS)

Jaber, Khalid Mohammad; Alia, Osama Moh'd.; Shuaib, Mohammed Mahmod

2018-03-01

Finding the optimal parameters that can reproduce experimental data (such as the velocity-density relation and the specific flow rate) is a very important component of the validation and calibration of microscopic crowd dynamic models. Heavy computational demand during parameter search is a known limitation that exists in a previously developed model known as the Harmony Search-Based Social Force Model (HS-SFM). In this paper, a parallel-based mechanism is proposed to reduce the computational time and memory resource utilisation required to find these parameters. More specifically, two MATLAB-based multicore techniques (parfor and create independent jobs) using shared memory are developed by taking advantage of the multithreading capabilities of parallel computing, resulting in a new framework called the Parallel Harmony Search-Based Social Force Model (P-HS-SFM). The experimental results show that the parfor-based P-HS-SFM achieved a better computational time of about 26 h, an efficiency improvement of ? 54% and a speedup factor of 2.196 times in comparison with the HS-SFM sequential processor. The performance of the P-HS-SFM using the create independent jobs approach is also comparable to parfor with a computational time of 26.8 h, an efficiency improvement of about 30% and a speedup of 2.137 times.

Why interdisciplinary research enriches the study of crime. Comment on "Statistical physics of crime: A review" by M.R. D'Orsogna and M. Perc

NASA Astrophysics Data System (ADS)

Donnay, Karsten

2015-03-01

The past several years have seen a rapidly growing interest in the use of advanced quantitative methodologies and formalisms adapted from the natural sciences to study a broad range of social phenomena. The research field of computational social science [1,2], for example, uses digital artifacts of human online activity to cast a new light on social dynamics. Similarly, the studies reviewed by D'Orsogna and Perc showcase a diverse set of advanced quantitative techniques to study the dynamics of crime. Methods used range from partial differential equations and self-exciting point processes to agent-based models, evolutionary game theory and network science [3].

Experiences of women with breast cancer: exchanging social support over the CHESS computer network.

PubMed

Shaw, B R; McTavish, F; Hawkins, R; Gustafson, D H; Pingree, S

2000-01-01

Using an existential-phenomenological approach, this paper describes how women with breast cancer experience the giving and receiving of social support in a computer-mediated context. Women viewed their experiences with the computer-mediated support group as an additional and unique source of support in facing their illness. Anonymity within the support group fostered equalized participation and allowed women to communicate in ways that would have been more difficult in a face-to-face context. The asynchronous communication was a frustration to some participants, but some indicated that the format allowed for more thoughtful interaction. Motivations for seeking social support appeared to be a dynamic process, with a consistent progression from a position of receiving support to that of giving support. The primary benefits women received from participation in the group were communicating with other people who shared similar problems and helping others, which allowed them to change their focus from a preoccupation with their own sickness to thinking of others. Consistent with past research is the finding that women in this study expressed that social support is a multidimensional phenomenon and that their computer-mediated support group provided abundant emotional support, encouragement, and informational support. Excerpts from the phenomenological interviews are used to review and highlight key theoretical concepts from the research literatures on computer-mediated communication, social support, and the psychosocial needs of women with breast cancer.

Mnemonic convergence in social networks: The emergent properties of cognition at a collective level.

PubMed

Coman, Alin; Momennejad, Ida; Drach, Rae D; Geana, Andra

2016-07-19

The development of shared memories, beliefs, and norms is a fundamental characteristic of human communities. These emergent outcomes are thought to occur owing to a dynamic system of information sharing and memory updating, which fundamentally depends on communication. Here we report results on the formation of collective memories in laboratory-created communities. We manipulated conversational network structure in a series of real-time, computer-mediated interactions in fourteen 10-member communities. The results show that mnemonic convergence, measured as the degree of overlap among community members' memories, is influenced by both individual-level information-processing phenomena and by the conversational social network structure created during conversational recall. By studying laboratory-created social networks, we show how large-scale social phenomena (i.e., collective memory) can emerge out of microlevel local dynamics (i.e., mnemonic reinforcement and suppression effects). The social-interactionist approach proposed herein points to optimal strategies for spreading information in social networks and provides a framework for measuring and forging collective memories in communities of individuals.

Opinion formation in time-varying social networks: The case of the naming game

NASA Astrophysics Data System (ADS)

Maity, Suman Kalyan; Manoj, T. Venkat; Mukherjee, Animesh

2012-09-01

We study the dynamics of the naming game as an opinion formation model on time-varying social networks. This agent-based model captures the essential features of the agreement dynamics by means of a memory-based negotiation process. Our study focuses on the impact of time-varying properties of the social network of the agents on the naming game dynamics. In particular, we perform a computational exploration of this model using simulations on top of real networks. We investigate the outcomes of the dynamics on two different types of time-varying data: (1) the networks vary on a day-to-day basis and (2) the networks vary within very short intervals of time (20 sec). In the first case, we find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the naming game in these networks maintains clusters of coexisting opinions indefinitely leading to metastability. In the second case, we investigate the evolution of the naming game in perfect synchronization with the time evolution of the underlying social network shedding new light on the traditional emergent properties of the game that differ largely from what has been reported in the existing literature.

Coordination dynamics in a socially situated nervous system

PubMed Central

Coey, Charles A.; Varlet, Manuel; Richardson, Michael J.

2012-01-01

Traditional theories of cognitive science have typically accounted for the organization of human behavior by detailing requisite computational/representational functions and identifying neurological mechanisms that might perform these functions. Put simply, such approaches hold that neural activity causes behavior. This same general framework has been extended to accounts of human social behavior via concepts such as “common-coding” and “co-representation” and much recent neurological research has been devoted to brain structures that might execute these social-cognitive functions. Although these neural processes are unquestionably involved in the organization and control of human social interactions, there is good reason to question whether they should be accorded explanatory primacy. Alternatively, we propose that a full appreciation of the role of neural processes in social interactions requires appropriately situating them in their context of embodied-embedded constraints. To this end, we introduce concepts from dynamical systems theory and review research demonstrating that the organization of human behavior, including social behavior, can be accounted for in terms of self-organizing processes and lawful dynamics of animal-environment systems. Ultimately, we hope that these alternative concepts can complement the recent advances in cognitive neuroscience and thereby provide opportunities to develop a complete and coherent account of human social interaction. PMID:22701413

Social Insects: A Model System for Network Dynamics

NASA Astrophysics Data System (ADS)

Charbonneau, Daniel; Blonder, Benjamin; Dornhaus, Anna

Social insect colonies (ants, bees, wasps, and termites) show sophisticated collective problem-solving in the face of variable constraints. Individuals exchange information and materials such as food. The resulting network structure and dynamics can inform us about the mechanisms by which the insects achieve particular collective behaviors and these can be transposed to man-made and social networks. We discuss how network analysis can answer important questions about social insects, such as how effective task allocation or information flow is realized. We put forward the idea that network analysis methods are under-utilized in social insect research, and that they can provide novel ways to view the complexity of collective behavior, particularly if network dynamics are taken into account. To illustrate this, we present an example of network tasks performed by ant workers, linked by instances of workers switching from one task to another. We show how temporal network analysis can propose and test new hypotheses on mechanisms of task allocation, and how adding temporal elements to static networks can drastically change results. We discuss the benefits of using social insects as models for complex systems in general. There are multiple opportunities emergent technologies and analysis methods in facilitating research on social insect network. The potential for interdisciplinary work could significantly advance diverse fields such as behavioral ecology, computer sciences, and engineering.

System dynamic simulation: A new method in social impact assessment (SIA)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karami, Shobeir, E-mail: shobeirkarami@gmail.com; Karami, Ezatollah, E-mail: ekarami@shirazu.ac.ir; Buys, Laurie, E-mail: l.buys@qut.edu.au

Many complex social questions are difficult to address adequately with conventional methods and techniques, due to the complicated dynamics, and hard to quantify social processes. Despite these difficulties researchers and practitioners have attempted to use conventional methods not only in evaluative modes but also in predictive modes to inform decision making. The effectiveness of SIAs would be increased if they were used to support the project design processes. This requires deliberate use of lessons from retrospective assessments to inform predictive assessments. Social simulations may be a useful tool for developing a predictive SIA method. There have been limited attempts tomore » develop computer simulations that allow social impacts to be explored and understood before implementing development projects. In light of this argument, this paper aims to introduce system dynamic (SD) simulation as a new predictive SIA method in large development projects. We propose the potential value of the SD approach to simulate social impacts of development projects. We use data from the SIA of Gareh-Bygone floodwater spreading project to illustrate the potential of SD simulation in SIA. It was concluded that in comparison to traditional SIA methods SD simulation can integrate quantitative and qualitative inputs from different sources and methods and provides a more effective and dynamic assessment of social impacts for development projects. We recommend future research to investigate the full potential of SD in SIA in comparing different situations and scenarios.« less

Computational Social Science: Exciting Progress and Future Challenges

NASA Astrophysics Data System (ADS)

Watts, Duncan

The past 15 years have witnessed a remarkable increase in both the scale and scope of social and behavioral data available to researchers, leading some to herald the emergence of a new field: ``computational social science.'' Against these exciting developments stands a stubborn fact: that in spite of many thousands of published papers, there has been surprisingly little progress on the ``big'' questions that motivated the field in the first place--questions concerning systemic risk in financial systems, problem solving in complex organizations, and the dynamics of epidemics or social movements, among others. In this talk I highlight some examples of research that would not have been possible just a handful of years ago and that illustrate the promise of CSS. At the same time, they illustrate its limitations. I then conclude with some thoughts on how CSS can bridge the gap between its current state and its potential.

Dynamics of Cooperation in a Task Completion Social Dilemma

PubMed Central

Passino, Kevin M.

2017-01-01

We study the situation where the members of a community have the choice to participate in the completion of a common task. The process of completing the task involves only costs and no benefits to the individuals that participate in this process. However, completing the task results in changes that significantly benefit the community and that exceed the participation efforts. A task completion social dilemma arises when the short-term participation costs dissipate any interest in the community members to contribute to the task completion process and therefore to obtain the benefits that result from completing the task. In this work, we model the task completion problem using a dynamical system that characterizes the participation dynamics in the community and the task completion process. We show how this model naturally allows for the incorporation of several mechanisms that facilitate the emergence of cooperation and that have been studied in previous research on social dilemmas, including communication across a network, and indirect reciprocity through relative reputation. We provide mathematical analyses and computer simulations to study the qualitative properties of the participation dynamics in the community for different scenarios. PMID:28125721

Stochastic Dynamics through Hierarchically Embedded Markov Chains

NASA Astrophysics Data System (ADS)

Vasconcelos, Vítor V.; Santos, Fernando P.; Santos, Francisco C.; Pacheco, Jorge M.

2017-02-01

Studying dynamical phenomena in finite populations often involves Markov processes of significant mathematical and/or computational complexity, which rapidly becomes prohibitive with increasing population size or an increasing number of individual configuration states. Here, we develop a framework that allows us to define a hierarchy of approximations to the stationary distribution of general systems that can be described as discrete Markov processes with time invariant transition probabilities and (possibly) a large number of states. This results in an efficient method for studying social and biological communities in the presence of stochastic effects—such as mutations in evolutionary dynamics and a random exploration of choices in social systems—including situations where the dynamics encompasses the existence of stable polymorphic configurations, thus overcoming the limitations of existing methods. The present formalism is shown to be general in scope, widely applicable, and of relevance to a variety of interdisciplinary problems.

Stochastic Dynamics through Hierarchically Embedded Markov Chains.

PubMed

Vasconcelos, Vítor V; Santos, Fernando P; Santos, Francisco C; Pacheco, Jorge M

2017-02-03

Studying dynamical phenomena in finite populations often involves Markov processes of significant mathematical and/or computational complexity, which rapidly becomes prohibitive with increasing population size or an increasing number of individual configuration states. Here, we develop a framework that allows us to define a hierarchy of approximations to the stationary distribution of general systems that can be described as discrete Markov processes with time invariant transition probabilities and (possibly) a large number of states. This results in an efficient method for studying social and biological communities in the presence of stochastic effects-such as mutations in evolutionary dynamics and a random exploration of choices in social systems-including situations where the dynamics encompasses the existence of stable polymorphic configurations, thus overcoming the limitations of existing methods. The present formalism is shown to be general in scope, widely applicable, and of relevance to a variety of interdisciplinary problems.

Bipartite Graphs as Models of Population Structures in Evolutionary Multiplayer Games

PubMed Central

Peña, Jorge; Rochat, Yannick

2012-01-01

By combining evolutionary game theory and graph theory, “games on graphs” study the evolutionary dynamics of frequency-dependent selection in population structures modeled as geographical or social networks. Networks are usually represented by means of unipartite graphs, and social interactions by two-person games such as the famous prisoner’s dilemma. Unipartite graphs have also been used for modeling interactions going beyond pairwise interactions. In this paper, we argue that bipartite graphs are a better alternative to unipartite graphs for describing population structures in evolutionary multiplayer games. To illustrate this point, we make use of bipartite graphs to investigate, by means of computer simulations, the evolution of cooperation under the conventional and the distributed N-person prisoner’s dilemma. We show that several implicit assumptions arising from the standard approach based on unipartite graphs (such as the definition of replacement neighborhoods, the intertwining of individual and group diversity, and the large overlap of interaction neighborhoods) can have a large impact on the resulting evolutionary dynamics. Our work provides a clear example of the importance of construction procedures in games on graphs, of the suitability of bigraphs and hypergraphs for computational modeling, and of the importance of concepts from social network analysis such as centrality, centralization and bipartite clustering for the understanding of dynamical processes occurring on networked population structures. PMID:22970237

Connectionism, parallel constraint satisfaction processes, and gestalt principles: (re) introducing cognitive dynamics to social psychology.

PubMed

Read, S J; Vanman, E J; Miller, L C

1997-01-01

We argue that recent work in connectionist modeling, in particular the parallel constraint satisfaction processes that are central to many of these models, has great importance for understanding issues of both historical and current concern for social psychologists. We first provide a brief description of connectionist modeling, with particular emphasis on parallel constraint satisfaction processes. Second, we examine the tremendous similarities between parallel constraint satisfaction processes and the Gestalt principles that were the foundation for much of modem social psychology. We propose that parallel constraint satisfaction processes provide a computational implementation of the principles of Gestalt psychology that were central to the work of such seminal social psychologists as Asch, Festinger, Heider, and Lewin. Third, we then describe how parallel constraint satisfaction processes have been applied to three areas that were key to the beginnings of modern social psychology and remain central today: impression formation and causal reasoning, cognitive consistency (balance and cognitive dissonance), and goal-directed behavior. We conclude by discussing implications of parallel constraint satisfaction principles for a number of broader issues in social psychology, such as the dynamics of social thought and the integration of social information within the narrow time frame of social interaction.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Walker, La Tonya Nicole; Malczynski, Leonard A.

DYNAMO is a computer program for building and running 'continuous' simulation models. It was developed by the Industrial Dynamics Group at the Massachusetts Institute of Technology for simulating dynamic feedback models of business, economic, and social systems. The history of the system dynamics method since 1957 includes many classic models built in DYANMO. It was not until the late 1980s that software was built to take advantage of the rise of personal computers and graphical user interfaces that DYNAMO was supplanted. There is much learning and insight to be gained from examining the DYANMO models and their accompanying research papers.more » We believe that it is a worthwhile exercise to convert DYNAMO models to more recent software packages. We have made an attempt to make it easier to turn these models into a more current system dynamics software language, Powersim © Studio produced by Powersim AS 2 of Bergen, Norway. This guide shows how to convert DYNAMO syntax into Studio syntax.« less

Dynamic properties of successful smiles

PubMed Central

Helwig, Nathaniel E.; Sohre, Nick E.; Ruprecht, Mark R.; Guy, Stephen J.; Lyford-Pike, Sofía

2017-01-01

Facial expression of emotion is a foundational aspect of social interaction and nonverbal communication. In this study, we use a computer-animated 3D facial tool to investigate how dynamic properties of a smile are perceived. We created smile animations where we systematically manipulated the smile’s angle, extent, dental show, and dynamic symmetry. Then we asked a diverse sample of 802 participants to rate the smiles in terms of their effectiveness, genuineness, pleasantness, and perceived emotional intent. We define a “successful smile” as one that is rated effective, genuine, and pleasant in the colloquial sense of these words. We found that a successful smile can be expressed via a variety of different spatiotemporal trajectories, involving an intricate balance of mouth angle, smile extent, and dental show combined with dynamic symmetry. These findings have broad applications in a variety of areas, such as facial reanimation surgery, rehabilitation, computer graphics, and psychology. PMID:28658294

Social Dynamics in Web Page through Inter-Agent Interaction

NASA Astrophysics Data System (ADS)

Takeuchi, Yugo; Katagiri, Yasuhiro

Social persuasion abounds in human-human interactions. Attitudes and behaviors of people are invariably influenced by the attitudes and behaviors of other people as well as our social roles/relationships toward them. In the pedagogic scene, the relationship between teacher and learner produces one of the most typical interactions, in which the teacher makes the learner spontaneously study what he/she teaches. This study is an attempt to elucidate the nature and effectiveness of social persuasion in human-computer interaction environments. We focus on the social dynamics of multi-party interactions that involve both human-agent and inter-agent interactions. An experiment is conducted in a virtual web-instruction setting employing two types of agents: conductor agents who accompany and guide each learner throughout his/her learning sessions, and domain-expert agents who provide explanations and instructions for each stage of the instructional materials. In this experiment, subjects are assigned two experimental conditions: the authorized condition, in which an agent respectfully interacts with another agent, and the non-authorized condition, in which an agent carelessly interacts with another agent. The results indicate performance improvements in the authorized condition of inter-agent interactions. An analysis is given from the perspective of the transfer of authority from inter-agent to human-agent interactions based on social conformity. We argue for pedagogic advantages of social dynamics created by multiple animated character agents.

An Online Algorithm for Maximizing Submodular Functions

DTIC Science & Technology

2007-12-20

dynamics of the social network are known. In theory, our online algorithms could be used to adapt a marketing campaign to unknown or time-varying social...An Online Algorithm for Maximizing Submodular Functions Matthew Streeter Daniel Golovin December 20, 2007 CMU-CS-07-171 School of Computer Science...number. 1. REPORT DATE 20 DEC 2007 2. REPORT TYPE 3. DATES COVERED 00-00-2007 to 00-00-2007 4. TITLE AND SUBTITLE An Online Algorithm for

Rethinking Human-Centered Computing: Finding the Customer and Negotiated Interactions at the Airport

NASA Technical Reports Server (NTRS)

Wales, Roxana; O'Neill, John; Mirmalek, Zara

2003-01-01

The breakdown in the air transportation system over the past several years raises an interesting question for researchers: How can we help improve the reliability of airline operations? In offering some answers to this question, we make a statement about Huuman-Centered Computing (HCC). First we offer the definition that HCC is a multi-disciplinary research and design methodology focused on supporting humans as they use technology by including cognitive and social systems, computational tools and the physical environment in the analysis of organizational systems. We suggest that a key element in understanding organizational systems is that there are external cognitive and social systems (customers) as well as internal cognitive and social systems (employees) and that they interact dynamically to impact the organization and its work. The design of human-centered intelligent systems must take this outside-inside dynamic into account. In the past, the design of intelligent systems has focused on supporting the work and improvisation requirements of employees but has often assumed that customer requirements are implicitly satisfied by employee requirements. Taking a customer-centric perspective provides a different lens for understanding this outside-inside dynamic, the work of the organization and the requirements of both customers and employees In this article we will: 1) Demonstrate how the use of ethnographic methods revealed the important outside-inside dynamic in an airline, specifically the consequential relationship between external customer requirements and perspectives and internal organizational processes and perspectives as they came together in a changing environment; 2) Describe how taking a customer centric perspective identifies places where the impact of the outside-inside dynamic is most critical and requires technology that can be adaptive; 3) Define and discuss the place of negotiated interactions in airline operations, identifying how these interactions between customers and airline employees provided new insights into design problems in the airline system; 4) Show how taking a customer-centric perspective influences the HCC design of an airline system and make recommendations for new architectures and intelligent devices that will enable airline systems to adapt flexibly to delay situations, supporting both customers and airline employees.

Learning within Incoherent Structures: The Space of Online Discussion Forums.

ERIC Educational Resources Information Center

Thomas, Matthew J. W.

2002-01-01

Presents results from a study of undergraduate students' learning outcomes and patterns of interaction within an online discussion forum. Topics include social dynamics of computer-mediated communication versus face-to-face communication; cognitive engagement; critical and reflective thinking; and student interaction. (Author/LRW)

Mnemonic convergence in social networks: The emergent properties of cognition at a collective level

PubMed Central

Coman, Alin; Momennejad, Ida; Drach, Rae D.; Geana, Andra

2016-01-01

The development of shared memories, beliefs, and norms is a fundamental characteristic of human communities. These emergent outcomes are thought to occur owing to a dynamic system of information sharing and memory updating, which fundamentally depends on communication. Here we report results on the formation of collective memories in laboratory-created communities. We manipulated conversational network structure in a series of real-time, computer-mediated interactions in fourteen 10-member communities. The results show that mnemonic convergence, measured as the degree of overlap among community members’ memories, is influenced by both individual-level information-processing phenomena and by the conversational social network structure created during conversational recall. By studying laboratory-created social networks, we show how large-scale social phenomena (i.e., collective memory) can emerge out of microlevel local dynamics (i.e., mnemonic reinforcement and suppression effects). The social-interactionist approach proposed herein points to optimal strategies for spreading information in social networks and provides a framework for measuring and forging collective memories in communities of individuals. PMID:27357678

Development of a dynamic computational model of social cognitive theory.

PubMed

Riley, William T; Martin, Cesar A; Rivera, Daniel E; Hekler, Eric B; Adams, Marc A; Buman, Matthew P; Pavel, Misha; King, Abby C

2016-12-01

Social cognitive theory (SCT) is among the most influential theories of behavior change and has been used as the conceptual basis of health behavior interventions for smoking cessation, weight management, and other health behaviors. SCT and other behavior theories were developed primarily to explain differences between individuals, but explanatory theories of within-person behavioral variability are increasingly needed as new technologies allow for intensive longitudinal measures and interventions adapted from these inputs. These within-person explanatory theoretical applications can be modeled as dynamical systems. SCT constructs, such as reciprocal determinism, are inherently dynamical in nature, but SCT has not been modeled as a dynamical system. This paper describes the development of a dynamical system model of SCT using fluid analogies and control systems principles drawn from engineering. Simulations of this model were performed to assess if the model performed as predicted based on theory and empirical studies of SCT. This initial model generates precise and testable quantitative predictions for future intensive longitudinal research. Dynamic modeling approaches provide a rigorous method for advancing health behavior theory development and refinement and for guiding the development of more potent and efficient interventions.

The Fragility of Individual-Based Explanations of Social Hierarchies: A Test Using Animal Pecking Orders

PubMed Central

2016-01-01

The standard approach in accounting for hierarchical differentiation in biology and the social sciences considers a hierarchy as a static distribution of individuals possessing differing amounts of some valued commodity, assumes that the hierarchy is generated by micro-level processes involving individuals, and attempts to reverse engineer the processes that produced the hierarchy. However, sufficient experimental and analytical results are available to evaluate this standard approach in the case of animal dominance hierarchies (pecking orders). Our evaluation using evidence from hierarchy formation in small groups of both hens and cichlid fish reveals significant deficiencies in the three tenets of the standard approach in accounting for the organization of dominance hierarchies. In consequence, we suggest that a new approach is needed to explain the organization of pecking orders and, very possibly, by implication, for other kinds of social hierarchies. We develop an example of such an approach that considers dominance hierarchies to be dynamic networks, uses dynamic sequences of interaction (dynamic network motifs) to explain the organization of dominance hierarchies, and derives these dynamic sequences directly from observation of hierarchy formation. We test this dynamical explanation using computer simulation and find a good fit with actual dynamics of hierarchy formation in small groups of hens. We hypothesize that the same dynamic sequences are used in small groups of many other animal species forming pecking orders, and we discuss the data required to evaluate our hypothesis. Finally, we briefly consider how our dynamic approach may be generalized to other kinds of social hierarchies using the example of the distribution of empty gastropod (snail) shells occupied in populations of hermit crabs. PMID:27410230

Walking Ahead: The Headed Social Force Model.

PubMed

Farina, Francesco; Fontanelli, Daniele; Garulli, Andrea; Giannitrapani, Antonio; Prattichizzo, Domenico

2017-01-01

Human motion models are finding an increasing number of novel applications in many different fields, such as building design, computer graphics and robot motion planning. The Social Force Model is one of the most popular alternatives to describe the motion of pedestrians. By resorting to a physical analogy, individuals are assimilated to point-wise particles subject to social forces which drive their dynamics. Such a model implicitly assumes that humans move isotropically. On the contrary, empirical evidence shows that people do have a preferred direction of motion, walking forward most of the time. Lateral motions are observed only in specific circumstances, such as when navigating in overcrowded environments or avoiding unexpected obstacles. In this paper, the Headed Social Force Model is introduced in order to improve the realism of the trajectories generated by the classical Social Force Model. The key feature of the proposed approach is the inclusion of the pedestrians' heading into the dynamic model used to describe the motion of each individual. The force and torque representing the model inputs are computed as suitable functions of the force terms resulting from the traditional Social Force Model. Moreover, a new force contribution is introduced in order to model the behavior of people walking together as a single group. The proposed model features high versatility, being able to reproduce both the unicycle-like trajectories typical of people moving in open spaces and the point-wise motion patterns occurring in high density scenarios. Extensive numerical simulations show an increased regularity of the resulting trajectories and confirm a general improvement of the model realism.

Why we interact: on the functional role of the striatum in the subjective experience of social interaction.

PubMed

Pfeiffer, Ulrich J; Schilbach, Leonhard; Timmermans, Bert; Kuzmanovic, Bojana; Georgescu, Alexandra L; Bente, Gary; Vogeley, Kai

2014-11-01

There is ample evidence that human primates strive for social contact and experience interactions with conspecifics as intrinsically rewarding. Focusing on gaze behavior as a crucial means of human interaction, this study employed a unique combination of neuroimaging, eye-tracking, and computer-animated virtual agents to assess the neural mechanisms underlying this component of behavior. In the interaction task, participants believed that during each interaction the agent's gaze behavior could either be controlled by another participant or by a computer program. Their task was to indicate whether they experienced a given interaction as an interaction with another human participant or the computer program based on the agent's reaction. Unbeknownst to them, the agent was always controlled by a computer to enable a systematic manipulation of gaze reactions by varying the degree to which the agent engaged in joint attention. This allowed creating a tool to distinguish neural activity underlying the subjective experience of being engaged in social and non-social interaction. In contrast to previous research, this allows measuring neural activity while participants experience active engagement in real-time social interactions. Results demonstrate that gaze-based interactions with a perceived human partner are associated with activity in the ventral striatum, a core component of reward-related neurocircuitry. In contrast, interactions with a computer-driven agent activate attention networks. Comparisons of neural activity during interaction with behaviorally naïve and explicitly cooperative partners demonstrate different temporal dynamics of the reward system and indicate that the mere experience of engagement in social interaction is sufficient to recruit this system. Copyright © 2014 Elsevier Inc. All rights reserved.

Combining Computational Fluid Dynamics and Agent-Based Modeling: A New Approach to Evacuation Planning

PubMed Central

Epstein, Joshua M.; Pankajakshan, Ramesh; Hammond, Ross A.

2011-01-01

We introduce a novel hybrid of two fields—Computational Fluid Dynamics (CFD) and Agent-Based Modeling (ABM)—as a powerful new technique for urban evacuation planning. CFD is a predominant technique for modeling airborne transport of contaminants, while ABM is a powerful approach for modeling social dynamics in populations of adaptive individuals. The hybrid CFD-ABM method is capable of simulating how large, spatially-distributed populations might respond to a physically realistic contaminant plume. We demonstrate the overall feasibility of CFD-ABM evacuation design, using the case of a hypothetical aerosol release in Los Angeles to explore potential effectiveness of various policy regimes. We conclude by arguing that this new approach can be powerfully applied to arbitrary population centers, offering an unprecedented preparedness and catastrophic event response tool. PMID:21687788

The Beneficial Role of Random Strategies in Social and Financial Systems

NASA Astrophysics Data System (ADS)

Biondo, Alessio Emanuele; Pluchino, Alessandro; Rapisarda, Andrea

2013-05-01

In this paper we focus on the beneficial role of random strategies in social sciences by means of simple mathematical and computational models. We briefly review recent results obtained by two of us in previous contributions for the case of the Peter principle and the efficiency of a Parliament. Then, we develop a new application of random strategies to the case of financial trading and discuss in detail our findings about forecasts of markets dynamics.

Vertical Interaction in Open Software Engineering Communities

DTIC Science & Technology

2009-03-01

Program in CASOS (NSF,DGE-9972762), the Office of Naval Research under Dynamic Network Analysis program (N00014-02-1-0973, the Air Force Office of...W91WAW07C0063) for research in the area of dynamic network analysis. Additional support was provided by CASOS - the center for Computational Analysis of Social...methods across the domain. For a given project, de - velopers can choose from dozens of models, tools, platforms, and languages for specification, design

On Social Optima of Non-Cooperative Mean Field Games

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Sen; Zhang, Wei; Zhao, Lin

This paper studies the social optima in noncooperative mean-field games for a large population of agents with heterogeneous stochastic dynamic systems. Each agent seeks to maximize an individual utility functional, and utility functionals of different agents are coupled through a mean field term that depends on the mean of the population states/controls. The paper has the following contributions. First, we derive a set of control strategies for the agents that possess *-Nash equilibrium property, and converge to the mean-field Nash equilibrium as the population size goes to infinity. Second, we study the social optimal in the mean field game. Wemore » derive the conditions, termed the socially optimal conditions, under which the *-Nash equilibrium of the mean field game maximizes the social welfare. Third, a primal-dual algorithm is proposed to compute the *-Nash equilibrium of the mean field game. Since the *-Nash equilibrium of the mean field game is socially optimal, we can compute the equilibrium by solving the social welfare maximization problem, which can be addressed by a decentralized primal-dual algorithm. Numerical simulations are presented to demonstrate the effectiveness of the proposed approach.« less

Dynamical networks of influence in small group discussions.

PubMed

Moussaïd, Mehdi; Noriega Campero, Alejandro; Almaatouq, Abdullah

2018-01-01

In many domains of life, business and management, numerous problems are addressed by small groups of individuals engaged in face-to-face discussions. While research in social psychology has a long history of studying the determinants of small group performances, the internal dynamics that govern a group discussion are not yet well understood. Here, we rely on computational methods based on network analyses and opinion dynamics to describe how individuals influence each other during a group discussion. We consider the situation in which a small group of three individuals engages in a discussion to solve an estimation task. We propose a model describing how group members gradually influence each other and revise their judgments over the course of the discussion. The main component of the model is an influence network-a weighted, directed graph that determines the extent to which individuals influence each other during the discussion. In simulations, we first study the optimal structure of the influence network that yields the best group performances. Then, we implement a social learning process by which individuals adapt to the past performance of their peers, thereby affecting the structure of the influence network in the long run. We explore the mechanisms underlying the emergence of efficient or maladaptive networks and show that the influence network can converge towards the optimal one, but only when individuals exhibit a social discounting bias by downgrading the relative performances of their peers. Finally, we find a late-speaker effect, whereby individuals who speak later in the discussion are perceived more positively in the long run and are thus more influential. The numerous predictions of the model can serve as a basis for future experiments, and this work opens research on small group discussion to computational social sciences.

Dynamical networks of influence in small group discussions

PubMed Central

Noriega Campero, Alejandro; Almaatouq, Abdullah

2018-01-01

In many domains of life, business and management, numerous problems are addressed by small groups of individuals engaged in face-to-face discussions. While research in social psychology has a long history of studying the determinants of small group performances, the internal dynamics that govern a group discussion are not yet well understood. Here, we rely on computational methods based on network analyses and opinion dynamics to describe how individuals influence each other during a group discussion. We consider the situation in which a small group of three individuals engages in a discussion to solve an estimation task. We propose a model describing how group members gradually influence each other and revise their judgments over the course of the discussion. The main component of the model is an influence network—a weighted, directed graph that determines the extent to which individuals influence each other during the discussion. In simulations, we first study the optimal structure of the influence network that yields the best group performances. Then, we implement a social learning process by which individuals adapt to the past performance of their peers, thereby affecting the structure of the influence network in the long run. We explore the mechanisms underlying the emergence of efficient or maladaptive networks and show that the influence network can converge towards the optimal one, but only when individuals exhibit a social discounting bias by downgrading the relative performances of their peers. Finally, we find a late-speaker effect, whereby individuals who speak later in the discussion are perceived more positively in the long run and are thus more influential. The numerous predictions of the model can serve as a basis for future experiments, and this work opens research on small group discussion to computational social sciences. PMID:29338013

Global brain dynamics during social exclusion predict subsequent behavioral conformity

PubMed Central

Wasylyshyn, Nick; Hemenway Falk, Brett; Garcia, Javier O; Cascio, Christopher N; O’Donnell, Matthew Brook; Bingham, C Raymond; Simons-Morton, Bruce; Vettel, Jean M; Falk, Emily B

2018-01-01

Abstract Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (n = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI session and a subsequent driving simulator session in which they drove alone and in the presence of a peer who expressed risk-averse or risk-accepting driving norms. We computed the difference in functional connectivity between social exclusion and social inclusion from each node in the brain to nodes in two brain networks, one previously associated with mentalizing (medial prefrontal cortex, temporoparietal junction, precuneus, temporal poles) and another with social pain (dorsal anterior cingulate cortex, anterior insula). Using predictive modeling, this measure of global connectivity during exclusion predicted the extent of conformity to peer pressure during driving in the subsequent experimental session. These findings extend our understanding of how global neural dynamics guide social behavior, revealing functional network activity that captures individual differences. PMID:29529310

A Strategic Interaction Model of Punishment Favoring Contagion of Honest Behavior

PubMed Central

Cremene, Marcel; Dumitrescu, D.; Cremene, Ligia

2014-01-01

The punishment effect on social behavior is analyzed within the strategic interaction framework of Cellular Automata and computational Evolutionary Game Theory. A new game, called Social Honesty (SH), is proposed. The SH game is analyzed in spatial configurations. Probabilistic punishment is used as a dishonesty deterrence mechanism. In order to capture the intrinsic uncertainty of social environments, payoffs are described as random variables. New dynamics, with a new relation between punishment probability and punishment severity, are revealed. Punishment probability proves to be more important than punishment severity in guiding convergence towards honesty as predominant behavior. This result is confirmed by empirical evidence and reported experiments. Critical values and transition intervals for punishment probability and severity are identified and analyzed. Clusters of honest or dishonest players emerge spontaneously from the very first rounds of interaction and are determinant for the future dynamics and outcomes. PMID:24489917

LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS

PubMed Central

Almquist, Zack W.; Butts, Carter T.

2015-01-01

Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach. PMID:26120218

LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS.

PubMed

Almquist, Zack W; Butts, Carter T

2014-08-01

Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach.

Modeling behavior dynamics using computational psychometrics within virtual worlds.

PubMed

Cipresso, Pietro

2015-01-01

In case of fire in a building, how will people behave in the crowd? The behavior of each individual affects the behavior of others and, conversely, each one behaves considering the crowd as a whole and the individual others. In this article, I propose a three-step method to explore a brand new way to study behavior dynamics. The first step relies on the creation of specific situations with standard techniques (such as mental imagery, text, video, and audio) and an advanced technique [Virtual Reality (VR)] to manipulate experimental settings. The second step concerns the measurement of behavior in one, two, or many individuals focusing on parameters extractions to provide information about the behavior dynamics. Finally, the third step, which uses the parameters collected and measured in the previous two steps in order to simulate possible scenarios to forecast through computational models, understand, and explain behavior dynamics at the social level. An experimental study was also included to demonstrate the three-step method and a possible scenario.

An economic model of friendship and enmity for measuring social balance in networks

NASA Astrophysics Data System (ADS)

Lee, Kyu-Min; Shin, Euncheol; You, Seungil

2017-12-01

We propose a dynamic economic model of networks where agents can be friends or enemies with one another. This is a decentralized relationship model in that agents decide whether to change their relationships so as to minimize their imbalanced triads. In this model, there is a single parameter, which we call social temperature, that captures the degree to which agents care about social balance in their relationships. We show that the global structure of relationship configuration converges to a unique stationary distribution. Using this stationary distribution, we characterize the maximum likelihood estimator of the social temperature parameter. Since the estimator is computationally challenging to calculate from real social network datasets, we provide a simple simulation algorithm and verify its performance with real social network datasets.

Why Do Computer Viruses Survive In The Internet?

NASA Astrophysics Data System (ADS)

Ifti, Margarita; Neumann, Paul

2010-01-01

We use the three-species cyclic competition model (Rock-Paper-Scissors), described by reactions A+B→2B, B+C→2C, C+A→2A, for emulating a computer network with e-mail viruses. Different topologies of the network bring about different dynamics of the epidemics. When the parameters of the network are varied, it is observed that very high clustering coefficients are necessary for a pandemics to happen. The differences between the networks of computer users, e-mail networks, and social networks, as well as their role in determining the nature of epidemics are also discussed.

Nature as a network of morphological infocomputational processes for cognitive agents

NASA Astrophysics Data System (ADS)

Dodig-Crnkovic, Gordana

2017-01-01

This paper presents a view of nature as a network of infocomputational agents organized in a dynamical hierarchy of levels. It provides a framework for unification of currently disparate understandings of natural, formal, technical, behavioral and social phenomena based on information as a structure, differences in one system that cause the differences in another system, and computation as its dynamics, i.e. physical process of morphological change in the informational structure. We address some of the frequent misunderstandings regarding the natural/morphological computational models and their relationships to physical systems, especially cognitive systems such as living beings. Natural morphological infocomputation as a conceptual framework necessitates generalization of models of computation beyond the traditional Turing machine model presenting symbol manipulation, and requires agent-based concurrent resource-sensitive models of computation in order to be able to cover the whole range of phenomena from physics to cognition. The central role of agency, particularly material vs. cognitive agency is highlighted.

Shortfall online: The development of an educational computer game for teaching sustainable engineering to Millennial Generation students

NASA Astrophysics Data System (ADS)

Gennett, Zachary Andrew

Millennial Generation students bring significant learning and teaching challenges to the classroom, because of their unique learning styles, breadth of interests related to social and environmental issues, and intimate experiences with technology. As a result, there has been an increased willingness at many universities to experiment with pedagogical strategies that depart from a traditional "learning by listening" model, and move toward more innovative methods involving active learning through computer games. In particular, current students typically express a strong interest in sustainability in which economic concerns must be weighed relative to environmental and social responsibilities. A game-based setting could prove very effective for fostering an operational understanding of these tradeoffs, and especially the social dimension which remains largely underdeveloped relative to the economic and environmental aspects. Through an examination of the educational potential of computer games, this study hypothesizes that to acquire the skills necessary to manage and understand the complexities of sustainability, Millennial Generation students must be engaged in active learning exercises that present dynamic problems and foster a high level of social interaction. This has led to the development of an educational computer game, entitled Shortfall, which simulates a business milieu for testing alternative paths regarding the principles of sustainability. This study examines the evolution of Shortfall from an educational board game that teaches the principles of environmentally benign manufacturing, to a completely networked computer game, entitled Shortfall Online that teaches the principles of sustainability. A capital-based theory of sustainability is adopted to more accurately convey the tradeoffs and opportunity costs among economic prosperity, environmental preservation, and societal responsibilities. While the economic and environmental aspects of sustainability have received considerable attention in traditional pedagogical approaches, specific focus is provided for the social dimension of sustainability, as it had remained largely underdeveloped. To measure social sustainability and provide students with an understanding of its significance, a prospective metric utilizing a social capital peer-evaluation survey, unique to Shortfall, is developed.

Does the mask govern the mind?: effects of arbitrary gender representation on quantitative task performance in avatar-represented virtual groups.

PubMed

Lee, Jong-Eun Roselyn; Nass, Clifford I; Bailenson, Jeremy N

2014-04-01

Virtual environments employing avatars for self-representation-including the opportunity to represent or misrepresent social categories-raise interesting and intriguing questions as to how one's avatar-based social category shapes social identity dynamics, particularly when stereotypes prevalent in the offline world apply to the social categories visually represented by avatars. The present experiment investigated how social category representation via avatars (i.e., graphical representations of people in computer-mediated environments) affects stereotype-relevant task performance. In particular, building on and extending the Proteus effect model, we explored whether and how stereotype lift (i.e., a performance boost caused by the awareness of a domain-specific negative stereotype associated with outgroup members) occurred in virtual group settings in which avatar-based gender representation was arbitrary. Female and male participants (N=120) were randomly assigned either a female avatar or a male avatar through a process masked as a random drawing. They were then placed in a numerical minority status with respect to virtual gender-as the only virtual female (male) in a computer-mediated triad with two opposite-gendered avatars-and performed a mental arithmetic task either competitively or cooperatively. The data revealed that participants who were arbitrarily represented by a male avatar and competed against two ostensible female avatars showed strongest performance compared to others on the arithmetic task. This pattern occurred regardless of participants' actual gender, pointing to a virtual stereotype lift effect. Additional mediation tests showed that task motivation partially mediated the effect. Theoretical and practical implications for social identity dynamics in avatar-based virtual environments are discussed.

A Micro-Level Data-Calibrated Agent-Based Model: The Synergy between Microsimulation and Agent-Based Modeling.

PubMed

Singh, Karandeep; Ahn, Chang-Won; Paik, Euihyun; Bae, Jang Won; Lee, Chun-Hee

2018-01-01

Artificial life (ALife) examines systems related to natural life, its processes, and its evolution, using simulations with computer models, robotics, and biochemistry. In this article, we focus on the computer modeling, or "soft," aspects of ALife and prepare a framework for scientists and modelers to be able to support such experiments. The framework is designed and built to be a parallel as well as distributed agent-based modeling environment, and does not require end users to have expertise in parallel or distributed computing. Furthermore, we use this framework to implement a hybrid model using microsimulation and agent-based modeling techniques to generate an artificial society. We leverage this artificial society to simulate and analyze population dynamics using Korean population census data. The agents in this model derive their decisional behaviors from real data (microsimulation feature) and interact among themselves (agent-based modeling feature) to proceed in the simulation. The behaviors, interactions, and social scenarios of the agents are varied to perform an analysis of population dynamics. We also estimate the future cost of pension policies based on the future population structure of the artificial society. The proposed framework and model demonstrates how ALife techniques can be used by researchers in relation to social issues and policies.

Effect of Zealotry in High-dimensional Opinion Dynamics Models

DTIC Science & Technology

2015-02-18

the several platforms for portable computing to choose from, such as Apple iPad, Amazon Kindle, and Samsung Galaxy Note. A third example is the choice... diverse topologies, Chaos 17, 026111 (2007). [16] S. K. Maity, T. V. Manoj, and A. Mukherjee, Opinion formation in time-varying social networks: The case of

Reach and speed of judgment propagation in the laboratory.

PubMed

Moussaïd, Mehdi; Herzog, Stefan M; Kämmer, Juliane E; Hertwig, Ralph

2017-04-18

In recent years, a large body of research has demonstrated that judgments and behaviors can propagate from person to person. Phenomena as diverse as political mobilization, health practices, altruism, and emotional states exhibit similar dynamics of social contagion. The precise mechanisms of judgment propagation are not well understood, however, because it is difficult to control for confounding factors such as homophily or dynamic network structures. We introduce an experimental design that renders possible the stringent study of judgment propagation. In this design, experimental chains of individuals can revise their initial judgment in a visual perception task after observing a predecessor's judgment. The positioning of a very good performer at the top of a chain created a performance gap, which triggered waves of judgment propagation down the chain. We evaluated the dynamics of judgment propagation experimentally. Despite strong social influence within pairs of individuals, the reach of judgment propagation across a chain rarely exceeded a social distance of three to four degrees of separation. Furthermore, computer simulations showed that the speed of judgment propagation decayed exponentially with the social distance from the source. We show that information distortion and the overweighting of other people's errors are two individual-level mechanisms hindering judgment propagation at the scale of the chain. Our results contribute to the understanding of social-contagion processes, and our experimental method offers numerous new opportunities to study judgment propagation in the laboratory.

Reach and speed of judgment propagation in the laboratory

PubMed Central

Herzog, Stefan M.; Kämmer, Juliane E.; Hertwig, Ralph

2017-01-01

In recent years, a large body of research has demonstrated that judgments and behaviors can propagate from person to person. Phenomena as diverse as political mobilization, health practices, altruism, and emotional states exhibit similar dynamics of social contagion. The precise mechanisms of judgment propagation are not well understood, however, because it is difficult to control for confounding factors such as homophily or dynamic network structures. We introduce an experimental design that renders possible the stringent study of judgment propagation. In this design, experimental chains of individuals can revise their initial judgment in a visual perception task after observing a predecessor’s judgment. The positioning of a very good performer at the top of a chain created a performance gap, which triggered waves of judgment propagation down the chain. We evaluated the dynamics of judgment propagation experimentally. Despite strong social influence within pairs of individuals, the reach of judgment propagation across a chain rarely exceeded a social distance of three to four degrees of separation. Furthermore, computer simulations showed that the speed of judgment propagation decayed exponentially with the social distance from the source. We show that information distortion and the overweighting of other people’s errors are two individual-level mechanisms hindering judgment propagation at the scale of the chain. Our results contribute to the understanding of social-contagion processes, and our experimental method offers numerous new opportunities to study judgment propagation in the laboratory. PMID:28373540

Multi-Relational Characterization of Dynamic Social Network Communities

NASA Astrophysics Data System (ADS)

Lin, Yu-Ru; Sundaram, Hari; Kelliher, Aisling

The emergence of the mediated social web - a distributed network of participants creating rich media content and engaging in interactive conversations through Internet-based communication technologies - has contributed to the evolution of powerful social, economic and cultural change. Online social network sites and blogs, such as Facebook, Twitter, Flickr and LiveJournal, thrive due to their fundamental sense of "community". The growth of online communities offers both opportunities and challenges for researchers and practitioners. Participation in online communities has been observed to influence people's behavior in diverse ways ranging from financial decision-making to political choices, suggesting the rich potential for diverse applications. However, although studies on the social web have been extensive, discovering communities from online social media remains challenging, due to the interdisciplinary nature of this subject. In this article, we present our recent work on characterization of communities in online social media using computational approaches grounded on the observations from social science.

Augmenting Trust Establishment in Dynamic Systems with Social Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lagesse, Brent J; Kumar, Mohan; Venkatesh, Svetha

2010-01-01

Social networking has recently flourished in popularity through the use of social websites. Pervasive computing resources have allowed people stay well-connected to each other through access to social networking resources. We take the position that utilizing information produced by relationships within social networks can assist in the establishment of trust for other pervasive computing applications. Furthermore, we describe how such a system can augment a sensor infrastructure used for event observation with information from mobile sensors (ie, mobile phones with cameras) controlled by potentially untrusted third parties. Pervasive computing systems are invisible systems, oriented around the user. As a result,more » many future pervasive systems are likely to include a social aspect to the system. The social communities that are developed in these systems can augment existing trust mechanisms with information about pre-trusted entities or entities to initially consider when beginning to establish trust. An example of such a system is the Collaborative Virtual Observation (CoVO) system fuses sensor information from disaparate sources in soft real-time to recreate a scene that provides observation of an event that has recently transpired. To accomplish this, CoVO must efficently access services whilst protecting the data from corruption from unknown remote nodes. CoVO combines dynamic service composition with virtual observation to utilize existing infrastructure with third party services available in the environment. Since these services are not under the control of the system, they may be unreliable or malicious. When an event of interest occurs, the given infrastructure (bus cameras, etc.) may not sufficiently cover the necessary information (be it in space, time, or sensor type). To enhance observation of the event, infrastructure is augmented with information from sensors in the environment that the infrastructure does not control. These sensors may be unreliable, uncooperative, or even malicious. Additionally, to execute queries in soft real-time, processing must be distributed to available systems in the environment. We propose to use information from social networks to satisfy these requirements. In this paper, we present our position that knowledge gained from social activities can be used to augment trust mechanisms in pervasive computing. The system uses social behavior of nodes to predict a subset that it wants to query for information. In this context, social behavior such as transit patterns and schedules (which can be used to determine if a queried node is likely to be reliable) or known relationships, such as a phone's address book, that can be used to determine networks of nodes that may also be able to assist in retrieving information. Neither implicit nor explicit relationships necessarily imply that the user trusts an entity, but rather will provide a starting place for establishing trust. The proposed framework utilizes social network information to assist in trust establishment when third-party sensors are used for sensing events.« less

Neural Correlates of Racial Ingroup Bias in Observing Computer-Animated Social Encounters.

PubMed

Katsumi, Yuta; Dolcos, Sanda

2017-01-01

Despite evidence for the role of group membership in the neural correlates of social cognition, the mechanisms associated with processing non-verbal behaviors displayed by racially ingroup vs. outgroup members remain unclear. Here, 20 Caucasian participants underwent fMRI recording while observing social encounters with ingroup and outgroup characters displaying dynamic and static non-verbal behaviors. Dynamic behaviors included approach and avoidance behaviors, preceded or not by a handshake; both dynamic and static behaviors were followed by participants' ratings. Behaviorally, participants showed bias toward their ingroup members, demonstrated by faster/slower reaction times for evaluating ingroup static/approach behaviors, respectively. At the neural level, despite overall similar responses in the action observation network to ingroup and outgroup encounters, the medial prefrontal cortex showed dissociable activation, possibly reflecting spontaneous processing of ingroup static behaviors and positive evaluations of ingroup approach behaviors. The anterior cingulate and superior frontal cortices also showed sensitivity to race, reflected in coordinated and reduced activation for observing ingroup static behaviors. Finally, the posterior superior temporal sulcus showed uniquely increased activity to observing ingroup handshakes. These findings shed light on the mechanisms of racial ingroup bias in observing social encounters, and have implications for understanding factors related to successful interactions with individuals from diverse backgrounds.

Cortical oscillatory dynamics in a social interaction model.

PubMed

Knyazev, Gennady G; Slobodskoj-Plusnin, Jaroslav Y; Bocharov, Andrey V; Pylkova, Liudmila V

2013-03-15

In this study we sought to investigate cortical oscillatory dynamics accompanying three major kinds of social behavior: aggressive, friendly, and avoidant. Behavioral and EEG data were collected in 48 participants during a computer game modeling social interactions with virtual 'persons'. 3D source reconstruction and independent component analysis were applied to EEG data. Results showed that social behavior was partly reactive and partly proactive with subject's personality playing an important role in shaping this behavior. Most salient differences were found between avoidance and approach behaviors, whereas the two kinds of approach behavior (i.e., aggression and friendship) did not differ from each other. Comparative to avoidance, approach behaviors were associated with higher induced responses in most frequency bands which were mostly observed in cortical areas overlapping with the default mode network. The difference between approach- and avoidance-related oscillatory dynamics was more salient in subjects predisposed to approach behaviors (i.e., in aggressive or sociable subjects) and was less pronounced in subjects predisposed to avoidance behavior (i.e., in high trait anxiety scorers). There was a trend to higher low frequency phase-locking in motor area in approach than in avoid condition. Results are discussed in light of the concept linking induced responses with top-down and evoked responses with bottom-up processes. Copyright © 2012 Elsevier B.V. All rights reserved.

Neural Correlates of Racial Ingroup Bias in Observing Computer-Animated Social Encounters

PubMed Central

Katsumi, Yuta; Dolcos, Sanda

2018-01-01

Despite evidence for the role of group membership in the neural correlates of social cognition, the mechanisms associated with processing non-verbal behaviors displayed by racially ingroup vs. outgroup members remain unclear. Here, 20 Caucasian participants underwent fMRI recording while observing social encounters with ingroup and outgroup characters displaying dynamic and static non-verbal behaviors. Dynamic behaviors included approach and avoidance behaviors, preceded or not by a handshake; both dynamic and static behaviors were followed by participants’ ratings. Behaviorally, participants showed bias toward their ingroup members, demonstrated by faster/slower reaction times for evaluating ingroup static/approach behaviors, respectively. At the neural level, despite overall similar responses in the action observation network to ingroup and outgroup encounters, the medial prefrontal cortex showed dissociable activation, possibly reflecting spontaneous processing of ingroup static behaviors and positive evaluations of ingroup approach behaviors. The anterior cingulate and superior frontal cortices also showed sensitivity to race, reflected in coordinated and reduced activation for observing ingroup static behaviors. Finally, the posterior superior temporal sulcus showed uniquely increased activity to observing ingroup handshakes. These findings shed light on the mechanisms of racial ingroup bias in observing social encounters, and have implications for understanding factors related to successful interactions with individuals from diverse backgrounds. PMID:29354042

Signalling and obfuscation for congestion control

NASA Astrophysics Data System (ADS)

Mareček, Jakub; Shorten, Robert; Yu, Jia Yuan

2015-10-01

We aim to reduce the social cost of congestion in many smart city applications. In our model of congestion, agents interact over limited resources after receiving signals from a central agent that observes the state of congestion in real time. Under natural models of agent populations, we develop new signalling schemes and show that by introducing a non-trivial amount of uncertainty in the signals, we reduce the social cost of congestion, i.e., improve social welfare. The signalling schemes are efficient in terms of both communication and computation, and are consistent with past observations of the congestion. Moreover, the resulting population dynamics converge under reasonable assumptions.

Objective measurement of head movement differences in children with and without autism spectrum disorder.

PubMed

Martin, Katherine B; Hammal, Zakia; Ren, Gang; Cohn, Jeffrey F; Cassell, Justine; Ogihara, Mitsunori; Britton, Jennifer C; Gutierrez, Anibal; Messinger, Daniel S

2018-01-01

Deficits in motor movement in children with autism spectrum disorder (ASD) have typically been characterized qualitatively by human observers. Although clinicians have noted the importance of atypical head positioning (e.g. social peering and repetitive head banging) when diagnosing children with ASD, a quantitative understanding of head movement in ASD is lacking. Here, we conduct a quantitative comparison of head movement dynamics in children with and without ASD using automated, person-independent computer-vision based head tracking (Zface). Because children with ASD often exhibit preferential attention to nonsocial versus social stimuli, we investigated whether children with and without ASD differed in their head movement dynamics depending on stimulus sociality. The current study examined differences in head movement dynamics in children with ( n  = 21) and without ASD ( n  = 21). Children were video-recorded while watching a 16-min video of social and nonsocial stimuli. Three dimensions of rigid head movement-pitch (head nods), yaw (head turns), and roll (lateral head inclinations)-were tracked using Zface. The root mean square of pitch, yaw, and roll was calculated to index the magnitude of head angular displacement (quantity of head movement) and angular velocity (speed). Compared with children without ASD, children with ASD exhibited greater yaw displacement, indicating greater head turning, and greater velocity of yaw and roll, indicating faster head turning and inclination. Follow-up analyses indicated that differences in head movement dynamics were specific to the social rather than the nonsocial stimulus condition. Head movement dynamics (displacement and velocity) were greater in children with ASD than in children without ASD, providing a quantitative foundation for previous clinical reports. Head movement differences were evident in lateral (yaw and roll) but not vertical (pitch) movement and were specific to a social rather than nonsocial condition. When presented with social stimuli, children with ASD had higher levels of head movement and moved their heads more quickly than children without ASD. Children with ASD may use head movement to modulate their perception of social scenes.

Persistence in a Random Bond Ising Model of Socio-Econo Dynamics

NASA Astrophysics Data System (ADS)

Jain, S.; Yamano, T.

We study the persistence phenomenon in a socio-econo dynamics model using computer simulations at a finite temperature on hypercubic lattices in dimensions up to five. The model includes a "social" local field which contains the magnetization at time t. The nearest neighbour quenched interactions are drawn from a binary distribution which is a function of the bond concentration, p. The decay of the persistence probability in the model depends on both the spatial dimension and p. We find no evidence of "blocking" in this model. We also discuss the implications of our results for possible applications in the social and economic fields. It is suggested that the absence, or otherwise, of blocking could be used as a criterion to decide on the validity of a given model in different scenarios.

A Dynamic Analysis of Why Learners Develop a Preference for Autonomous Learners in Computer-Mediated Communication

ERIC Educational Resources Information Center

Rienties, Bart; Tempelaar, Dirk; Giesbers, Bas; Segers, Mien; Gijselaers, Wim

2014-01-01

A large number of studies in CMC have assessed how social interaction, processes and learning outcomes are intertwined. The present research explores how the degree of self-determination of learners, that is the motivational orientation of a learner, influences the communication and interaction patterns in an online Problem Based Learning…

A cognitive-consistency based model of population wide attitude change.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lakkaraju, Kiran; Speed, Ann Elizabeth

Attitudes play a significant role in determining how individuals process information and behave. In this paper we have developed a new computational model of population wide attitude change that captures the social level: how individuals interact and communicate information, and the cognitive level: how attitudes and concept interact with each other. The model captures the cognitive aspect by representing each individuals as a parallel constraint satisfaction network. The dynamics of this model are explored through a simple attitude change experiment where we vary the social network and distribution of attitudes in a population.

Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research.

PubMed

Spruijt-Metz, Donna; Hekler, Eric; Saranummi, Niilo; Intille, Stephen; Korhonen, Ilkka; Nilsen, Wendy; Rivera, Daniel E; Spring, Bonnie; Michie, Susan; Asch, David A; Sanna, Alberto; Salcedo, Vicente Traver; Kukakfa, Rita; Pavel, Misha

2015-09-01

Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static "snapshots" of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing "gold standard" measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a "knowledge commons," which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.

Dynamic Communicability Predicts Infectiousness

NASA Astrophysics Data System (ADS)

Mantzaris, Alexander V.; Higham, Desmond J.

Using real, time-dependent social interaction data, we look at correlations between some recently proposed dynamic centrality measures and summaries from large-scale epidemic simulations. The evolving network arises from email exchanges. The centrality measures, which are relatively inexpensive to compute, assign rankings to individual nodes based on their ability to broadcast information over the dynamic topology. We compare these with node rankings based on infectiousness that arise when a full stochastic SI simulation is performed over the dynamic network. More precisely, we look at the proportion of the network that a node is able to infect over a fixed time period, and the length of time that it takes for a node to infect half the network. We find that the dynamic centrality measures are an excellent, and inexpensive, proxy for the full simulation-based measures.

Health Status and Health Dynamics in an Empirical Model of Expected Longevity*

PubMed Central

Benítez-Silva, Hugo; Ni, Huan

2010-01-01

Expected longevity is an important factor influencing older individuals’ decisions such as consumption, savings, purchase of life insurance and annuities, claiming of Social Security benefits, and labor supply. It has also been shown to be a good predictor of actual longevity, which in turn is highly correlated with health status. A relatively new literature on health investments under uncertainty, which builds upon the seminal work by Grossman (1972), has directly linked longevity with characteristics, behaviors, and decisions by utility maximizing agents. Our empirical model can be understood within that theoretical framework as estimating a production function of longevity. Using longitudinal data from the Health and Retirement Study, we directly incorporate health dynamics in explaining the variation in expected longevities, and compare two alternative measures of health dynamics: the self-reported health change, and the computed health change based on self-reports of health status. In 38% of the reports in our sample, computed health changes are inconsistent with the direct report on health changes over time. And another 15% of the sample can suffer from information losses if computed changes are used to assess changes in actual health. These potentially serious problems raise doubts regarding the use and interpretation of the computed health changes and even the lagged measures of self-reported health as controls for health dynamics in a variety of empirical settings. Our empirical results, controlling for both subjective and objective measures of health status and unobserved heterogeneity in reporting, suggest that self-reported health changes are a preferred measure of health dynamics. PMID:18187217

Consensus Emerging from the Bottom-up: the Role of Cognitive Variables in Opinion Dynamics

NASA Astrophysics Data System (ADS)

Giardini, Francesca; Vilone, Daniele; Conte, Rosaria

2015-09-01

The study of opinions - e.g., their formation and change, and their effects on our society - by means of theoretical and numerical models has been one of the main goals of sociophysics until now, but it is one of the defining topics addressed by social psychology and complexity science. Despite the flourishing of different models and theories, several key questions still remain unanswered. The aim of this paper is to provide a cognitively grounded computational model of opinions in which they are described as mental representations and defined in terms of distinctive mental features. We also define how these representations change dynamically through different processes, describing the interplay between mental and social dynamics of opinions. We present two versions of the model, one with discrete opinions (voter model-like), and one with continuous ones (Deffuant-like). By means of numerical simulations, we compare the behaviour of our cognitive model with the classical sociophysical models, and we identify interesting differences in the dynamics of consensus for each of the models considered.

Complexity in Nature and Society: Complexity Management in the Age of Globalization

NASA Astrophysics Data System (ADS)

Mainzer, Klaus

The theory of nonlinear complex systems has become a proven problem-solving approach in the natural sciences from cosmic and quantum systems to cellular organisms and the brain. Even in modern engineering science self-organizing systems are developed to manage complex networks and processes. It is now recognized that many of our ecological, social, economic, and political problems are also of a global, complex, and nonlinear nature. What are the laws of sociodynamics? Is there a socio-engineering of nonlinear problem solving? What can we learn from nonlinear dynamics for complexity management in social, economic, financial and political systems? Is self-organization an acceptable strategy to handle the challenges of complexity in firms, institutions and other organizations? It is a main thesis of the talk that nature and society are basically governed by nonlinear and complex information dynamics. How computational is sociodynamics? What can we hope for social, economic and political problem solving in the age of globalization?.

Using Bayesian Nonparametric Hidden Semi-Markov Models to Disentangle Affect Processes during Marital Interaction

PubMed Central

Griffin, William A.; Li, Xun

2016-01-01

Sequential affect dynamics generated during the interaction of intimate dyads, such as married couples, are associated with a cascade of effects—some good and some bad—on each partner, close family members, and other social contacts. Although the effects are well documented, the probabilistic structures associated with micro-social processes connected to the varied outcomes remain enigmatic. Using extant data we developed a method of classifying and subsequently generating couple dynamics using a Hierarchical Dirichlet Process Hidden semi-Markov Model (HDP-HSMM). Our findings indicate that several key aspects of existing models of marital interaction are inadequate: affect state emissions and their durations, along with the expected variability differences between distressed and nondistressed couples are present but highly nuanced; and most surprisingly, heterogeneity among highly satisfied couples necessitate that they be divided into subgroups. We review how this unsupervised learning technique generates plausible dyadic sequences that are sensitive to relationship quality and provide a natural mechanism for computational models of behavioral and affective micro-social processes. PMID:27187319

An Agent-Based Epidemic Simulation of Social Behaviors Affecting HIV Transmission among Taiwanese Homosexuals

PubMed Central

2015-01-01

Computational simulations are currently used to identify epidemic dynamics, to test potential prevention and intervention strategies, and to study the effects of social behaviors on HIV transmission. The author describes an agent-based epidemic simulation model of a network of individuals who participate in high-risk sexual practices, using number of partners, condom usage, and relationship length to distinguish between high- and low-risk populations. Two new concepts—free links and fixed links—are used to indicate tendencies among individuals who either have large numbers of short-term partners or stay in long-term monogamous relationships. An attempt was made to reproduce epidemic curves of reported HIV cases among male homosexuals in Taiwan prior to using the agent-based model to determine the effects of various policies on epidemic dynamics. Results suggest that when suitable adjustments are made based on available social survey statistics, the model accurately simulates real-world behaviors on a large scale. PMID:25815047

Computationally modeling interpersonal trust.

PubMed

Lee, Jin Joo; Knox, W Bradley; Wormwood, Jolie B; Breazeal, Cynthia; Desteno, David

2013-01-01

We present a computational model capable of predicting-above human accuracy-the degree of trust a person has toward their novel partner by observing the trust-related nonverbal cues expressed in their social interaction. We summarize our prior work, in which we identify nonverbal cues that signal untrustworthy behavior and also demonstrate the human mind's readiness to interpret those cues to assess the trustworthiness of a social robot. We demonstrate that domain knowledge gained from our prior work using human-subjects experiments, when incorporated into the feature engineering process, permits a computational model to outperform both human predictions and a baseline model built in naiveté of this domain knowledge. We then present the construction of hidden Markov models to investigate temporal relationships among the trust-related nonverbal cues. By interpreting the resulting learned structure, we observe that models built to emulate different levels of trust exhibit different sequences of nonverbal cues. From this observation, we derived sequence-based temporal features that further improve the accuracy of our computational model. Our multi-step research process presented in this paper combines the strength of experimental manipulation and machine learning to not only design a computational trust model but also to further our understanding of the dynamics of interpersonal trust.

Graceful Failure and Societal Resilience Analysis Via Agent-Based Modeling and Simulation

NASA Astrophysics Data System (ADS)

Schopf, P. S.; Cioffi-Revilla, C.; Rogers, J. D.; Bassett, J.; Hailegiorgis, A. B.

2014-12-01

Agent-based social modeling is opening up new methodologies for the study of societal response to weather and climate hazards, and providing measures of resiliency that can be studied in many contexts, particularly in coupled human and natural-technological systems (CHANTS). Since CHANTS are complex adaptive systems, societal resiliency may or may not occur, depending on dynamics that lack closed form solutions. Agent-based modeling has been shown to provide a viable theoretical and methodological approach for analyzing and understanding disasters and societal resiliency in CHANTS. Our approach advances the science of societal resilience through computational modeling and simulation methods that complement earlier statistical and mathematical approaches. We present three case studies of social dynamics modeling that demonstrate the use of these agent based models. In Central Asia, we exmaine mutltiple ensemble simulations with varying climate statistics to see how droughts and zuds affect populations, transmission of wealth across generations, and the overall structure of the social system. In Eastern Africa, we explore how successive episodes of drought events affect the adaptive capacity of rural households. Human displacement, mainly, rural to urban migration, and livelihood transition particularly from pastoral to farming are observed as rural households interacting dynamically with the biophysical environment and continually adjust their behavior to accommodate changes in climate. In the far north case we demonstrate one of the first successful attempts to model the complete climate-permafrost-infrastructure-societal interaction network as a complex adaptive system/CHANTS implemented as a ``federated'' agent-based model using evolutionary computation. Analysis of population changes resulting from extreme weather across these and other cases provides evidence for the emergence of new steady states and shifting patterns of resilience.

"I'm* Two Rabbits" / "J'ai un Rouge Pullover*". How Corrective Feedback Is Handled in Collaborative Exchange Programmes between Early Language Learners

ERIC Educational Resources Information Center

Choffat-Durr, Anne; Macaire, Dominique

2012-01-01

This article presents how, in the social dynamics of two classrooms involved in an exchange programme, young learners provide their peers with asynchronous feedback taking place in the digital medium. Within two Call Triangles that interact thanks to Computer Mediated Communication tools, teachers sharing the same methodological precept on…

Virtual Social Environments as a Tool for Psychological Assessment: Dynamics of Interaction with a Virtual Spouse

ERIC Educational Resources Information Center

Schonbrodt, Felix D.; Asendorpf, Jens B.

2011-01-01

Computer games are advocated as a promising tool bridging the gap between the controllability of a lab experiment and the mundane realism of a field experiment. At the same time, many authors stress the importance of observing real behavior instead of asking participants about possible or intended behaviors. In this article, the authors introduce…

Learning dynamics in social dilemmas

PubMed Central

Macy, Michael W.; Flache, Andreas

2002-01-01

The Nash equilibrium, the main solution concept in analytical game theory, cannot make precise predictions about the outcome of repeated mixed-motive games. Nor can it tell us much about the dynamics by which a population of players moves from one equilibrium to another. These limitations, along with concerns about the cognitive demands of forward-looking rationality, have motivated efforts to explore backward-looking alternatives to analytical game theory. Most of the effort has been invested in evolutionary models of population dynamics. We shift attention to a learning-theoretic alternative. Computational experiments with adaptive agents identify a fundamental solution concept for social dilemmas–−stochastic collusion–−based on a random walk from a self-limiting noncooperative equilibrium into a self-reinforcing cooperative equilibrium. However, we show that this solution is viable only within a narrow range of aspiration levels. Below the lower threshold, agents are pulled into a deficient equilibrium that is a stronger attractor than mutual cooperation. Above the upper threshold, agents are dissatisfied with mutual cooperation. Aspirations that adapt with experience (producing habituation to stimuli) do not gravitate into the window of viability; rather, they are the worst of both worlds. Habituation destabilizes cooperation and stabilizes defection. Results from the two-person problem suggest that applications to multiplex and embedded relationships will yield unexpected insights into the global dynamics of cooperation in social dilemmas. PMID:12011402

Dynamical minimalism: why less is more in psychology.

PubMed

Nowak, Andrzej

2004-01-01

The principle of parsimony, embraced in all areas of science, states that simple explanations are preferable to complex explanations in theory construction. Parsimony, however, can necessitate a trade-off with depth and richness in understanding. The approach of dynamical minimalism avoids this trade-off. The goal of this approach is to identify the simplest mechanisms and fewest variables capable of producing the phenomenon in question. A dynamical model in which change is produced by simple rules repetitively interacting with each other can exhibit unexpected and complex properties. It is thus possible to explain complex psychological and social phenomena with very simple models if these models are dynamic. In dynamical minimalist theories, then, the principle of parsimony can be followed without sacrificing depth in understanding. Computer simulations have proven especially useful for investigating the emergent properties of simple models.

Health status and health dynamics in an empirical model of expected longevity.

PubMed

Benítez-Silva, Hugo; Ni, Huan

2008-05-01

Expected longevity is an important factor influencing older individuals' decisions such as consumption, savings, purchase of life insurance and annuities, claiming of Social Security benefits, and labor supply. It has also been shown to be a good predictor of actual longevity, which in turn is highly correlated with health status. A relatively new literature on health investments under uncertainty, which builds upon the seminal work by Grossman [Grossman, M., 1972. On the concept of health capital and demand for health. Journal of Political Economy 80, 223-255] has directly linked longevity with characteristics, behaviors, and decisions by utility maximizing agents. Our empirical model can be understood within that theoretical framework as estimating a production function of longevity. Using longitudinal data from the Health and Retirement Study, we directly incorporate health dynamics in explaining the variation in expected longevities, and compare two alternative measures of health dynamics: the self-reported health change, and the computed health change based on self-reports of health status. In 38% of the reports in our sample, computed health changes are inconsistent with the direct report on health changes over time. And another 15% of the sample can suffer from information losses if computed changes are used to assess changes in actual health. These potentially serious problems raise doubts regarding the use and interpretation of the computed health changes and even the lagged measures of self-reported health as controls for health dynamics in a variety of empirical settings. Our empirical results, controlling for both subjective and objective measures of health status and unobserved heterogeneity in reporting, suggest that self-reported health changes are a preferred measure of health dynamics.

The Human Face as a Dynamic Tool for Social Communication.

PubMed

Jack, Rachael E; Schyns, Philippe G

2015-07-20

As a highly social species, humans frequently exchange social information to support almost all facets of life. One of the richest and most powerful tools in social communication is the face, from which observers can quickly and easily make a number of inferences - about identity, gender, sex, age, race, ethnicity, sexual orientation, physical health, attractiveness, emotional state, personality traits, pain or physical pleasure, deception, and even social status. With the advent of the digital economy, increasing globalization and cultural integration, understanding precisely which face information supports social communication and which produces misunderstanding is central to the evolving needs of modern society (for example, in the design of socially interactive digital avatars and companion robots). Doing so is challenging, however, because the face can be thought of as comprising a high-dimensional, dynamic information space, and this impacts cognitive science and neuroimaging, and their broader applications in the digital economy. New opportunities to address this challenge are arising from the development of new methods and technologies, coupled with the emergence of a modern scientific culture that embraces cross-disciplinary approaches. Here, we briefly review one such approach that combines state-of-the-art computer graphics, psychophysics and vision science, cultural psychology and social cognition, and highlight the main knowledge advances it has generated. In the light of current developments, we provide a vision of the future directions in the field of human facial communication within and across cultures. Copyright © 2015 Elsevier Ltd. All rights reserved.

Computational Social Creativity.

PubMed

Saunders, Rob; Bown, Oliver

2015-01-01

This article reviews the development of computational models of creativity where social interactions are central. We refer to this area as computational social creativity. Its context is described, including the broader study of creativity, the computational modeling of other social phenomena, and computational models of individual creativity. Computational modeling has been applied to a number of areas of social creativity and has the potential to contribute to our understanding of creativity. A number of requirements for computational models of social creativity are common in artificial life and computational social science simulations. Three key themes are identified: (1) computational social creativity research has a critical role to play in understanding creativity as a social phenomenon and advancing computational creativity by making clear epistemological contributions in ways that would be challenging for other approaches; (2) the methodologies developed in artificial life and computational social science carry over directly to computational social creativity; and (3) the combination of computational social creativity with individual models of creativity presents significant opportunities and poses interesting challenges for the development of integrated models of creativity that have yet to be realized.

Impact of Short Social Training on Prosocial Behaviors: An fMRI Study.

PubMed

Lukinova, Evgeniya; Myagkov, Mikhail

2016-01-01

Efficient brain-computer interfaces (BCIs) are in need of knowledge about the human brain and how it interacts, plays games, and socializes with other brains. A breakthrough can be achieved by revealing the microfoundations of sociality, an additional component of the utility function reflecting the value of contributing to group success derived from social identity. Building upon our previous behavioral work, we conduct a series of functional magnetic resonance imaging (fMRI) experiments (N = 10 in the Pilot Study and N = 15 in the Main Study) to measure whether and how sociality alters the functional activation of and connectivity between specific systems in the brain. The overarching hypothesis of this study is that sociality, even in a minimal form, serves as a natural mechanism of sustainable cooperation by fostering interaction between brain regions associated with social cognition and those related to value calculation. We use group-based manipulations to induce varying levels of sociality and compare behavior in two social dilemmas: Prisoner's Dilemma and variations of Ultimatum Game. We find that activation of the right inferior frontal gyrus, a region previously associated with cognitive control and modulation of the valuation system, is correlated with activity in the medial prefrontal cortex (mPFC) to a greater degree when participants make economic decisions in a game with an acquaintance, high sociality condition, compared to a game with a random individual, low sociality condition. These initial results suggest a specific biological mechanism through which sociality facilitates cooperation, fairness and provision of public goods at the cost of individual gain. Future research should examine neural dynamics in the brain during the computation of utility in the context of strategic games that involve social interaction for a larger sample of subjects.

Impact of Short Social Training on Prosocial Behaviors: An fMRI Study

PubMed Central

Lukinova, Evgeniya; Myagkov, Mikhail

2016-01-01

Efficient brain–computer interfaces (BCIs) are in need of knowledge about the human brain and how it interacts, plays games, and socializes with other brains. A breakthrough can be achieved by revealing the microfoundations of sociality, an additional component of the utility function reflecting the value of contributing to group success derived from social identity. Building upon our previous behavioral work, we conduct a series of functional magnetic resonance imaging (fMRI) experiments (N = 10 in the Pilot Study and N = 15 in the Main Study) to measure whether and how sociality alters the functional activation of and connectivity between specific systems in the brain. The overarching hypothesis of this study is that sociality, even in a minimal form, serves as a natural mechanism of sustainable cooperation by fostering interaction between brain regions associated with social cognition and those related to value calculation. We use group-based manipulations to induce varying levels of sociality and compare behavior in two social dilemmas: Prisoner’s Dilemma and variations of Ultimatum Game. We find that activation of the right inferior frontal gyrus, a region previously associated with cognitive control and modulation of the valuation system, is correlated with activity in the medial prefrontal cortex (mPFC) to a greater degree when participants make economic decisions in a game with an acquaintance, high sociality condition, compared to a game with a random individual, low sociality condition. These initial results suggest a specific biological mechanism through which sociality facilitates cooperation, fairness and provision of public goods at the cost of individual gain. Future research should examine neural dynamics in the brain during the computation of utility in the context of strategic games that involve social interaction for a larger sample of subjects. PMID:27458349

Robot Comedy Lab: experimenting with the social dynamics of live performance

PubMed Central

Katevas, Kleomenis; Healey, Patrick G. T.; Harris, Matthew Tobias

2015-01-01

The success of live comedy depends on a performer's ability to “work” an audience. Ethnographic studies suggest that this involves the co-ordinated use of subtle social signals such as body orientation, gesture, gaze by both performers and audience members. Robots provide a unique opportunity to test the effects of these signals experimentally. Using a life-size humanoid robot, programmed to perform a stand-up comedy routine, we manipulated the robot's patterns of gesture and gaze and examined their effects on the real-time responses of a live audience. The strength and type of responses were captured using SHORE™computer vision analytics. The results highlight the complex, reciprocal social dynamics of performer and audience behavior. People respond more positively when the robot looks at them, negatively when it looks away and performative gestures also contribute to different patterns of audience response. This demonstrates how the responses of individual audience members depend on the specific interaction they're having with the performer. This work provides insights into how to design more effective, more socially engaging forms of robot interaction that can be used in a variety of service contexts. PMID:26379585

Predicting Node Degree Centrality with the Node Prominence Profile

PubMed Central

Yang, Yang; Dong, Yuxiao; Chawla, Nitesh V.

2014-01-01

Centrality of a node measures its relative importance within a network. There are a number of applications of centrality, including inferring the influence or success of an individual in a social network, and the resulting social network dynamics. While we can compute the centrality of any node in a given network snapshot, a number of applications are also interested in knowing the potential importance of an individual in the future. However, current centrality is not necessarily an effective predictor of future centrality. While there are different measures of centrality, we focus on degree centrality in this paper. We develop a method that reconciles preferential attachment and triadic closure to capture a node's prominence profile. We show that the proposed node prominence profile method is an effective predictor of degree centrality. Notably, our analysis reveals that individuals in the early stage of evolution display a distinctive and robust signature in degree centrality trend, adequately predicted by their prominence profile. We evaluate our work across four real-world social networks. Our findings have important implications for the applications that require prediction of a node's future degree centrality, as well as the study of social network dynamics. PMID:25429797

A common neural network differentially mediates direct and social fear learning.

PubMed

Lindström, Björn; Haaker, Jan; Olsson, Andreas

2018-02-15

Across species, fears often spread between individuals through social learning. Yet, little is known about the neural and computational mechanisms underlying social learning. Addressing this question, we compared social and direct (Pavlovian) fear learning showing that they showed indistinguishable behavioral effects, and involved the same cross-modal (self/other) aversive learning network, centered on the amygdala, the anterior insula (AI), and the anterior cingulate cortex (ACC). Crucially, the information flow within this network differed between social and direct fear learning. Dynamic causal modeling combined with reinforcement learning modeling revealed that the amygdala and AI provided input to this network during direct and social learning, respectively. Furthermore, the AI gated learning signals based on surprise (associability), which were conveyed to the ACC, in both learning modalities. Our findings provide insights into the mechanisms underlying social fear learning, with implications for understanding common psychological dysfunctions, such as phobias and other anxiety disorders. Copyright © 2017 Elsevier Inc. All rights reserved.

Aeronautical engineering: A continuing bibliography with indexes (supplement 267)

NASA Technical Reports Server (NTRS)

1991-01-01

This bibliography lists 661 reports, articles, and other documents introduced into the NASA scientific and technical information system in June, 1991. Subject coverage includes design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; theoretical and applied aspects of aerodynamics and general fluid dynamics; electrical engineering; aircraft control; remote sensing; computer sciences; nuclear physics; and social sciences.

Nonlinear Socio-Ecological Dynamics and First Principles ofCollective Choice Behavior of ``Homo Socialis"

NASA Astrophysics Data System (ADS)

Sonis, M.

Socio-ecological dynamics emerged from the field of Mathematical SocialSciences and opened up avenues for re-examination of classical problems of collective behavior in Social and Spatial sciences. The ``engine" of this collective behavior is the subjective mental evaluation of level of utilities in the future, presenting sets of composite socio-economic-temporal-locational advantages. These dynamics present new laws of collective multi-population behavior which are the meso-level counterparts of the utility optimization individual behavior. The central core of the socio-ecological choice dynamics includes the following first principle of the collective choice behavior of ``Homo Socialis" based on the existence of ``collective consciousness": the choice behavior of ``Homo Socialis" is a collective meso-level choice behavior such that the relative changes in choice frequencies depend on the distribution of innovation alternatives between adopters of innovations. The mathematical basis of the Socio-Ecological Dynamics includes two complementary analytical approaches both based on the use of computer modeling as a theoretical and simulation tool. First approach is the ``continuous approach" --- the systems of ordinary and partial differential equations reflecting the continuous time Volterra ecological formalism in a form of antagonistic and/or cooperative collective hyper-games between different sub-sets of choice alternatives. Second approach is the ``discrete approach" --- systems of difference equations presenting a new branch of the non-linear discrete dynamics --- the Discrete Relative m-population/n-innovations Socio-Spatial Dynamics (Dendrinos and Sonis, 1990). The generalization of the Volterra formalism leads further to the meso-level variational principle of collective choice behavior determining the balance between the resulting cumulative social spatio-temporal interactions among the population of adopters susceptible to the choice alternatives and the cumulative equalization of the power of elites supporting different choice alternatives. This balance governs the dynamic innovation choice process and constitutes the dynamic meso-level counterpart of the micro-economic individual utility maximization principle.

Using cognitive architectures to study issues in team cognition in a complex task environment

NASA Astrophysics Data System (ADS)

Smart, Paul R.; Sycara, Katia; Tang, Yuqing

2014-05-01

Cognitive social simulation is a computer simulation technique that aims to improve our understanding of the dynamics of socially-situated and socially-distributed cognition. This makes cognitive social simulation techniques particularly appealing as a means to undertake experiments into team cognition. The current paper reports on the results of an ongoing effort to develop a cognitive social simulation capability that can be used to undertake studies into team cognition using the ACT-R cognitive architecture. This capability is intended to support simulation experiments using a team-based problem solving task, which has been used to explore the effect of different organizational environments on collective problem solving performance. The functionality of the ACT-R-based cognitive social simulation capability is presented and a number of areas of future development work are outlined. The paper also describes the motivation for adopting cognitive architectures in the context of social simulation experiments and presents a number of research areas where cognitive social simulation may be useful in developing a better understanding of the dynamics of team cognition. These include the use of cognitive social simulation to study the role of cognitive processes in determining aspects of communicative behavior, as well as the impact of communicative behavior on the shaping of task-relevant cognitive processes (e.g., the social shaping of individual and collective memory as a result of communicative exchanges). We suggest that the ability to perform cognitive social simulation experiments in these areas will help to elucidate some of the complex interactions that exist between cognitive, social, technological and informational factors in the context of team-based problem-solving activities.

Avoiding numerical pitfalls in social force models

NASA Astrophysics Data System (ADS)

Köster, Gerta; Treml, Franz; Gödel, Marion

2013-06-01

The social force model of Helbing and Molnár is one of the best known approaches to simulate pedestrian motion, a collective phenomenon with nonlinear dynamics. It is based on the idea that the Newtonian laws of motion mostly carry over to pedestrian motion so that human trajectories can be computed by solving a set of ordinary differential equations for velocity and acceleration. The beauty and simplicity of this ansatz are strong reasons for its wide spread. However, the numerical implementation is not without pitfalls. Oscillations, collisions, and instabilities occur even for very small step sizes. Classic solution ideas from molecular dynamics do not apply to the problem because the system is not Hamiltonian despite its source of inspiration. Looking at the model through the eyes of a mathematician, however, we realize that the right hand side of the differential equation is nondifferentiable and even discontinuous at critical locations. This produces undesirable behavior in the exact solution and, at best, severe loss of accuracy in efficient numerical schemes even in short range simulations. We suggest a very simple mollified version of the social force model that conserves the desired dynamic properties of the original many-body system but elegantly and cost efficiently resolves several of the issues concerning stability and numerical resolution.

Dynamics of epidemic spreading with vaccination: Impact of social pressure and engagement

NASA Astrophysics Data System (ADS)

Pires, Marcelo A.; Crokidakis, Nuno

2017-02-01

In this work we consider a model of epidemic spreading coupled with an opinion dynamics in a fully-connected population. Regarding the opinion dynamics, the individuals may be in two distinct states, namely in favor or against a vaccination campaign. Individuals against the vaccination follow a standard SIS model, whereas the pro-vaccine individuals can also be in a third compartment, namely Vaccinated. In addition, the opinions change according to the majority-rule dynamics in groups with three individuals. We also consider that the vaccine can give permanent or temporary immunization to the individuals. By means of analytical calculations and computer simulations, we show that the opinion dynamics can drastically affect the disease propagation, and that the engagement of the pro-vaccine individuals can be crucial for stopping the epidemic spreading. The full numerical code for simulating the model is available from the authors' webpage.

Network neuroscience

PubMed Central

Bassett, Danielle S; Sporns, Olaf

2017-01-01

Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We review emerging trends in network neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system. PMID:28230844

EarthCube: A Community-Driven Cyberinfrastructure for the Geosciences

NASA Astrophysics Data System (ADS)

Koskela, Rebecca; Ramamurthy, Mohan; Pearlman, Jay; Lehnert, Kerstin; Ahern, Tim; Fredericks, Janet; Goring, Simon; Peckham, Scott; Powers, Lindsay; Kamalabdi, Farzad; Rubin, Ken; Yarmey, Lynn

2017-04-01

EarthCube is creating a dynamic, System of Systems (SoS) infrastructure and data tools to collect, access, analyze, share, and visualize all forms of geoscience data and resources, using advanced collaboration, technological, and computational capabilities. EarthCube, as a joint effort between the U.S. National Science Foundation Directorate for Geosciences and the Division of Advanced Cyberinfrastructure, is a quickly growing community of scientists across all geoscience domains, as well as geoinformatics researchers and data scientists. EarthCube has attracted an evolving, dynamic virtual community of more than 2,500 contributors, including earth, ocean, polar, planetary, atmospheric, geospace, computer and social scientists, educators, and data and information professionals. During 2017, EarthCube will transition to the implementation phase. The implementation will balance "innovation" and "production" to advance cross-disciplinary science goals as well as the development of future data scientists. This presentation will describe the current architecture design for the EarthCube cyberinfrastructure and implementation plan.

Detecting agency from the biological motion of veridical vs animated agents

PubMed Central

Kelley, William M.; Heatherton, Todd F.; Macrae, C. Neil

2007-01-01

The ability to detect agency is fundamental for understanding the social world. Underlying this capacity are neural circuits that respond to patterns of intentional biological motion in the superior temporal sulcus and temporoparietal junction. Here we show that the brain's blood oxygenation level dependent (BOLD) response to such motion is modulated by the representation of the actor. Dynamic social interactions were portrayed by either live-action agents or computer-animated agents, enacting the exact same patterns of biological motion. Using an event-related design, we found that the BOLD response associated with the perception and interpretation of agency was greater when identical physical movements were performed by real rather than animated agents. This finding has important implications for previous work on biological motion that has relied upon computer-animated stimuli and demonstrates that the neural substrates of social perception are finely tuned toward real-world agents. In addition, the response in lateral temporal areas was observed in the absence of instructions to make mental inferences, thus demonstrating the spontaneous implementation of the intentional stance. PMID:18985141

Decomposition-Based Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Social Networks

PubMed Central

Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

2014-01-01

Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms. PMID:24723806

Opinion dynamics on interacting networks: media competition and social influence.

PubMed

Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

2014-05-27

The inner dynamics of the multiple actors of the informations systems - i.e, T.V., newspapers, blogs, social network platforms, - play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist.

Opinion dynamics on interacting networks: media competition and social influence

NASA Astrophysics Data System (ADS)

Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

2014-05-01

The inner dynamics of the multiple actors of the informations systems - i.e, T.V., newspapers, blogs, social network platforms, - play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist.

Decomposition-based multiobjective evolutionary algorithm for community detection in dynamic social networks.

PubMed

Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

2014-01-01

Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.

GDSCalc: A Web-Based Application for Evaluating Discrete Graph Dynamical Systems

PubMed Central

Elmeligy Abdelhamid, Sherif H.; Kuhlman, Chris J.; Marathe, Madhav V.; Mortveit, Henning S.; Ravi, S. S.

2015-01-01

Discrete dynamical systems are used to model various realistic systems in network science, from social unrest in human populations to regulation in biological networks. A common approach is to model the agents of a system as vertices of a graph, and the pairwise interactions between agents as edges. Agents are in one of a finite set of states at each discrete time step and are assigned functions that describe how their states change based on neighborhood relations. Full characterization of state transitions of one system can give insights into fundamental behaviors of other dynamical systems. In this paper, we describe a discrete graph dynamical systems (GDSs) application called GDSCalc for computing and characterizing system dynamics. It is an open access system that is used through a web interface. We provide an overview of GDS theory. This theory is the basis of the web application; i.e., an understanding of GDS provides an understanding of the software features, while abstracting away implementation details. We present a set of illustrative examples to demonstrate its use in education and research. Finally, we compare GDSCalc with other discrete dynamical system software tools. Our perspective is that no single software tool will perform all computations that may be required by all users; tools typically have particular features that are more suitable for some tasks. We situate GDSCalc within this space of software tools. PMID:26263006

GDSCalc: A Web-Based Application for Evaluating Discrete Graph Dynamical Systems.

PubMed

Elmeligy Abdelhamid, Sherif H; Kuhlman, Chris J; Marathe, Madhav V; Mortveit, Henning S; Ravi, S S

2015-01-01

Discrete dynamical systems are used to model various realistic systems in network science, from social unrest in human populations to regulation in biological networks. A common approach is to model the agents of a system as vertices of a graph, and the pairwise interactions between agents as edges. Agents are in one of a finite set of states at each discrete time step and are assigned functions that describe how their states change based on neighborhood relations. Full characterization of state transitions of one system can give insights into fundamental behaviors of other dynamical systems. In this paper, we describe a discrete graph dynamical systems (GDSs) application called GDSCalc for computing and characterizing system dynamics. It is an open access system that is used through a web interface. We provide an overview of GDS theory. This theory is the basis of the web application; i.e., an understanding of GDS provides an understanding of the software features, while abstracting away implementation details. We present a set of illustrative examples to demonstrate its use in education and research. Finally, we compare GDSCalc with other discrete dynamical system software tools. Our perspective is that no single software tool will perform all computations that may be required by all users; tools typically have particular features that are more suitable for some tasks. We situate GDSCalc within this space of software tools.

Framework and Implications of Virtual Neurorobotics

PubMed Central

Goodman, Philip H.; Zou, Quan; Dascalu, Sergiu-Mihai

2008-01-01

Despite decades of societal investment in artificial learning systems, truly “intelligent” systems have yet to be realized. These traditional models are based on input-output pattern optimization and/or cognitive production rule modeling. One response has been social robotics, using the interaction of human and robot to capture important cognitive dynamics such as cooperation and emotion; to date, these systems still incorporate traditional learning algorithms. More recently, investigators are focusing on the core assumptions of the brain “algorithm” itself—trying to replicate uniquely “neuromorphic” dynamics such as action potential spiking and synaptic learning. Only now are large-scale neuromorphic models becoming feasible, due to the availability of powerful supercomputers and an expanding supply of parameters derived from research into the brain's interdependent electrophysiological, metabolomic and genomic networks. Personal computer technology has also led to the acceptance of computer-generated humanoid images, or “avatars”, to represent intelligent actors in virtual realities. In a recent paper, we proposed a method of virtual neurorobotics (VNR) in which the approaches above (social-emotional robotics, neuromorphic brain architectures, and virtual reality projection) are hybridized to rapidly forward-engineer and develop increasingly complex, intrinsically intelligent systems. In this paper, we synthesize our research and related work in the field and provide a framework for VNR, with wider implications for research and practical applications. PMID:18982115

Dynamic social networks facilitate cooperation in the N-player Prisoner’s Dilemma

NASA Astrophysics Data System (ADS)

Rezaei, Golriz; Kirley, Michael

2012-12-01

Understanding how cooperative behaviour evolves in network communities, where the individual members interact via social dilemma games, is an on-going challenge. In this paper, we introduce a social network based model to investigate the evolution of cooperation in the N-player Prisoner’s Dilemma game. As such, this work complements previous studies focused on multi-player social dilemma games and endogenous networks. Agents in our model, employ different game-playing strategies reflecting varying cognitive capacities. When an agent plays cooperatively, a social link is formed with each of the other N-1 group members. Subsequent cooperative actions reinforce this link. However, when an agent defects, the links in the social network are broken. Computational simulations across a range of parameter settings are used to examine different scenarios: varying population and group sizes; the group formation process (or partner selection); and agent decision-making strategies under varying dilemma constraints (cost-to-benefit ratios), including a “discriminator” strategy where the action is based on a function of the weighted links within an agent’s social network. The simulation results show that the proposed social network model is able to evolve and maintain cooperation. As expected, as the value of N increases the equilibrium proportion of cooperators in the population decreases. In addition, this outcome is dependent on the dilemma constraint (cost-to-benefit ratio). However, in some circumstances the dynamic social network plays an increasingly important role in promoting and sustaining cooperation, especially when the agents adopt the discriminator strategy. The adjustment of social links results in the formation of communities of “like-minded” agents. Subsequently, this local optimal behaviour promotes the evolution of cooperative behaviour at the system level.

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning.

PubMed

Hong, Weizhe; Kennedy, Ann; Burgos-Artizzu, Xavier P; Zelikowsky, Moriel; Navonne, Santiago G; Perona, Pietro; Anderson, David J

2015-09-22

A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body "pose" of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics.

Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning

PubMed Central

Hong, Weizhe; Kennedy, Ann; Burgos-Artizzu, Xavier P.; Zelikowsky, Moriel; Navonne, Santiago G.; Perona, Pietro; Anderson, David J.

2015-01-01

A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body “pose” of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics. PMID:26354123

The epidemic threshold theorem with social and contact heterogeneity

NASA Astrophysics Data System (ADS)

Hincapié Palacio, Doracelly; Ospina Giraldo, Juan; Gómez Arias, Rubén Darío

2008-03-01

The threshold theorem of an epidemic SIR model was compared when infectious and susceptible individuals have homogeneous mixing and heterogeneous social status and when individuals of random networks have contact heterogeneity. Particularly the effect of vaccination in such models is considered when: individuals or nodes are exposed to impoverished, vaccination and loss of immunity. An equilibrium analysis and local stability of small perturbations about the equilibrium values were implemented using computer algebra. Numerical simulations were executed in order to describe the dynamic of transmission of diseases and changes of the basic reproductive rate. The implications of these results are examined around the threats to the global public health security.

Epidemic dynamics and endemic states in complex networks

NASA Astrophysics Data System (ADS)

Pastor-Satorras, Romualdo; Vespignani, Alessandro

2001-06-01

We study by analytical methods and large scale simulations a dynamical model for the spreading of epidemics in complex networks. In networks with exponentially bounded connectivity we recover the usual epidemic behavior with a threshold defining a critical point below that the infection prevalence is null. On the contrary, on a wide range of scale-free networks we observe the absence of an epidemic threshold and its associated critical behavior. This implies that scale-free networks are prone to the spreading and the persistence of infections whatever spreading rate the epidemic agents might possess. These results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks.

GeoChronos: An On-line Collaborative Platform for Earth Observation Scientists

NASA Astrophysics Data System (ADS)

Gamon, J. A.; Kiddle, C.; Curry, R.; Markatchev, N.; Zonta-Pastorello, G., Jr.; Rivard, B.; Sanchez-Azofeifa, G. A.; Simmonds, R.; Tan, T.

2009-12-01

Recent advances in cyberinfrastructure are offering new solutions to the growing challenges of managing and sharing large data volumes. Web 2.0 and social networking technologies, provide the means for scientists to collaborate and share information more effectively. Cloud computing technologies can provide scientists with transparent and on-demand access to applications served over the Internet in a dynamic and scalable manner. Semantic Web technologies allow for data to be linked together in a manner understandable by machines, enabling greater automation. Combining all of these technologies together can enable the creation of very powerful platforms. GeoChronos (http://geochronos.org/), part of a CANARIE Network Enabled Platforms project, is an online collaborative platform that incorporates these technologies to enable members of the earth observation science community to share data and scientific applications and to collaborate more effectively. The GeoChronos portal is built on an open source social networking platform called Elgg. Elgg provides a full set of social networking functionalities similar to Facebook including blogs, tags, media/document sharing, wikis, friends/contacts, groups, discussions, message boards, calendars, status, activity feeds and more. An underlying cloud computing infrastructure enables scientists to access dynamically provisioned applications via the portal for visualizing and analyzing data. Users are able to access and run the applications from any computer that has a Web browser and Internet connectivity and do not need to manage and maintain the applications themselves. Semantic Web Technologies, such as the Resource Description Framework (RDF) are being employed for relating and linking together spectral, satellite, meteorological and other data. Social networking functionality plays an integral part in facilitating the sharing of data and applications. Examples of recent GeoChronos users during the early testing phase have included the IAI International Wireless Sensor Networking Summer School at the University of Alberta, and the IAI Tropi-Dry community. Current GeoChronos activities include the development of a web-based spectral library and related analytical and visualization tools, in collaboration with members of the SpecNet community. The GeoChronos portal will be open to all members of the earth observation science community when the project nears completion at the end of 2010.

Perceptual crossing: the simplest online paradigm

PubMed Central

Auvray, Malika; Rohde, Marieke

2012-01-01

Researchers in social cognition increasingly realize that many phenomena cannot be understood by investigating offline situations only, focusing on individual mechanisms and an observer perspective. There are processes of dynamic emergence specific to online situations, when two or more persons are engaged in a real-time interaction that are more than just the sum of the individual capacities or behaviors, and these require the study of online social interaction. Auvray et al.'s (2009) perceptual crossing paradigm offers possibly the simplest paradigm for studying such online interactions: two persons, a one-dimensional space, one bit of information, and a yes/no answer. This study has provoked a lot of resonance in different areas of research, including experimental psychology, computer/robot modeling, philosophy, psychopathology, and even in the field of design. In this article, we review and critically assess this body of literature. We give an overview of both behavioral experimental research and simulated agent modeling done using the perceptual crossing paradigm. We discuss different contexts in which work on perceptual crossing has been cited. This includes the controversy about the possible constitutive role of perceptual crossing for social cognition. We conclude with an outlook on future research possibilities, in particular those that could elucidate the link between online interaction dynamics and individual social cognition. PMID:22723776

The benefits of social influence in optimized cultural markets.

PubMed

Abeliuk, Andrés; Berbeglia, Gerardo; Cebrian, Manuel; Van Hentenryck, Pascal

2015-01-01

Social influence has been shown to create significant unpredictability in cultural markets, providing one potential explanation why experts routinely fail at predicting commercial success of cultural products. As a result, social influence is often presented in a negative light. Here, we show the benefits of social influence for cultural markets. We present a policy that uses product quality, appeal, position bias and social influence to maximize expected profits in the market. Our computational experiments show that our profit-maximizing policy leverages social influence to produce significant performance benefits for the market, while our theoretical analysis proves that our policy outperforms in expectation any policy not displaying social signals. Our results contrast with earlier work which focused on showing the unpredictability and inequalities created by social influence. Not only do we show for the first time that, under our policy, dynamically showing consumers positive social signals increases the expected profit of the seller in cultural markets. We also show that, in reasonable settings, our profit-maximizing policy does not introduce significant unpredictability and identifies "blockbusters". Overall, these results shed new light on the nature of social influence and how it can be leveraged for the benefits of the market.

Tracking and Analyzing Individual Distress Following Terrorist Attacks Using Social Media Streams.

PubMed

Lin, Yu-Ru; Margolin, Drew; Wen, Xidao

2017-08-01

Risk research has theorized a number of mechanisms that might trigger, prolong, or potentially alleviate individuals' distress following terrorist attacks. These mechanisms are difficult to examine in a single study, however, because the social conditions of terrorist attacks are difficult to simulate in laboratory experiments and appropriate preattack baselines are difficult to establish with surveys. To address this challenge, we propose the use of computational focus groups and a novel analysis framework to analyze a social media stream that archives user history and location. The approach uses time-stamped behavior to quantify an individual's preattack behavior after an attack has occurred, enabling the assessment of time-specific changes in the intensity and duration of an individual's distress, as well as the assessment of individual and social-level covariates. To exemplify the methodology, we collected over 18 million tweets from 15,509 users located in Paris on November 13, 2015, and measured the degree to which they expressed anxiety, anger, and sadness after the attacks. The analysis resulted in findings that would be difficult to observe through other methods, such as that news media exposure had competing, time-dependent effects on anxiety, and that gender dynamics are complicated by baseline behavior. Opportunities for integrating computational focus group analysis with traditional methods are discussed. © 2017 Society for Risk Analysis.

Blackmail propagation on small-world networks

NASA Astrophysics Data System (ADS)

Shao, Zhi-Gang; Jian-Ping Sang; Zou, Xian-Wu; Tan, Zhi-Jie; Jin, Zhun-Zhi

2005-06-01

The dynamics of the blackmail propagation model based on small-world networks is investigated. It is found that for a given transmitting probability λ the dynamical behavior of blackmail propagation transits from linear growth type to logistical growth one with the network randomness p increases. The transition takes place at the critical network randomness pc=1/N, where N is the total number of nodes in the network. For a given network randomness p the dynamical behavior of blackmail propagation transits from exponential decrease type to logistical growth one with the transmitting probability λ increases. The transition occurs at the critical transmitting probability λc=1/, where is the average number of the nearest neighbors. The present work will be useful for understanding computer virus epidemics and other spreading phenomena on communication and social networks.

Organizational Strategy and Business Environment Effects Based on a Computation Method

NASA Astrophysics Data System (ADS)

Reklitis, Panagiotis; Konstantopoulos, Nikolaos; Trivellas, Panagiotis

2007-12-01

According to many researchers of organizational theory, a great number of problems encountered by the manufacturing firms are due to their ineffectiveness to respond to significant changes of their external environment and align their competitive strategy accordingly. From this point of view, the pursuit of the appropriate generic strategy is vital for firms facing a dynamic and highly competitive environment. In the present paper, we adopt Porter's typology to operationalise organizational strategy (cost leadership, innovative and marketing differentiation, and focus) considering changes in the external business environment (dynamism, complexity and munificence). Although simulation of social events is a quite difficult task, since there are so many considerations (not all well understood) involved, in the present study we developed a dynamic system based on the conceptual framework of strategy-environment associations.

EvoGraph: On-The-Fly Efficient Mining of Evolving Graphs on GPU

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sengupta, Dipanjan; Song, Shuaiwen

With the prevalence of the World Wide Web and social networks, there has been a growing interest in high performance analytics for constantly-evolving dynamic graphs. Modern GPUs provide massive AQ1 amount of parallelism for efficient graph processing, but the challenges remain due to their lack of support for the near real-time streaming nature of dynamic graphs. Specifically, due to the current high volume and velocity of graph data combined with the complexity of user queries, traditional processing methods by first storing the updates and then repeatedly running static graph analytics on a sequence of versions or snapshots are deemed undesirablemore » and computational infeasible on GPU. We present EvoGraph, a highly efficient and scalable GPU- based dynamic graph analytics framework.« less

Alignment of dynamic networks.

PubMed

Vijayan, V; Critchlow, D; Milenkovic, T

2017-07-15

Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems' static network representations, as is currently done. For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA ++. This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. Importantly, the two dynamic conservation measures can be optimized with any state-of-the-art NA method and not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to static NA, on synthetic and real-world networks, in computational biology and social domains. DynaMAGNA++ is parallelized and has a user-friendly graphical interface. http://nd.edu/∼cone/DynaMAGNA++/ . tmilenko@nd.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Alignment of dynamic networks

PubMed Central

Vijayan, V.; Critchlow, D.; Milenković, T.

2017-01-01

Abstract Motivation: Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems’ static network representations, as is currently done. Results: For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA ++. This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. Importantly, the two dynamic conservation measures can be optimized with any state-of-the-art NA method and not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to static NA, on synthetic and real-world networks, in computational biology and social domains. DynaMAGNA++ is parallelized and has a user-friendly graphical interface. Availability and implementation: http://nd.edu/∼cone/DynaMAGNA++/. Contact: tmilenko@nd.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28881980

Equifinality in empirical studies of cultural transmission.

PubMed

Barrett, Brendan J

2018-01-31

Cultural systems exhibit equifinal behavior - a single final state may be arrived at via different mechanisms and/or from different initial states. Potential for equifinality exists in all empirical studies of cultural transmission including controlled experiments, observational field research, and computational simulations. Acknowledging and anticipating the existence of equifinality is important in empirical studies of social learning and cultural evolution; it helps us understand the limitations of analytical approaches and can improve our ability to predict the dynamics of cultural transmission. Here, I illustrate and discuss examples of equifinality in studies of social learning, and how certain experimental designs might be prone to it. I then review examples of equifinality discussed in the social learning literature, namely the use of s-shaped diffusion curves to discern individual from social learning and operational definitions and analytical approaches used in studies of conformist transmission. While equifinality exists to some extent in all studies of social learning, I make suggestions for how to address instances of it, with an emphasis on using data simulation and methodological verification alongside modern statistical approaches that emphasize prediction and model comparison. In cases where evaluated learning mechanisms are equifinal due to non-methodological factors, I suggest that this is not always a problem if it helps us predict cultural change. In some cases, equifinal learning mechanisms might offer insight into how both individual learning, social learning strategies and other endogenous social factors might by important in structuring cultural dynamics and within- and between-group heterogeneity. Copyright © 2018 Elsevier B.V. All rights reserved.

Exploding the Black Box: Personal Computing, the Notebook Battery Crisis, and Postindustrial Systems Thinking.

PubMed

Eisler, Matthew N

Historians of science and technology have generally ignored the role of power sources in the development of consumer electronics. In this they have followed the predilections of historical actors. Research, development, and manufacturing of batteries has historically occurred at a social and intellectual distance from the research, development, and manufacturing of the devices they power. Nevertheless, power source technoscience should properly be understood as an allied yet estranged field of electronics. The separation between the fields has had important consequences for the design and manufacturing of mobile consumer electronics. This paper explores these dynamics in the co-construction of notebook batteries and computers. In so doing, it challenges assumptions of historians and industrial engineers and planners about the nature of computer systems in particular and the development of technological systems. The co-construction of notebook computers and batteries, and the occasional catastrophic failure of their compatibility, challenges systems thinking more generally.

High-Efficiency High-Resolution Global Model Developments at the NASA Goddard Data Assimilation Office

NASA Technical Reports Server (NTRS)

Lin, Shian-Jiann; Atlas, Robert (Technical Monitor)

2002-01-01

The Data Assimilation Office (DAO) has been developing a new generation of ultra-high resolution General Circulation Model (GCM) that is suitable for 4-D data assimilation, numerical weather predictions, and climate simulations. These three applications have conflicting requirements. For 4-D data assimilation and weather predictions, it is highly desirable to run the model at the highest possible spatial resolution (e.g., 55 km or finer) so as to be able to resolve and predict socially and economically important weather phenomena such as tropical cyclones, hurricanes, and severe winter storms. For climate change applications, the model simulations need to be carried out for decades, if not centuries. To reduce uncertainty in climate change assessments, the next generation model would also need to be run at a fine enough spatial resolution that can at least marginally simulate the effects of intense tropical cyclones. Scientific problems (e.g., parameterization of subgrid scale moist processes) aside, all three areas of application require the model's computational performance to be dramatically improved as compared to the previous generation. In this talk, I will present the current and future developments of the "finite-volume dynamical core" at the Data Assimilation Office. This dynamical core applies modem monotonicity preserving algorithms and is genuinely conservative by construction, not by an ad hoc fixer. The "discretization" of the conservation laws is purely local, which is clearly advantageous for resolving sharp gradient flow features. In addition, the local nature of the finite-volume discretization also has a significant advantage on distributed memory parallel computers. Together with a unique vertically Lagrangian control volume discretization that essentially reduces the dimension of the computational problem from three to two, the finite-volume dynamical core is very efficient, particularly at high resolutions. I will also present the computational design of the dynamical core using a hybrid distributed-shared memory programming paradigm that is portable to virtually any of today's high-end parallel super-computing clusters.

High-Efficiency High-Resolution Global Model Developments at the NASA Goddard Data Assimilation Office

NASA Technical Reports Server (NTRS)

Lin, Shian-Jiann; Atlas, Robert (Technical Monitor)

2002-01-01

The Data Assimilation Office (DAO) has been developing a new generation of ultra-high resolution General Circulation Model (GCM) that is suitable for 4-D data assimilation, numerical weather predictions, and climate simulations. These three applications have conflicting requirements. For 4-D data assimilation and weather predictions, it is highly desirable to run the model at the highest possible spatial resolution (e.g., 55 kin or finer) so as to be able to resolve and predict socially and economically important weather phenomena such as tropical cyclones, hurricanes, and severe winter storms. For climate change applications, the model simulations need to be carried out for decades, if not centuries. To reduce uncertainty in climate change assessments, the next generation model would also need to be run at a fine enough spatial resolution that can at least marginally simulate the effects of intense tropical cyclones. Scientific problems (e.g., parameterization of subgrid scale moist processes) aside, all three areas of application require the model's computational performance to be dramatically improved as compared to the previous generation. In this talk, I will present the current and future developments of the "finite-volume dynamical core" at the Data Assimilation Office. This dynamical core applies modem monotonicity preserving algorithms and is genuinely conservative by construction, not by an ad hoc fixer. The "discretization" of the conservation laws is purely local, which is clearly advantageous for resolving sharp gradient flow features. In addition, the local nature of the finite-volume discretization also has a significant advantage on distributed memory parallel computers. Together with a unique vertically Lagrangian control volume discretization that essentially reduces the dimension of the computational problem from three to two, the finite-volume dynamical core is very efficient, particularly at high resolutions. I will also present the computational design of the dynamical core using a hybrid distributed- shared memory programming paradigm that is portable to virtually any of today's high-end parallel super-computing clusters.

Virtual Reality for Research in Social Neuroscience.

PubMed

Parsons, Thomas D; Gaggioli, Andrea; Riva, Giuseppe

2017-04-16

The emergence of social neuroscience has significantly advanced our understanding of the relationship that exists between social processes and their neurobiological underpinnings. Social neuroscience research often involves the use of simple and static stimuli lacking many of the potentially important aspects of real world activities and social interactions. Whilst this research has merit, there is a growing interest in the presentation of dynamic stimuli in a manner that allows researchers to assess the integrative processes carried out by perceivers over time. Herein, we discuss the potential of virtual reality for enhancing ecological validity while maintaining experimental control in social neuroscience research. Virtual reality is a technology that allows for the creation of fully interactive, three-dimensional computerized models of social situations that can be fully controlled by the experimenter. Furthermore, the introduction of interactive virtual characters-either driven by a human or by a computer-allows the researcher to test, in a systematic and independent manner, the effects of various social cues. We first introduce key technical features and concepts related to virtual reality. Next, we discuss the potential of this technology for enhancing social neuroscience protocols, drawing on illustrative experiments from the literature.

Social determinants of health inequalities: towards a theoretical perspective using systems science.

PubMed

Jayasinghe, Saroj

2015-08-25

A systems approach offers a novel conceptualization to natural and social systems. In recent years, this has led to perceiving population health outcomes as an emergent property of a dynamic and open, complex adaptive system. The current paper explores these themes further and applies the principles of systems approach and complexity science (i.e. systems science) to conceptualize social determinants of health inequalities. The conceptualization can be done in two steps: viewing health inequalities from a systems approach and extending it to include complexity science. Systems approach views health inequalities as patterns within the larger rubric of other facets of the human condition, such as educational outcomes and economic development. This anlysis requires more sophisticated models such as systems dynamic models. An extension of the approach is to view systems as complex adaptive systems, i.e. systems that are 'open' and adapt to the environment. They consist of dynamic adapting subsystems that exhibit non-linear interactions, while being 'open' to a similarly dynamic environment of interconnected systems. They exhibit emergent properties that cannot be estimated with precision by using the known interactions among its components (such as economic development, political freedom, health system, culture etc.). Different combinations of the same bundle of factors or determinants give rise to similar patterns or outcomes (i.e. property of convergence), and minor variations in the initial condition could give rise to widely divergent outcomes. Novel approaches using computer simulation models (e.g. agent-based models) would shed light on possible mechanisms as to how factors or determinants interact and lead to emergent patterns of health inequalities of populations.

Studies of Opinion Stability for Small Dynamic Networks with Opportunistic Agents

NASA Astrophysics Data System (ADS)

Sobkowicz, Pawel

There are numerous examples of societies with extremely stable mix of contrasting opinions. We argue that this stability is a result of an interplay between society network topology adjustment and opinion changing processes. To support this position we present a computer model of opinion formation based on some novel assumptions, designed to bring the model closer to social reality. In our model, the agents, in addition to changing their opinions due to influence of the rest of society and external propaganda, have the ability to modify their social network, forming links with agents sharing the same opinions and cutting the links with those they disagree with. To improve the model further we divide the agents into "fanatics" and "opportunists," depending on how easy it is to change their opinions. The simulations show significant differences compared to traditional models, where network links are static. In particular, for the dynamical model where inter-agent links are adjustable, the final network structure and opinion distribution is shown to resemble real world observations, such as social structures and persistence of minority groups even when most of the society is against them and the propaganda is strong.

Transmission of severe acute respiratory syndrome in dynamical small-world networks

NASA Astrophysics Data System (ADS)

Masuda, Naoki; Konno, Norio; Aihara, Kazuyuki

2004-03-01

The outbreak of severe acute respiratory syndrome (SARS) is still threatening the world because of a possible resurgence. In the current situation that effective medical treatments such as antiviral drugs are not discovered yet, dynamical features of the epidemics should be clarified for establishing strategies for tracing, quarantine, isolation, and regulating social behavior of the public at appropriate costs. Here we propose a network model for SARS epidemics and discuss why superspreaders emerged and why SARS spread especially in hospitals, which were key factors of the recent outbreak. We suggest that superspreaders are biologically contagious patients, and they may amplify the spreads by going to potentially contagious places such as hospitals. To avoid mass transmission in hospitals, it may be a good measure to treat suspected cases without hospitalizing them. Finally, we indicate that SARS probably propagates in small-world networks associated with human contacts and that the biological nature of individuals and social group properties are factors more important than the heterogeneous rates of social contacts among individuals. This is in marked contrast with epidemics of sexually transmitted diseases or computer viruses to which scale-free network models often apply.

Data-driven modelling of social forces and collective behaviour in zebrafish.

PubMed

Zienkiewicz, Adam K; Ladu, Fabrizio; Barton, David A W; Porfiri, Maurizio; Bernardo, Mario Di

2018-04-14

Zebrafish are rapidly emerging as a powerful model organism in hypothesis-driven studies targeting a number of functional and dysfunctional processes. Mathematical models of zebrafish behaviour can inform the design of experiments, through the unprecedented ability to perform pilot trials on a computer. At the same time, in-silico experiments could help refining the analysis of real data, by enabling the systematic investigation of key neurobehavioural factors. Here, we establish a data-driven model of zebrafish social interaction. Specifically, we derive a set of interaction rules to capture the primary response mechanisms which have been observed experimentally. Contrary to previous studies, we include dynamic speed regulation in addition to turning responses, which together provide attractive, repulsive and alignment interactions between individuals. The resulting multi-agent model provides a novel, bottom-up framework to describe both the spontaneous motion and individual-level interaction dynamics of zebrafish, inferred directly from experimental observations. Copyright © 2018 Elsevier Ltd. All rights reserved.

Killer Apps: Developing Novel Applications That Enhance Team Coordination, Communication, and Effectiveness

PubMed Central

Buengeler, Claudia; Klonek, Florian; Lehmann-Willenbrock, Nale; Morency, Louis-Philippe; Poppe, Ronald

2017-01-01

As part of the Lorentz workshop, “Interdisciplinary Insights into Group and Team Dynamics,” held in Leiden, Netherlands, this article describes how Geeks and Groupies (computer and social scientists) may benefit from interdisciplinary collaboration toward the development of killer apps in team contexts that are meaningful and challenging for both. First, we discuss interaction processes during team meetings as a research topic for both Groupies and Geeks. Second, we highlight teamwork in health care settings as an interdisciplinary research challenge. Third, we discuss how an automated solution for optimal team design could benefit team effectiveness and feed into team-based interventions. Fourth, we discuss team collaboration in massive open online courses as a challenge for both Geeks and Groupies. We argue for the necessary integration of social and computational research insights and approaches. In the hope of inspiring future interdisciplinary collaborations, we develop criteria for evaluating killer apps—including the four proposed here—and discuss future research challenges and opportunities that potentially derive from these developments. PMID:28989264

Cybersim: geographic, temporal, and organizational dynamics of malware propagation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Santhi, Nandakishore; Yan, Guanhua; Eidenbenz, Stephan

2010-01-01

Cyber-infractions into a nation's strategic security envelope pose a constant and daunting challenge. We present the modular CyberSim tool which has been developed in response to the need to realistically simulate at a national level, software vulnerabilities and resulting mal ware propagation in online social networks. CyberSim suite (a) can generate realistic scale-free networks from a database of geocoordinated computers to closely model social networks arising from personal and business email contacts and online communities; (b) maintains for each,bost a list of installed software, along with the latest published vulnerabilities; (d) allows designated initial nodes where malware gets introduced; (e)more » simulates, using distributed discrete event-driven technology, the spread of malware exploiting a specific vulnerability, with packet delay and user online behavior models; (f) provides a graphical visualization of spread of infection, its severity, businesses affected etc to the analyst. We present sample simulations on a national level network with millions of computers.« less

Killer Apps: Developing Novel Applications That Enhance Team Coordination, Communication, and Effectiveness.

PubMed

Buengeler, Claudia; Klonek, Florian; Lehmann-Willenbrock, Nale; Morency, Louis-Philippe; Poppe, Ronald

2017-10-01

As part of the Lorentz workshop, "Interdisciplinary Insights into Group and Team Dynamics," held in Leiden, Netherlands, this article describes how Geeks and Groupies (computer and social scientists) may benefit from interdisciplinary collaboration toward the development of killer apps in team contexts that are meaningful and challenging for both. First, we discuss interaction processes during team meetings as a research topic for both Groupies and Geeks. Second, we highlight teamwork in health care settings as an interdisciplinary research challenge. Third, we discuss how an automated solution for optimal team design could benefit team effectiveness and feed into team-based interventions. Fourth, we discuss team collaboration in massive open online courses as a challenge for both Geeks and Groupies. We argue for the necessary integration of social and computational research insights and approaches. In the hope of inspiring future interdisciplinary collaborations, we develop criteria for evaluating killer apps-including the four proposed here-and discuss future research challenges and opportunities that potentially derive from these developments.

Opinion dynamics on interacting networks: media competition and social influence

PubMed Central

Quattrociocchi, Walter; Caldarelli, Guido; Scala, Antonio

2014-01-01

The inner dynamics of the multiple actors of the informations systems – i.e, T.V., newspapers, blogs, social network platforms, – play a fundamental role on the evolution of the public opinion. Coherently with the recent history of the information system (from few main stream media to the massive diffusion of socio-technical system), in this work we investigate how main stream media signed interaction might shape the opinion space. In particular we focus on how different size (in the number of media) and interaction patterns of the information system may affect collective debates and thus the opinions' distribution. We introduce a sophisticated computational model of opinion dynamics which accounts for the coexistence of media and gossip as separated mechanisms and for their feedback loops. The model accounts also for the effect of the media communication patterns by considering both the simple case where each medium mimics the behavior of the most successful one (to maximize the audience) and the case where there is polarization and thus competition among media memes. We show that plurality and competition within information sources lead to stable configurations where several and distant cultures coexist. PMID:24861995

Social networks and spreading of epidemics

NASA Astrophysics Data System (ADS)

Trimper, Steffen; Zheng, Dafang; Brandau, Marian

2004-05-01

Epidemiological processes are studied within a recently proposed social network model using the susceptible-infected-refractory dynamics (SIR) of an epidemic. Within the network model, a population of individuals may be characterized by H independent hierarchies or dimensions, each of which consists of groupings of individuals into layers of subgroups. Detailed numerical simulations reveals that for H > 1, the global spreading results regardless of the degree of homophily α of the individuals forming a social circle. For H = 1, a transition from a global to a local spread occurs as the population becomes decomposed into increasingly homophilous groups. Multiple dimensions in classifying individuals (nodes) thus make a society (computer network) highly susceptible to large scale outbreaks of infectious diseases (viruses). The SIR-model can be extended by the inclusion of waiting times resulting in modified distribution function of the recovered.

Multiobjective Optimal Control Methodology for the Analysis of Certain Sociodynamic Problems

DTIC Science & Technology

2009-03-01

but less expensive in both time and memory. 137 References [1] R. Albert and A-L Barabasi. Statistical mechanics of complex networks. Reviews of Modern...Review, E(51):4282–4286, 1995. [24] D. Helbing, P. Molnar, and F. Schweitzer . Computer simulation of pedestrian dynamics and trail formation. May 1998...Patterson AFB, OH, 2001. [49] F. Schweitzer . Brownian Agents and Active Particles. Springer, Santa Fe, NM, 2003. [50] P. Sen. Complexities of social

Crowd Sensing-Enabling Security Service Recommendation for Social Fog Computing Systems

PubMed Central

Wu, Jun; Su, Zhou; Li, Jianhua

2017-01-01

Fog computing, shifting intelligence and resources from the remote cloud to edge networks, has the potential of providing low-latency for the communication from sensing data sources to users. For the objects from the Internet of Things (IoT) to the cloud, it is a new trend that the objects establish social-like relationships with each other, which efficiently brings the benefits of developed sociality to a complex environment. As fog service become more sophisticated, it will become more convenient for fog users to share their own services, resources, and data via social networks. Meanwhile, the efficient social organization can enable more flexible, secure, and collaborative networking. Aforementioned advantages make the social network a potential architecture for fog computing systems. In this paper, we design an architecture for social fog computing, in which the services of fog are provisioned based on “friend” relationships. To the best of our knowledge, this is the first attempt at an organized fog computing system-based social model. Meanwhile, social networking enhances the complexity and security risks of fog computing services, creating difficulties of security service recommendations in social fog computing. To address this, we propose a novel crowd sensing-enabling security service provisioning method to recommend security services accurately in social fog computing systems. Simulation results show the feasibilities and efficiency of the crowd sensing-enabling security service recommendation method for social fog computing systems. PMID:28758943

Crowd Sensing-Enabling Security Service Recommendation for Social Fog Computing Systems.

PubMed

Wu, Jun; Su, Zhou; Wang, Shen; Li, Jianhua

2017-07-30

Fog computing, shifting intelligence and resources from the remote cloud to edge networks, has the potential of providing low-latency for the communication from sensing data sources to users. For the objects from the Internet of Things (IoT) to the cloud, it is a new trend that the objects establish social-like relationships with each other, which efficiently brings the benefits of developed sociality to a complex environment. As fog service become more sophisticated, it will become more convenient for fog users to share their own services, resources, and data via social networks. Meanwhile, the efficient social organization can enable more flexible, secure, and collaborative networking. Aforementioned advantages make the social network a potential architecture for fog computing systems. In this paper, we design an architecture for social fog computing, in which the services of fog are provisioned based on "friend" relationships. To the best of our knowledge, this is the first attempt at an organized fog computing system-based social model. Meanwhile, social networking enhances the complexity and security risks of fog computing services, creating difficulties of security service recommendations in social fog computing. To address this, we propose a novel crowd sensing-enabling security service provisioning method to recommend security services accurately in social fog computing systems. Simulation results show the feasibilities and efficiency of the crowd sensing-enabling security service recommendation method for social fog computing systems.

A Novel Interdisciplinary Approach to Socio-Technical Complexity

NASA Astrophysics Data System (ADS)

Bassetti, Chiara

The chapter presents a novel interdisciplinary approach that integrates micro-sociological analysis into computer-vision and pattern-recognition modeling and algorithms, the purpose being to tackle socio-technical complexity at a systemic yet micro-grounded level. The approach is empirically-grounded and both theoretically- and analytically-driven, yet systemic and multidimensional, semi-supervised and computable, and oriented towards large scale applications. The chapter describes the proposed approach especially as for its sociological foundations, and as applied to the analysis of a particular setting --i.e. sport-spectator crowds. Crowds, better defined as large gatherings, are almost ever-present in our societies, and capturing their dynamics is crucial. From social sciences to public safety management and emergency response, modeling and predicting large gatherings' presence and dynamics, thus possibly preventing critical situations and being able to properly react to them, is fundamental. This is where semi/automated technologies can make the difference. The work presented in this chapter is intended as a scientific step towards such an objective.

Dynamic online surveys and experiments with the free open-source software dynQuest.

PubMed

Rademacher, Jens D M; Lippke, Sonia

2007-08-01

With computers and the World Wide Web widely available, collecting data through Web browsers is an attractive method utilized by the social sciences. In this article, conducting PC- and Web-based trials with the software package dynQuest is described. The software manages dynamic questionnaire-based trials over the Internet or on single computers, possibly as randomized control trials (RCT), if two or more groups are involved. The choice of follow-up questions can depend on previous responses, as needed for matched interventions. Data are collected in a simple text-based database that can be imported easily into other programs for postprocessing and statistical analysis. The software consists of platform-independent scripts written in the programming language PERL that use the common gateway interface between Web browser and server for submission of data through HTML forms. Advantages of dynQuest are parsimony, simplicity in use and installation, transparency, and reliability. The program is available as open-source freeware from the authors.

Integrating System Dynamics and Bayesian Networks with Application to Counter-IED Scenarios

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jarman, Kenneth D.; Brothers, Alan J.; Whitney, Paul D.

2010-06-06

The practice of choosing a single modeling paradigm for predictive analysis can limit the scope and relevance of predictions and their utility to decision-making processes. Considering multiple modeling methods simultaneously may improve this situation, but a better solution provides a framework for directly integrating different, potentially complementary modeling paradigms to enable more comprehensive modeling and predictions, and thus better-informed decisions. The primary challenges of this kind of model integration are to bridge language and conceptual gaps between modeling paradigms, and to determine whether natural and useful linkages can be made in a formal mathematical manner. To address these challenges inmore » the context of two specific modeling paradigms, we explore mathematical and computational options for linking System Dynamics (SD) and Bayesian network (BN) models and incorporating data into the integrated models. We demonstrate that integrated SD/BN models can naturally be described as either state space equations or Dynamic Bayes Nets, which enables the use of many existing computational methods for simulation and data integration. To demonstrate, we apply our model integration approach to techno-social models of insurgent-led attacks and security force counter-measures centered on improvised explosive devices.« less

Comparing the social skills of students addicted to computer games with normal students.

PubMed

Zamani, Eshrat; Kheradmand, Ali; Cheshmi, Maliheh; Abedi, Ahmad; Hedayati, Nasim

2010-01-01

This study aimed to investigate and compare the social skills of studentsaddicted to computer games with normal students. The dependentvariable in the present study is the social skills. The study population included all the students in the second grade ofpublic secondary school in the city of Isfahan at the educational year of2009-2010. The sample size included 564 students selected using thecluster random sampling method. Data collection was conducted usingQuestionnaire of Addiction to Computer Games and Social SkillsQuestionnaire (The Teenage Inventory of Social Skill or TISS). The results of the study showed that generally, there was a significantdifference between the social skills of students addicted to computer gamesand normal students. In addition, the results indicated that normal studentshad a higher level of social skills in comparison with students addicted tocomputer games. As the study results showed, addiction to computer games may affectthe quality and quantity of social skills. In other words, the higher theaddiction to computer games, the less the social skills. The individualsaddicted to computer games have less social skills.).

Comparing the Social Skills of Students Addicted to Computer Games with Normal Students

PubMed Central

Zamani, Eshrat; Kheradmand, Ali; Cheshmi, Maliheh; Abedi, Ahmad; Hedayati, Nasim

2010-01-01

Background This study aimed to investigate and compare the social skills of studentsaddicted to computer games with normal students. The dependentvariable in the present study is the social skills. Methods The study population included all the students in the second grade ofpublic secondary school in the city of Isfahan at the educational year of2009-2010. The sample size included 564 students selected using thecluster random sampling method. Data collection was conducted usingQuestionnaire of Addiction to Computer Games and Social SkillsQuestionnaire (The Teenage Inventory of Social Skill or TISS). Findings The results of the study showed that generally, there was a significantdifference between the social skills of students addicted to computer gamesand normal students. In addition, the results indicated that normal studentshad a higher level of social skills in comparison with students addicted tocomputer games. Conclusion As the study results showed, addiction to computer games may affectthe quality and quantity of social skills. In other words, the higher theaddiction to computer games, the less the social skills. The individualsaddicted to computer games have less social skills.). PMID:24494102

Network dynamics of social influence in the wisdom of crowds

PubMed Central

Brackbill, Devon; Centola, Damon

2017-01-01

A longstanding problem in the social, biological, and computational sciences is to determine how groups of distributed individuals can form intelligent collective judgments. Since Galton’s discovery of the “wisdom of crowds” [Galton F (1907) Nature 75:450–451], theories of collective intelligence have suggested that the accuracy of group judgments requires individuals to be either independent, with uncorrelated beliefs, or diverse, with negatively correlated beliefs [Page S (2008) The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies]. Previous experimental studies have supported this view by arguing that social influence undermines the wisdom of crowds. These results showed that individuals’ estimates became more similar when subjects observed each other’s beliefs, thereby reducing diversity without a corresponding increase in group accuracy [Lorenz J, Rauhut H, Schweitzer F, Helbing D (2011) Proc Natl Acad Sci USA 108:9020–9025]. By contrast, we show general network conditions under which social influence improves the accuracy of group estimates, even as individual beliefs become more similar. We present theoretical predictions and experimental results showing that, in decentralized communication networks, group estimates become reliably more accurate as a result of information exchange. We further show that the dynamics of group accuracy change with network structure. In centralized networks, where the influence of central individuals dominates the collective estimation process, group estimates become more likely to increase in error. PMID:28607070

Network dynamics of social influence in the wisdom of crowds.

PubMed

Becker, Joshua; Brackbill, Devon; Centola, Damon

2017-06-27

A longstanding problem in the social, biological, and computational sciences is to determine how groups of distributed individuals can form intelligent collective judgments. Since Galton's discovery of the "wisdom of crowds" [Galton F (1907) Nature 75:450-451], theories of collective intelligence have suggested that the accuracy of group judgments requires individuals to be either independent, with uncorrelated beliefs, or diverse, with negatively correlated beliefs [Page S (2008) The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies ]. Previous experimental studies have supported this view by arguing that social influence undermines the wisdom of crowds. These results showed that individuals' estimates became more similar when subjects observed each other's beliefs, thereby reducing diversity without a corresponding increase in group accuracy [Lorenz J, Rauhut H, Schweitzer F, Helbing D (2011) Proc Natl Acad Sci USA 108:9020-9025]. By contrast, we show general network conditions under which social influence improves the accuracy of group estimates, even as individual beliefs become more similar. We present theoretical predictions and experimental results showing that, in decentralized communication networks, group estimates become reliably more accurate as a result of information exchange. We further show that the dynamics of group accuracy change with network structure. In centralized networks, where the influence of central individuals dominates the collective estimation process, group estimates become more likely to increase in error.

Detection of communities with Naming Game-based methods

PubMed Central

Ribeiro, Carlos Henrique Costa

2017-01-01

Complex networks are often organized in groups or communities of agents that share the same features and/or functions, and this structural organization is built naturally with the formation of the system. In social networks, we argue that the dynamic of linguistic interactions of agreement among people can be a crucial factor in generating this community structure, given that sharing opinions with another person bounds them together, and disagreeing constantly would probably weaken the relationship. We present here a computational model of opinion exchange that uncovers the community structure of a network. Our aim is not to present a new community detection method proper, but to show how a model of social communication dynamics can reveal the (simple and overlapping) community structure in an emergent way. Our model is based on a standard Naming Game, but takes into consideration three social features: trust, uncertainty and opinion preference, that are built over time as agents communicate among themselves. We show that the separate addition of each social feature in the Naming Game results in gradual improvements with respect to community detection. In addition, the resulting uncertainty and trust values classify nodes and edges according to role and position in the network. Also, our model has shown a degree of accuracy both for non-overlapping and overlapping communities that are comparable with most algorithms specifically designed for topological community detection. PMID:28797097

Social opinion dynamics is not chaotic

NASA Astrophysics Data System (ADS)

Lim, Chjan; Zhang, Weituo

2016-08-01

Motivated by the research on social opinion dynamics over large and dense networks, a general framework for verifying the monotonicity property of multi-agent dynamics is introduced. This allows a derivation of sociologically meaningful sufficient conditions for monotonicity that are tailor-made for social opinion dynamics, which typically have high nonlinearity. A direct consequence of monotonicity is that social opinion dynamics is nonchaotic. A key part of this framework is the definition of a partial order relation that is suitable for a large class of social opinion dynamics such as the generalized naming games. Comparisons are made to previous techniques to verify monotonicity. Using the results obtained, we extend many of the consequences of monotonicity to this class of social dynamics, including several corollaries on their asymptotic behavior, such as global convergence to consensus and tipping points of a minority fraction of zealots or leaders.

Exemplary Social Studies Teachers Use of Computer-Supported Instruction in the Classroom

ERIC Educational Resources Information Center

Acikalin, Mehmet

2010-01-01

Educators increasingly support the use of computer-supported instruction in social studies education. However few studies have been conducted to study teacher use of computer-supported instruction in social studies education. This study was therefore designed to examine the use of exemplary social studies teachers' computer-supported instruction…

Modified two-layer social force model for emergency earthquake evacuation

NASA Astrophysics Data System (ADS)

Zhang, Hao; Liu, Hong; Qin, Xin; Liu, Baoxi

2018-02-01

Studies of crowd behavior with related research on computer simulation provide an effective basis for architectural design and effective crowd management. Based on low-density group organization patterns, a modified two-layer social force model is proposed in this paper to simulate and reproduce a group gathering process. First, this paper studies evacuation videos from the Luan'xian earthquake in 2012, and extends the study of group organization patterns to a higher density. Furthermore, taking full advantage of the strength in crowd gathering simulations, a new method on grouping and guidance is proposed while using crowd dynamics. Second, a real-life grouping situation in earthquake evacuation is simulated and reproduced. Comparing with the fundamental social force model and existing guided crowd model, the modified model reduces congestion time and truly reflects group behaviors. Furthermore, the experiment result also shows that a stable group pattern and a suitable leader could decrease collision and allow a safer evacuation process.

On Market-Based Coordination of Thermostatically Controlled Loads With User Preference

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Sen; Zhang, Wei; Lian, Jianming

2014-12-15

This paper presents a market-based control framework to coordinate a group of autonomous Thermostatically Controlled Loads (TCL) to achieve the system-level objectives with pricing incentives. The problem is formulated as maximizing the social welfare subject to feeder power constraint. It allows the coordinator to affect the aggregated power of a group of dynamical systems, and creates an interactive market where the users and the coordinator cooperatively determine the optimal energy allocation and energy price. The optimal pricing strategy is derived, which maximizes social welfare while respecting the feeder power constraint. The bidding strategy is also designed to compute the optimalmore » price in real time (e.g., every 5 minutes) based on local device information. The coordination framework is validated with realistic simulations in GridLab-D. Extensive simulation results demonstrate that the proposed approach effectively maximizes the social welfare and decreases power congestion at key times.« less

Assessing Behavioral Stages From Social Media Data.

PubMed

Liu, Jason; Weitzman, Elissa R; Chunara, Rumi

2017-01-01

Important work rooted in psychological theory posits that health behavior change occurs through a series of discrete stages. Our work builds on the field of social computing by identifying how social media data can be used to resolve behavior stages at high resolution (e.g. hourly/daily) for key population subgroups and times. In essence this approach opens new opportunities to advance psychological theories and better understand how our health is shaped based on the real, dynamic, and rapid actions we make every day. To do so, we bring together domain knowledge and machine learning methods to form a hierarchical classification of Twitter data that resolves different stages of behavior. We identify and examine temporal patterns of the identified stages, with alcohol as a use case (planning or looking to drink, currently drinking, and reflecting on drinking). Known seasonal trends are compared with findings from our methods. We discuss the potential health policy implications of detecting high frequency behavior stages.

Integrating GIS and ABM to Explore Spatiotemporal Dynamics

NASA Astrophysics Data System (ADS)

Sun, M.; Jiang, Y.; Yang, C.

2013-12-01

Agent-based modeling as a methodology for the bottom-up exploration with the account of adaptive behavior and heterogeneity of system components can help discover the development and pattern of the complex social and environmental system. However, ABM is a computationally intensive process especially when the number of system components becomes large and the agent-agent/agent-environmental interaction is modeled very complex. Most of traditional ABM frameworks developed based on CPU do not have a satisfying computing capacity. To address the problem and as the emergence of advanced techniques, GPU computing with CUDA can provide powerful parallel structure to enable the complex simulation of spatiotemporal dynamics. In this study, we first develop a GPU-based ABM system. Secondly, in order to visualize the dynamics generated from the movement of agent and the change of agent/environmental attributes during the simulation, we integrate GIS into the ABM system. Advanced geovisualization technologies can be utilized for representing the spatiotemporal change events, such as proper 2D/3D maps with state-of-the-art symbols, space-time cube and multiple layers each of which presents pattern in one time-stamp, etc. Thirdly, visual analytics which include interactive tools (e.g. grouping, filtering, linking, etc.) is included in our ABM-GIS system to help users conduct real-time data exploration during the progress of simulation. Analysis like flow analysis and spatial cluster analysis can be integrated according to the geographical problem we want to explore.

Matrix product algorithm for stochastic dynamics on networks applied to nonequilibrium Glauber dynamics

NASA Astrophysics Data System (ADS)

Barthel, Thomas; De Bacco, Caterina; Franz, Silvio

2018-01-01

We introduce and apply an efficient method for the precise simulation of stochastic dynamical processes on locally treelike graphs. Networks with cycles are treated in the framework of the cavity method. Such models correspond, for example, to spin-glass systems, Boolean networks, neural networks, or other technological, biological, and social networks. Building upon ideas from quantum many-body theory, our approach is based on a matrix product approximation of the so-called edge messages—conditional probabilities of vertex variable trajectories. Computation costs and accuracy can be tuned by controlling the matrix dimensions of the matrix product edge messages (MPEM) in truncations. In contrast to Monte Carlo simulations, the algorithm has a better error scaling and works for both single instances as well as the thermodynamic limit. We employ it to examine prototypical nonequilibrium Glauber dynamics in the kinetic Ising model. Because of the absence of cancellation effects, observables with small expectation values can be evaluated accurately, allowing for the study of decay processes and temporal correlations.

Computer Simulation in the Social Sciences/Social Studies.

ERIC Educational Resources Information Center

Klassen, Daniel L.

Computers are beginning to be used more frequently as instructional tools in secondary school social studies. This is especially true of "new social studies" programs; i.e., programs which subordinate mere mastery of factual content to the recognition of and ability to deal with the social imperatives of the future. Computer-assisted…

Preliminary evaluation of the effect of a dynamic preferential runway system upon community noise disturbance

NASA Technical Reports Server (NTRS)

Patterson, H. P.; Edmiston, R. P.; Connor, W. K.

1972-01-01

A dynamic preferential runway system (DPRS) was developed for John F. Kennedy International Airport for the purpose of controlling short term noise exposure in the neighboring communities. The DPRS is a computer-aided procedure for optimum selection of runways from the standpoint of noise and is based upon a community disturbance model which takes into account flyover levels, size of exposed populations, time of day and week, and persistence of overflights. A preliminary evaluation of the DPRS is presented on the basis of social survey data and telephone complaint records, for the trial period of August and September, 1971. Comparative use is made of data taken in a previous survey of the same community areas in 1969.

Bridging Social and Semantic Computing - Design and Evaluation of User Interfaces for Hybrid Systems

ERIC Educational Resources Information Center

Bostandjiev, Svetlin Alex I.

2012-01-01

The evolution of the Web brought new interesting problems to computer scientists that we loosely classify in the fields of social and semantic computing. Social computing is related to two major paradigms: computations carried out by a large amount of people in a collective intelligence fashion (i.e. wikis), and performing computations on social…

Business Models of E-Government: Research on Dynamic E-Government Based on Web Services

NASA Astrophysics Data System (ADS)

Li, Yan; Yang, Jiumin

Government transcends all sectors in a society. It provides not only the legal, political and economic infrastructure to support other sectors, but also exerts significant influence on the social factors that contribute to their development. With its maturity of technologies and management, e-government will eventually enter into the time of 'one-stop' services. Among others, the technology of Web services is the major contributor to this achievement. Web services provides a new way of standard-based software technology, letting programmers combine existing computer system in new ways over the Internet within one business or across many, and would thereby bring about profound and far-reaching impacts on e-government. This paper introduced the business modes of e-government, architecture of dynamic e-government and its key technologies. Finally future prospect of dynamic e-government was also briefly discussed.

Exploring the dynamics of collective cognition using a computational model of cognitive dissonance

NASA Astrophysics Data System (ADS)

Smart, Paul R.; Sycara, Katia; Richardson, Darren P.

2013-05-01

The socially-distributed nature of cognitive processing in a variety of organizational settings means that there is increasing scientific interest in the factors that affect collective cognition. In military coalitions, for example, there is a need to understand how factors such as communication network topology, trust, cultural differences and the potential for miscommunication affects the ability of distributed teams to generate high quality plans, to formulate effective decisions and to develop shared situation awareness. The current paper presents a computational model and associated simulation capability for performing in silico experimental analyses of collective sensemaking. This model can be used in combination with the results of human experimental studies in order to improve our understanding of the factors that influence collective sensemaking processes.

Loyalty to Computer Terminals: Is it Anthropomorphism or Consistency?

ERIC Educational Resources Information Center

Sundar, S. Shyam

2004-01-01

The psychological tendency to behave socially with a computer is quite well documented in the literature. But does the short-term socialness of human-computer interaction extend over to long-term social relationships with computers? In particular, do we show loyalty to particular computer terminals over a period of time? An electronic observation…

Inverse targeting —An effective immunization strategy

NASA Astrophysics Data System (ADS)

Schneider, C. M.; Mihaljev, T.; Herrmann, H. J.

2012-05-01

We propose a new method to immunize populations or computer networks against epidemics which is more efficient than any continuous immunization method considered before. The novelty of our method resides in the way of determining the immunization targets. First we identify those individuals or computers that contribute the least to the disease spreading measured through their contribution to the size of the largest connected cluster in the social or a computer network. The immunization process follows the list of identified individuals or computers in inverse order, immunizing first those which are most relevant for the epidemic spreading. We have applied our immunization strategy to several model networks and two real networks, the Internet and the collaboration network of high-energy physicists. We find that our new immunization strategy is in the case of model networks up to 14%, and for real networks up to 33% more efficient than immunizing dynamically the most connected nodes in a network. Our strategy is also numerically efficient and can therefore be applied to large systems.

Citizen social science: a methodology to facilitate and evaluate workplace learning in continuing interprofessional education.

PubMed

Dadich, Ann

2014-05-01

Workplace learning in continuing interprofessional education (CIPE) can be difficult to facilitate and evaluate, which can create a number of challenges for this type of learning. This article presents an innovative method to foster and investigate workplace learning in CIPE - citizen social science. Citizen social science involves clinicians as co-researchers in the systematic examination of social phenomena. When facilitated by an open-source online social networking platform, clinicians can participate via computer, smartphone, or tablet in ways that suit their needs and preferences. Furthermore, as co-researchers they can help to reveal the dynamic interplay that facilitates workplace learning in CIPE. Although yet to be tested, citizen social science offers four potential benefits: it recognises and accommodates the complexity of workplace learning in CIPE; it has the capacity to both foster and evaluate the phenomena; it can be used in situ, capturing and having direct relevance to the complexity of the workplace; and by advancing both theoretical and methodological debates on CIPE, it may reveal opportunities to improve and sustain workplace learning. By describing an example situated in the youth health sector, this article demonstrates how these benefits might be realised.

Hacking Social Networks: Examining the Viability of Using Computer Network Attack Against Social Networks

DTIC Science & Technology

2007-03-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS Approved for public release; distribution is unlimited. HACKING SOCIAL NETWORKS : EXAMINING THE...VIABILITY OF USING COMPUTER NETWORK ATTACK AGAINST SOCIAL NETWORKS by Russell G. Schuhart II March 2007 Thesis Advisor: David Tucker Second Reader...Master’s Thesis 4. TITLE AND SUBTITLE: Hacking Social Networks : Examining the Viability of Using Computer Network Attack Against Social Networks 6. AUTHOR

Building a Community of Research Practice: Intragroup Team Social Dynamics in Interdisciplinary Mixed Methods

ERIC Educational Resources Information Center

Hemmings, Annette; Beckett, Gulbahar; Kennerly, Susan; Yap, Tracey

2013-01-01

This article explicates the intragroup social dynamics and work of a nursing and education research team as a community of research practice interested in organizational cultures and occupational subcultures. Dynamics were characterized by processes of socialization through reeducation and group social identity formation that enabled members to…

Entropy of dynamical social networks

NASA Astrophysics Data System (ADS)

Zhao, Kun; Karsai, Marton; Bianconi, Ginestra

2012-02-01

Dynamical social networks are evolving rapidly and are highly adaptive. Characterizing the information encoded in social networks is essential to gain insight into the structure, evolution, adaptability and dynamics. Recently entropy measures have been used to quantify the information in email correspondence, static networks and mobility patterns. Nevertheless, we still lack methods to quantify the information encoded in time-varying dynamical social networks. In this talk we present a model to quantify the entropy of dynamical social networks and use this model to analyze the data of phone-call communication. We show evidence that the entropy of the phone-call interaction network changes according to circadian rhythms. Moreover we show that social networks are extremely adaptive and are modified by the use of technologies such as mobile phone communication. Indeed the statistics of duration of phone-call is described by a Weibull distribution and is significantly different from the distribution of duration of face-to-face interactions in a conference. Finally we investigate how much the entropy of dynamical social networks changes in realistic models of phone-call or face-to face interactions characterizing in this way different type human social behavior.

78 FR 1275 - Privacy Act of 1974; Computer Matching Program

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-08

... Social Security Administration (Computer Matching Agreement 1071). SUMMARY: In accordance with the... of its new computer matching program with the Social Security Administration (SSA). DATES: OPM will... conditions under which SSA will disclose Social Security benefit data to OPM via direct computer link. OPM...

Computer-Based Education in the Social Studies.

ERIC Educational Resources Information Center

Ehman, Lee H.; Glenn, Allen D.

Computers have not revolutionized social studies curricula because so few teachers use them. But research does indicate that computers are flexible instructional tools that can assist in the development of attitudes, intellectual motivation, and inquiry skills. Social studies educators need to consider expanded computer use in their classrooms…

Evaluating a Computational Model of Social Causality and Responsibility

DTIC Science & Technology

2006-01-01

Evaluating a Computational Model of Social Causality and Responsibility Wenji Mao University of Southern California Institute for Creative...empirically evaluate a computa- tional model of social causality and responsibility against human social judgments. Results from our experimental...developed a general computational model of social cau- sality and responsibility [10, 11] that formalizes the factors people use in reasoning about

Computers in Social Studies.

ERIC Educational Resources Information Center

Kendall, Diane S.; Budin, Howard

1987-01-01

Provides an introduction to the articles in this issue. Maintains that social studies teachers and textbooks will not be replaced by computers. States that the relationship between the social studies and computers is limited only by our imaginations. (JDH)

Computation and Dynamics: Classical and Quantum

NASA Astrophysics Data System (ADS)

Kisil, Vladimir V.

2010-05-01

We discuss classical and quantum computations in terms of corresponding Hamiltonian dynamics. This allows us to introduce quantum computations which involve parallel processing of both: the data and programme instructions. Using mixed quantum-classical dynamics we look for a full cost of computations on quantum computers with classical terminals.

The Process and Effect of Supportive Message Expression and Reception in Online Breast Cancer Support Groups

PubMed Central

Kim, Eunkyung; Han, Jeong Yeob; Moon, Tae Joon; Shaw, Bret; Shah, Dhavan V.; McTavish, Fiona M.; Gustafson, David H.

2011-01-01

Objective To better understand the process and effect of social support exchanges within computer-mediated social support (CMSS) groups for breast cancer patients, this study examines 1) the dynamic interplay between emotional support giving and receiving and 2) the relative effects of emotional support giving and receiving on patients’ psychosocial health outcomes. Methods Data was collected from 177 patients who participated in online cancer support groups within the Comprehensive Health Enhancement Support System (CHESS) during the 4-month intervention. Data included 1) pretest and/or posttest survey scores of demographic, disease-related, and psychosocial factors, 2) automatically collected CHESS usage data, and 3) computer-aided content analysis of social support messages posts. Results Hierarchical regression analyses revealed that those who receive higher levels of support from others have fewer breast cancer-related concerns (β= −.15, p<.05), while those who give higher levels of support to others reframe their own problems in a positive light and adopt more positive strategies for coping (β= .16, p<.05). In addition to these positive effects, partial correlation analysis indicated that these two supportive behaviors are reciprocal. Conclusions We concluded that supportive exchanges of receiving and giving play positive, but different, roles in predicting psychosocial health outcomes. Moreover, emotional support giving and receiving tend to reinforce each other. Our findings help practitioners, health care providers, and health system designers make sense of diverse social support processes among cancer patients participating within CMSS groups. PMID:21416553

Studies on the population dynamics of a rumor-spreading model in online social networks

NASA Astrophysics Data System (ADS)

Dong, Suyalatu; Fan, Feng-Hua; Huang, Yong-Chang

2018-02-01

This paper sets up a rumor spreading model in online social networks based on the European fox rabies SIR model. The model considers the impact of changing number of online social network users, combines the transmission dynamics to set up a population dynamics of rumor spreading model in online social networks. Simulation is carried out on online social network, and results show that the new rumor spreading model is in accordance with the real propagation characteristics in online social networks.

Computer Mediated Social Network Approach to Software Support and Maintenance

DTIC Science & Technology

2010-06-01

Page 1        Computer Mediated  Social   Network  Approach to  Software Support and Maintenance     LTC J. Carlos Vega  *Student Paper*    Point...DATES COVERED 00-00-2010 to 00-00-2010 4. TITLE AND SUBTITLE Computer Mediated Social Network Approach to Software Support and Maintenance...This research highlights the preliminary findings on the potential of computer mediated social networks . This research focused on social networks as

Handheld Computers in the Classroom: Integration Strategies for Social Studies Educators.

ERIC Educational Resources Information Center

Ray, Beverly

Handheld computers have gone beyond the world of business and are now finding their way into the hands of social studies teachers and students. This paper discusses how social studies teachers can use handheld computers to aid anytime/ anywhere course management. The integration of handheld technology into the classroom provides social studies…

Convergence analysis of particle swarm optimization (PSO) method on the with-in host dengue infection treatment model

NASA Astrophysics Data System (ADS)

Handayani, D.; Nuraini, N.; Tse, O.; Saragih, R.; Naiborhu, J.

2016-04-01

PSO is a computational optimization method motivated by the social behavior of organisms like bird flocking, fish schooling and human social relations. PSO is one of the most important swarm intelligence algorithms. In this study, we analyze the convergence of PSO when it is applied to with-in host dengue infection treatment model simulation in our early research. We used PSO method to construct the initial adjoin equation and to solve a control problem. Its properties of control input on the continuity of objective function and ability of adapting to the dynamic environment made us have to analyze the convergence of PSO. With the convergence analysis of PSO we will have some parameters that ensure the convergence result of numerical simulations on this model using PSO.

Live interaction distinctively shapes social gaze dynamics in rhesus macaques.

PubMed

Dal Monte, Olga; Piva, Matthew; Morris, Jason A; Chang, Steve W C

2016-10-01

The dynamic interaction of gaze between individuals is a hallmark of social cognition. However, very few studies have examined social gaze dynamics after mutual eye contact during real-time interactions. We used a highly quantifiable paradigm to assess social gaze dynamics between pairs of monkeys and modeled these dynamics using an exponential decay function to investigate sustained attention after mutual eye contact. When monkeys were interacting with real partners compared with static images and movies of the same monkeys, we found a significant increase in the proportion of fixations to the eyes and a smaller dispersion of fixations around the eyes, indicating enhanced focal attention to the eye region. Notably, dominance and familiarity between the interacting pairs induced separable components of gaze dynamics that were unique to live interactions. Gaze dynamics of dominant monkeys after mutual eye contact were associated with a greater number of fixations to the eyes, whereas those of familiar pairs were associated with a faster rate of decrease in this eye-directed attention. Our findings endorse the notion that certain key aspects of social cognition are only captured during interactive social contexts and dependent on the elapsed time relative to socially meaningful events. Copyright © 2016 the American Physiological Society.

Live interaction distinctively shapes social gaze dynamics in rhesus macaques

PubMed Central

Piva, Matthew; Morris, Jason A.; Chang, Steve W. C.

2016-01-01

The dynamic interaction of gaze between individuals is a hallmark of social cognition. However, very few studies have examined social gaze dynamics after mutual eye contact during real-time interactions. We used a highly quantifiable paradigm to assess social gaze dynamics between pairs of monkeys and modeled these dynamics using an exponential decay function to investigate sustained attention after mutual eye contact. When monkeys were interacting with real partners compared with static images and movies of the same monkeys, we found a significant increase in the proportion of fixations to the eyes and a smaller dispersion of fixations around the eyes, indicating enhanced focal attention to the eye region. Notably, dominance and familiarity between the interacting pairs induced separable components of gaze dynamics that were unique to live interactions. Gaze dynamics of dominant monkeys after mutual eye contact were associated with a greater number of fixations to the eyes, whereas those of familiar pairs were associated with a faster rate of decrease in this eye-directed attention. Our findings endorse the notion that certain key aspects of social cognition are only captured during interactive social contexts and dependent on the elapsed time relative to socially meaningful events. PMID:27486105

Nonlinear dynamics as an engine of computation.

PubMed

Kia, Behnam; Lindner, John F; Ditto, William L

2017-03-06

Control of chaos teaches that control theory can tame the complex, random-like behaviour of chaotic systems. This alliance between control methods and physics-cybernetical physics-opens the door to many applications, including dynamics-based computing. In this article, we introduce nonlinear dynamics and its rich, sometimes chaotic behaviour as an engine of computation. We review our work that has demonstrated how to compute using nonlinear dynamics. Furthermore, we investigate the interrelationship between invariant measures of a dynamical system and its computing power to strengthen the bridge between physics and computation.This article is part of the themed issue 'Horizons of cybernetical physics'. © 2017 The Author(s).

Nonlinear dynamics as an engine of computation

PubMed Central

Lindner, John F.; Ditto, William L.

2017-01-01

Control of chaos teaches that control theory can tame the complex, random-like behaviour of chaotic systems. This alliance between control methods and physics—cybernetical physics—opens the door to many applications, including dynamics-based computing. In this article, we introduce nonlinear dynamics and its rich, sometimes chaotic behaviour as an engine of computation. We review our work that has demonstrated how to compute using nonlinear dynamics. Furthermore, we investigate the interrelationship between invariant measures of a dynamical system and its computing power to strengthen the bridge between physics and computation. This article is part of the themed issue ‘Horizons of cybernetical physics’. PMID:28115619

Rural-Urban Migration in D-Dimensional Lattices

NASA Astrophysics Data System (ADS)

Espíndola, Aquino L.; Penna, T. J. P.; Silveira, Jaylson J.

The rural-urban migration phenomenon is analyzed by using an agent-based computational model. Agents are placed on lattices which dimensions varying from d =2 up to d =7. The localization of the agents in the lattice defines that their social neighborhood (rural or urban) is not related to their spatial distribution. The effect of the dimension of lattice is studied by analyzing the variation of the main parameters that characterizes the migratory process. The dynamics displays strong effects even for around one million of sites, in higher dimensions (d =6, 7).

From Tabletop RPG to Interactive Storytelling: Definition of a Story Manager for Videogames

NASA Astrophysics Data System (ADS)

Delmas, Guylain; Champagnat, Ronan; Augeraud, Michel

Adding narrative in computer game is complicated because it may restrict player interactivity. Our aim is to design a controller that dynamically built a plot, through the game execution, centred on player's actions. Tabletop Role-playing games manage to deal with this goal. This paper presents a study of role-playing games, their organization, and the models commonly used for narrative generation. It then deduces a proposition of components and data structures for interactive storytelling in videogames. A prototype of a social game has been developed as example.

From seconds to months: an overview of multi-scale dynamics of mobile telephone calls

NASA Astrophysics Data System (ADS)

Saramäki, Jari; Moro, Esteban

2015-06-01

Big Data on electronic records of social interactions allow approaching human behaviour and sociality from a quantitative point of view with unforeseen statistical power. Mobile telephone Call Detail Records (CDRs), automatically collected by telecom operators for billing purposes, have proven especially fruitful for understanding one-to-one communication patterns as well as the dynamics of social networks that are reflected in such patterns. We present an overview of empirical results on the multi-scale dynamics of social dynamics and networks inferred from mobile telephone calls. We begin with the shortest timescales and fastest dynamics, such as burstiness of call sequences between individuals, and "zoom out" towards longer temporal and larger structural scales, from temporal motifs formed by correlated calls between multiple individuals to long-term dynamics of social groups. We conclude this overview with a future outlook.

Use of a Computer Language in Teaching Dynamic Programming. Final Report.

ERIC Educational Resources Information Center

Trimble, C. J.; And Others

Most optimization problems of any degree of complexity must be solved using a computer. In the teaching of dynamic programing courses, it is often desirable to use a computer in problem solution. The solution process involves conceptual formulation and computational Solution. Generalized computer codes for dynamic programing problem solution…

Fluid Centrality: A Social Network Analysis of Social-Technical Relations in Computer-Mediated Communication

ERIC Educational Resources Information Center

Enriquez, Judith Guevarra

2010-01-01

In this article, centrality is explored as a measure of computer-mediated communication (CMC) in networked learning. Centrality measure is quite common in performing social network analysis (SNA) and in analysing social cohesion, strength of ties and influence in CMC, and computer-supported collaborative learning research. It argues that measuring…

Epidemic modeling in complex realities.

PubMed

Colizza, Vittoria; Barthélemy, Marc; Barrat, Alain; Vespignani, Alessandro

2007-04-01

In our global world, the increasing complexity of social relations and transport infrastructures are key factors in the spread of epidemics. In recent years, the increasing availability of computer power has enabled both to obtain reliable data allowing one to quantify the complexity of the networks on which epidemics may propagate and to envision computational tools able to tackle the analysis of such propagation phenomena. These advances have put in evidence the limits of homogeneous assumptions and simple spatial diffusion approaches, and stimulated the inclusion of complex features and heterogeneities relevant in the description of epidemic diffusion. In this paper, we review recent progresses that integrate complex systems and networks analysis with epidemic modelling and focus on the impact of the various complex features of real systems on the dynamics of epidemic spreading.

Leveraging Social Computing for Personalized Crisis Communication using Social Media.

PubMed

Leykin, Dmitry; Aharonson-Daniel, Limor; Lahad, Mooli

2016-03-24

The extensive use of social media in modern life redefines social interaction and communication. Communication plays an important role in mitigating, or exacerbating, the psychological and behavioral responses to critical incidents and disasters. As recent disasters demonstrated, people tend to converge to social media during and following emergencies. Authorities can then use this media and other computational methods to gain insights from the public, mainly to enhance situational awareness, but also to improve their communication with the public and public adherence to instructions. The current review presents a conceptual framework for studying psychological aspects of crisis and risk communication using the social media through social computing. Advanced analytical tools can be integrated in the processes and objectives of crisis communication. The availability of the computational techniques can improve communication with the public by a process of Hyper-Targeted Crisis Communication. The review suggests that using advanced computational tools for target-audience profiling and linguistic matching in social media, can facilitate more sensitive and personalized emergency communication.

Evidence-based guidelines for the informal use of computers by children to promote the development of academic, cognitive and social skills.

PubMed

Tran, Phuoc; Subrahmanyam, Kaveri

2013-01-01

The use of computers in the home has become very common among young children. This paper reviews research on the effects of informal computer use and identifies potential pathways through which computers may impact children's development. Based on the evidence reviewed, we present the following guidelines to arrange informal computer experiences that will promote the development of children's academic, cognitive and social skills: (1) children should be encouraged to use computers for moderate amounts of time (2-3 days a week for an hour or two per day) and (2) children's use of computers should (a) include non-violent action-based computer games as well as educational games, (b) not displace social activities but should instead be arranged to provide opportunities for social engagement with peers and family members and (c) involve content with pro-social and non-violent themes. We conclude the paper with questions that must be addressed in future research. This paper reviews research on the effects of informal computer use on children's academic, cognitive and social skills. Based on the evidence presented, we have presented guidelines to enable parents, teachers and other adults to arrange informal computer experiences so as to maximise their potential benefit for children's development.

Computational Workbench for Multibody Dynamics

NASA Technical Reports Server (NTRS)

Edmonds, Karina

2007-01-01

PyCraft is a computer program that provides an interactive, workbenchlike computing environment for developing and testing algorithms for multibody dynamics. Examples of multibody dynamic systems amenable to analysis with the help of PyCraft include land vehicles, spacecraft, robots, and molecular models. PyCraft is based on the Spatial-Operator- Algebra (SOA) formulation for multibody dynamics. The SOA operators enable construction of simple and compact representations of complex multibody dynamical equations. Within the Py-Craft computational workbench, users can, essentially, use the high-level SOA operator notation to represent the variety of dynamical quantities and algorithms and to perform computations interactively. PyCraft provides a Python-language interface to underlying C++ code. Working with SOA concepts, a user can create and manipulate Python-level operator classes in order to implement and evaluate new dynamical quantities and algorithms. During use of PyCraft, virtually all SOA-based algorithms are available for computational experiments.

Fluctuation of similarity (FLUS) to detect transitions between distinct dynamical regimes in short time series

PubMed Central

Malik, Nishant; Marwan, Norbert; Zou, Yong; Mucha, Peter J.; Kurths, Jürgen

2016-01-01

A method to identify distinct dynamical regimes and transitions between those regimes in a short univariate time series was recently introduced [1], employing the computation of fluctuations in a measure of nonlinear similarity based on local recurrence properties. In the present work, we describe the details of the analytical relationships between this newly introduced measure and the well known concepts of attractor dimensions and Lyapunov exponents. We show that the new measure has linear dependence on the effective dimension of the attractor and it measures the variations in the sum of the Lyapunov spectrum. To illustrate the practical usefulness of the method, we identify various types of dynamical transitions in different nonlinear models. We present testbed examples for the new method’s robustness against noise and missing values in the time series. We also use this method to analyze time series of social dynamics, specifically an analysis of the U.S. crime record time series from 1975 to 1993. Using this method, we find that dynamical complexity in robberies was influenced by the unemployment rate until the late 1980’s. We have also observed a dynamical transition in homicide and robbery rates in the late 1980’s and early 1990’s, leading to increase in the dynamical complexity of these rates. PMID:25019852

Using online social networks to track a pandemic: A systematic review.

PubMed

Al-Garadi, Mohammed Ali; Khan, Muhammad Sadiq; Varathan, Kasturi Dewi; Mujtaba, Ghulam; Al-Kabsi, Abdelkodose M

2016-08-01

The popularity and proliferation of online social networks (OSNs) have created massive social interaction among users that generate an extensive amount of data. An OSN offers a unique opportunity for studying and understanding social interaction and communication among far larger populations now more than ever before. Recently, OSNs have received considerable attention as a possible tool to track a pandemic because they can provide an almost real-time surveillance system at a less costly rate than traditional surveillance systems. A systematic literature search for studies with the primary aim of using OSN to detect and track a pandemic was conducted. We conducted an electronic literature search for eligible English articles published between 2004 and 2015 using PUBMED, IEEExplore, ACM Digital Library, Google Scholar, and Web of Science. First, the articles were screened on the basis of titles and abstracts. Second, the full texts were reviewed. All included studies were subjected to quality assessment. OSNs have rich information that can be utilized to develop an almost real-time pandemic surveillance system. The outcomes of OSN surveillance systems have demonstrated high correlations with the findings of official surveillance systems. However, the limitation in using OSN to track pandemic is in collecting representative data with sufficient population coverage. This challenge is related to the characteristics of OSN data. The data are dynamic, large-sized, and unstructured, thus requiring advanced algorithms and computational linguistics. OSN data contain significant information that can be used to track a pandemic. Different from traditional surveys and clinical reports, in which the data collection process is time consuming at costly rates, OSN data can be collected almost in real time at a cheaper cost. Additionally, the geographical and temporal information can provide exploratory analysis of spatiotemporal dynamics of infectious disease spread. However, on one hand, an OSN-based surveillance system requires comprehensive adoption, enhanced geographical identification system, and advanced algorithms and computational linguistics to eliminate its limitations and challenges. On the other hand, OSN is probably to never replace traditional surveillance, but it can offer complementary data that can work best when integrated with traditional data. Copyright © 2016 Elsevier Inc. All rights reserved.

Determining sociability, social space, and social presence in (a)synchronous collaborative groups.

PubMed

Kreijns, Karel; Kirschner, Paul A; Jochems, Wim; Van Buuren, Hans

2004-04-01

The effectiveness of group learning in asynchronous distributed learning groups depends on the social interaction that takes place. This social interaction affects both cognitive and socioemotional processes that take place during learning, group forming, establishment of group structures, and group dynamics. Though now known to be important, this aspect is often ignored, denied or forgotten by educators and researchers who tend to concentrate on cognitive processes and on-task contexts. This "one-sided" educational focus largely determines the set of requirements in the design of computer-supported collaborative learning (CSCL) environments resulting in functional CSCL environments. In contrast, our research is aimed at the design and implementation of sociable CSCL environments which may increase the likelihood that a sound social space will emerge. We use a theoretical framework that is based upon an ecological approach to social interaction, centering on the concept of social affordances, the concept of the sociability of CSCL environments, and social presence theory. The hypothesis is that the higher the sociability, the more likely that social interaction will take place or will increase, and the more likely that this will result in an emerging sound social space. In the present research, the variables of interest are sociability, social space, and social presence. This study deals with the construction and validation of three instruments to determine sociability, social space, and social presence in (a)synchronous collaborating groups. The findings suggest that the instruments have potential to be useful as measures for the respective variables. However, it must be realized that these measures are "first steps."

Facial movements strategically camouflage involuntary social signals of face morphology.

PubMed

Gill, Daniel; Garrod, Oliver G B; Jack, Rachael E; Schyns, Philippe G

2014-05-01

Animals use social camouflage as a tool of deceit to increase the likelihood of survival and reproduction. We tested whether humans can also strategically deploy transient facial movements to camouflage the default social traits conveyed by the phenotypic morphology of their faces. We used the responses of 12 observers to create models of the dynamic facial signals of dominance, trustworthiness, and attractiveness. We applied these dynamic models to facial morphologies differing on perceived dominance, trustworthiness, and attractiveness to create a set of dynamic faces; new observers rated each dynamic face according to the three social traits. We found that specific facial movements camouflage the social appearance of a face by modulating the features of phenotypic morphology. A comparison of these facial expressions with those similarly derived for facial emotions showed that social-trait expressions, rather than being simple one-to-one overgeneralizations of emotional expressions, are a distinct set of signals composed of movements from different emotions. Our generative face models represent novel psychophysical laws for social sciences; these laws predict the perception of social traits on the basis of dynamic face identities.

Hippocampal temporal-parietal junction interaction in the production of psychotic symptoms: a framework for understanding the schizophrenic syndrome

PubMed Central

Wible, Cynthia G.

2012-01-01

A framework is described for understanding the schizophrenic syndrome at the brain systems level. It is hypothesized that over-activation of dynamic gesture and social perceptual processes in the temporal-parietal occipital junction (TPJ), posterior superior temporal sulcus (PSTS) and surrounding regions produce the syndrome (including positive and negative symptoms, their prevalence, prodromal signs, and cognitive deficits). Hippocampal system hyper-activity and atrophy have been consistently found in schizophrenia. Hippocampal activity is highly correlated with activity in the TPJ and may be a source of over-excitation of the TPJ and surrounding regions. Strong evidence for this comes from in-vivo recordings in humans during psychotic episodes. Many positive symptoms of schizophrenia can be reframed as the erroneous sense of a presence or other who is observing, acting, speaking, or controlling; these qualia are similar to those evoked during abnormal activation of the TPJ. The TPJ and PSTS play a key role in the perception (and production) of dynamic social, emotional, and attentional gestures for the self and others (e.g., body/face/eye gestures, audiovisual speech and prosody, and social attentional gestures such as eye gaze). The single cell representation of dynamic gestures is multimodal (auditory, visual, tactile), matching the predominant hallucinatory categories in schizophrenia. Inherent in the single cell perceptual signal of dynamic gesture representations is a computation of intention, agency, and anticipation or expectancy (for the self and others). Stimulation of the TPJ resulting in activation of the self representation has been shown to result a feeling of a presence or multiple presences (due to heautoscopy) and also bizarre tactile experiences. Neurons in the TPJ are also tuned, or biased to detect threat related emotions. Abnormal over-activation in this system could produce the conscious hallucination of a voice (audiovisual speech), a person or a touch. Over-activation could interfere with attentional/emotional gesture perception and production (negative symptoms). It could produce the unconscious feeling of being watched, followed, or of a social situation unfolding along with accompanying abnormal perception of intent and agency (delusions). Abnormal activity in the TPJ would also be predicted to create several cognitive disturbances that are characteristic of schizophrenia, including abnormalities in attention, predictive social processing, working memory, and a bias to erroneously perceive threat. PMID:22737114

Social significance of community structure: Statistical view

NASA Astrophysics Data System (ADS)

Li, Hui-Jia; Daniels, Jasmine J.

2015-01-01

Community structure analysis is a powerful tool for social networks that can simplify their topological and functional analysis considerably. However, since community detection methods have random factors and real social networks obtained from complex systems always contain error edges, evaluating the significance of a partitioned community structure is an urgent and important question. In this paper, integrating the specific characteristics of real society, we present a framework to analyze the significance of a social community. The dynamics of social interactions are modeled by identifying social leaders and corresponding hierarchical structures. Instead of a direct comparison with the average outcome of a random model, we compute the similarity of a given node with the leader by the number of common neighbors. To determine the membership vector, an efficient community detection algorithm is proposed based on the position of the nodes and their corresponding leaders. Then, using a log-likelihood score, the tightness of the community can be derived. Based on the distribution of community tightness, we establish a connection between p -value theory and network analysis, and then we obtain a significance measure of statistical form . Finally, the framework is applied to both benchmark networks and real social networks. Experimental results show that our work can be used in many fields, such as determining the optimal number of communities, analyzing the social significance of a given community, comparing the performance among various algorithms, etc.

Social Network Analysis and Nutritional Behavior: An Integrated Modeling Approach

PubMed Central

Senior, Alistair M.; Lihoreau, Mathieu; Buhl, Jerome; Raubenheimer, David; Simpson, Stephen J.

2016-01-01

Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent research combining state-space models of nutritional geometry with agent-based models (ABMs), show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit ABMs that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition). Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interactions in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments. PMID:26858671

Functional cortical network in alpha band correlates with social bargaining.

PubMed

Billeke, Pablo; Zamorano, Francisco; Chavez, Mario; Cosmelli, Diego; Aboitiz, Francisco

2014-01-01

Solving demanding tasks requires fast and flexible coordination among different brain areas. Everyday examples of this are the social dilemmas in which goals tend to clash, requiring one to weigh alternative courses of action in limited time. In spite of this fact, there are few studies that directly address the dynamics of flexible brain network integration during social interaction. To study the preceding, we carried out EEG recordings while subjects played a repeated version of the Ultimatum Game in both human (social) and computer (non-social) conditions. We found phase synchrony (inter-site-phase-clustering) modulation in alpha band that was specific to the human condition and independent of power modulation. The strength and patterns of the inter-site-phase-clustering of the cortical networks were also modulated, and these modulations were mainly in frontal and parietal regions. Moreover, changes in the individuals' alpha network structure correlated with the risk of the offers made only in social conditions. This correlation was independent of changes in power and inter-site-phase-clustering strength. Our results indicate that, when subjects believe they are participating in a social interaction, a specific modulation of functional cortical networks in alpha band takes place, suggesting that phase synchrony of alpha oscillations could serve as a mechanism by which different brain areas flexibly interact in order to adapt ongoing behavior in socially demanding contexts.

Functional Cortical Network in Alpha Band Correlates with Social Bargaining

PubMed Central

Billeke, Pablo; Zamorano, Francisco; Chavez, Mario; Cosmelli, Diego; Aboitiz, Francisco

2014-01-01

Solving demanding tasks requires fast and flexible coordination among different brain areas. Everyday examples of this are the social dilemmas in which goals tend to clash, requiring one to weigh alternative courses of action in limited time. In spite of this fact, there are few studies that directly address the dynamics of flexible brain network integration during social interaction. To study the preceding, we carried out EEG recordings while subjects played a repeated version of the Ultimatum Game in both human (social) and computer (non-social) conditions. We found phase synchrony (inter-site-phase-clustering) modulation in alpha band that was specific to the human condition and independent of power modulation. The strength and patterns of the inter-site-phase-clustering of the cortical networks were also modulated, and these modulations were mainly in frontal and parietal regions. Moreover, changes in the individuals’ alpha network structure correlated with the risk of the offers made only in social conditions. This correlation was independent of changes in power and inter-site-phase-clustering strength. Our results indicate that, when subjects believe they are participating in a social interaction, a specific modulation of functional cortical networks in alpha band takes place, suggesting that phase synchrony of alpha oscillations could serve as a mechanism by which different brain areas flexibly interact in order to adapt ongoing behavior in socially demanding contexts. PMID:25286240

Supporting Teachers' Management of Middle School Social Dynamics: The Scouting Report Process

ERIC Educational Resources Information Center

Farmer, Thomas W.; Chen, Chin-Chih; Hamm, Jill V.; Moates, Meredith M.; Mehtaji, Meera; Lee, David; Huneke, Michelle R.

2016-01-01

This describes the "scouting report" as an approach that social and behavior intervention specialists can use to help middle-level teachers create social contexts that support productive social roles and relationships of students with disabilities. Building from research on early adolescent social dynamics and context-based interventions…

Computer Training for Seniors: An Academic-Community Partnership

ERIC Educational Resources Information Center

Sanders, Martha J.; O'Sullivan, Beth; DeBurra, Katherine; Fedner, Alesha

2013-01-01

Computer technology is integral to information retrieval, social communication, and social interaction. However, only 47% of seniors aged 65 and older use computers. The purpose of this study was to determine the impact of a client-centered computer program on computer skills, attitudes toward computer use, and generativity in novice senior…

Social Dynamic of Yogyakarta Citizens in Facing the Uncertainty of Traditional Value and Modernity: The Integrated Value of Social Study

ERIC Educational Resources Information Center

Sridiyatmiko, Gunawan

2016-01-01

The principal issue of this study is "how does the society dynamic of Yogyakarta in facing the polemic of traditional, modernity, and social study values which can be developed in social study learning at school? The general aim of this study is to find how the society dynamic phenomenon which happened in Yogyakarta mainly in Kraton, Kauman,…

A Perceptual Pathway to Bias: Interracial Exposure Reduces Abrupt Shifts in Real-Time Race Perception That Predict Mixed-Race Bias.

PubMed

Freeman, Jonathan B; Pauker, Kristin; Sanchez, Diana T

2016-04-01

In two national samples, we examined the influence of interracial exposure in one's local environment on the dynamic process underlying race perception and its evaluative consequences. Using a mouse-tracking paradigm, we found in Study 1 that White individuals with low interracial exposure exhibited a unique effect of abrupt, unstable White-Black category shifting during real-time perception of mixed-race faces, consistent with predictions from a neural-dynamic model of social categorization and computational simulations. In Study 2, this shifting effect was replicated and shown to predict a trust bias against mixed-race individuals and to mediate the effect of low interracial exposure on that trust bias. Taken together, the findings demonstrate that interracial exposure shapes the dynamics through which racial categories activate and resolve during real-time perceptions, and these initial perceptual dynamics, in turn, may help drive evaluative biases against mixed-race individuals. Thus, lower-level perceptual aspects of encounters with racial ambiguity may serve as a foundation for mixed-race prejudice. © The Author(s) 2016.

Sensible organizations: technology and methodology for automatically measuring organizational behavior.

PubMed

Olguin Olguin, Daniel; Waber, Benjamin N; Kim, Taemie; Mohan, Akshay; Ara, Koji; Pentland, Alex

2009-02-01

We present the design, implementation, and deployment of a wearable computing platform for measuring and analyzing human behavior in organizational settings. We propose the use of wearable electronic badges capable of automatically measuring the amount of face-to-face interaction, conversational time, physical proximity to other people, and physical activity levels in order to capture individual and collective patterns of behavior. Our goal is to be able to understand how patterns of behavior shape individuals and organizations. By using on-body sensors in large groups of people for extended periods of time in naturalistic settings, we have been able to identify, measure, and quantify social interactions, group behavior, and organizational dynamics. We deployed this wearable computing platform in a group of 22 employees working in a real organization over a period of one month. Using these automatic measurements, we were able to predict employees' self-assessments of job satisfaction and their own perceptions of group interaction quality by combining data collected with our platform and e-mail communication data. In particular, the total amount of communication was predictive of both of these assessments, and betweenness in the social network exhibited a high negative correlation with group interaction satisfaction. We also found that physical proximity and e-mail exchange had a negative correlation of r = -0.55 (p 0.01), which has far-reaching implications for past and future research on social networks.

Internal-external stimulus competition in a system of interacting moving particles: Persuasion versus propaganda

NASA Astrophysics Data System (ADS)

Clementi, N. C.; Revelli, J. A.; Sibona, G. J.

2015-07-01

We propose a general nonlinear analytical framework to study the effect of an external stimulus in the internal state of a population of moving particles. This novel scheme allows us to study a broad range of excitation transport phenomena. In particular, considering social systems, it gives insight of the spatial dynamics influence in the competition between propaganda (mass media) and convincement. By extending the framework presented by Terranova et al. [Europhys. Lett. 105, 30007 (2014), 10.1209/0295-5075/105/30007], we now allow changes in individual's opinions due to a reflection induced by mass media. The equations of the model could be solved numerically, and, for some special cases, it is possible to derive analytical solutions for the steady states. We implement computational simulations for different social and dynamical systems to check the accuracy of our scheme and to study a broader variety of scenarios. In particular, we compare the numerical outcome with the analytical results for two possible real cases, finding a good agreement. From the results, we observe that mass media dominates the opinion state in slow dynamics communities; whereas, for higher agent active speeds, the rate of interactions increases and the opinion state is determined by a competition between propaganda and persuasion. This difference suggests that kinetics can not be neglected in the study of transport of any excitation over a particle system.

Internal-external stimulus competition in a system of interacting moving particles: Persuasion versus propaganda.

PubMed

Clementi, N C; Revelli, J A; Sibona, G J

2015-07-01

We propose a general nonlinear analytical framework to study the effect of an external stimulus in the internal state of a population of moving particles. This novel scheme allows us to study a broad range of excitation transport phenomena. In particular, considering social systems, it gives insight of the spatial dynamics influence in the competition between propaganda (mass media) and convincement. By extending the framework presented by Terranova et al. [Europhys. Lett. 105, 30007 (2014)], we now allow changes in individual's opinions due to a reflection induced by mass media. The equations of the model could be solved numerically, and, for some special cases, it is possible to derive analytical solutions for the steady states. We implement computational simulations for different social and dynamical systems to check the accuracy of our scheme and to study a broader variety of scenarios. In particular, we compare the numerical outcome with the analytical results for two possible real cases, finding a good agreement. From the results, we observe that mass media dominates the opinion state in slow dynamics communities; whereas, for higher agent active speeds, the rate of interactions increases and the opinion state is determined by a competition between propaganda and persuasion. This difference suggests that kinetics can not be neglected in the study of transport of any excitation over a particle system.

A Dynamical Approach Toward Understanding Mechanisms of Team Science: Change, Kinship, Tension, and Heritage in a Transdisciplinary Team

PubMed Central

2013-01-01

Abstract Since the concept of team science gained recognition among biomedical researchers, social scientists have been challenged with investigating evidence of team mechanisms and functional dynamics within transdisciplinary teams. Identification of these mechanisms has lacked substantial research using grounded theory models to adequately describe their dynamical qualities. Research trends continue to favor the measurement of teams by isolating occurrences of production over relational mechanistic team tendencies. This study uses a social constructionist‐grounded multilevel mixed methods approach to identify social dynamics and mechanisms within a transdisciplinary team. A National Institutes of Health—funded research team served as a sample. Data from observations, interviews, and focus groups were qualitatively coded to generate micro/meso level analyses. Social mechanisms operative within this biomedical scientific team were identified. Dynamics that support such mechanisms were documented and explored. Through theoretical and emergent coding, four social mechanisms dominated in the analysis—change, kinship, tension, and heritage. Each contains relational social dynamics. This micro/meso level study suggests such mechanisms and dynamics are key features of team science and as such can inform problems of integration, praxis, and engagement in teams. PMID:23919361

A dynamical approach toward understanding mechanisms of team science: change, kinship, tension, and heritage in a transdisciplinary team.

PubMed

Lotrecchiano, Gaetano R

2013-08-01

Since the concept of team science gained recognition among biomedical researchers, social scientists have been challenged with investigating evidence of team mechanisms and functional dynamics within transdisciplinary teams. Identification of these mechanisms has lacked substantial research using grounded theory models to adequately describe their dynamical qualities. Research trends continue to favor the measurement of teams by isolating occurrences of production over relational mechanistic team tendencies. This study uses a social constructionist-grounded multilevel mixed methods approach to identify social dynamics and mechanisms within a transdisciplinary team. A National Institutes of Health-funded research team served as a sample. Data from observations, interviews, and focus groups were qualitatively coded to generate micro/meso level analyses. Social mechanisms operative within this biomedical scientific team were identified. Dynamics that support such mechanisms were documented and explored. Through theoretical and emergent coding, four social mechanisms dominated in the analysis-change, kinship, tension, and heritage. Each contains relational social dynamics. This micro/meso level study suggests such mechanisms and dynamics are key features of team science and as such can inform problems of integration, praxis, and engagement in teams. © 2013 Wiley Periodicals, Inc.

The World Climate Exercise: Is (Simulated) Experience Our Best Teacher?

NASA Astrophysics Data System (ADS)

Rath, K.; Rooney-varga, J. N.; Jones, A.; Johnston, E.; Sterman, J.

2015-12-01

Meeting the challenge of climate change will clearly require 'deep learning' - learning that motivates a search for underlying meaning, a willingness to exert the sustained effort needed to understand complex problems, and innovative problem-solving. This type of learning is dependent on the level of the learner's engagement with the material, their intrinsic motivation to learn, intention to understand, and relevance of the material to the learner. Here, we present evidence for deep learning about climate change through a simulation-based role-playing exercise, World Climate. The exercise puts participants into the roles of delegates to the United Nations climate negotiations and asks them to create an international climate deal. They find out the implications of their decisions, according to the best available science, through the same decision-support computer simulation used to provide feedback for the real-world negotiations, C-ROADS. World Climate provides an opportunity for participants have an immersive, social experience in which they learn first-hand about both the social dynamics of climate change decision-making, through role-play, and the dynamics of the climate system, through an interactive computer simulation. Evaluation results so far have shown that the exercise is highly engaging and memorable and that it motivates large majorities of participants (>70%) to take action on climate change. In addition, we have found that it leads to substantial gains in understanding key systems thinking concepts (e.g., the stock-flow behavior of atmospheric CO2), as well as improvements in understanding of climate change causes and impacts. While research is still needed to better understand the impacts of simulation-based role-playing exercises like World Climate on behavior change, long-term understanding, transfer of systems thinking skills across topics, and the importance of social learning during the exercise, our results to date indicate that it is a powerful, active learning tool that has strong potential to foster deep learning about climate change.

Climate Literacy in the Classroom: Supporting Teachers in the Transition to NGSS

NASA Astrophysics Data System (ADS)

Rogers, M. J. B.; Merrill, J.; Harcourt, P.; Petrone, C.; Shea, N.; Mead, H.

2014-12-01

Meeting the challenge of climate change will clearly require 'deep learning' - learning that motivates a search for underlying meaning, a willingness to exert the sustained effort needed to understand complex problems, and innovative problem-solving. This type of learning is dependent on the level of the learner's engagement with the material, their intrinsic motivation to learn, intention to understand, and relevance of the material to the learner. Here, we present evidence for deep learning about climate change through a simulation-based role-playing exercise, World Climate. The exercise puts participants into the roles of delegates to the United Nations climate negotiations and asks them to create an international climate deal. They find out the implications of their decisions, according to the best available science, through the same decision-support computer simulation used to provide feedback for the real-world negotiations, C-ROADS. World Climate provides an opportunity for participants have an immersive, social experience in which they learn first-hand about both the social dynamics of climate change decision-making, through role-play, and the dynamics of the climate system, through an interactive computer simulation. Evaluation results so far have shown that the exercise is highly engaging and memorable and that it motivates large majorities of participants (>70%) to take action on climate change. In addition, we have found that it leads to substantial gains in understanding key systems thinking concepts (e.g., the stock-flow behavior of atmospheric CO2), as well as improvements in understanding of climate change causes and impacts. While research is still needed to better understand the impacts of simulation-based role-playing exercises like World Climate on behavior change, long-term understanding, transfer of systems thinking skills across topics, and the importance of social learning during the exercise, our results to date indicate that it is a powerful, active learning tool that has strong potential to foster deep learning about climate change.

Yearning to Give Back: Searching for Social Purpose in Computer Science and Engineering.

PubMed

Carrigan, Coleen M

2017-01-01

Computing is highly segregated and stratified by gender. While there is abundant scholarship investigating this problem, emerging evidence suggests that a hierarchy of value exists between the social and technical dimensions of Computer Science and Engineering (CSE) and this plays a role in the underrepresentation of women in the field. This ethnographic study of women's experiences in computing offers evidence of a systemic preference for the technical dimensions of computing over the social and a correlation between gender and social aspirations. Additionally, it suggests there is a gap between the exaltation of computing's social contributions and the realities of them. My participants expressed a yearning to contribute to the collective well-being of society using their computing skills. I trace moments of rupture in my participants' stories, moments when they felt these aspirations were in conflict with the cultural values in their organizations. I interpret these ruptures within a consideration of yearning, a need my participants had to contribute meaningfully to society that remained unfulfilled. The yearning to align one's altruistic values with one's careers aspirations in CSE illuminates an area for greater exploration on the path to realizing gender equity in computing. I argue that before a case can be made that careers in computing do indeed contribute to social and civil engagements, we must first address the meaning of the social within the values, ideologies and practices of CSE institutions and next, develop ways to measure and evaluate the field's contributions to society.

Yearning to Give Back: Searching for Social Purpose in Computer Science and Engineering

PubMed Central

Carrigan, Coleen M.

2017-01-01

Computing is highly segregated and stratified by gender. While there is abundant scholarship investigating this problem, emerging evidence suggests that a hierarchy of value exists between the social and technical dimensions of Computer Science and Engineering (CSE) and this plays a role in the underrepresentation of women in the field. This ethnographic study of women's experiences in computing offers evidence of a systemic preference for the technical dimensions of computing over the social and a correlation between gender and social aspirations. Additionally, it suggests there is a gap between the exaltation of computing's social contributions and the realities of them. My participants expressed a yearning to contribute to the collective well-being of society using their computing skills. I trace moments of rupture in my participants' stories, moments when they felt these aspirations were in conflict with the cultural values in their organizations. I interpret these ruptures within a consideration of yearning, a need my participants had to contribute meaningfully to society that remained unfulfilled. The yearning to align one's altruistic values with one's careers aspirations in CSE illuminates an area for greater exploration on the path to realizing gender equity in computing. I argue that before a case can be made that careers in computing do indeed contribute to social and civil engagements, we must first address the meaning of the social within the values, ideologies and practices of CSE institutions and next, develop ways to measure and evaluate the field's contributions to society. PMID:28790936

Structures and Dynamics of Social Networks: Selection, Influence, and Self-Organization

ERIC Educational Resources Information Center

Go, Myong-Hyun

2010-01-01

This dissertation studies the social structures and dynamics of human networks: how peers at the micro level and physical environments at the macro level interact with the individual preferences and attributes and shape social dynamics. It is composed of three parts. The first essay, "Friendship Choices and Group Effects in Adolescent…

Leveraging Social Computing for Personalized Crisis Communication using Social Media

PubMed Central

Leykin, Dmitry; Aharonson-Daniel, Limor; Lahad, Mooli

2016-01-01

Introduction: The extensive use of social media in modern life redefines social interaction and communication. Communication plays an important role in mitigating, or exacerbating, the psychological and behavioral responses to critical incidents and disasters. As recent disasters demonstrated, people tend to converge to social media during and following emergencies. Authorities can then use this media and other computational methods to gain insights from the public, mainly to enhance situational awareness, but also to improve their communication with the public and public adherence to instructions. Methods: The current review presents a conceptual framework for studying psychological aspects of crisis and risk communication using the social media through social computing. Results: Advanced analytical tools can be integrated in the processes and objectives of crisis communication. The availability of the computational techniques can improve communication with the public by a process of Hyper-Targeted Crisis Communication. Discussion: The review suggests that using advanced computational tools for target-audience profiling and linguistic matching in social media, can facilitate more sensitive and personalized emergency communication. PMID:27092290

Unperturbed Schelling Segregation in Two or Three Dimensions

NASA Astrophysics Data System (ADS)

Barmpalias, George; Elwes, Richard; Lewis-Pye, Andrew

2016-09-01

Schelling's models of segregation, first described in 1969 (Am Econ Rev 59:488-493, 1969) are among the best known models of self-organising behaviour. Their original purpose was to identify mechanisms of urban racial segregation. But his models form part of a family which arises in statistical mechanics, neural networks, social science, and beyond, where populations of agents interact on networks. Despite extensive study, unperturbed Schelling models have largely resisted rigorous analysis, prior results generally focusing on variants in which noise is introduced into the dynamics, the resulting system being amenable to standard techniques from statistical mechanics or stochastic evolutionary game theory (Young in Individual strategy and social structure: an evolutionary theory of institutions, Princeton University Press, Princeton, 1998). A series of recent papers (Brandt et al. in: Proceedings of the 44th annual ACM symposium on theory of computing (STOC 2012), 2012); Barmpalias et al. in: 55th annual IEEE symposium on foundations of computer science, Philadelphia, 2014, J Stat Phys 158:806-852, 2015), has seen the first rigorous analyses of 1-dimensional unperturbed Schelling models, in an asymptotic framework largely unknown in statistical mechanics. Here we provide the first such analysis of 2- and 3-dimensional unperturbed models, establishing most of the phase diagram, and answering a challenge from Brandt et al. in: Proceedings of the 44th annual ACM symposium on theory of computing (STOC 2012), 2012).

A Novel Computer-Based Set-Up to Study Movement Coordination in Human Ensembles

PubMed Central

Alderisio, Francesco; Lombardi, Maria; Fiore, Gianfranco; di Bernardo, Mario

2017-01-01

Existing experimental works on movement coordination in human ensembles mostly investigate situations where each subject is connected to all the others through direct visual and auditory coupling, so that unavoidable social interaction affects their coordination level. Here, we present a novel computer-based set-up to study movement coordination in human groups so as to minimize the influence of social interaction among participants and implement different visual pairings between them. In so doing, players can only take into consideration the motion of a designated subset of the others. This allows the evaluation of the exclusive effects on coordination of the structure of interconnections among the players in the group and their own dynamics. In addition, our set-up enables the deployment of virtual computer players to investigate dyadic interaction between a human and a virtual agent, as well as group synchronization in mixed teams of human and virtual agents. We show how this novel set-up can be employed to study coordination both in dyads and in groups over different structures of interconnections, in the presence as well as in the absence of virtual agents acting as followers or leaders. Finally, in order to illustrate the capabilities of the architecture, we describe some preliminary results. The platform is available to any researcher who wishes to unfold the mechanisms underlying group synchronization in human ensembles and shed light on its socio-psychological aspects. PMID:28649217

Dynamic Socialized Gaussian Process Models for Human Behavior Prediction in a Health Social Network

PubMed Central

Shen, Yelong; Phan, NhatHai; Xiao, Xiao; Jin, Ruoming; Sun, Junfeng; Piniewski, Brigitte; Kil, David; Dou, Dejing

2016-01-01

Modeling and predicting human behaviors, such as the level and intensity of physical activity, is a key to preventing the cascade of obesity and helping spread healthy behaviors in a social network. In our conference paper, we have developed a social influence model, named Socialized Gaussian Process (SGP), for socialized human behavior modeling. Instead of explicitly modeling social influence as individuals' behaviors influenced by their friends' previous behaviors, SGP models the dynamic social correlation as the result of social influence. The SGP model naturally incorporates personal behavior factor and social correlation factor (i.e., the homophily principle: Friends tend to perform similar behaviors) into a unified model. And it models the social influence factor (i.e., an individual's behavior can be affected by his/her friends) implicitly in dynamic social correlation schemes. The detailed experimental evaluation has shown the SGP model achieves better prediction accuracy compared with most of baseline methods. However, a Socialized Random Forest model may perform better at the beginning compared with the SGP model. One of the main reasons is the dynamic social correlation function is purely based on the users' sequential behaviors without considering other physical activity-related features. To address this issue, we further propose a novel “multi-feature SGP model” (mfSGP) which improves the SGP model by using multiple physical activity-related features in the dynamic social correlation learning. Extensive experimental results illustrate that the mfSGP model clearly outperforms all other models in terms of prediction accuracy and running time. PMID:27746515

20 CFR 229.65 - Initial reduction.

Code of Federal Regulations, 2010 CFR

2010-04-01

... wage (see § 225.2 of this chapter) used to compute the DIB O/M under the Social Security Act rules... that exceed the maximum used in computing social security benefits) for the 5 consecutive years after... earnings that exceed the maximum used in computing social security benefits) for the year of highest...

Mathematical modeling of infectious disease dynamics

PubMed Central

Siettos, Constantinos I.; Russo, Lucia

2013-01-01

Over the last years, an intensive worldwide effort is speeding up the developments in the establishment of a global surveillance network for combating pandemics of emergent and re-emergent infectious diseases. Scientists from different fields extending from medicine and molecular biology to computer science and applied mathematics have teamed up for rapid assessment of potentially urgent situations. Toward this aim mathematical modeling plays an important role in efforts that focus on predicting, assessing, and controlling potential outbreaks. To better understand and model the contagious dynamics the impact of numerous variables ranging from the micro host–pathogen level to host-to-host interactions, as well as prevailing ecological, social, economic, and demographic factors across the globe have to be analyzed and thoroughly studied. Here, we present and discuss the main approaches that are used for the surveillance and modeling of infectious disease dynamics. We present the basic concepts underpinning their implementation and practice and for each category we give an annotated list of representative works. PMID:23552814

Hyperbolic scaling and computing in social crowds: Comment on "Human behaviours in evacuation crowd dynamics: From modelling to "big data" toward crisis management" by Nicola Bellomo et al.

NASA Astrophysics Data System (ADS)

Outada, Nisrine

2016-09-01

I have read with great interest the paper [5] where the authors present an overview and critical analysis of the literature on the modeling of the crowd dynamics with special attention to evacuation dynamics. The approach developed is based on suitable development of methods of the kinetic theory. Interactions, which lead to the decision choice, are modeled by theoretical tools of stochastic evolutionary game theory [11,12]. However, the paper [5] provides not only a survey focused on topics of great interest for our society, but also it looks ahead to a variety of interesting and challenging mathematical problems. Specifically, I am interested in the derivation of macroscopic (hydrodynamic) models from the underlying description given from the kinetic theory approach, more specifically by the kinetic theory for active particles [8]. A general reference on crowd modeling is the recently published book [10].

Time Allocation in Social Networks: Correlation Between Social Structure and Human Communication Dynamics

NASA Astrophysics Data System (ADS)

Miritello, Giovanna; Lara, Rubén; Moro, Esteban

Recent research has shown the deep impact of the dynamics of human interactions (or temporal social networks) on the spreading of information, opinion formation, etc. In general, the bursty nature of human interactions lowers the interaction between people to the extent that both the speed and reach of information diffusion are diminished. Using a large database of 20 million users of mobile phone calls we show evidence this effect is not homogeneous in the social network but in fact, there is a large correlation between this effect and the social topological structure around a given individual. In particular, we show that social relations of hubs in a network are relatively weaker from the dynamical point than those that are poorer connected in the information diffusion process. Our results show the importance of the temporal patterns of communication when analyzing and modeling dynamical process on social networks.

Flipping Patients and Frames: The Patient in Relational Medicine

PubMed Central

Zukier, Henri

2017-01-01

Medicine has evolved in two opposite directions. Evidence-based medicine focuses more on laboratory and computer data than on the patient. Yet experimental data also provide growing evidence for the importance of the patient’s social-psychological “demand” side of medicine, to complement the doctor’s bio-cognitive “supply” side. The patient’s mindset has major diagnostic and therapeutic effects. The patient’s experience is shaped by perceptions of four dimensions: meaning, agency, self-image, and temporal focus. The patient’s perceptions are linked in part to the therapeutic context, through the interaction between doctor and patient. In that proximal setting, the dimensions can be reshaped, for better and worse. These dynamics point to the inherently interactional nature of medicine and to the significant role of medical social sciences in the therapeutic context. PMID:28786808

Principles for the wise use of computers by children.

PubMed

Straker, L; Pollock, C; Maslen, B

2009-11-01

Computer use by children at home and school is now common in many countries. Child computer exposure varies with the type of computer technology available and the child's age, gender and social group. This paper reviews the current exposure data and the evidence for positive and negative effects of computer use by children. Potential positive effects of computer use by children include enhanced cognitive development and school achievement, reduced barriers to social interaction, enhanced fine motor skills and visual processing and effective rehabilitation. Potential negative effects include threats to child safety, inappropriate content, exposure to violence, bullying, Internet 'addiction', displacement of moderate/vigorous physical activity, exposure to junk food advertising, sleep displacement, vision problems and musculoskeletal problems. The case for child specific evidence-based guidelines for wise use of computers is presented based on children using computers differently to adults, being physically, cognitively and socially different to adults, being in a state of change and development and the potential to impact on later adult risk. Progress towards child-specific guidelines is reported. Finally, a set of guideline principles is presented as the basis for more detailed guidelines on the physical, cognitive and social impact of computer use by children. The principles cover computer literacy, technology safety, child safety and privacy and appropriate social, cognitive and physical development. The majority of children in affluent communities now have substantial exposure to computers. This is likely to have significant effects on child physical, cognitive and social development. Ergonomics can provide and promote guidelines for wise use of computers by children and by doing so promote the positive effects and reduce the negative effects of computer-child, and subsequent computer-adult, interaction.

Processing of facial affect in social drinkers: a dose-response study of alcohol using dynamic emotion expressions.

PubMed

Kamboj, Sunjeev K; Joye, Alyssa; Bisby, James A; Das, Ravi K; Platt, Bradley; Curran, H Valerie

2013-05-01

Studies of affect recognition can inform our understanding of the interpersonal effects of alcohol and help develop a more complete neuropsychological profile of this drug. The objective of the study was to examine affect recognition in social drinkers using a novel dynamic affect-recognition task, sampling performance across a range of evolutionarily significant target emotions and neutral expressions. Participants received 0, 0.4 or 0.8 g/kg alcohol in a double-blind, independent groups design. Relatively naturalistic changes in facial expression-from neutral (mouth open) to increasing intensities of target emotions, as well as neutral (mouth closed)-were simulated using computer-generated dynamic morphs. Accuracy and reaction time were measured and a two-high-threshold model applied to hits and false-alarm data to determine sensitivity and response bias. While there was no effect on the principal emotion expressions (happiness, sadness, fear, anger and disgust), compared to those receiving 0.8 g/kg of alcohol and placebo, participants administered with 0.4 g/kg alcohol tended to show an enhanced response bias to neutral expressions. Exploration of this effect suggested an accompanying tendency to misattribute neutrality to sad expressions following the 0.4-g/kg dose. The 0.4-g/kg alcohol-but not 0.8 g/kg-produced a limited and specific modification in affect recognition evidenced by a neutral response bias and possibly an accompanying tendency to misclassify sad expressions as neutral. In light of previous findings on involuntary negative memory following the 0.4-g/kg dose, we suggest that moderate-but not high-doses of alcohol have a special relevance to emotional processing in social drinkers.

Group Effects on Individual Attitudes Toward Social Responsibility.

PubMed

Secchi, Davide; Bui, Hong T M

2018-01-01

This study uses a quasi-experimental design to investigate what happens to individual socially responsible attitudes when they are exposed to group dynamics. Findings show that group engagement increases individual attitudes toward social responsibility. We also found that individuals with low attitudes toward social responsibility are more likely to change their opinions when group members show more positive attitudes toward social responsibility. Conversely, individuals with high attitudes do not change much, independent of group characteristics. To better analyze the effect of group dynamics, the study proposes to split social responsibility into relative and absolute components. Findings show that relative social responsibility is correlated with but different from absolute social responsibility although the latter is more susceptible than the former to group dynamics.

Asymptotic theory of time-varying social networks with heterogeneous activity and tie allocation.

PubMed

Ubaldi, Enrico; Perra, Nicola; Karsai, Márton; Vezzani, Alessandro; Burioni, Raffaella; Vespignani, Alessandro

2016-10-24

The dynamic of social networks is driven by the interplay between diverse mechanisms that still challenge our theoretical and modelling efforts. Amongst them, two are known to play a central role in shaping the networks evolution, namely the heterogeneous propensity of individuals to i) be socially active and ii) establish a new social relationships with their alters. Here, we empirically characterise these two mechanisms in seven real networks describing temporal human interactions in three different settings: scientific collaborations, Twitter mentions, and mobile phone calls. We find that the individuals' social activity and their strategy in choosing ties where to allocate their social interactions can be quantitatively described and encoded in a simple stochastic network modelling framework. The Master Equation of the model can be solved in the asymptotic limit. The analytical solutions provide an explicit description of both the system dynamic and the dynamical scaling laws characterising crucial aspects about the evolution of the networks. The analytical predictions match with accuracy the empirical observations, thus validating the theoretical approach. Our results provide a rigorous dynamical system framework that can be extended to include other processes shaping social dynamics and to generate data driven predictions for the asymptotic behaviour of social networks.

Asymptotic theory of time-varying social networks with heterogeneous activity and tie allocation

NASA Astrophysics Data System (ADS)

Ubaldi, Enrico; Perra, Nicola; Karsai, Márton; Vezzani, Alessandro; Burioni, Raffaella; Vespignani, Alessandro

2016-10-01

The dynamic of social networks is driven by the interplay between diverse mechanisms that still challenge our theoretical and modelling efforts. Amongst them, two are known to play a central role in shaping the networks evolution, namely the heterogeneous propensity of individuals to i) be socially active and ii) establish a new social relationships with their alters. Here, we empirically characterise these two mechanisms in seven real networks describing temporal human interactions in three different settings: scientific collaborations, Twitter mentions, and mobile phone calls. We find that the individuals’ social activity and their strategy in choosing ties where to allocate their social interactions can be quantitatively described and encoded in a simple stochastic network modelling framework. The Master Equation of the model can be solved in the asymptotic limit. The analytical solutions provide an explicit description of both the system dynamic and the dynamical scaling laws characterising crucial aspects about the evolution of the networks. The analytical predictions match with accuracy the empirical observations, thus validating the theoretical approach. Our results provide a rigorous dynamical system framework that can be extended to include other processes shaping social dynamics and to generate data driven predictions for the asymptotic behaviour of social networks.

Continuous Opinion Dynamics Under Bounded Confidence:. a Survey

NASA Astrophysics Data System (ADS)

Lorenz, Jan

Models of continuous opinion dynamics under bounded confidence have been presented independently by Krause and Hegselmann and by Deffuant et al. in 2000. They have raised a fair amount of attention in the communities of social simulation, sociophysics and complexity science. The researchers working on it come from disciplines such as physics, mathematics, computer science, social psychology and philosophy. In these models agents hold continuous opinions which they can gradually adjust if they hear the opinions of others. The idea of bounded confidence is that agents only interact if they are close in opinion to each other. Usually, the models are analyzed with agent-based simulations in a Monte Carlo style, but they can also be reformulated on the agent's density in the opinion space in a master equation style. The contribution of this survey is fourfold. First, it will present the agent-based and density-based modeling frameworks including the cases of multidimensional opinions and heterogeneous bounds of confidence. Second, it will give the bifurcation diagrams of cluster configuration in the homogeneous model with uniformly distributed initial opinions. Third, it will review the several extensions and the evolving phenomena which have been studied so far, and fourth it will state some open questions.

Automatic decoding of facial movements reveals deceptive pain expressions

PubMed Central

Bartlett, Marian Stewart; Littlewort, Gwen C.; Frank, Mark G.; Lee, Kang

2014-01-01

Summary In highly social species such as humans, faces have evolved to convey rich information for social interaction, including expressions of emotions and pain [1–3]. Two motor pathways control facial movement [4–7]. A subcortical extrapyramidal motor system drives spontaneous facial expressions of felt emotions. A cortical pyramidal motor system controls voluntary facial expressions. The pyramidal system enables humans to simulate facial expressions of emotions not actually experienced. Their simulation is so successful that they can deceive most observers [8–11]. Machine vision may, however, be able to distinguish deceptive from genuine facial signals by identifying the subtle differences between pyramidally and extrapyramidally driven movements. Here we show that human observers could not discriminate real from faked expressions of pain better than chance, and after training, improved accuracy to a modest 55%. However a computer vision system that automatically measures facial movements and performs pattern recognition on those movements attained 85% accuracy. The machine system’s superiority is attributable to its ability to differentiate the dynamics of genuine from faked expressions. Thus by revealing the dynamics of facial action through machine vision systems, our approach has the potential to elucidate behavioral fingerprints of neural control systems involved in emotional signaling. PMID:24656830

Epidemic dynamics on a risk-based evolving social network

NASA Astrophysics Data System (ADS)

Antwi, Shadrack; Shaw, Leah

2013-03-01

Social network models have been used to study how behavior affects the dynamics of an infection in a population. Motivated by HIV, we consider how a trade-off between benefits and risks of sexual connections determine network structure and disease prevalence. We define a stochastic network model with formation and breaking of links as changes in sexual contacts. Each node has an intrinsic benefit its neighbors derive from connecting to it. Nodes' infection status is not apparent to others, but nodes with more connections (higher degree) are assumed more likely to be infected. The probability to form and break links is determined by a payoff computed from the benefit and degree-dependent risk. The disease is represented by a SI (susceptible-infected) model. We study network and epidemic evolution via Monte Carlo simulation and analytically predict the behavior with a heterogeneous mean field approach. The dependence of network connectivity and infection threshold on parameters is determined, and steady state degree distribution and epidemic levels are obtained. We also study a situation where system-wide infection levels alter perception of risk and cause nodes to adjust their behavior. This is a case of an adaptive network, where node status feeds back to change network geometry.

The Dimensions of "Social Dynamics" in Comparative Studies on Higher Education

ERIC Educational Resources Information Center

Välimaa, Jussi; Nokkala, Terhi

2014-01-01

This article discusses social dynamics of higher education which is one of the most crucial but neglected perspectives in comparative studies of higher education. We pay attention to the importance of time, space and contexts--both geographical and socio-cultural ones--to reveal how they influence on different social dynamics in various systems of…

Aperiodic dynamics in a deterministic adaptive network model of attitude formation in social groups

NASA Astrophysics Data System (ADS)

Ward, Jonathan A.; Grindrod, Peter

2014-07-01

Adaptive network models, in which node states and network topology coevolve, arise naturally in models of social dynamics that incorporate homophily and social influence. Homophily relates the similarity between pairs of nodes' states to their network coupling strength, whilst social influence causes coupled nodes' states to convergence. In this paper we propose a deterministic adaptive network model of attitude formation in social groups that includes these effects, and in which the attitudinal dynamics are represented by an activato-inhibitor process. We illustrate that consensus, corresponding to all nodes adopting the same attitudinal state and being fully connected, may destabilise via Turing instability, giving rise to aperiodic dynamics with sensitive dependence on initial conditions. These aperiodic dynamics correspond to the formation and dissolution of sub-groups that adopt contrasting attitudes. We discuss our findings in the context of cultural polarisation phenomena. Social influence. This reflects the fact that people tend to modify their behaviour and attitudes in response to the opinions of others [22-26]. We model social influence via diffusion: agents adjust their state according to a weighted sum (dictated by the evolving network) of the differences between their state and the states of their neighbours. Homophily. This relates the similarity of individuals' states to their frequency and strength of interaction [27]. Thus in our model, homophily drives the evolution of the weighted ‘social' network. A precise formulation of our model is given in Section 2. Social influence and homophily underpin models of social dynamics [21], which cover a wide range of sociological phenomena, including the diffusion of innovations [28-32], complex contagions [33-36], collective action [37-39], opinion dynamics [19,20,40,10,11,13,15,41,16], the emergence of social norms [42-44], group stability [45], social differentiation [46] and, of particular relevance here, cultural dissemination [47,12,48].Combining the effects of social influence and homophily naturally gives rise to an adaptive network, since social influence causes the states of agents that are strongly connected to become more similar, while homophily strengthens connections between agents whose states are already similar.1

Virtual faces expressing emotions: an initial concomitant and construct validity study.

PubMed

Joyal, Christian C; Jacob, Laurence; Cigna, Marie-Hélène; Guay, Jean-Pierre; Renaud, Patrice

2014-01-01

Facial expressions of emotions represent classic stimuli for the study of social cognition. Developing virtual dynamic facial expressions of emotions, however, would open-up possibilities, both for fundamental and clinical research. For instance, virtual faces allow real-time Human-Computer retroactions between physiological measures and the virtual agent. The goal of this study was to initially assess concomitants and construct validity of a newly developed set of virtual faces expressing six fundamental emotions (happiness, surprise, anger, sadness, fear, and disgust). Recognition rates, facial electromyography (zygomatic major and corrugator supercilii muscles), and regional gaze fixation latencies (eyes and mouth regions) were compared in 41 adult volunteers (20 ♂, 21 ♀) during the presentation of video clips depicting real vs. virtual adults expressing emotions. Emotions expressed by each set of stimuli were similarly recognized, both by men and women. Accordingly, both sets of stimuli elicited similar activation of facial muscles and similar ocular fixation times in eye regions from man and woman participants. Further validation studies can be performed with these virtual faces among clinical populations known to present social cognition difficulties. Brain-Computer Interface studies with feedback-feedforward interactions based on facial emotion expressions can also be conducted with these stimuli.

Dynamic Collaboration Infrastructure for Hydrologic Science

NASA Astrophysics Data System (ADS)

Tarboton, D. G.; Idaszak, R.; Castillo, C.; Yi, H.; Jiang, F.; Jones, N.; Goodall, J. L.

2016-12-01

Data and modeling infrastructure is becoming increasingly accessible to water scientists. HydroShare is a collaborative environment that currently offers water scientists the ability to access modeling and data infrastructure in support of data intensive modeling and analysis. It supports the sharing of and collaboration around "resources" which are social objects defined to include both data and models in a structured standardized format. Users collaborate around these objects via comments, ratings, and groups. HydroShare also supports web services and cloud based computation for the execution of hydrologic models and analysis and visualization of hydrologic data. However, the quantity and variety of data and modeling infrastructure available that can be accessed from environments like HydroShare is increasing. Storage infrastructure can range from one's local PC to campus or organizational storage to storage in the cloud. Modeling or computing infrastructure can range from one's desktop to departmental clusters to national HPC resources to grid and cloud computing resources. How does one orchestrate this vast number of data and computing infrastructure without needing to correspondingly learn each new system? A common limitation across these systems is the lack of efficient integration between data transport mechanisms and the corresponding high-level services to support large distributed data and compute operations. A scientist running a hydrology model from their desktop may require processing a large collection of files across the aforementioned storage and compute resources and various national databases. To address these community challenges a proof-of-concept prototype was created integrating HydroShare with RADII (Resource Aware Data-centric collaboration Infrastructure) to provide software infrastructure to enable the comprehensive and rapid dynamic deployment of what we refer to as "collaborative infrastructure." In this presentation we discuss the results of this proof-of-concept prototype which enabled HydroShare users to readily instantiate virtual infrastructure marshaling arbitrary combinations, varieties, and quantities of distributed data and computing infrastructure in addressing big problems in hydrology.

Social Dynamics Modeling and Inference

DTIC Science & Technology

2018-03-29

AFRL-AFOSR-JP-TR-2018-0027 Social Dynamics Modeling and Inference Kwang-Cheng Chen NATIONAL TAIWAN UNIVERSITY Final Report 03/29/2018 DISTRIBUTION A...DATES COVERED (From - To)      14 May 2014 to 13 May 2017 4.  TITLE AND SUBTITLE Social Dynamics Modeling and Inference 5a.  CONTRACT NUMBER 5b.  GRANT...behavior in human society, to set up the foundation of future possible inference and even control of social collective behavior. Two primary

Drill Sergeant or Math Teacher: Teacher Socialization and Computer Advertisements.

ERIC Educational Resources Information Center

Gribble, Mary; And Others

This paper addresses the question of teacher socialization through contrived images, i.e., the influence of advertising as part of an educational and socialization process. It examines ways in which computer advertisements directed towards teachers influence their perceptions of how computers can and should be used, and how the same advertisements…

Social Construction of Computer Experience in a First-Grade Classroom: Social Processes and Mediating Artifacts.

ERIC Educational Resources Information Center

Wang, X. Christine; Ching, Cynthia Carter

2003-01-01

This ethnographic study investigated first-graders' social construction of their classroom computer experience. Findings showed that children constantly negotiate between their own individual and collective goals in the classroom as they create their own definition of computer use while conforming to the teacher's rules. Considers the usefulness…

Computational Methods for Structural Mechanics and Dynamics

NASA Technical Reports Server (NTRS)

Stroud, W. Jefferson (Editor); Housner, Jerrold M. (Editor); Tanner, John A. (Editor); Hayduk, Robert J. (Editor)

1989-01-01

Topics addressed include: transient dynamics; transient finite element method; transient analysis in impact and crash dynamic studies; multibody computer codes; dynamic analysis of space structures; multibody mechanics and manipulators; spatial and coplanar linkage systems; flexible body simulation; multibody dynamics; dynamical systems; and nonlinear characteristics of joints.

Using Advanced Computing in Applied Dynamics: From the Dynamics of Granular Material to the Motion of the Mars Rover

DTIC Science & Technology

2013-08-26

USING ADVANCED COMPUTING IN APPLIED DYNAMICS : FROM THE DYNAMICS OF GRANULAR MATERIAL TO THE MOTION OF THE MARS ROVER Dan Negrut NVIDIA CUDA...USING ADVANCED COMPUTING IN APPLIED DYNAMICS : FROM THE DYNAMICS OF GRANULAR MATERIAL TO THE MOTION OF THE MARS ROVER 5a. CONTRACT NUMBER W911NF-11-F...University of Parma, Italy • Drs. Paramsothy Jayakumar & David Lamb, US Army TARDEC • Mihai Anitescu, University of Chicago & Argonne National Lab

Models, Entropy and Information of Temporal Social Networks

NASA Astrophysics Data System (ADS)

Zhao, Kun; Karsai, Márton; Bianconi, Ginestra

Temporal social networks are characterized by heterogeneous duration of contacts, which can either follow a power-law distribution, such as in face-to-face interactions, or a Weibull distribution, such as in mobile-phone communication. Here we model the dynamics of face-to-face interaction and mobile phone communication by a reinforcement dynamics, which explains the data observed in these different types of social interactions. We quantify the information encoded in the dynamics of these networks by the entropy of temporal networks. Finally, we show evidence that human dynamics is able to modulate the information present in social network dynamics when it follows circadian rhythms and when it is interfacing with a new technology such as the mobile-phone communication technology.

A Harris-Todaro Agent-Based Model to Rural-Urban Migration

NASA Astrophysics Data System (ADS)

Espíndola, Aquino L.; Silveira, Jaylson J.; Penna, T. J. P.

2006-09-01

The Harris-Todaro model of the rural-urban migration process is revisited under an agent-based approach. The migration of the workers is interpreted as a process of social learning by imitation, formalized by a computational model. By simulating this model, we observe a transitional dynamics with continuous growth of the urban fraction of overall population toward an equilibrium. Such an equilibrium is characterized by stabilization of rural-urban expected wages differential (generalized Harris-Todaro equilibrium condition), urban concentration and urban unemployment. These classic results obtained originally by Harris and Todaro are emergent properties of our model.

An integrated communications demand model

NASA Astrophysics Data System (ADS)

Doubleday, C. F.

1980-11-01

A computer model of communications demand is being developed to permit dynamic simulations of the long-term evolution of demand for communications media in the U.K. to be made under alternative assumptions about social, economic and technological trends in British Telecom's business environment. The context and objectives of the project and the potential uses of the model are reviewed, and four key concepts in the demand for communications media, around which the model is being structured are discussed: (1) the generation of communications demand; (2) substitution between media; (3) technological convergence; and (4) competition. Two outline perspectives on the model itself are given.

How Ebola impacts social dynamics in gorillas: a multistate modelling approach.

PubMed

Genton, Céline; Pierre, Amandine; Cristescu, Romane; Lévréro, Florence; Gatti, Sylvain; Pierre, Jean-Sébastien; Ménard, Nelly; Le Gouar, Pascaline

2015-01-01

Emerging infectious diseases can induce rapid changes in population dynamics and threaten population persistence. In socially structured populations, the transfers of individuals between social units, for example, from breeding groups to non-breeding groups, shape population dynamics. We suggest that diseases may affect these crucial transfers. We aimed to determine how disturbance by an emerging disease affects demographic rates of gorillas, especially transfer rates within populations and immigration rates into populations. We compared social dynamics and key demographic parameters in a gorilla population affected by Ebola using a long-term observation data set including pre-, during and post-outbreak periods. We also studied a population of undetermined epidemiological status in order to assess whether this population was affected by the disease. We developed a multistate model that can handle transition between social units while optimizing the number of states. During the Ebola outbreak, social dynamics displayed increased transfers from a breeding to a non-breeding status for both males and females. Six years after the outbreak, demographic and most of social dynamics parameters had returned to their initial rates, suggesting a certain resilience in the response to disruption. The formation of breeding groups increased just after Ebola, indicating that environmental conditions were still attractive. However, population recovery was likely delayed because compensatory immigration was probably impeded by the potential impact of Ebola in the surrounding areas. The population of undetermined epidemiological status behaved similarly to the other population before Ebola. Our results highlight the need to integrate social dynamics in host-population demographic models to better understand the role of social structure in the sensitivity and the response to disease disturbances. © 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society.

Social Dynamics Management and Functional Behavioral Assessment

ERIC Educational Resources Information Center

Lee, David L.

2018-01-01

Managing social dynamics is a critical aspect of creating a positive learning environment in classrooms. In this paper three key interrelated ideas, reinforcement, function, and motivating operations, are discussed with relation to managing social behavior.

Automated social skills training with audiovisual information.

PubMed

Tanaka, Hiroki; Sakti, Sakriani; Neubig, Graham; Negoro, Hideki; Iwasaka, Hidemi; Nakamura, Satoshi

2016-08-01

People with social communication difficulties tend to have superior skills using computers, and as a result computer-based social skills training systems are flourishing. Social skills training, performed by human trainers, is a well-established method to obtain appropriate skills in social interaction. Previous works have attempted to automate one or several parts of social skills training through human-computer interaction. However, while previous work on simulating social skills training considered only acoustic and linguistic features, human social skills trainers take into account visual features (e.g. facial expression, posture). In this paper, we create and evaluate a social skills training system that closes this gap by considering audiovisual features regarding ratio of smiling, yaw, and pitch. An experimental evaluation measures the difference in effectiveness of social skill training when using audio features and audiovisual features. Results showed that the visual features were effective to improve users' social skills.

Gender Issues in Technology Use: Perceived Social Support, Computer Self-Efficacy and Value Beliefs, and Computer Use beyond School

ERIC Educational Resources Information Center

Vekiri, Ioanna; Chronaki, Anna

2008-01-01

In this study, we examined relations between outside school computer experiences, perceived social support for using computers, and self-efficacy and value beliefs about computer learning for 340 Greek elementary school boys and girls. Participants responded to a questionnaire about their access to computer use outside school (e.g. frequency of…

The Social Organisation of Help during Young Children's Use of the Computer

ERIC Educational Resources Information Center

Davidson, Christina

2012-01-01

This article examines some of the ways that young children seek and provide help through social interaction during use of the computer in the home. Although social interaction is considered an important aspect of young children's use of computers, there are still few studies that provide detailed analysis of how young children accomplish that…

Dynamics of Opinion Forming in Structurally Balanced Social Networks

PubMed Central

Altafini, Claudio

2012-01-01

A structurally balanced social network is a social community that splits into two antagonistic factions (typical example being a two-party political system). The process of opinion forming on such a community is most often highly predictable, with polarized opinions reflecting the bipartition of the network. The aim of this paper is to suggest a class of dynamical systems, called monotone systems, as natural models for the dynamics of opinion forming on structurally balanced social networks. The high predictability of the outcome of a decision process is explained in terms of the order-preserving character of the solutions of this class of dynamical systems. If we represent a social network as a signed graph in which individuals are the nodes and the signs of the edges represent friendly or hostile relationships, then the property of structural balance corresponds to the social community being splittable into two antagonistic factions, each containing only friends. PMID:22761667

Aeroelastic Modeling of a Nozzle Startup Transient

NASA Technical Reports Server (NTRS)

Wang, Ten-See; Zhao, Xiang; Zhang, Sijun; Chen, Yen-Sen

2014-01-01

Lateral nozzle forces are known to cause severe structural damage to any new rocket engine in development during test. While three-dimensional, transient, turbulent, chemically reacting computational fluid dynamics methodology has been demonstrated to capture major side load physics with rigid nozzles, hot-fire tests often show nozzle structure deformation during major side load events, leading to structural damages if structural strengthening measures were not taken. The modeling picture is incomplete without the capability to address the two-way responses between the structure and fluid. The objective of this study is to develop a tightly coupled aeroelastic modeling algorithm by implementing the necessary structural dynamics component into an anchored computational fluid dynamics methodology. The computational fluid dynamics component is based on an unstructured-grid, pressure-based computational fluid dynamics formulation, while the computational structural dynamics component is developed under the framework of modal analysis. Transient aeroelastic nozzle startup analyses at sea level were performed, and the computed transient nozzle fluid-structure interaction physics presented,

Development of an Aeroelastic Modeling Capability for Transient Nozzle Side Load Analysis

NASA Technical Reports Server (NTRS)

Wang, Ten-See; Zhao, Xiang; Zhang, Sijun; Chen, Yen-Sen

2013-01-01

Lateral nozzle forces are known to cause severe structural damage to any new rocket engine in development during test. While three-dimensional, transient, turbulent, chemically reacting computational fluid dynamics methodology has been demonstrated to capture major side load physics with rigid nozzles, hot-fire tests often show nozzle structure deformation during major side load events, leading to structural damages if structural strengthening measures were not taken. The modeling picture is incomplete without the capability to address the two-way responses between the structure and fluid. The objective of this study is to develop a coupled aeroelastic modeling capability by implementing the necessary structural dynamics component into an anchored computational fluid dynamics methodology. The computational fluid dynamics component is based on an unstructured-grid, pressure-based computational fluid dynamics formulation, while the computational structural dynamics component is developed in the framework of modal analysis. Transient aeroelastic nozzle startup analyses of the Block I Space Shuttle Main Engine at sea level were performed. The computed results from the aeroelastic nozzle modeling are presented.

75 FR 5166 - Privacy Act of 1974, as Amended; Computer Matching Program (Social Security Administration...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-01

... SOCIAL SECURITY ADMINISTRATION [Docket No. SSA 2009-0043] Privacy Act of 1974, as Amended; Computer Matching Program (Social Security Administration/Railroad Retirement Board (SSA/RRB))-- Match Number 1308 AGENCY: Social Security Administration (SSA). ACTION: Notice of renewal of an existing...

Fundamental structures of dynamic social networks.

PubMed

Sekara, Vedran; Stopczynski, Arkadiusz; Lehmann, Sune

2016-09-06

Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-min time slices, we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores is preceded by coordination behavior in the communication networks and demonstrating that social behavior can be predicted with high precision.

Fundamental structures of dynamic social networks

PubMed Central

Sekara, Vedran; Stopczynski, Arkadiusz; Lehmann, Sune

2016-01-01

Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-min time slices, we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores is preceded by coordination behavior in the communication networks and demonstrating that social behavior can be predicted with high precision. PMID:27555584

Static and Dynamic Facial Cues Differentially Affect the Consistency of Social Evaluations.

PubMed

Hehman, Eric; Flake, Jessica K; Freeman, Jonathan B

2015-08-01

Individuals are quite sensitive to others' appearance cues when forming social evaluations. Cues such as facial emotional resemblance are based on facial musculature and thus dynamic. Cues such as a face's structure are based on the underlying bone and are thus relatively static. The current research examines the distinction between these types of facial cues by investigating the consistency in social evaluations arising from dynamic versus static cues. Specifically, across four studies using real faces, digitally generated faces, and downstream behavioral decisions, we demonstrate that social evaluations based on dynamic cues, such as intentions, have greater variability across multiple presentations of the same identity than do social evaluations based on static cues, such as ability. Thus, although evaluations of intentions vary considerably across different instances of a target's face, evaluations of ability are relatively fixed. The findings highlight the role of facial cues' consistency in the stability of social evaluations. © 2015 by the Society for Personality and Social Psychology, Inc.

Breastfeeding and Social Media among First-Time African American Mothers

PubMed Central

Asiodu, Ifeyinwa V.; Waters, Catherine M.; Dailey, Dawn E.; Lee, Kathryn A.; Lyndon, Audrey

2015-01-01

Objective To describe the use of social media during the antepartum and postpartum periods among first-time African American mothers and their support persons. Design A qualitative critical ethnographic research design within the contexts of Family Life Course Development Theory and Black Feminist Theory. Setting Participants were recruited from community-based, public health, and home visiting programs. Participants A purposive sample was recruited, consisting of 14 pregnant African American women and eight support persons. Methods Pregnant and postpartum African American women and their support persons were interviewed separately during the antepartum and postpartum periods. Data were analyzed thematically. Results Participants frequently used social media for educational and social support and searched the internet for perinatal and parenting information. Most participants reported using at least one mobile application during their pregnancies and after giving birth. Social media were typically accessed through smartphones and/or computers using different websites and applications. While participants gleaned considerable information about infant development from these applications, they had difficulty finding and recalling information about infant feeding. Conclusion Social media are an important vehicle to disseminate infant feeding information; however, they are not currently being used to full potential. Our findings suggest that future interventions geared towards African American mothers and their support persons should include social media approaches. The way individuals gather, receive, and interpret information is dynamic. The increasing popularity and use of social media platforms offers the opportunity to create more innovative, targeted mobile health interventions for infant feeding and breastfeeding promotion. PMID:25712127

Fluctuation of similarity to detect transitions between distinct dynamical regimes in short time series

NASA Astrophysics Data System (ADS)

Malik, Nishant; Marwan, Norbert; Zou, Yong; Mucha, Peter J.; Kurths, Jürgen

2014-06-01

A method to identify distinct dynamical regimes and transitions between those regimes in a short univariate time series was recently introduced [N. Malik et al., Europhys. Lett. 97, 40009 (2012), 10.1209/0295-5075/97/40009], employing the computation of fluctuations in a measure of nonlinear similarity based on local recurrence properties. In this work, we describe the details of the analytical relationships between this newly introduced measure and the well-known concepts of attractor dimensions and Lyapunov exponents. We show that the new measure has linear dependence on the effective dimension of the attractor and it measures the variations in the sum of the Lyapunov spectrum. To illustrate the practical usefulness of the method, we identify various types of dynamical transitions in different nonlinear models. We present testbed examples for the new method's robustness against noise and missing values in the time series. We also use this method to analyze time series of social dynamics, specifically an analysis of the US crime record time series from 1975 to 1993. Using this method, we find that dynamical complexity in robberies was influenced by the unemployment rate until the late 1980s. We have also observed a dynamical transition in homicide and robbery rates in the late 1980s and early 1990s, leading to increase in the dynamical complexity of these rates.

Research on application of intelligent computation based LUCC model in urbanization process

NASA Astrophysics Data System (ADS)

Chen, Zemin

2007-06-01

Global change study is an interdisciplinary and comprehensive research activity with international cooperation, arising in 1980s, with the largest scopes. The interaction between land use and cover change, as a research field with the crossing of natural science and social science, has become one of core subjects of global change study as well as the front edge and hot point of it. It is necessary to develop research on land use and cover change in urbanization process and build an analog model of urbanization to carry out description, simulation and analysis on dynamic behaviors in urban development change as well as to understand basic characteristics and rules of urbanization process. This has positive practical and theoretical significance for formulating urban and regional sustainable development strategy. The effect of urbanization on land use and cover change is mainly embodied in the change of quantity structure and space structure of urban space, and LUCC model in urbanization process has been an important research subject of urban geography and urban planning. In this paper, based upon previous research achievements, the writer systematically analyzes the research on land use/cover change in urbanization process with the theories of complexity science research and intelligent computation; builds a model for simulating and forecasting dynamic evolution of urban land use and cover change, on the basis of cellular automation model of complexity science research method and multi-agent theory; expands Markov model, traditional CA model and Agent model, introduces complexity science research theory and intelligent computation theory into LUCC research model to build intelligent computation-based LUCC model for analog research on land use and cover change in urbanization research, and performs case research. The concrete contents are as follows: 1. Complexity of LUCC research in urbanization process. Analyze urbanization process in combination with the contents of complexity science research and the conception of complexity feature to reveal the complexity features of LUCC research in urbanization process. Urban space system is a complex economic and cultural phenomenon as well as a social process, is the comprehensive characterization of urban society, economy and culture, and is a complex space system formed by society, economy and nature. It has dissipative structure characteristics, such as opening, dynamics, self-organization, non-balance etc. Traditional model cannot simulate these social, economic and natural driving forces of LUCC including main feedback relation from LUCC to driving force. 2. Establishment of Markov extended model of LUCC analog research in urbanization process. Firstly, use traditional LUCC research model to compute change speed of regional land use through calculating dynamic degree, exploitation degree and consumption degree of land use; use the theory of fuzzy set to rewrite the traditional Markov model, establish structure transfer matrix of land use, forecast and analyze dynamic change and development trend of land use, and present noticeable problems and corresponding measures in urbanization process according to research results. 3. Application of intelligent computation research and complexity science research method in LUCC analog model in urbanization process. On the basis of detailed elaboration of the theory and the model of LUCC research in urbanization process, analyze the problems of existing model used in LUCC research (namely, difficult to resolve many complexity phenomena in complex urban space system), discuss possible structure realization forms of LUCC analog research in combination with the theories of intelligent computation and complexity science research. Perform application analysis on BP artificial neural network and genetic algorithms of intelligent computation and CA model and MAS technology of complexity science research, discuss their theoretical origins and their own characteristics in detail, elaborate the feasibility of them in LUCC analog research, and bring forward improvement methods and measures on existing problems of this kind of model. 4. Establishment of LUCC analog model in urbanization process based on theories of intelligent computation and complexity science. Based on the research on abovementioned BP artificial neural network, genetic algorithms, CA model and multi-agent technology, put forward improvement methods and application assumption towards their expansion on geography, build LUCC analog model in urbanization process based on CA model and Agent model, realize the combination of learning mechanism of BP artificial neural network and fuzzy logic reasoning, express the regulation with explicit formula, and amend the initial regulation through self study; optimize network structure of LUCC analog model and methods and procedures of model parameters with genetic algorithms. In this paper, I introduce research theory and methods of complexity science into LUCC analog research and presents LUCC analog model based upon CA model and MAS theory. Meanwhile, I carry out corresponding expansion on traditional Markov model and introduce the theory of fuzzy set into data screening and parameter amendment of improved model to improve the accuracy and feasibility of Markov model in the research on land use/cover change.

Incorporating Social System Dynamics into the Food-Energy-Water System Resilience-Sustainability Modeling Process

NASA Astrophysics Data System (ADS)

Givens, J.; Padowski, J.; Malek, K.; Guzman, C.; Boll, J.; Adam, J. C.; Witinok-Huber, R.

2017-12-01

In the face of climate change and multi-scalar governance objectives, achieving resilience of food-energy-water (FEW) systems requires interdisciplinary approaches. Through coordinated modeling and management efforts, we study "Innovations in the Food-Energy-Water Nexus (INFEWS)" through a case-study in the Columbia River Basin. Previous research on FEW system management and resilience includes some attention to social dynamics (e.g., economic, governance); however, more research is needed to better address social science perspectives. Decisions ultimately taken in this river basin would occur among stakeholders encompassing various institutional power structures including multiple U.S. states, tribal lands, and sovereign nations. The social science lens draws attention to the incompatibility between the engineering definition of resilience (i.e., return to equilibrium or a singular stable state) and the ecological and social system realities, more explicit in the ecological interpretation of resilience (i.e., the ability of a system to move into a different, possibly more resilient state). Social science perspectives include but are not limited to differing views on resilience as normative, system persistence versus transformation, and system boundary issues. To expand understanding of resilience and objectives for complex and dynamic systems, concepts related to inequality, heterogeneity, power, agency, trust, values, culture, history, conflict, and system feedbacks must be more tightly integrated into FEW research. We identify gaps in knowledge and data, and the value and complexity of incorporating social components and processes into systems models. We posit that socio-biophysical system resilience modeling would address important complex, dynamic social relationships, including non-linear dynamics of social interactions, to offer an improved understanding of sustainable management in FEW systems. Conceptual modeling that is presented in our study, represents a starting point for a continued research agenda that incorporates social dynamics into FEW system resilience and management.

Use of measurement theory for operationalization and quantification of psychological constructs in systems dynamics modelling

NASA Astrophysics Data System (ADS)

Fitkov-Norris, Elena; Yeghiazarian, Ara

2016-11-01

The analytical tools available to social scientists have traditionally been adapted from tools originally designed for analysis of natural science phenomena. This article discusses the applicability of systems dynamics - a qualitative based modelling approach, as a possible analysis and simulation tool that bridges the gap between social and natural sciences. After a brief overview of the systems dynamics modelling methodology, the advantages as well as limiting factors of systems dynamics to the potential applications in the field of social sciences and human interactions are discussed. The issues arise with regards to operationalization and quantification of latent constructs at the simulation building stage of the systems dynamics methodology and measurement theory is proposed as a ready and waiting solution to the problem of dynamic model calibration, with a view of improving simulation model reliability and validity and encouraging the development of standardised, modular system dynamics models that can be used in social science research.

Dynamically analyzing cell interactions in biological environments using multiagent social learning framework.

PubMed

Zhang, Chengwei; Li, Xiaohong; Li, Shuxin; Feng, Zhiyong

2017-09-20

Biological environment is uncertain and its dynamic is similar to the multiagent environment, thus the research results of the multiagent system area can provide valuable insights to the understanding of biology and are of great significance for the study of biology. Learning in a multiagent environment is highly dynamic since the environment is not stationary anymore and each agent's behavior changes adaptively in response to other coexisting learners, and vice versa. The dynamics becomes more unpredictable when we move from fixed-agent interaction environments to multiagent social learning framework. Analytical understanding of the underlying dynamics is important and challenging. In this work, we present a social learning framework with homogeneous learners (e.g., Policy Hill Climbing (PHC) learners), and model the behavior of players in the social learning framework as a hybrid dynamical system. By analyzing the dynamical system, we obtain some conditions about convergence or non-convergence. We experimentally verify the predictive power of our model using a number of representative games. Experimental results confirm the theoretical analysis. Under multiagent social learning framework, we modeled the behavior of agent in biologic environment, and theoretically analyzed the dynamics of the model. We present some sufficient conditions about convergence or non-convergence and prove them theoretically. It can be used to predict the convergence of the system.

Joyfully Map Social Dynamics When Designing Web-Based Courses

ERIC Educational Resources Information Center

Ahamer, Gilbert

2013-01-01

This paper provides a concept and a notation for optimizing the design of social processes in gaming and learning for individuals, groups of individuals and society as a whole. Traditional approaches to the mapping and designing of the emerging social dynamics in a joyful, social education setting have fallen short of producing desirable results…

OT promotes closer interpersonal distance among highly empathic individuals.

PubMed

Perry, Anat; Mankuta, David; Shamay-Tsoory, Simone G

2015-01-01

The space between people, or 'interpersonal distance', creates and defines the dynamics of social interactions and is a salient cue signaling responsiveness and feeling comfortable. This distance is implicit yet clearly felt, especially if someone stands closer or farther away than expected. Increasing evidence suggests that Oxytocin (OT) serves as a social hormone in humans, and that one of its roles may be to alter the perceptual salience of social cues. Considering that empathic ability may shape the way individuals process social stimuli, we predicted that OT will differentially affect preferred interpersonal distance depending on individual differences in empathy. Participants took part in two interpersonal distance experiments: In the first, they had to stop a (computer visualized) protagonist when feeling most comfortable; in the second, they were asked to choose the room in which they would later discuss intimate topics with another. Both experiments revealed an interaction between the effect of OT and empathy level. Among highly empathic individuals, OT promoted the choice of closer interpersonal distances. Yet, OT had an opposite effect on individuals with low empathic traits. We conclude that the enhancement of social cues following OT administration may have opposite effects on individuals with different empathic abilities. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Smile esthetics: age related changes, and objective differences between social and spontaneous smiles.

PubMed

Dindaroğlu, Furkan; Doğan, Servet; Erdinç, Ashhan M Ertan

2011-01-01

To evaluate the importance of age in orthodontic treatment by studying the dependence of smile and resting parameters on age and to expose differences between social and spontaneous smiles. Subjects consisted of 67 individuals aged between 17 and 55. The video recordings were transferred to a computer 200 still frames were captured for each individual 50 were captured in resting position, 50 during speech, 50for social, and 50for spontaneous smiles. One picture was selectedfrom each group based on how pictures reflected the desired point ANOVA and Scheffe Post-hoc tests were performed on smile measurements. In all the resting parameters, statistically significant differences were observed among age groups. Also, the response of these parameters to age differs between men and women. Statistically significant diferences were found in some smile parameters among diferent age groups, for both smile types. We find significant differences between social and spontaneous smiles. Age related alterations should be taken into consideration during treatment planning, especially in women. Due to its high consistency, there are advantages with using a spontaneous smile in soft-hard tissue evaluations. We also emphasize the necessity to take dynamic registrations for a true functional evaluation.

Infectious disease transmission and contact networks in wildlife and livestock.

PubMed

Craft, Meggan E

2015-05-26

The use of social and contact networks to answer basic and applied questions about infectious disease transmission in wildlife and livestock is receiving increased attention. Through social network analysis, we understand that wild animal and livestock populations, including farmed fish and poultry, often have a heterogeneous contact structure owing to social structure or trade networks. Network modelling is a flexible tool used to capture the heterogeneous contacts of a population in order to test hypotheses about the mechanisms of disease transmission, simulate and predict disease spread, and test disease control strategies. This review highlights how to use animal contact data, including social networks, for network modelling, and emphasizes that researchers should have a pathogen of interest in mind before collecting or using contact data. This paper describes the rising popularity of network approaches for understanding transmission dynamics in wild animal and livestock populations; discusses the common mismatch between contact networks as measured in animal behaviour and relevant parasites to match those networks; and highlights knowledge gaps in how to collect and analyse contact data. Opportunities for the future include increased attention to experiments, pathogen genetic markers and novel computational tools. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Infectious disease transmission and contact networks in wildlife and livestock

PubMed Central

Craft, Meggan E.

2015-01-01

The use of social and contact networks to answer basic and applied questions about infectious disease transmission in wildlife and livestock is receiving increased attention. Through social network analysis, we understand that wild animal and livestock populations, including farmed fish and poultry, often have a heterogeneous contact structure owing to social structure or trade networks. Network modelling is a flexible tool used to capture the heterogeneous contacts of a population in order to test hypotheses about the mechanisms of disease transmission, simulate and predict disease spread, and test disease control strategies. This review highlights how to use animal contact data, including social networks, for network modelling, and emphasizes that researchers should have a pathogen of interest in mind before collecting or using contact data. This paper describes the rising popularity of network approaches for understanding transmission dynamics in wild animal and livestock populations; discusses the common mismatch between contact networks as measured in animal behaviour and relevant parasites to match those networks; and highlights knowledge gaps in how to collect and analyse contact data. Opportunities for the future include increased attention to experiments, pathogen genetic markers and novel computational tools. PMID:25870393

Using Computers for Research into Social Relations.

ERIC Educational Resources Information Center

Holden, George W.

1988-01-01

Discusses computer-presented social situations (CPSS), i.e., microcomputer-based simulations developed to provide a new methodological tool for social scientists interested in the study of social relations. Two CPSSs are described: DaySim, used to help identify types of parenting; and DateSim, used to study interpersonal attraction. (21…

77 FR 74913 - Privacy Act of 1974, as Amended; Computer Matching Program (Social Security Administration (SSA...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-18

... SOCIAL SECURITY ADMINISTRATION [Docket No. SSA 2012-0055] Privacy Act of 1974, as Amended; Computer Matching Program (Social Security Administration (SSA)/Office of Personnel Management (OPM))--Match Number 1307 AGENCY: Social Security Administration. ACTION: Notice of a renewal of an existing...

Are Heavy Users of Computer Games and Social Media More Computer Literate?

ERIC Educational Resources Information Center

Appel, Markus

2012-01-01

Adolescents spend a substantial part of their leisure time with playing games and using social media such as Facebook. The present paper examines the link between adolescents' computer and Internet activities and computer literacy (defined as the ability to work with a computer efficiently). A cross-sectional study with N = 200 adolescents, aged…

Association between playing computer games and mental and social health among male adolescents in Iran in 2014.

PubMed

Mohammadi, Mehrnoosh; RezaeiDehaghani, Abdollah; Mehrabi, Tayebeh; RezaeiDehaghani, Ali

2016-01-01

As adolescents spend much time on playing computer games, their mental and social effects should be considered. The present study aimed to investigate the association between playing computer games and the mental and social health among male adolescents in Iran in 2014. This is a cross-sectional study conducted on 210 adolescents selected by multi-stage random sampling. Data were collected by Goldberg and Hillier general health (28 items) and Kiez social health questionnaires. The association was tested by Pearson and Spearman correlation coefficients, one-way analysis of variance (ANOVA), and independent t-test. Computer games related factors such as the location, type, length, the adopted device, and mode of playing games were investigated. Results showed that 58.9% of the subjects played games on a computer alone for 1 h at home. Results also revealed that the subjects had appropriate mental health and 83.2% had moderate social health. Results showed a poor significant association between the length of games and social health (r = -0.15, P = 0.03), the type of games and mental health (r = -0.16, P = 0.01), and the device used in playing games and social health (F = 0.95, P = 0.03). The findings showed that adolescents' mental and social health is negatively associated with their playing computer games. Therefore, to promote their health, educating them about the correct way of playing computer games is essential and their parents and school authorities, including nurses working at schools, should determine its relevant factors such as the type, length, and device used in playing such games.

Design and Implementation of High-Performance GIS Dynamic Objects Rendering Engine

NASA Astrophysics Data System (ADS)

Zhong, Y.; Wang, S.; Li, R.; Yun, W.; Song, G.

2017-12-01

Spatio-temporal dynamic visualization is more vivid than static visualization. It important to use dynamic visualization techniques to reveal the variation process and trend vividly and comprehensively for the geographical phenomenon. To deal with challenges caused by dynamic visualization of both 2D and 3D spatial dynamic targets, especially for different spatial data types require high-performance GIS dynamic objects rendering engine. The main approach for improving the rendering engine with vast dynamic targets relies on key technologies of high-performance GIS, including memory computing, parallel computing, GPU computing and high-performance algorisms. In this study, high-performance GIS dynamic objects rendering engine is designed and implemented for solving the problem based on hybrid accelerative techniques. The high-performance GIS rendering engine contains GPU computing, OpenGL technology, and high-performance algorism with the advantage of 64-bit memory computing. It processes 2D, 3D dynamic target data efficiently and runs smoothly with vast dynamic target data. The prototype system of high-performance GIS dynamic objects rendering engine is developed based SuperMap GIS iObjects. The experiments are designed for large-scale spatial data visualization, the results showed that the high-performance GIS dynamic objects rendering engine have the advantage of high performance. Rendering two-dimensional and three-dimensional dynamic objects achieve 20 times faster on GPU than on CPU.

Faster than Real-Time Dynamic Simulation for Large-Size Power System with Detailed Dynamic Models using High-Performance Computing Platform

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Renke; Jin, Shuangshuang; Chen, Yousu

This paper presents a faster-than-real-time dynamic simulation software package that is designed for large-size power system dynamic simulation. It was developed on the GridPACKTM high-performance computing (HPC) framework. The key features of the developed software package include (1) faster-than-real-time dynamic simulation for a WECC system (17,000 buses) with different types of detailed generator, controller, and relay dynamic models, (2) a decoupled parallel dynamic simulation algorithm with optimized computation architecture to better leverage HPC resources and technologies, (3) options for HPC-based linear and iterative solvers, (4) hidden HPC details, such as data communication and distribution, to enable development centered on mathematicalmore » models and algorithms rather than on computational details for power system researchers, and (5) easy integration of new dynamic models and related algorithms into the software package.« less

Near real-time traffic routing

NASA Technical Reports Server (NTRS)

Yang, Chaowei (Inventor); Xie, Jibo (Inventor); Zhou, Bin (Inventor); Cao, Ying (Inventor)

2012-01-01

A near real-time physical transportation network routing system comprising: a traffic simulation computing grid and a dynamic traffic routing service computing grid. The traffic simulator produces traffic network travel time predictions for a physical transportation network using a traffic simulation model and common input data. The physical transportation network is divided into a multiple sections. Each section has a primary zone and a buffer zone. The traffic simulation computing grid includes multiple of traffic simulation computing nodes. The common input data includes static network characteristics, an origin-destination data table, dynamic traffic information data and historical traffic data. The dynamic traffic routing service computing grid includes multiple dynamic traffic routing computing nodes and generates traffic route(s) using the traffic network travel time predictions.

77 FR 64834 - Computational Fluid Dynamics Best Practice Guidelines for Dry Cask Applications

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-23

... NUCLEAR REGULATORY COMMISSION [NRC-2012-0250] Computational Fluid Dynamics Best Practice... public comments on draft NUREG-2152, ``Computational Fluid Dynamics Best Practice Guidelines for Dry Cask... System (ADAMS): You may access publicly-available documents online in the NRC Library at http://www.nrc...

Tenth Workshop for Computational Fluid Dynamic Applications in Rocket Propulsion, part 1

NASA Technical Reports Server (NTRS)

Williams, R. W. (Compiler)

1992-01-01

Experimental and computational fluid dynamic activities in rocket propulsion were discussed. The workshop was an open meeting of government, industry, and academia. A broad number of topics were discussed including computational fluid dynamic methodology, liquid and solid rocket propulsion, turbomachinery, combustion, heat transfer, and grid generation.

Tenth Workshop for Computational Fluid Dynamic Applications in Rocket Propulsion, part 2

NASA Technical Reports Server (NTRS)

Williams, R. W. (Compiler)

1992-01-01

Presented here are 59 abstracts and presentations and three invited presentations given at the Tenth Workshop for Computational Fluid Dynamic Applications in Rocket Propulsion held at the George C. Marshall Space Flight Center, April 28-30, 1992. The purpose of the workshop is to discuss experimental and computational fluid dynamic activities in rocket propulsion. The workshop is an open meeting for government, industry, and academia. A broad number of topics are discussed, including a computational fluid dynamic methodology, liquid and solid rocket propulsion, turbomachinery, combustion, heat transfer, and grid generation.

Eleventh Workshop for Computational Fluid Dynamic Applications in Rocket Propulsion

NASA Technical Reports Server (NTRS)

Williams, R. W. (Compiler)

1993-01-01

Conference publication includes 79 abstracts and presentations and 3 invited presentations given at the Eleventh Workshop for Computational Fluid Dynamic Applications in Rocket Propulsion held at George C. Marshall Space Flight Center, April 20-22, 1993. The purpose of the workshop is to discuss experimental and computational fluid dynamic activities in rocket propulsion. The workshop is an open meeting for government, industry, and academia. A broad number of topics are discussed including computational fluid dynamic methodology, liquid and solid rocket propulsion, turbomachinery, combustion, heat transfer, and grid generation.

Eleventh Workshop for Computational Fluid Dynamic Applications in Rocket Propulsion, Part 1

NASA Technical Reports Server (NTRS)

Williams, Robert W. (Compiler)

1993-01-01

Conference publication includes 79 abstracts and presentations given at the Eleventh Workshop for Computational Fluid Dynamic Applications in Rocket Propulsion held at the George C. Marshall Space Flight Center, April 20-22, 1993. The purpose of this workshop is to discuss experimental and computational fluid dynamic activities in rocket propulsion. The workshop is an open meeting for government, industry, and academia. A broad number of topics are discussed including computational fluid dynamic methodology, liquid and solid rocket propulsion, turbomachinery, combustion, heat transfer, and grid generation.

Stochastic and Boltzmann-like models for behavioral changes, and their relation to game theory

NASA Astrophysics Data System (ADS)

Helbing, Dirk

1993-03-01

In the last decade, stochastic models have shown to be very useful for quantitative modelling of social processes. Here, a configurational master equation for the description of behavioral changes by pair interactions of individuals is developed. Three kinds of social pair interactions are distinguished: Avoidance processes, compromising processes, and imitative processes. Computational results are presented for a special case of imitative processes: the competition of two equivalent strategies. They show a phase transition that describes the self-organization of a behavioral convention. This phase transition is further analyzed by examining the equations for the most probable behavioral distribution, which are Boltzmann-like equations. Special cases of Boltzmann-like equations do not obey the H-theorem and have oscillatory or even chaotic solutions. A suitable Taylor approximation leads to the so-called game dynamical equations (also known as selection-mutation equations in the theory of evolution).

ICT-Supported Education; Learning Styles for Individual Knowledge Building

NASA Astrophysics Data System (ADS)

Haugen, Harald; Ask, Bodil; Bjørke, Sven Åke

School surveys and reports on integration of ICT in teaching and learning indicate that the technology is mainly used in traditional learning environments. Furthermore, the most frequently used software in the classrooms are general tools like word processors, presentation tools and Internet browsers. Recent attention among youngsters on social software / web 2.0, contemporary pedagogical approaches like social constructivism and long time experiences with system dynamics and simulations, seem to have a hard time being accepted by teachers and curriculum designers. How can teachers be trained to understand and apply these possibilities optimally that are now available in the classroom and online, on broadband connections and with high capacity computers? Some views on practices with the above-mentioned alternative approaches to learning are presented in this paper, focusing particularly on the options for online work and learning programmes. Here we have first hand experience with adult and mature academics, but also some background with other target groups.

Artificial Exo-Society Modeling: a New Tool for SETI Research

NASA Astrophysics Data System (ADS)

Gardner, James N.

2002-01-01

One of the newest fields of complexity research is artificial society modeling. Methodologically related to artificial life research, artificial society modeling utilizes agent-based computer simulation tools like SWARM and SUGARSCAPE developed by the Santa Fe Institute, Los Alamos National Laboratory and the Bookings Institution in an effort to introduce an unprecedented degree of rigor and quantitative sophistication into social science research. The broad aim of artificial society modeling is to begin the development of a more unified social science that embeds cultural evolutionary processes in a computational environment that simulates demographics, the transmission of culture, conflict, economics, disease, the emergence of groups and coadaptation with an environment in a bottom-up fashion. When an artificial society computer model is run, artificial societal patterns emerge from the interaction of autonomous software agents (the "inhabitants" of the artificial society). Artificial society modeling invites the interpretation of society as a distributed computational system and the interpretation of social dynamics as a specialized category of computation. Artificial society modeling techniques offer the potential of computational simulation of hypothetical alien societies in much the same way that artificial life modeling techniques offer the potential to model hypothetical exobiological phenomena. NASA recently announced its intention to begin exploring the possibility of including artificial life research within the broad portfolio of scientific fields comprised by the interdisciplinary astrobiology research endeavor. It may be appropriate for SETI researchers to likewise commence an exploration of the possible inclusion of artificial exo-society modeling within the SETI research endeavor. Artificial exo-society modeling might be particularly useful in a post-detection environment by (1) coherently organizing the set of data points derived from a detected ETI signal, (2) mapping trends in the data points over time (assuming receipt of an extended ETI signal), and (3) projecting such trends forward to derive alternative cultural evolutionary scenarios for the exo-society under analysis. The latter exercise might be particularly useful to compensate for the inevitable time lag between generation of an ETI signal and receipt of an ETI signal on Earth. For this reason, such an exercise might be a helpful adjunct to the decisional process contemplated by Paragraph 9 of the Declaration of Principles Concerning Activities Following the Detection of Extraterrestrial Intelligence.

Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status Updates

PubMed Central

Kern, Margaret L; Fulcher, Ben D; Rickard, Nikki S

2018-01-01

Background Frequent expression of negative emotion words on social media has been linked to depression. However, metrics have relied on average values, not dynamic measures of emotional volatility. Objective The aim of this study was to report on the associations between depression severity and the variability (time-unstructured) and instability (time-structured) in emotion word expression on Facebook and Twitter across status updates. Methods Status updates and depression severity ratings of 29 Facebook users and 49 Twitter users were collected through the app MoodPrism. The average proportion of positive and negative emotion words used, within-person variability, and instability were computed. Results Negative emotion word instability was a significant predictor of greater depression severity on Facebook (rs(29)=.44, P=.02, 95% CI 0.09-0.69), even after controlling for the average proportion of negative emotion words used (partial rs(26)=.51, P=.006) and within-person variability (partial rs(26)=.49, P=.009). A different pattern emerged on Twitter where greater negative emotion word variability indicated lower depression severity (rs(49)=−.34, P=.01, 95% CI −0.58 to 0.09). Differences between Facebook and Twitter users in their emotion word patterns and psychological characteristics were also explored. Conclusions The findings suggest that negative emotion word instability may be a simple yet sensitive measure of time-structured variability, useful when screening for depression through social media, though its usefulness may depend on the social media platform. PMID:29739736

The risk of developing a work disability across the adulthood years.

PubMed

Rank, Mark R; Hirschl, Thomas A

2014-04-01

Work disability has implications for individual health, national health care expenditures, economic productivity, and the social safety net. Knowledge about population dynamics and risk factors associated with work disability are not delineated by cross-sectional research. In this paper the authors estimate, for the first time, the prospective lifetime risk that a head of household will report a work disability. Using forty years of longitudinal data from the Panel Study of Income Dynamics (PSID), we estimate the lifetime risk of developing a work disability and conduct a logistic regression analysis to examine personal characteristics that increase the likelihood of a self-reported work disability. Life table methods are used to calculate lifetime prevalence, and to compute covariate effects. Between the ages of 25 and 60, over half (54.6%) of U.S. household heads will self-report a work disability, and approximately one quarter (24.1%) will self-report a severe work disability. Persons with income below 150% of the federal poverty level, or lower educational attainment, have an increased likelihood of reporting a work disability. This study finds that more than half of U.S. household heads will self-report a work disability, which is a higher prevalence than in existing cross-sectional estimates. The social context for this finding is that work disability is a major driver of spending on health care services and the social safety net. Copyright © 2014 Elsevier Inc. All rights reserved.

Eliciting and Receiving Online Support: Using Computer-Aided Content Analysis to Examine the Dynamics of Online Social Support

PubMed Central

Kraut, Robert E; Levine, John M

2015-01-01

Background Although many people with serious diseases participate in online support communities, little research has investigated how participants elicit and provide social support on these sites. Objective The first goal was to propose and test a model of the dynamic process through which participants in online support communities elicit and provide emotional and informational support. The second was to demonstrate the value of computer coding of conversational data using machine learning techniques (1) by replicating results derived from human-coded data about how people elicit support and (2) by answering questions that are intractable with small samples of human-coded data, namely how exposure to different types of social support predicts continued participation in online support communities. The third was to provide a detailed description of these machine learning techniques to enable other researchers to perform large-scale data analysis in these communities. Methods Communication among approximately 90,000 registered users of an online cancer support community was analyzed. The corpus comprised 1,562,459 messages organized into 68,158 discussion threads. Amazon Mechanical Turk workers coded (1) 1000 thread-starting messages on 5 attributes (positive and negative emotional self-disclosure, positive and negative informational self-disclosure, questions) and (2) 1000 replies on emotional and informational support. Their judgments were used to train machine learning models that automatically estimated the amount of these 7 attributes in the messages. Across attributes, the average Pearson correlation between human-based judgments and computer-based judgments was .65. Results Part 1 used human-coded data to investigate relationships between (1) 4 kinds of self-disclosure and question asking in thread-starting posts and (2) the amount of emotional and informational support in the first reply. Self-disclosure about negative emotions (beta=.24, P<.001), negative events (beta=.25, P<.001), and positive events (beta=.10, P=.02) increased emotional support. However, asking questions depressed emotional support (beta=–.21, P<.001). In contrast, asking questions increased informational support (beta=.38, P<.001), whereas positive informational self-disclosure depressed it (beta=–.09, P=.003). Self-disclosure led to the perception of emotional needs, which elicited emotional support, whereas asking questions led to the perception of informational needs, which elicited informational support. Part 2 used machine-coded data to replicate these results. Part 3 analyzed the machine-coded data and showed that exposure to more emotional support predicted staying in the group longer 33% (hazard ratio=0.67, P<.001), whereas exposure to more informational support predicted leaving the group sooner (hazard ratio=1.05, P<.001). Conclusions Self-disclosure is effective in eliciting emotional support, whereas question asking is effective in eliciting informational support. Moreover, perceptions that people desire particular kinds of support influence the support they receive. Finally, the type of support people receive affects the likelihood of their staying in or leaving the group. These results demonstrate the utility of machine learning methods for investigating the dynamics of social support exchange in online support communities. PMID:25896033

Eliciting and receiving online support: using computer-aided content analysis to examine the dynamics of online social support.

PubMed

Wang, Yi-Chia; Kraut, Robert E; Levine, John M

2015-04-20

Although many people with serious diseases participate in online support communities, little research has investigated how participants elicit and provide social support on these sites. The first goal was to propose and test a model of the dynamic process through which participants in online support communities elicit and provide emotional and informational support. The second was to demonstrate the value of computer coding of conversational data using machine learning techniques (1) by replicating results derived from human-coded data about how people elicit support and (2) by answering questions that are intractable with small samples of human-coded data, namely how exposure to different types of social support predicts continued participation in online support communities. The third was to provide a detailed description of these machine learning techniques to enable other researchers to perform large-scale data analysis in these communities. Communication among approximately 90,000 registered users of an online cancer support community was analyzed. The corpus comprised 1,562,459 messages organized into 68,158 discussion threads. Amazon Mechanical Turk workers coded (1) 1000 thread-starting messages on 5 attributes (positive and negative emotional self-disclosure, positive and negative informational self-disclosure, questions) and (2) 1000 replies on emotional and informational support. Their judgments were used to train machine learning models that automatically estimated the amount of these 7 attributes in the messages. Across attributes, the average Pearson correlation between human-based judgments and computer-based judgments was .65. Part 1 used human-coded data to investigate relationships between (1) 4 kinds of self-disclosure and question asking in thread-starting posts and (2) the amount of emotional and informational support in the first reply. Self-disclosure about negative emotions (beta=.24, P<.001), negative events (beta=.25, P<.001), and positive events (beta=.10, P=.02) increased emotional support. However, asking questions depressed emotional support (beta=-.21, P<.001). In contrast, asking questions increased informational support (beta=.38, P<.001), whereas positive informational self-disclosure depressed it (beta=-.09, P=.003). Self-disclosure led to the perception of emotional needs, which elicited emotional support, whereas asking questions led to the perception of informational needs, which elicited informational support. Part 2 used machine-coded data to replicate these results. Part 3 analyzed the machine-coded data and showed that exposure to more emotional support predicted staying in the group longer 33% (hazard ratio=0.67, P<.001), whereas exposure to more informational support predicted leaving the group sooner (hazard ratio=1.05, P<.001). Self-disclosure is effective in eliciting emotional support, whereas question asking is effective in eliciting informational support. Moreover, perceptions that people desire particular kinds of support influence the support they receive. Finally, the type of support people receive affects the likelihood of their staying in or leaving the group. These results demonstrate the utility of machine learning methods for investigating the dynamics of social support exchange in online support communities.

Social Play at the Computer: Preschoolers Scaffold and Support Peers' Computer Competence.

ERIC Educational Resources Information Center

Freeman, Nancy K.; Somerindyke, Jennifer

2001-01-01

Describes preschoolers' collaboration during free play in a computer lab, focusing on the computer's contribution to active, peer-mediated learning. Discusses these observations in terms of Parten's insights on children's social play and Vygotsky's socio-cultural learning theory, noting that the children scaffolded each other's growing computer…

Integration of Computational Geometry, Finite Element, and Multibody System Algorithms for the Development of New Computational Methodology for High-Fidelity Vehicle Systems Modeling and Simulation. ADDENDUM

DTIC Science & Technology

2013-11-12

Dr. Paramsothy Jayakumar (586) 282-4896 Computational Dynamics Inc.   0    Name of Contractor Computational Dynamics Inc. (CDI) 1809...Dr. Paramsothy Jayakumar TARDEC Computational Dynamics Inc.   1    Project Summary This project aims at addressing and remedying the serious...Shabana, A.A., Jayakumar , P., and Letherwood, M., “Soil Models and Vehicle System Dynamics”, Applied Mechanics Reviews, Vol. 65(4), 2013, doi

Socio-affective touch expression database

PubMed Central

Op de Beeck, Hans

2018-01-01

Socio-affective touch communication conveys a vast amount of information about emotions and intentions in social contexts. In spite of the complexity of the socio-affective touch expressions we use daily, previous studies addressed only a few aspects of social touch mainly focusing on hedonics, such as stroking, leaving a wide range of social touch behaviour unexplored. To overcome this limit, we present the Socio-Affective Touch Expression Database (SATED), which includes a large range of dynamic interpersonal socio-affective touch events varying in valence and arousal. The original database contained 26 different social touch expressions each performed by three actor pairs. To validate each touch expression, we conducted two behavioural experiments investigating perceived naturalness and affective values. Based on the rated naturalness and valence, 13 socio-affective touch expressions along with 12 corresponding non-social touch events were selected as a complete set, achieving 75 video clips in total. Moreover, we quantified motion energy for each touch expression to investigate its intrinsic correlations with perceived affective values and its similarity among actor- and action-pairs. As a result, the touch expression database is not only systematically defined and well-controlled, but also spontaneous and natural, while eliciting clear affective responses. This database will allow a fine-grained investigation of complex interpersonal socio-affective touch in the realm of social psychology and neuroscience along with potential application areas in affective computing and neighbouring fields. PMID:29364988

Comparative Implementation of High Performance Computing for Power System Dynamic Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Shuangshuang; Huang, Zhenyu; Diao, Ruisheng

Dynamic simulation for transient stability assessment is one of the most important, but intensive, computations for power system planning and operation. Present commercial software is mainly designed for sequential computation to run a single simulation, which is very time consuming with a single processer. The application of High Performance Computing (HPC) to dynamic simulations is very promising in accelerating the computing process by parallelizing its kernel algorithms while maintaining the same level of computation accuracy. This paper describes the comparative implementation of four parallel dynamic simulation schemes in two state-of-the-art HPC environments: Message Passing Interface (MPI) and Open Multi-Processing (OpenMP).more » These implementations serve to match the application with dedicated multi-processor computing hardware and maximize the utilization and benefits of HPC during the development process.« less

Understanding Social Contagion in Adoption Processes Using Dynamic Social Networks.

PubMed

Herrera, Mauricio; Armelini, Guillermo; Salvaj, Erica

2015-01-01

There are many studies in the marketing and diffusion literature of the conditions in which social contagion affects adoption processes. Yet most of these studies assume that social interactions do not change over time, even though actors in social networks exhibit different likelihoods of being influenced across the diffusion period. Rooted in physics and epidemiology theories, this study proposes a Susceptible Infectious Susceptible (SIS) model to assess the role of social contagion in adoption processes, which takes changes in social dynamics over time into account. To study the adoption over a span of ten years, the authors used detailed data sets from a community of consumers and determined the importance of social contagion, as well as how the interplay of social and non-social influences from outside the community drives adoption processes. Although social contagion matters for diffusion, it is less relevant in shaping adoption when the study also includes social dynamics among members of the community. This finding is relevant for managers and entrepreneurs who trust in word-of-mouth marketing campaigns whose effect may be overestimated if marketers fail to acknowledge variations in social interactions.

Understanding Social Contagion in Adoption Processes Using Dynamic Social Networks

PubMed Central

2015-01-01

There are many studies in the marketing and diffusion literature of the conditions in which social contagion affects adoption processes. Yet most of these studies assume that social interactions do not change over time, even though actors in social networks exhibit different likelihoods of being influenced across the diffusion period. Rooted in physics and epidemiology theories, this study proposes a Susceptible Infectious Susceptible (SIS) model to assess the role of social contagion in adoption processes, which takes changes in social dynamics over time into account. To study the adoption over a span of ten years, the authors used detailed data sets from a community of consumers and determined the importance of social contagion, as well as how the interplay of social and non-social influences from outside the community drives adoption processes. Although social contagion matters for diffusion, it is less relevant in shaping adoption when the study also includes social dynamics among members of the community. This finding is relevant for managers and entrepreneurs who trust in word-of-mouth marketing campaigns whose effect may be overestimated if marketers fail to acknowledge variations in social interactions. PMID:26505473

Newtonian chimpanzees? A molecular dynamics approach to understanding decision-making by wild chimpanzees

NASA Astrophysics Data System (ADS)

Westley, Matthew; Sen, Surajit; Sinha, Anindya

2014-07-01

In this study, we computationally investigate decision-making by individuals and the ensuing social structure of a primate species, chimpanzees, using Newton's equations of classical mechanics, as opposed to agentbased analyses in which individual chimpanzees make independent decisions. Our model uses molecular dynamics simulation techniques to solve Newton's equations and is able to approximate the movements of female and male chimpanzees, especially in relation to the available food resources, in a manner that is consistent with their observed behavior in natural habitats. It is noteworthy that our Newtonian dynamics-based model may allow us to make certain specific observations of their behaviour, some of which may be difficult to achieve through agent-based modelling exercises or even field studies. Chimpanzees tend to live in fission-fusion social groups, with varying number of individuals, in which both females and males tend to display intrasexual competition for valuable food resources while the males also compete for oestrus females. Most populations of the species are also restricted to a small range of habitats, a clear indication that they are especially vulnerable to the availability and distribution of food sources. With reasonable assumptions of chimpanzee behaviour, we have been able to analyse the clustering behaviour of individuals in relation to local food sources as also patterns of their migration across groups. Our simulated results are qualitatively consistent with field observations conducted on a particular semi-isolated population of chimpanzees in Bossou, Guinea, in western Africa.

An evaluation of dynamic lip-tooth characteristics during speech and smile in adolescents.

PubMed

Ackerman, Marc B; Brensinger, Colleen; Landis, J Richard

2004-02-01

This retrospective study was conducted to measure lip-tooth characteristics of adolescents. Pretreatment video clips of 1242 consecutive patients were screened for Class-I skeletal and dental patterns. After all inclusion criteria were applied, the final sample consisted of 50 patients (27 boys, 23 girls) with a mean age of 12.5 years. The raw digital video stream of each patient was edited to select a single image frame representing the patient saying the syllable "chee" and a second single image representing the patient's posed social smile and saved as part of a 12-frame image sequence. Each animation image was analyzed using a SmileMesh computer application to measure the smile index (the ratio of the intercommissure width divided by the interlabial gap), intercommissure width (mm), interlabial gap (mm), percent incisor below the intercommissure line, and maximum incisor exposure (mm). The data were analyzed using SAS (version 8.1). All recorded differences in linear measures had to be > or = 2 mm. The results suggest that anterior tooth display at speech and smile should be recorded independently but evaluated as part of a dynamic range. Asking patients to say "cheese" and then smile is no longer a valid method to elicit the parameters of anterior tooth display. When planning the vertical positions of incisors during orthodontic treatment, the orthodontist should view the dynamics of anterior tooth display as a continuum delineated by the time points of rest, speech, posed social smile, and a Duchenne smile.

Face processing regions are sensitive to distinct aspects of temporal sequence in facial dynamics.

PubMed

Reinl, Maren; Bartels, Andreas

2014-11-15

Facial movement conveys important information for social interactions, yet its neural processing is poorly understood. Computational models propose that shape- and temporal sequence sensitive mechanisms interact in processing dynamic faces. While face processing regions are known to respond to facial movement, their sensitivity to particular temporal sequences has barely been studied. Here we used fMRI to examine the sensitivity of human face-processing regions to two aspects of directionality in facial movement trajectories. We presented genuine movie recordings of increasing and decreasing fear expressions, each of which were played in natural or reversed frame order. This two-by-two factorial design matched low-level visual properties, static content and motion energy within each factor, emotion-direction (increasing or decreasing emotion) and timeline (natural versus artificial). The results showed sensitivity for emotion-direction in FFA, which was timeline-dependent as it only occurred within the natural frame order, and sensitivity to timeline in the STS, which was emotion-direction-dependent as it only occurred for decreased fear. The occipital face area (OFA) was sensitive to the factor timeline. These findings reveal interacting temporal sequence sensitive mechanisms that are responsive to both ecological meaning and to prototypical unfolding of facial dynamics. These mechanisms are temporally directional, provide socially relevant information regarding emotional state or naturalness of behavior, and agree with predictions from modeling and predictive coding theory. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Modeling emergent border-crossing behaviors during pandemics

NASA Astrophysics Data System (ADS)

Santos, Eunice E.; Santos, Eugene; Korah, John; Thompson, Jeremy E.; Gu, Qi; Kim, Keum Joo; Li, Deqing; Russell, Jacob; Subramanian, Suresh; Zhang, Yuxi; Zhao, Yan

2013-06-01

Modeling real-world scenarios is a challenge for traditional social science researchers, as it is often hard to capture the intricacies and dynamisms of real-world situations without making simplistic assumptions. This imposes severe limitations on the capabilities of such models and frameworks. Complex population dynamics during natural disasters such as pandemics is an area where computational social science can provide useful insights and explanations. In this paper, we employ a novel intent-driven modeling paradigm for such real-world scenarios by causally mapping beliefs, goals, and actions of individuals and groups to overall behavior using a probabilistic representation called Bayesian Knowledge Bases (BKBs). To validate our framework we examine emergent behavior occurring near a national border during pandemics, specifically the 2009 H1N1 pandemic in Mexico. The novelty of the work in this paper lies in representing the dynamism at multiple scales by including both coarse-grained (events at the national level) and finegrained (events at two separate border locations) information. This is especially useful for analysts in disaster management and first responder organizations who need to be able to understand both macro-level behavior and changes in the immediate vicinity, to help with planning, prevention, and mitigation. We demonstrate the capabilities of our framework in uncovering previously hidden connections and explanations by comparing independent models of the border locations with their fused model to identify emergent behaviors not found in either independent location models nor in a simple linear combination of those models.

Research in Structures and Dynamics, 1984

NASA Technical Reports Server (NTRS)

Hayduk, R. J. (Compiler); Noor, A. K. (Compiler)

1984-01-01

A symposium on advanced and trends in structures and dynamics was held to communicate new insights into physical behavior and to identify trends in the solution procedures for structures and dynamics problems. Pertinent areas of concern were (1) multiprocessors, parallel computation, and database management systems, (2) advances in finite element technology, (3) interactive computing and optimization, (4) mechanics of materials, (5) structural stability, (6) dynamic response of structures, and (7) advanced computer applications.

Nonlinear dynamics based digital logic and circuits.

PubMed

Kia, Behnam; Lindner, John F; Ditto, William L

2015-01-01

We discuss the role and importance of dynamics in the brain and biological neural networks and argue that dynamics is one of the main missing elements in conventional Boolean logic and circuits. We summarize a simple dynamics based computing method, and categorize different techniques that we have introduced to realize logic, functionality, and programmability. We discuss the role and importance of coupled dynamics in networks of biological excitable cells, and then review our simple coupled dynamics based method for computing. In this paper, for the first time, we show how dynamics can be used and programmed to implement computation in any given base, including but not limited to base two.

Parsing Heterogeneity in Autism Spectrum Disorders: Visual Scanning of Dynamic Social Scenes in School-Aged Children

ERIC Educational Resources Information Center

Rice, Katherine; Moriuchi, Jennifer M.; Jones, Warren; Klin, Ami

2012-01-01

Objective: To examine patterns of variability in social visual engagement and their relationship to standardized measures of social disability in a heterogeneous sample of school-aged children with autism spectrum disorders (ASD). Method: Eye-tracking measures of visual fixation during free-viewing of dynamic social scenes were obtained for 109…

A novel system for tracking social preference dynamics in mice reveals sex- and strain-specific characteristics.

PubMed

Netser, Shai; Haskal, Shani; Magalnik, Hen; Wagner, Shlomo

2017-01-01

Deciphering the biological mechanisms underlying social behavior in animal models requires standard behavioral paradigms that can be unbiasedly employed in an observer- and laboratory-independent manner. During the past decade, the three-chamber test has become such a standard paradigm used to evaluate social preference (sociability) and social novelty preference in mice. This test suffers from several caveats, including its reliance on spatial navigation skills and negligence of behavioral dynamics. Here, we present a novel experimental apparatus and an automated analysis system which offer an alternative to the three-chamber test while solving the aforementioned caveats. The custom-made apparatus is simple for production, and the analysis system is publically available as an open-source software, enabling its free use. We used this system to compare the dynamics of social behavior during the social preference and social novelty preference tests between male and female C57BL/6J mice. We found that in both tests, male mice keep their preference towards one of the stimuli for longer periods than females. We then employed our system to define several new parameters of social behavioral dynamics in mice and revealed that social preference behavior is segregated in time into two distinct phases. An early exploration phase, characterized by high rate of transitions between stimuli and short bouts of stimulus investigation, is followed by an interaction phase with low transition rate and prolonged interactions, mainly with the preferred stimulus. Finally, we compared the dynamics of social behavior between C57BL/6J and BTBR male mice, the latter of which are considered as asocial strain serving as a model for autism spectrum disorder. We found that BTBR mice ( n  = 8) showed a specific deficit in transition from the exploration phase to the interaction phase in the social preference test, suggesting a reduced tendency towards social interaction. We successfully employed our new experimental system to unravel previously unidentified sex- and strain-specific differences in the dynamics of social behavior in mice. Thus, the system presented here facilitates a more thorough and detailed analysis of social behavior in small rodent models, enabling a better comparison between strains and treatments.

Designing Social Presence in e-Learning Environments: Testing the Effect of Interactivity on Children

ERIC Educational Resources Information Center

Tung, Fang-Wu; Deng, Yi-Shin

2006-01-01

The "computers are social actors" paradigm asserts that human-to-computer interactions are fundamentally social responses. Earlier research has shown that effective management of the social presence in user interface design can improve user engagement and motivation. Much of this research has focused on adult subjects. This study…

Review of game theory applications for situation awareness

NASA Astrophysics Data System (ADS)

Blasch, Erik; Shen, Dan; Pham, Khanh D.; Chen, Genshe

2015-05-01

Game theoretical methods have been used for spectral awareness, space situational awareness (SSA), cyber situational awareness (CSA), and Intelligence, Surveillance, and Reconnaissance situation awareness (ISA). Each of these cases, awareness is supported by sensor estimation for assessment and the situation is determined from the actions of multiple players. Game theory assumes rational actors in a defined scenario; however, variations in social, cultural and behavioral factors include the dynamic nature of the context. In a dynamic data-driven application system (DDDAS), modeling must include both the measurements but also how models are used by different actors with different priorities. In this paper, we highlight the applications of game theory by reviewing the literature to determine the current state of the art and future needs. Future developments would include building towards knowledge awareness with information technology (e.g., data aggregation, access, indexing); multiscale analysis (e.g., space, time, and frequency), and software methods (e.g., architectures, cloud computing, protocols).

Ethical and regulatory challenges of research using pervasive sensing and other emerging technologies: IRB perspectives.

PubMed

Nebeker, Camille; Harlow, John; Espinoza Giacinto, Rebeca; Orozco-Linares, Rubi; Bloss, Cinnamon S; Weibel, Nadir

2017-01-01

Vast quantities of personal health information and private identifiable information are being created through mobile apps, wearable sensors, and social networks. While new strategies and tools for obtaining health data have expanded researchers' abilities to design and test personalized and adaptive health interventions, the deployment of pervasive sensing and computational techniques to gather research data is raising ethical challenges for Institutional Review Boards (IRBs) charged with protecting research participants. To explore experiences with, and perceptions about, technology-enabled research, and identify solutions for promoting responsible conduct of this research we conducted focus groups with human research protection program and IRB affiliates. Our findings outline the need for increased collaboration across stakeholders in terms of: (1) shared and dynamic resources that improve awareness of technologies and decrease potential threats to participant privacy and data confidentiality, and (2) development of appropriate and dynamic standards through collaboration with stakeholders in the research ethics community.

Absence of influential spreaders in rumor dynamics

NASA Astrophysics Data System (ADS)

Borge-Holthoefer, Javier; Moreno, Yamir

2012-02-01

Recent research [Kitsak, Gallos, Havlin, Liljeros, Muchnik, Stanley, and Makse, Nature Physics1745-247310.1038/nphys1746 6, 888 (2010)] has suggested that coreness, and not degree, constitutes a better topological descriptor to identify influential spreaders in complex networks. This hypothesis has been verified in the context of disease spreading. Here, we instead focus on rumor spreading models, which are more suited for social contagion and information propagation. To this end, we perform extensive computer simulations on top of several real-world networks and find opposite results. Namely, we show that the spreading capabilities of the nodes do not depend on their k-core index, which instead determines whether or not a given node prevents the diffusion of a rumor to a system-wide scale. Our findings are relevant both for sociological studies of contagious dynamics and for the design of efficient commercial viral processes.

Psychology and social networks: a dynamic network theory perspective.

PubMed

Westaby, James D; Pfaff, Danielle L; Redding, Nicholas

2014-04-01

Research on social networks has grown exponentially in recent years. However, despite its relevance, the field of psychology has been relatively slow to explain the underlying goal pursuit and resistance processes influencing social networks in the first place. In this vein, this article aims to demonstrate how a dynamic network theory perspective explains the way in which social networks influence these processes and related outcomes, such as goal achievement, performance, learning, and emotional contagion at the interpersonal level of analysis. The theory integrates goal pursuit, motivation, and conflict conceptualizations from psychology with social network concepts from sociology and organizational science to provide a taxonomy of social network role behaviors, such as goal striving, system supporting, goal preventing, system negating, and observing. This theoretical perspective provides psychologists with new tools to map social networks (e.g., dynamic network charts), which can help inform the development of change interventions. Implications for social, industrial-organizational, and counseling psychology as well as conflict resolution are discussed, and new opportunities for research are highlighted, such as those related to dynamic network intelligence (also known as cognitive accuracy), levels of analysis, methodological/ethical issues, and the need to theoretically broaden the study of social networking and social media behavior. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

The Virtual Teacher (VT) Paradigm: Learning New Patterns of Interpersonal Coordination Using the Human Dynamic Clamp

PubMed Central

2015-01-01

The Virtual Teacher paradigm, a version of the Human Dynamic Clamp (HDC), is introduced into studies of learning patterns of inter-personal coordination. Combining mathematical modeling and experimentation, we investigate how the HDC may be used as a Virtual Teacher (VT) to help humans co-produce and internalize new inter-personal coordination pattern(s). Human learners produced rhythmic finger movements whilst observing a computer-driven avatar, animated by dynamic equations stemming from the well-established Haken-Kelso-Bunz (1985) and Schöner-Kelso (1988) models of coordination. We demonstrate that the VT is successful in shifting the pattern co-produced by the VT-human system toward any value (Experiment 1) and that the VT can help humans learn unstable relative phasing patterns (Experiment 2). Using transfer entropy, we find that information flow from one partner to the other increases when VT-human coordination loses stability. This suggests that variable joint performance may actually facilitate interaction, and in the long run learning. VT appears to be a promising tool for exploring basic learning processes involved in social interaction, unraveling the dynamics of information flow between interacting partners, and providing possible rehabilitation opportunities. PMID:26569608

The Virtual Teacher (VT) Paradigm: Learning New Patterns of Interpersonal Coordination Using the Human Dynamic Clamp.

PubMed

Kostrubiec, Viviane; Dumas, Guillaume; Zanone, Pier-Giorgio; Kelso, J A Scott

2015-01-01

The Virtual Teacher paradigm, a version of the Human Dynamic Clamp (HDC), is introduced into studies of learning patterns of inter-personal coordination. Combining mathematical modeling and experimentation, we investigate how the HDC may be used as a Virtual Teacher (VT) to help humans co-produce and internalize new inter-personal coordination pattern(s). Human learners produced rhythmic finger movements whilst observing a computer-driven avatar, animated by dynamic equations stemming from the well-established Haken-Kelso-Bunz (1985) and Schöner-Kelso (1988) models of coordination. We demonstrate that the VT is successful in shifting the pattern co-produced by the VT-human system toward any value (Experiment 1) and that the VT can help humans learn unstable relative phasing patterns (Experiment 2). Using transfer entropy, we find that information flow from one partner to the other increases when VT-human coordination loses stability. This suggests that variable joint performance may actually facilitate interaction, and in the long run learning. VT appears to be a promising tool for exploring basic learning processes involved in social interaction, unraveling the dynamics of information flow between interacting partners, and providing possible rehabilitation opportunities.

Using periodic orbits to compute chaotic transport rates between resonance zones.

PubMed

Sattari, Sulimon; Mitchell, Kevin A

2017-11-01

Transport properties of chaotic systems are computable from data extracted from periodic orbits. Given a sufficient number of periodic orbits, the escape rate can be computed using the spectral determinant, a function that incorporates the eigenvalues and periods of periodic orbits. The escape rate computed from periodic orbits converges to the true value as more and more periodic orbits are included. Escape from a given region of phase space can be computed by considering only periodic orbits that lie within the region. An accurate symbolic dynamics along with a corresponding partitioning of phase space is useful for systematically obtaining all periodic orbits up to a given period, to ensure that no important periodic orbits are missing in the computation. Homotopic lobe dynamics (HLD) is an automated technique for computing accurate partitions and symbolic dynamics for maps using the topological forcing of intersections of stable and unstable manifolds of a few periodic anchor orbits. In this study, we apply the HLD technique to compute symbolic dynamics and periodic orbits, which are then used to find escape rates from different regions of phase space for the Hénon map. We focus on computing escape rates in parameter ranges spanning hyperbolic plateaus, which are parameter intervals where the dynamics is hyperbolic and the symbolic dynamics does not change. After the periodic orbits are computed for a single parameter value within a hyperbolic plateau, periodic orbit continuation is used to compute periodic orbits over an interval that spans the hyperbolic plateau. The escape rates computed from a few thousand periodic orbits agree with escape rates computed from Monte Carlo simulations requiring hundreds of billions of orbits.

Quantitative and Qualitative Evaluation of The Structural Designing of Medical Informatics Dynamic Encyclopedia.

PubMed

Safdari, Reza; Shahmoradi, Leila; Hosseini-Beheshti, Molouk-Sadat; Nejad, Ahmadreza Farzaneh; Hosseiniravandi, Mohammad

2015-10-01

Encyclopedias and their compilation have become so prevalent as a valid cultural medium in the world. The daily development of computer industry and the expansion of various sciences have made indispensable the compilation of electronic, specialized encyclopedias, especially the web-based ones. This is an applied-developmental study conducted in 2014. First, the main terms in the field of medical informatics were gathered using MeSH Online 2014 and the supplementary terms of each were determined, and then the tree diagram of the terms was drawn based on their relationship in MeSH. Based on the studies done by the researchers, the tree diagram of the encyclopedia was drawn with respect to the existing areas in this field, and the terms gathered were put in related domains. In MeSH, 75 preferred terms together with 249 supplementary ones were indexed. One of the informatics' sub-branches is biomedical informatics and health which itself consists of three sub-divisions of bioinformatics, clinical informatics, and health informatics. Medical informatics which is a subdivision of clinical informatics has developed from the three fields of medical sciences, management and social sciences, and computational sciences and mathematics. Medical Informatics is created of confluence and fusion and applications of the three major scientific branches include health and biological sciences, social sciences and management sciences, computing and mathematical sciences, and according to that the structure of MeSH is weak for future development of Encyclopedia of Medical Informatics.

Quantum wavepacket ab initio molecular dynamics: an approach for computing dynamically averaged vibrational spectra including critical nuclear quantum effects.

PubMed

Sumner, Isaiah; Iyengar, Srinivasan S

2007-10-18

We have introduced a computational methodology to study vibrational spectroscopy in clusters inclusive of critical nuclear quantum effects. This approach is based on the recently developed quantum wavepacket ab initio molecular dynamics method that combines quantum wavepacket dynamics with ab initio molecular dynamics. The computational efficiency of the dynamical procedure is drastically improved (by several orders of magnitude) through the utilization of wavelet-based techniques combined with the previously introduced time-dependent deterministic sampling procedure measure to achieve stable, picosecond length, quantum-classical dynamics of electrons and nuclei in clusters. The dynamical information is employed to construct a novel cumulative flux/velocity correlation function, where the wavepacket flux from the quantized particle is combined with classical nuclear velocities to obtain the vibrational density of states. The approach is demonstrated by computing the vibrational density of states of [Cl-H-Cl]-, inclusive of critical quantum nuclear effects, and our results are in good agreement with experiment. A general hierarchical procedure is also provided, based on electronic structure harmonic frequencies, classical ab initio molecular dynamics, computation of nuclear quantum-mechanical eigenstates, and employing quantum wavepacket ab initio dynamics to understand vibrational spectroscopy in hydrogen-bonded clusters that display large degrees of anharmonicities.

Social Robots vs. Computer Display: Does the Way Social Stories Are Delivered Make a Difference for Their Effectiveness on ASD Children?

ERIC Educational Resources Information Center

Pop, Cristina A.; Simut, Ramona E.; Pintea, Sebastian; Saldien, Jelle; Rusu, Alina S.; Vanderfaeillie, Johan; David, Daniel O.; Lefeber, Dirk; Vanderborght, Bram

2013-01-01

Background and Objectives: The aim of this exploratory study is to test whether social stories presented by a social robot have a greater effect than ones presented on a computer display in increasing the independency in expressing social abilities of children with autism spectrum disorders (ASD). Although much progress has been made in developing…

A roadmap towards personalized immunology.

PubMed

Delhalle, Sylvie; Bode, Sebastian F N; Balling, Rudi; Ollert, Markus; He, Feng Q

2018-01-01

Big data generation and computational processing will enable medicine to evolve from a "one-size-fits-all" approach to precise patient stratification and treatment. Significant achievements using "Omics" data have been made especially in personalized oncology. However, immune cells relative to tumor cells show a much higher degree of complexity in heterogeneity, dynamics, memory-capability, plasticity and "social" interactions. There is still a long way ahead on translating our capability to identify potentially targetable personalized biomarkers into effective personalized therapy in immune-centralized diseases. Here, we discuss the recent advances and successful applications in "Omics" data utilization and network analysis on patients' samples of clinical trials and studies, as well as the major challenges and strategies towards personalized stratification and treatment for infectious or non-communicable inflammatory diseases such as autoimmune diseases or allergies. We provide a roadmap and highlight experimental, clinical, computational analysis, data management, ethical and regulatory issues to accelerate the implementation of personalized immunology.

Computational Fluid Dynamics: Past, Present, And Future

NASA Technical Reports Server (NTRS)

Kutler, Paul

1988-01-01

Paper reviews development of computational fluid dynamics and explores future prospects of technology. Report covers such topics as computer technology, turbulence, development of solution methodology, developemnt of algorithms, definition of flow geometries, generation of computational grids, and pre- and post-data processing.

The impact of socio-technical communication styles on the diversity and innovation potential of global science collaboratories

DOE PAGES

Ozmen, Ozgur; Yilmaz, Levent; Smith, Jeffrey

2016-02-09

Emerging cyber-infrastructure tools are enabling scientists to transparently co-develop, share, and communicate about real-time diverse forms of knowledge artifacts. In these environments, communication preferences of scientists are posited as an important factor affecting innovation capacity and robustness of social and knowledge network structures. Scientific knowledge creation in such communities is called global participatory science (GPS). Recently, using agent-based modeling and collective action theory as a basis, a complex adaptive social communication network model (CollectiveInnoSim) is implemented. This work leverages CollectiveInnoSim implementing communication preferences of scientists. Social network metrics and knowledge production patterns are used as proxy metrics to infer innovationmore » potential of emergent knowledge and collaboration networks. The objective is to present the underlying communication dynamics of GPS in a form of computational model and delineate the impacts of various communication preferences of scientists on innovation potential of the collaboration network. Ultimately, the insight gained can help policy-makers to design GPS environments and promote innovation.« less

The impact of socio-technical communication styles on the diversity and innovation potential of global science collaboratories

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ozmen, Ozgur; Yilmaz, Levent; Smith, Jeffrey

Emerging cyber-infrastructure tools are enabling scientists to transparently co-develop, share, and communicate about real-time diverse forms of knowledge artifacts. In these environments, communication preferences of scientists are posited as an important factor affecting innovation capacity and robustness of social and knowledge network structures. Scientific knowledge creation in such communities is called global participatory science (GPS). Recently, using agent-based modeling and collective action theory as a basis, a complex adaptive social communication network model (CollectiveInnoSim) is implemented. This work leverages CollectiveInnoSim implementing communication preferences of scientists. Social network metrics and knowledge production patterns are used as proxy metrics to infer innovationmore » potential of emergent knowledge and collaboration networks. The objective is to present the underlying communication dynamics of GPS in a form of computational model and delineate the impacts of various communication preferences of scientists on innovation potential of the collaboration network. Ultimately, the insight gained can help policy-makers to design GPS environments and promote innovation.« less

Glass-like dynamics in confined and congested ant traffic.

PubMed

Gravish, Nick; Gold, Gregory; Zangwill, Andrew; Goodisman, Michael A D; Goldman, Daniel I

2015-09-07

The collective movement of animal groups often occurs in confined spaces. As animal groups are challenged to move at high density, their mobility dynamics may resemble the flow of densely packed non-living soft materials such as colloids, grains, or polymers. However, unlike inert soft-materials, self-propelled collective living systems often display social interactions whose influence on collective mobility are only now being explored. In this paper, we study the mobility of bi-directional traffic flow in a social insect (the fire ant Solenopsis invicta) as we vary the diameter of confining foraging tunnels. In all tunnel diameters, we observe the emergence of spatially heterogeneous regions of fast and slow traffic that are induced through two phenomena: physical obstruction, arising from the inability of individual ants to interpenetrate, and time-delay resulting from social interaction in which ants stop to briefly antennate. Density correlation functions reveal that the relaxation dynamics of high density traffic fluctuations scale linearly with fluctuation size and are sensitive to tunnel diameter. We separate the roles of physical obstruction and social interactions in traffic flow using cellular automata based simulation. Social interaction between ants is modeled as a dwell time (Tint) over which interacting ants remain stationary in the tunnel. Investigation over a range of densities and Tint reveals that the slowing dynamics of collective motion in social living systems are consistent with dynamics near a fragile glass transition in inert soft-matter systems. In particular, flow is relatively insensitive to density until a critical density is reached. As social interaction affinity is increased (increasing Tint) traffic dynamics change and resemble a strong glass transition. Thus, social interactions play an important role in the mobility of collective living systems at high density. Our experiments and model demonstrate that the concepts of soft-matter physics aid understanding of the mobility of collective living systems, and motivate further inquiry into the dynamics of densely confined social living systems.

Similar but Different: Dynamic Social Network Analysis Highlights Fundamental Differences between the Fission-Fusion Societies of Two Equid Species, the Onager and Grevy’s Zebra

PubMed Central

Rubenstein, Daniel I.; Sundaresan, Siva R.; Fischhoff, Ilya R.; Tantipathananandh, Chayant; Berger-Wolf, Tanya Y.

2015-01-01

Understanding why animal societies take on the form that they do has benefited from insights gained by applying social network analysis to patterns of individual associations. Such analyses typically aggregate data over long time periods even though most selective forces that shape sociality have strong temporal elements. By explicitly incorporating the temporal signal in social interaction data we re-examine the network dynamics of the social systems of the evolutionarily closely-related Grevy’s zebras and wild asses that show broadly similar social organizations. By identifying dynamic communities, previously hidden differences emerge: Grevy’s zebras show more modularity than wild asses and in wild asses most communities consist of solitary individuals; and in Grevy’s zebras, lactating females show a greater propensity to switch communities than non-lactating females and males. Both patterns were missed by static network analyses and in general, adding a temporal dimension provides insights into differences associated with the size and persistence of communities as well as the frequency and synchrony of their formation. Dynamic network analysis provides insights into the functional significance of these social differences and highlights the way dynamic community analysis can be applied to other species. PMID:26488598

Association between playing computer games and mental and social health among male adolescents in Iran in 2014

PubMed Central

Mohammadi, Mehrnoosh; RezaeiDehaghani, Abdollah; Mehrabi, Tayebeh; RezaeiDehaghani, Ali

2016-01-01

Background: As adolescents spend much time on playing computer games, their mental and social effects should be considered. The present study aimed to investigate the association between playing computer games and the mental and social health among male adolescents in Iran in 2014. Materials and Methods: This is a cross-sectional study conducted on 210 adolescents selected by multi-stage random sampling. Data were collected by Goldberg and Hillier general health (28 items) and Kiez social health questionnaires. The association was tested by Pearson and Spearman correlation coefficients, one-way analysis of variance (ANOVA), and independent t-test. Computer games related factors such as the location, type, length, the adopted device, and mode of playing games were investigated. Results: Results showed that 58.9% of the subjects played games on a computer alone for 1 h at home. Results also revealed that the subjects had appropriate mental health and 83.2% had moderate social health. Results showed a poor significant association between the length of games and social health (r = −0.15, P = 0.03), the type of games and mental health (r = −0.16, P = 0.01), and the device used in playing games and social health (F = 0.95, P = 0.03). Conclusions: The findings showed that adolescents’ mental and social health is negatively associated with their playing computer games. Therefore, to promote their health, educating them about the correct way of playing computer games is essential and their parents and school authorities, including nurses working at schools, should determine its relevant factors such as the type, length, and device used in playing such games. PMID:27095988

A computer-generated animated face stimulus set for psychophysiological research

PubMed Central

Naples, Adam; Nguyen-Phuc, Alyssa; Coffman, Marika; Kresse, Anna; Faja, Susan; Bernier, Raphael; McPartland., James

2014-01-01

Human faces are fundamentally dynamic, but experimental investigations of face perception traditionally rely on static images of faces. While naturalistic videos of actors have been used with success in some contexts, much research in neuroscience and psychophysics demands carefully controlled stimuli. In this paper, we describe a novel set of computer generated, dynamic, face stimuli. These grayscale faces are tightly controlled for low- and high-level visual properties. All faces are standardized in terms of size, luminance, and location and size of facial features. Each face begins with a neutral pose and transitions to an expression over the course of 30 frames. Altogether there are 222 stimuli spanning 3 different categories of movement: (1) an affective movement (fearful face); (2) a neutral movement (close-lipped, puffed cheeks with open eyes); and (3) a biologically impossible movement (upward dislocation of eyes and mouth). To determine whether early brain responses sensitive to low-level visual features differed between expressions, we measured the occipital P100 event related potential (ERP), which is known to reflect differences in early stages of visual processing and the N170, which reflects structural encoding of faces. We found no differences between faces at the P100, indicating that different face categories were well matched on low-level image properties. This database provides researchers with a well-controlled set of dynamic faces controlled on low-level image characteristics that are applicable to a range of research questions in social perception. PMID:25028164

Dynamic interactions in neural networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arbib, M.A.; Amari, S.

The study of neural networks is enjoying a great renaissance, both in computational neuroscience, the development of information processing models of living brains, and in neural computing, the use of neurally inspired concepts in the construction of intelligent machines. This volume presents models and data on the dynamic interactions occurring in the brain, and exhibits the dynamic interactions between research in computational neuroscience and in neural computing. The authors present current research, future trends and open problems.

Understanding Social Dynamics and School Shootings Requires Larger Context. Commentary on: "Bullying, Romantic Rejection, and Conflicts with Teachers: The Crucial Role of Social Dynamics in the Development of School Shootings--A Systematic Review"

ERIC Educational Resources Information Center

Klein, Jessie

2014-01-01

Jessie Klein begins her commentary on "Bullying, Romantic Rejection, and Conflicts with Teachers: The Crucial Role of Social Dynamics in the Development of School Shootings--A Systematic Review" (Sommer, Leuschner, and Scheithauer," International Journal of Developmental Science" v 8, n1-2, p3-24, 2014) by saying that to…

Thirteenth Workshop for Computational Fluid Dynamic Applications in Rocket Propulsion and Launch Vehicle Technology. Volume 2

NASA Technical Reports Server (NTRS)

Williams, R. W. (Compiler)

1996-01-01

This conference publication includes various abstracts and presentations given at the 13th Workshop for Computational Fluid Dynamic Applications in Rocket Propulsion and Launch Vehicle Technology held at the George C. Marshall Space Flight Center April 25-27 1995. The purpose of the workshop was to discuss experimental and computational fluid dynamic activities in rocket propulsion and launch vehicles. The workshop was an open meeting for government, industry, and academia. A broad number of topics were discussed including computational fluid dynamic methodology, liquid and solid rocket propulsion, turbomachinery, combustion, heat transfer, and grid generation.

Computational neuropharmacology: dynamical approaches in drug discovery.

PubMed

Aradi, Ildiko; Erdi, Péter

2006-05-01

Computational approaches that adopt dynamical models are widely accepted in basic and clinical neuroscience research as indispensable tools with which to understand normal and pathological neuronal mechanisms. Although computer-aided techniques have been used in pharmaceutical research (e.g. in structure- and ligand-based drug design), the power of dynamical models has not yet been exploited in drug discovery. We suggest that dynamical system theory and computational neuroscience--integrated with well-established, conventional molecular and electrophysiological methods--offer a broad perspective in drug discovery and in the search for novel targets and strategies for the treatment of neurological and psychiatric diseases.

Mouse Social Network Dynamics and Community Structure are Associated with Plasticity-Related Brain Gene Expression

PubMed Central

Williamson, Cait M.; Franks, Becca; Curley, James P.

2016-01-01

Laboratory studies of social behavior have typically focused on dyadic interactions occurring within a limited spatiotemporal context. However, this strategy prevents analyses of the dynamics of group social behavior and constrains identification of the biological pathways mediating individual differences in behavior. In the current study, we aimed to identify the spatiotemporal dynamics and hierarchical organization of a large social network of male mice. We also sought to determine if standard assays of social and exploratory behavior are predictive of social behavior in this social network and whether individual network position was associated with the mRNA expression of two plasticity-related genes, DNA methyltransferase 1 and 3a. Mice were observed to form a hierarchically organized social network and self-organized into two separate social network communities. Members of both communities exhibited distinct patterns of socio-spatial organization within the vivaria that was not limited to only agonistic interactions. We further established that exploratory and social behaviors in standard behavioral assays conducted prior to placing the mice into the large group was predictive of initial network position and behavior but were not associated with final social network position. Finally, we determined that social network position is associated with variation in mRNA levels of two neural plasticity genes, DNMT1 and DNMT3a, in the hippocampus but not the mPOA. This work demonstrates the importance of understanding the role of social context and complex social dynamics in determining the relationship between individual differences in social behavior and brain gene expression. PMID:27540359

Teachers' Organization of Participation Structures for Teaching Science with Computer Technology

NASA Astrophysics Data System (ADS)

Subramaniam, Karthigeyan

2016-08-01

This paper describes a qualitative study that investigated the nature of the participation structures and how the participation structures were organized by four science teachers when they constructed and communicated science content in their classrooms with computer technology. Participation structures focus on the activity structures and processes in social settings like classrooms thereby providing glimpses into the complex dynamics of teacher-students interactions, configurations, and conventions during collective meaning making and knowledge creation. Data included observations, interviews, and focus group interviews. Analysis revealed that the dominant participation structure evident within participants' instruction with computer technology was ( Teacher) initiation-( Student and Teacher) response sequences-( Teacher) evaluate participation structure. Three key events characterized the how participants organized this participation structure in their classrooms: setting the stage for interactive instruction, the joint activity, and maintaining accountability. Implications include the following: (1) teacher educators need to tap into the knowledge base that underscores science teachers' learning to teach philosophies when computer technology is used in instruction. (2) Teacher educators need to emphasize the essential idea that learning and cognition is not situated within the computer technology but within the pedagogical practices, specifically the participation structures. (3) The pedagogical practices developed with the integration or with the use of computer technology underscored by the teachers' own knowledge of classroom contexts and curriculum needs to be the focus for how students learn science content with computer technology instead of just focusing on how computer technology solely supports students learning of science content.

Sustainability science: accounting for nonlinear dynamics in policy and social-ecological systems

EPA Science Inventory

Resilience is an emergent property of complex systems. Understanding resilience is critical for sustainability science, as linked social-ecological systems and the policy process that governs them are characterized by non-linear dynamics. Non-linear dynamics in these systems mean...

Relationship between Gender Identity, Perceived Social Support for Using Computers, and Computer Self-Efficacy and Value Beliefs of Undergraduate Students

ERIC Educational Resources Information Center

Deechuay, Naraphol; Koul, Ravinder; Maneewan, Sorakrich; Lerdpornkulrat, Thanita

2016-01-01

This study investigated relationship between gender identity, social support for using computers and computer self-efficacy and value beliefs. Data was collected from first year undergraduate students at a university near Bangkok (72.3% females, mean age = 18.52 years). The respondents in our survey did not intend to major in computer sciences.…

Affective Feedback from Computers and its Effect on Perceived Ability and Affect: A Test of the Computers as Social Actor Hypothesis

ERIC Educational Resources Information Center

Mishra, Punya

2006-01-01

We report an experimental study that examined two questions: (a) The effect of affective feedback from computers on participants' motivation and self-perception of ability; and (b) whether people respond similarly to computer feedback as they do to human feedback. This study, framed within the Computers As Social Actors (CASA) framework,…

Sustained neural activity to gaze and emotion perception in dynamic social scenes

PubMed Central

Ulloa, José Luis; Puce, Aina; Hugueville, Laurent; George, Nathalie

2014-01-01

To understand social interactions, we must decode dynamic social cues from seen faces. Here, we used magnetoencephalography (MEG) to study the neural responses underlying the perception of emotional expressions and gaze direction changes as depicted in an interaction between two agents. Subjects viewed displays of paired faces that first established a social scenario of gazing at each other (mutual attention) or gazing laterally together (deviated group attention) and then dynamically displayed either an angry or happy facial expression. The initial gaze change elicited a significantly larger M170 under the deviated than the mutual attention scenario. At around 400 ms after the dynamic emotion onset, responses at posterior MEG sensors differentiated between emotions, and between 1000 and 2200 ms, left posterior sensors were additionally modulated by social scenario. Moreover, activity on right anterior sensors showed both an early and prolonged interaction between emotion and social scenario. These results suggest that activity in right anterior sensors reflects an early integration of emotion and social attention, while posterior activity first differentiated between emotions only, supporting the view of a dual route for emotion processing. Altogether, our data demonstrate that both transient and sustained neurophysiological responses underlie social processing when observing interactions between others. PMID:23202662

A longitudinal study of the sociosexual dynamics in a captive family group of wolves: the University of Connecticut wolf project.

PubMed

Jenks, Susan M

2011-11-01

An interest in the role of the social environment on the evolution of behavior led Professor Benson Ginsburg to studies of wolf social behavior. He initiated the University of Connecticut wolf project with a family group of wolves housed in a protected enclosure in an isolated area of campus. One aim of this project was to conduct a longitudinal study of a family group of wolves in order to understand the proximate behavioral mechanisms underlying mating dynamics with a degree of control and opportunistic observation that could not be achieved through field studies. The development of social relationships and the dynamics of mating were observed for 9 years. As in nature, agonistic relationships strongly influenced reproductive success, successful breeding was limited to a single pair each season, and the behavioral dynamics included status transitions with breeder rotations. Our work, when combined with the results of other captive wolf studies, has contributed valuable information to the general understanding of wolf social behavior, especially regarding the proximate behavior patterns underlying group social interactions and reproduction. This understanding has broadened perspectives on the dynamic interplay between social behavior and evolutionary processes.

The Social Origins of Networks and Diffusion.

PubMed

Centola, Damon

2015-03-01

Recent research on social contagion has demonstrated significant effects of network topology on the dynamics of diffusion. However, network topologies are not given a priori. Rather, they are patterns of relations that emerge from individual and structural features of society, such as population composition, group heterogeneity, homophily, and social consolidation. Following Blau and Schwartz, the author develops a model of social network formation that explores how social and structural constraints on tie formation generate emergent social topologies and then explores the effectiveness of these social networks for the dynamics of social diffusion. Results show that, at one extreme, high levels of consolidation can create highly balkanized communities with poor integration of shared norms and practices. As suggested by Blau and Schwartz, reducing consolidation creates more crosscutting circles and significantly improves the dynamics of social diffusion across the population. However, the author finds that further reducing consolidation creates highly intersecting social networks that fail to support the widespread diffusion of norms and practices, indicating that successful social diffusion can depend on moderate to high levels of structural consolidation.

Epidemics and dimensionality in hierarchical networks

NASA Astrophysics Data System (ADS)

Zheng, Da-Fang; Hui, P. M.; Trimper, Steffen; Zheng, Bo

2005-07-01

Epidemiological processes are studied within a recently proposed hierarchical network model using the susceptible-infected-refractory dynamics of an epidemic. Within the network model, a population may be characterized by H independent hierarchies or dimensions, each of which consists of groupings of individuals into layers of subgroups. Detailed numerical simulations reveal that for H>1, global spreading results regardless of the degree of homophily of the individuals forming a social circle. For H=1, a transition from global to local spread occurs as the population becomes decomposed into increasingly homophilous groups. Multiple dimensions in classifying individuals (nodes) thus make a society (computer network) highly susceptible to large-scale outbreaks of infectious diseases (viruses).

Collective dynamics of 'small-world' networks.

PubMed

Watts, D J; Strogatz, S H

1998-06-04

Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.

Modelling sociocognitive aspects of students' learning

NASA Astrophysics Data System (ADS)

Koponen, I. T.; Kokkonen, T.; Nousiainen, M.

2017-03-01

We present a computational model of sociocognitive aspects of learning. The model takes into account a student's individual cognition and sociodynamics of learning. We describe cognitive aspects of learning as foraging for explanations in the epistemic landscape, the structure (set by instructional design) of which guides the cognitive development through success or failure in foraging. We describe sociodynamic aspects as an agent-based model, where agents (learners) compare and adjust their conceptions of their own proficiency (self-proficiency) and that of their peers (peer-proficiency) in using explanatory schemes of different levels. We apply the model here in a case involving a three-tiered system of explanatory schemes, which can serve as a generic description of some well-known cases studied in empirical research on learning. The cognitive dynamics lead to the formation of dynamically robust outcomes of learning, seen as a strong preference for a certain explanatory schemes. The effects of social learning, however, can account for half of one's success in adopting higher-level schemes and greater proficiency. The model also predicts a correlation of dynamically emergent interaction patterns between agents and the learning outcomes.

Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees

PubMed Central

2011-01-01

Background The spread of infectious diseases crucially depends on the pattern of contacts between individuals. Knowledge of these patterns is thus essential to inform models and computational efforts. However, there are few empirical studies available that provide estimates of the number and duration of contacts between social groups. Moreover, their space and time resolutions are limited, so that data are not explicit at the person-to-person level, and the dynamic nature of the contacts is disregarded. In this study, we aimed to assess the role of data-driven dynamic contact patterns between individuals, and in particular of their temporal aspects, in shaping the spread of a simulated epidemic in the population. Methods We considered high-resolution data about face-to-face interactions between the attendees at a conference, obtained from the deployment of an infrastructure based on radiofrequency identification (RFID) devices that assessed mutual face-to-face proximity. The spread of epidemics along these interactions was simulated using an SEIR (Susceptible, Exposed, Infectious, Recovered) model, using both the dynamic network of contacts defined by the collected data, and two aggregated versions of such networks, to assess the role of the data temporal aspects. Results We show that, on the timescales considered, an aggregated network taking into account the daily duration of contacts is a good approximation to the full resolution network, whereas a homogeneous representation that retains only the topology of the contact network fails to reproduce the size of the epidemic. Conclusions These results have important implications for understanding the level of detail needed to correctly inform computational models for the study and management of real epidemics. Please see related article BMC Medicine, 2011, 9:88 PMID:21771290

Dissertation Defense Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

NASA Technical Reports Server (NTRS)

Groves, Curtis Edward

2014-01-01

Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional "validation by test only" mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions. Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in "Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations". This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions. The method accounts for all uncertainty terms from both numerical and input variables. Objective three is to compile a table of uncertainty parameters that could be used to estimate the error in a Computational Fluid Dynamics model of the Environmental Control System /spacecraft system. Previous studies have looked at the uncertainty in a Computational Fluid Dynamics model for a single output variable at a single point, for example the re-attachment length of a backward facing step. For the flow regime being analyzed (turbulent, three-dimensional, incompressible), the error at a single point can propagate into the solution both via flow physics and numerical methods. Calculating the uncertainty in using Computational Fluid Dynamics to accurately predict airflow speeds around encapsulated spacecraft in is imperative to the success of future missions.

Dissertation Defense: Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

NASA Technical Reports Server (NTRS)

Groves, Curtis Edward

2014-01-01

Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This paper describes an approach to quantify the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft without the use of test data. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional validation by test only mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions.Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computational Fluid Dynamics can be used to verify these requirements; however, the model must be validated by test data. This research includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available and open source solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT, STARCCM+, and OPENFOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid Dynamics model using the methodology found in Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations. This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions. The method accounts for all uncertainty terms from both numerical and input variables. Objective three is to compile a table of uncertainty parameters that could be used to estimate the error in a Computational Fluid Dynamics model of the Environmental Control System spacecraft system.Previous studies have looked at the uncertainty in a Computational Fluid Dynamics model for a single output variable at a single point, for example the re-attachment length of a backward facing step. For the flow regime being analyzed (turbulent, three-dimensional, incompressible), the error at a single point can propagate into the solution both via flow physics and numerical methods. Calculating the uncertainty in using Computational Fluid Dynamics to accurately predict airflow speeds around encapsulated spacecraft in is imperative to the success of future missions.

Computational Fluid Dynamics Uncertainty Analysis for Payload Fairing Spacecraft Environmental Control Systems

NASA Technical Reports Server (NTRS)

Groves, Curtis E.

2013-01-01

Spacecraft thermal protection systems are at risk of being damaged due to airflow produced from Environmental Control Systems. There are inherent uncertainties and errors associated with using Computational Fluid Dynamics to predict the airflow field around a spacecraft from the Environmental Control System. This proposal describes an approach to validate the uncertainty in using Computational Fluid Dynamics to predict airflow speeds around an encapsulated spacecraft. The research described here is absolutely cutting edge. Quantifying the uncertainty in analytical predictions is imperative to the success of any simulation-based product. The method could provide an alternative to traditional"validation by test only'' mentality. This method could be extended to other disciplines and has potential to provide uncertainty for any numerical simulation, thus lowering the cost of performing these verifications while increasing the confidence in those predictions. Spacecraft requirements can include a maximum airflow speed to protect delicate instruments during ground processing. Computationaf Fluid Dynamics can be used to veritY these requirements; however, the model must be validated by test data. The proposed research project includes the following three objectives and methods. Objective one is develop, model, and perform a Computational Fluid Dynamics analysis of three (3) generic, non-proprietary, environmental control systems and spacecraft configurations. Several commercially available solvers have the capability to model the turbulent, highly three-dimensional, incompressible flow regime. The proposed method uses FLUENT and OPEN FOAM. Objective two is to perform an uncertainty analysis of the Computational Fluid . . . Dynamics model using the methodology found in "Comprehensive Approach to Verification and Validation of Computational Fluid Dynamics Simulations". This method requires three separate grids and solutions, which quantify the error bars around Computational Fluid Dynamics predictions. The method accounts for all uncertainty terms from both numerical and input variables. Objective three is to compile a table of uncertainty parameters that could be used to estimate the error in a Computational Fluid Dynamics model of the Environmental Control System /spacecraft system. Previous studies have looked at the uncertainty in a Computational Fluid Dynamics model for a single output variable at a single point, for example the re-attachment length of a backward facing step. To date, the author is the only person to look at the uncertainty in the entire computational domain. For the flow regime being analyzed (turbulent, threedimensional, incompressible), the error at a single point can propagate into the solution both via flow physics and numerical methods. Calculating the uncertainty in using Computational Fluid Dynamics to accurately predict airflow speeds around encapsulated spacecraft in is imperative to the success of future missions.

Social coordination in toddler's word learning: interacting systems of perception and action

NASA Astrophysics Data System (ADS)

Pereira, Alfredo; Smith, Linda; Yu, Chen

2008-06-01

We measured turn-taking in terms of hand and head movements and asked if the global rhythm of the participants' body activity relates to word learning. Six dyads composed of parents and toddlers (M=18 months) interacted in a tabletop task wearing motion-tracking sensors on their hands and head. Parents were instructed to teach the labels of 10 novel objects and the child was later tested on a name-comprehension task. Using dynamic time warping, we compared the motion data of all body-part pairs, within and between partners. For every dyad, we also computed an overall measure of the quality of the interaction, that takes into consideration the state of interaction when the parent uttered an object label and the overall smoothness of the turn-taking. The overall interaction quality measure was correlated with the total number of words learned. In particular, head movements were inversely related to other partner's hand movements, and the degree of bodily coupling of parent and toddler predicted the words that children learned during the interaction. The implications of joint body dynamics to understanding joint coordination of activity in a social interaction, its scaffolding effect on the child's learning and its use in the development of artificial systems are discussed.

Collective Dynamics of Belief Evolution under Cognitive Coherence and Social Conformity.

PubMed

Rodriguez, Nathaniel; Bollen, Johan; Ahn, Yong-Yeol

2016-01-01

Human history has been marked by social instability and conflict, often driven by the irreconcilability of opposing sets of beliefs, ideologies, and religious dogmas. The dynamics of belief systems has been studied mainly from two distinct perspectives, namely how cognitive biases lead to individual belief rigidity and how social influence leads to social conformity. Here we propose a unifying framework that connects cognitive and social forces together in order to study the dynamics of societal belief evolution. Each individual is endowed with a network of interacting beliefs that evolves through interaction with other individuals in a social network. The adoption of beliefs is affected by both internal coherence and social conformity. Our framework may offer explanations for how social transitions can arise in otherwise homogeneous populations, how small numbers of zealots with highly coherent beliefs can overturn societal consensus, and how belief rigidity protects fringe groups and cults against invasion from mainstream beliefs, allowing them to persist and even thrive in larger societies. Our results suggest that strong consensus may be insufficient to guarantee social stability, that the cognitive coherence of belief-systems is vital in determining their ability to spread, and that coherent belief-systems may pose a serious problem for resolving social polarization, due to their ability to prevent consensus even under high levels of social exposure. We argue that the inclusion of cognitive factors into a social model could provide a more complete picture of collective human dynamics.

Computational Methods for Dynamic Stability and Control Derivatives

NASA Technical Reports Server (NTRS)

Green, Lawrence L.; Spence, Angela M.; Murphy, Patrick C.

2003-01-01

Force and moment measurements from an F-16XL during forced pitch oscillation tests result in dynamic stability derivatives, which are measured in combinations. Initial computational simulations of the motions and combined derivatives are attempted via a low-order, time-dependent panel method computational fluid dynamics code. The code dynamics are shown to be highly questionable for this application and the chosen configuration. However, three methods to computationally separate such combined dynamic stability derivatives are proposed. One of the separation techniques is demonstrated on the measured forced pitch oscillation data. Extensions of the separation techniques to yawing and rolling motions are discussed. In addition, the possibility of considering the angles of attack and sideslip state vector elements as distributed quantities, rather than point quantities, is introduced.

Computational Methods for Dynamic Stability and Control Derivatives

NASA Technical Reports Server (NTRS)

Green, Lawrence L.; Spence, Angela M.; Murphy, Patrick C.

2004-01-01

Force and moment measurements from an F-16XL during forced pitch oscillation tests result in dynamic stability derivatives, which are measured in combinations. Initial computational simulations of the motions and combined derivatives are attempted via a low-order, time-dependent panel method computational fluid dynamics code. The code dynamics are shown to be highly questionable for this application and the chosen configuration. However, three methods to computationally separate such combined dynamic stability derivatives are proposed. One of the separation techniques is demonstrated on the measured forced pitch oscillation data. Extensions of the separation techniques to yawing and rolling motions are discussed. In addition, the possibility of considering the angles of attack and sideslip state vector elements as distributed quantities, rather than point quantities, is introduced.

Computing with dynamical systems based on insulator-metal-transition oscillators

NASA Astrophysics Data System (ADS)

Parihar, Abhinav; Shukla, Nikhil; Jerry, Matthew; Datta, Suman; Raychowdhury, Arijit

2017-04-01

In this paper, we review recent work on novel computing paradigms using coupled oscillatory dynamical systems. We explore systems of relaxation oscillators based on linear state transitioning devices, which switch between two discrete states with hysteresis. By harnessing the dynamics of complex, connected systems, we embrace the philosophy of "let physics do the computing" and demonstrate how complex phase and frequency dynamics of such systems can be controlled, programmed, and observed to solve computationally hard problems. Although our discussion in this paper is limited to insulator-to-metallic state transition devices, the general philosophy of such computing paradigms can be translated to other mediums including optical systems. We present the necessary mathematical treatments necessary to understand the time evolution of these systems and demonstrate through recent experimental results the potential of such computational primitives.

A social implications of computing course which teaches computer ethics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pulliam, S.C.

1994-12-31

Computers are integral to today`s world, forming our society as well as responding to it, In recognition of this interaction, as well as in response to requirements by the Computer Science Accrediting Board (CSAB), many schools are incorporating computer ethics and values and addressing the social implications of computing within their curriculum. The approach discussed here is through a separate course, rather than relying on the integration of specific topics throughout the curriculum.

Dynamic modelling of an adsorption storage tank using a hybrid approach combining computational fluid dynamics and process simulation

USGS Publications Warehouse

Mota, J.P.B.; Esteves, I.A.A.C.; Rostam-Abadi, M.

2004-01-01

A computational fluid dynamics (CFD) software package has been coupled with the dynamic process simulator of an adsorption storage tank for methane fuelled vehicles. The two solvers run as independent processes and handle non-overlapping portions of the computational domain. The codes exchange data on the boundary interface of the two domains to ensure continuity of the solution and of its gradient. A software interface was developed to dynamically suspend and activate each process as necessary, and be responsible for data exchange and process synchronization. This hybrid computational tool has been successfully employed to accurately simulate the discharge of a new tank design and evaluate its performance. The case study presented here shows that CFD and process simulation are highly complementary computational tools, and that there are clear benefits to be gained from a close integration of the two. ?? 2004 Elsevier Ltd. All rights reserved.

Implementation of Parallel Dynamic Simulation on Shared-Memory vs. Distributed-Memory Environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Shuangshuang; Chen, Yousu; Wu, Di

2015-12-09

Power system dynamic simulation computes the system response to a sequence of large disturbance, such as sudden changes in generation or load, or a network short circuit followed by protective branch switching operation. It consists of a large set of differential and algebraic equations, which is computational intensive and challenging to solve using single-processor based dynamic simulation solution. High-performance computing (HPC) based parallel computing is a very promising technology to speed up the computation and facilitate the simulation process. This paper presents two different parallel implementations of power grid dynamic simulation using Open Multi-processing (OpenMP) on shared-memory platform, and Messagemore » Passing Interface (MPI) on distributed-memory clusters, respectively. The difference of the parallel simulation algorithms and architectures of the two HPC technologies are illustrated, and their performances for running parallel dynamic simulation are compared and demonstrated.« less

Structural and Dynamical Patterns on Online Social Networks: The Spanish May 15th Movement as a Case Study

PubMed Central

Borge-Holthoefer, Javier; Rivero, Alejandro; García, Iñigo; Cauhé, Elisa; Ferrer, Alfredo; Ferrer, Darío; Francos, David; Iñiguez, David; Pérez, María Pilar; Ruiz, Gonzalo; Sanz, Francisco; Serrano, Fermín; Viñas, Cristina; Tarancón, Alfonso; Moreno, Yamir

2011-01-01

The number of people using online social networks in their everyday life is continuously growing at a pace never saw before. This new kind of communication has an enormous impact on opinions, cultural trends, information spreading and even in the commercial success of new products. More importantly, social online networks have revealed as a fundamental organizing mechanism in recent country-wide social movements. In this paper, we provide a quantitative analysis of the structural and dynamical patterns emerging from the activity of an online social network around the ongoing May 15th (15M) movement in Spain. Our network is made up by users that exchanged tweets in a time period of one month, which includes the birth and stabilization of the 15M movement. We characterize in depth the growth of such dynamical network and find that it is scale-free with communities at the mesoscale. We also find that its dynamics exhibits typical features of critical systems such as robustness and power-law distributions for several quantities. Remarkably, we report that the patterns characterizing the spreading dynamics are asymmetric, giving rise to a clear distinction between information sources and sinks. Our study represents a first step towards the use of data from online social media to comprehend modern societal dynamics. PMID:21886834

Computational substrates of social value in interpersonal collaboration.

PubMed

Fareri, Dominic S; Chang, Luke J; Delgado, Mauricio R

2015-05-27

Decisions to engage in collaborative interactions require enduring considerable risk, yet provide the foundation for building and maintaining relationships. Here, we investigate the mechanisms underlying this process and test a computational model of social value to predict collaborative decision making. Twenty-six participants played an iterated trust game and chose to invest more frequently with their friends compared with a confederate or computer despite equal reinforcement rates. This behavior was predicted by our model, which posits that people receive a social value reward signal from reciprocation of collaborative decisions conditional on the closeness of the relationship. This social value signal was associated with increased activity in the ventral striatum and medial prefrontal cortex, which significantly predicted the reward parameters from the social value model. Therefore, we demonstrate that the computation of social value drives collaborative behavior in repeated interactions and provide a mechanistic account of reward circuit function instantiating this process. Copyright © 2015 the authors 0270-6474/15/358170-11$15.00/0.

Advanced Computational Methods for Security Constrained Financial Transmission Rights: Structure and Parallelism

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elbert, Stephen T.; Kalsi, Karanjit; Vlachopoulou, Maria

Financial Transmission Rights (FTRs) help power market participants reduce price risks associated with transmission congestion. FTRs are issued based on a process of solving a constrained optimization problem with the objective to maximize the FTR social welfare under power flow security constraints. Security constraints for different FTR categories (monthly, seasonal or annual) are usually coupled and the number of constraints increases exponentially with the number of categories. Commercial software for FTR calculation can only provide limited categories of FTRs due to the inherent computational challenges mentioned above. In this paper, a novel non-linear dynamical system (NDS) approach is proposed tomore » solve the optimization problem. The new formulation and performance of the NDS solver is benchmarked against widely used linear programming (LP) solvers like CPLEX™ and tested on large-scale systems using data from the Western Electricity Coordinating Council (WECC). The NDS is demonstrated to outperform the widely used CPLEX algorithms while exhibiting superior scalability. Furthermore, the NDS based solver can be easily parallelized which results in significant computational improvement.« less

The Implicit Contract: Implications for Health Social Work

ERIC Educational Resources Information Center

McCoyd, Judith L. M.

2010-01-01

Identifying common patient dynamics is useful for developing social work practice sensitivity in health social work. This article draws on findings from a study of women who terminated desired pregnancies because of fetal anomalies and identifies dynamics that may be applicable to many health settings. Data suggest that women have expectations…

Dynamic Social Impact: The Creation of Culture by Communication.

ERIC Educational Resources Information Center

Latane, Bibb

1996-01-01

Presents a theory of how individuals located in social space influence each other to create higher order patterns of cultural structure. Presents the theory as five propositions and six derivations, arguing that Dynamic Social Impact Theory accounts for four key features of culture: regional clustering, correlations among cultural elements,…

Breastfeeding and use of social media among first-time African American mothers.

PubMed

Asiodu, Ifeyinwa V; Waters, Catherine M; Dailey, Dawn E; Lee, Kathryn A; Lyndon, Audrey

2015-01-01

To describe the use of social media during the antepartum and postpartum periods among first-time African American mothers and their support persons. A qualitative critical ethnographic research design within the contexts of family life course development theory and Black feminist theory. Participants were recruited from community-based, public health, and home visiting programs. A purposive sample was recruited, consisting of 14 pregnant African American women and eight support persons. Pregnant and postpartum African American women and their support persons were interviewed separately during the antepartum and postpartum periods. Data were analyzed thematically. Participants frequently used social media for education and social support and searched the Internet for perinatal and parenting information. Most participants reported using at least one mobile application during their pregnancies and after giving birth. Social media were typically accessed through smartphones and/or computers using different websites and applications. Although participants gleaned considerable information about infant development from these applications, they had difficulty finding and recalling information about infant feeding. Social media are an important vehicle to disseminate infant feeding information; however, they are not currently being used to full potential. Our findings suggest that future interventions geared toward African American mothers and their support persons should include social media approaches. The way individuals gather, receive, and interpret information is dynamic. The increasing popularity and use of social media platforms offers the opportunity to create more innovative, targeted mobile health interventions for infant feeding and breastfeeding promotion. © 2015 AWHONN, the Association of Women's Health, Obstetric and Neonatal Nurses.

A Study To Increase Computer Applications in Social Work Management.

ERIC Educational Resources Information Center

Lucero, John A.

The purpose of this study was to address the use of computers in social work practice and to survey the field for tools, concepts, and trends that could assist social workers in their practice. In addition to a review of the relevant literature, information was requested from the Social Work Service and Ambulatory Care Database Section at Walter…

Social Skills Instruction for Urban Learners with Emotional and Behavioral Disorders: A Culturally Responsive and Computer-Based Intervention

ERIC Educational Resources Information Center

Robinson-Ervin, Porsha; Cartledge, Gwendolyn; Musti-Rao, Shobana; Gibson, Lenwood, Jr.; Keyes, Starr E.

2016-01-01

This study examined the effects of culturally relevant/responsive, computer-based social skills instruction on the social skill acquisition and generalization of 6 urban African American sixth graders with emotional and behavioral disorders (EBD). A multiple-probe across participants design was used to evaluate the effects of the social skills…

Computer Mediated Communication for Social and Academic Purposes: Profiles of Use and University Students' Gratifications

ERIC Educational Resources Information Center

Vrocharidou, Anatoli; Efthymiou, Ilias

2012-01-01

The present study approaches the Internet as a social space, where university students make use of computer mediated communication (CMC) applications, i.e. e-mail, instant messaging and social network sites, in order to satisfy social and academic needs. We focus on university students, because they represent one of the most avid groups of CMC…

Human Pacman: A Mobile Augmented Reality Entertainment System Based on Physical, Social, and Ubiquitous Computing

NASA Astrophysics Data System (ADS)

Cheok, Adrian David

This chapter details the Human Pacman system to illuminate entertainment computing which ventures to embed the natural physical world seamlessly with a fantasy virtual playground by capitalizing on infrastructure provided by mobile computing, wireless LAN, and ubiquitous computing. With Human Pacman, we have a physical role-playing computer fantasy together with real human-social and mobile-gaming that emphasizes on collaboration and competition between players in a wide outdoor physical area that allows natural wide-area human-physical movements. Pacmen and Ghosts are now real human players in the real world experiencing mixed computer graphics fantasy-reality provided by using the wearable computers on them. Virtual cookies and actual tangible physical objects are incorporated into the game play to provide novel experiences of seamless transitions between the real and virtual worlds. This is an example of a new form of gaming that anchors on physicality, mobility, social interaction, and ubiquitous computing.

Will a Category Cue Attract You? Motor Output Reveals Dynamic Competition across Person Construal

ERIC Educational Resources Information Center

Freeman, Jonathan B.; Ambady, Nalini; Rule, Nicholas O.; Johnson, Kerri L.

2008-01-01

People use social categories to perceive others, extracting category cues to glean membership. Growing evidence for continuous dynamics in real-time cognition suggests, contrary to prevailing social psychological accounts, that person construal may involve dynamic competition between simultaneously active representations. To test this, the authors…

Perceived problems with computer gaming and Internet use are associated with poorer social relations in adolescence.

PubMed

Rasmussen, Mette; Meilstrup, Charlotte Riebeling; Bendtsen, Pernille; Pedersen, Trine Pagh; Nielsen, Line; Madsen, Katrine Rich; Holstein, Bjørn E

2015-02-01

Young people's engagement in electronic gaming and Internet communication have caused concerns about potential harmful effects on their social relations, but the literature is inconclusive. The aim of this paper was to examine whether perceived problems with computer gaming and Internet communication are associated with young people's social relations. Cross-sectional questionnaire survey in 13 schools in the city of Aarhus, Denmark, in 2009. Response rate 89%, n = 2,100 students in grades 5, 7, and 9. Independent variables were perceived problems related to computer gaming and Internet use, respectively. Outcomes were measures of structural (number of days/week with friends, number of friends) and functional (confidence in others, being bullied, bullying others) dimensions of student's social relations. Perception of problems related to computer gaming were associated with almost all aspects of poor social relations among boys. Among girls, an association was only seen for bullying. For both boys and girls, perceived problems related to Internet use were associated with bullying only. Although the study is cross-sectional, the findings suggest that computer gaming and Internet use may be harmful to young people's social relations.

A Diffusive Strategic Dynamics for Social Systems

NASA Astrophysics Data System (ADS)

Agliari, Elena; Burioni, Raffaella; Contucci, Pierluigi

2010-05-01

We propose a model for the dynamics of a social system, which includes diffusive effects and a biased rule for spin-flips, reproducing the effect of strategic choices. This model is able to mimic some phenomena taking place during marketing or political campaigns. Using a cost function based on the Ising model defined on the typical quenched interaction environments for social systems (Erdös-Renyi graph, small-world and scale-free networks), we find, by numerical simulations, that a stable stationary state is reached, and we compare the final state to the one obtained with standard dynamics, by means of total magnetization and magnetic susceptibility. Our results show that the diffusive strategic dynamics features a critical interaction parameter strictly lower than the standard one. We discuss the relevance of our findings in social systems.

Thirteenth Workshop for Computational Fluid Dynamic Applications in Rocket Propulsion and Launch Vehicle Technology. Volume 1

NASA Technical Reports Server (NTRS)

Williams, R. W. (Compiler)

1996-01-01

The purpose of the workshop was to discuss experimental and computational fluid dynamic activities in rocket propulsion and launch vehicles. The workshop was an open meeting for government, industry, and academia. A broad number of topics were discussed including computational fluid dynamic methodology, liquid and solid rocket propulsion, turbomachinery, combustion, heat transfer, and grid generation.

Quantum ring-polymer contraction method: Including nuclear quantum effects at no additional computational cost in comparison to ab initio molecular dynamics

NASA Astrophysics Data System (ADS)

John, Christopher; Spura, Thomas; Habershon, Scott; Kühne, Thomas D.

2016-04-01

We present a simple and accurate computational method which facilitates ab initio path-integral molecular dynamics simulations, where the quantum-mechanical nature of the nuclei is explicitly taken into account, at essentially no additional computational cost in comparison to the corresponding calculation using classical nuclei. The predictive power of the proposed quantum ring-polymer contraction method is demonstrated by computing various static and dynamic properties of liquid water at ambient conditions using density functional theory. This development will enable routine inclusion of nuclear quantum effects in ab initio molecular dynamics simulations of condensed-phase systems.

How social and non-social information influence classification decisions: A computational modelling approach.

PubMed

Puskaric, Marin; von Helversen, Bettina; Rieskamp, Jörg

2017-08-01

Social information such as observing others can improve performance in decision making. In particular, social information has been shown to be useful when finding the best solution on one's own is difficult, costly, or dangerous. However, past research suggests that when making decisions people do not always consider other people's behaviour when it is at odds with their own experiences. Furthermore, the cognitive processes guiding the integration of social information with individual experiences are still under debate. Here, we conducted two experiments to test whether information about other persons' behaviour influenced people's decisions in a classification task. Furthermore, we examined how social information is integrated with individual learning experiences by testing different computational models. Our results show that social information had a small but reliable influence on people's classifications. The best computational model suggests that in categorization people first make up their own mind based on the non-social information, which is then updated by the social information.

A prisoner's dilemma experiment on cooperation with people and human-like computers.

PubMed

Kiesler, S; Sproull, L; Waters, K

1996-01-01

The authors investigated basic properties of social exchange and interaction with technology in an experiment on cooperation with a human-like computer partner or a real human partner. Talking with a computer partner may trigger social identity feelings or commitment norms. Participants played a prisoner's dilemma game with a confederate or a computer partner. Discussion, inducements to make promises, and partner cooperation varied across trials. On Trial 1, after discussion, most participants proposed cooperation. They kept their promises as much with a text-only computer as with a person, but less with a more human-like computer. Cooperation dropped sharply when any partner avoided discussion. The strong impact of discussion fits a social contract explanation of cooperation following discussion. Participants broke their promises to a computer more than to a person, however, indicating that people make heterogeneous commitments.

A full computation-relevant topological dynamics classification of elementary cellular automata.

PubMed

Schüle, Martin; Stoop, Ruedi

2012-12-01

Cellular automata are both computational and dynamical systems. We give a complete classification of the dynamic behaviour of elementary cellular automata (ECA) in terms of fundamental dynamic system notions such as sensitivity and chaoticity. The "complex" ECA emerge to be sensitive, but not chaotic and not eventually weakly periodic. Based on this classification, we conjecture that elementary cellular automata capable of carrying out complex computations, such as needed for Turing-universality, are at the "edge of chaos."

Social structure in a family group of Guanaco (Lama guanicoe, Ungulate): is female hierarchy based on 'prior attributes' or 'social dynamics'?

PubMed

Correa, Loreto A; Zapata, Beatriz; Samaniego, Horacio; Soto-Gamboa, Mauricio

2013-09-01

Social life involves costs and benefits mostly associated with how individuals interact with each other. The formation of hierarchies inside social groups has evolved as a common strategy to avoid high costs stemming from social interactions. Hierarchical relationships seem to be associated with different features such as body size, body condition and/or age, which determine dominance ability ('prior attributes' hypothesis). In contrast, the 'social dynamic' hypothesis suggests that an initial social context is a determinant in the formation of the hierarchy, more so than specific individual attributes. Hierarchical rank places individuals in higher positions, which presumably increases resource accessibility to their benefit, including opportunities for reproduction. We evaluate the maintenance of hierarchy in a family group of guanacos (Lama guanicoe) and evaluate the possible mechanisms involved in the stability of these interactions and their consequences. We estimate the linearity of social hierarchy and their dynamics. We find evidence of the formation of a highly linear hierarchy among females with males positioned at the bottom of the hierarchy. This hierarchy is not affected by physical characteristics or age, suggesting that it is established only through intra-group interactions. Rank is not related with calves' weight gain either; however, subordinated females, with lower rank, exhibit higher rates of allosuckling. We found no evidence of hierarchical structure in calves suggesting that hierarchical relationship in guanacos could be established during the formation of the family group. Hence, our results suggest that hierarchical dynamics could be related more to social dynamics than to prior attributes. We finally discuss the importance of hierarchies established by dominance and their role in minimizing social costs of interactions. Copyright © 2013 Elsevier B.V. All rights reserved.

Is Human-Computer Interaction Social or Parasocial?

ERIC Educational Resources Information Center

Sundar, S. Shyam

Conducted in the attribution-research paradigm of social psychology, a study examined whether human-computer interaction is fundamentally social (as in human-human interaction) or parasocial (as in human-television interaction). All 30 subjects (drawn from an undergraduate class on communication) were exposed to an identical interaction with…

Computer Simulation in Social Science.

ERIC Educational Resources Information Center

Garson, G. David

From a base in military models, computer simulation has evolved to provide a wide variety of applications in social science. General purpose simulation packages and languages such as FIRM, DYNAMO, and others have made significant contributions toward policy discussion in the social sciences and have well-documented efficacy in instructional…

Corporate funding and ideological polarization about climate change

PubMed Central

Farrell, Justin

2016-01-01

Drawing on large-scale computational data and methods, this research demonstrates how polarization efforts are influenced by a patterned network of political and financial actors. These dynamics, which have been notoriously difficult to quantify, are illustrated here with a computational analysis of climate change politics in the United States. The comprehensive data include all individual and organizational actors in the climate change countermovement (164 organizations), as well as all written and verbal texts produced by this network between 1993–2013 (40,785 texts, more than 39 million words). Two main findings emerge. First, that organizations with corporate funding were more likely to have written and disseminated texts meant to polarize the climate change issue. Second, and more importantly, that corporate funding influences the actual thematic content of these polarization efforts, and the discursive prevalence of that thematic content over time. These findings provide new, and comprehensive, confirmation of dynamics long thought to be at the root of climate change politics and discourse. Beyond the specifics of climate change, this paper has important implications for understanding ideological polarization more generally, and the increasing role of private funding in determining why certain polarizing themes are created and amplified. Lastly, the paper suggests that future studies build on the novel approach taken here that integrates large-scale textual analysis with social networks. PMID:26598653

Corporate funding and ideological polarization about climate change.

PubMed

Farrell, Justin

2016-01-05

Drawing on large-scale computational data and methods, this research demonstrates how polarization efforts are influenced by a patterned network of political and financial actors. These dynamics, which have been notoriously difficult to quantify, are illustrated here with a computational analysis of climate change politics in the United States. The comprehensive data include all individual and organizational actors in the climate change countermovement (164 organizations), as well as all written and verbal texts produced by this network between 1993-2013 (40,785 texts, more than 39 million words). Two main findings emerge. First, that organizations with corporate funding were more likely to have written and disseminated texts meant to polarize the climate change issue. Second, and more importantly, that corporate funding influences the actual thematic content of these polarization efforts, and the discursive prevalence of that thematic content over time. These findings provide new, and comprehensive, confirmation of dynamics long thought to be at the root of climate change politics and discourse. Beyond the specifics of climate change, this paper has important implications for understanding ideological polarization more generally, and the increasing role of private funding in determining why certain polarizing themes are created and amplified. Lastly, the paper suggests that future studies build on the novel approach taken here that integrates large-scale textual analysis with social networks.

Integration of Computational Geometry, Finite Element, and Multibody System Algorithms for the Development of New Computational Methodology for High-Fidelity Vehicle Systems Modeling and Simulation

DTIC Science & Technology

2013-04-11

vehicle dynamics. Unclassified Unclassified Unclassified UU 9 Dr. Paramsothy Jayakumar (586) 282-4896 Computational Dynamics Inc.   0    Name of...Technical Representative Dr. Paramsothy Jayakumar TARDEC Computational Dynamics Inc.   1    Project Summary This project aims at addressing and...applications. This literature review is being summarized and incorporated into the paper. The commentary provided by Dr. Jayakumar was addressed and

The investigation of tethered satellite system dynamics

NASA Technical Reports Server (NTRS)

Lorenzini, E.

1984-01-01

Tethered satellite system (TSS) dynamics were studied. The dynamic response of the TSS during the entire stationkeeping phase for the first electrodynamic mission was investigated. An out of plane swing amplitude and the tether's bowing were observed. The dynamics of the slack tether was studied and computer code, SLACK2, was improved both in capabilities and computational speed. Speed hazard related to tether breakage or plasma contactor failure was examined. Preliminary values of the potential difference after the failure and of the drop of the electric field along the tether axis have been computed. The update of the satellite rotational dynamics model is initiated.

Exploiting short-term memory in soft body dynamics as a computational resource

PubMed Central

Nakajima, K.; Li, T.; Hauser, H.; Pfeifer, R.

2014-01-01

Soft materials are not only highly deformable, but they also possess rich and diverse body dynamics. Soft body dynamics exhibit a variety of properties, including nonlinearity, elasticity and potentially infinitely many degrees of freedom. Here, we demonstrate that such soft body dynamics can be employed to conduct certain types of computation. Using body dynamics generated from a soft silicone arm, we show that they can be exploited to emulate functions that require memory and to embed robust closed-loop control into the arm. Our results suggest that soft body dynamics have a short-term memory and can serve as a computational resource. This finding paves the way towards exploiting passive body dynamics for control of a large class of underactuated systems. PMID:25185579

Guidelines for Computing Longitudinal Dynamic Stability Characteristics of a Subsonic Transport

NASA Technical Reports Server (NTRS)

Thompson, Joseph R.; Frank, Neal T.; Murphy, Patrick C.

2010-01-01

A systematic study is presented to guide the selection of a numerical solution strategy for URANS computation of a subsonic transport configuration undergoing simulated forced oscillation about its pitch axis. Forced oscillation is central to the prevalent wind tunnel methodology for quantifying aircraft dynamic stability derivatives from force and moment coefficients, which is the ultimate goal for the computational simulations. Extensive computations are performed that lead in key insights of the critical numerical parameters affecting solution convergence. A preliminary linear harmonic analysis is included to demonstrate the potential of extracting dynamic stability derivatives from computational solutions.

Dynamic Emulation Modelling (DEMo) of large physically-based environmental models

NASA Astrophysics Data System (ADS)

Galelli, S.; Castelletti, A.

2012-12-01

In environmental modelling large, spatially-distributed, physically-based models are widely adopted to describe the dynamics of physical, social and economic processes. Such an accurate process characterization comes, however, to a price: the computational requirements of these models are considerably high and prevent their use in any problem requiring hundreds or thousands of model runs to be satisfactory solved. Typical examples include optimal planning and management, data assimilation, inverse modelling and sensitivity analysis. An effective approach to overcome this limitation is to perform a top-down reduction of the physically-based model by identifying a simplified, computationally efficient emulator, constructed from and then used in place of the original model in highly resource-demanding tasks. The underlying idea is that not all the process details in the original model are equally important and relevant to the dynamics of the outputs of interest for the type of problem considered. Emulation modelling has been successfully applied in many environmental applications, however most of the literature considers non-dynamic emulators (e.g. metamodels, response surfaces and surrogate models), where the original dynamical model is reduced to a static map between input and the output of interest. In this study we focus on Dynamic Emulation Modelling (DEMo), a methodological approach that preserves the dynamic nature of the original physically-based model, with consequent advantages in a wide variety of problem areas. In particular, we propose a new data-driven DEMo approach that combines the many advantages of data-driven modelling in representing complex, non-linear relationships, but preserves the state-space representation typical of process-based models, which is both particularly effective in some applications (e.g. optimal management and data assimilation) and facilitates the ex-post physical interpretation of the emulator structure, thus enhancing the credibility of the model to stakeholders and decision-makers. Numerical results from the application of the approach to the reduction of 3D coupled hydrodynamic-ecological models in several real world case studies, including Marina Reservoir (Singapore) and Googong Reservoir (Australia), are illustrated.

A dynamic social systems model for considering structural factors in HIV prevention and detection

PubMed Central

Latkin, Carl; Weeks, Margaret; Glasman, Laura; Galletly, Carol; Albarracin, Dolores

2010-01-01

We present a model for HIV-related behaviors that emphasizes the dynamic and social nature of the structural factors that influence HIV prevention and detection. Key structural dimensions of the model include resources, science and technology, formal social control, informal social influences and control, social interconnectedness, and settings. These six dimensions can be conceptualized on macro, meso, and micro levels. Given the inherent complexity of structural factors and their interrelatedness, HIV prevention interventions may focus on different levels and dimensions. We employ a systems perspective to describe the interconnected and dynamic processes of change among social systems and their components. The topics of HIV testing and safer injection facilities are analyzed using this structural framework. Finally, we discuss methodological issues in the development and evaluation of structural interventions for HIV prevention and detection. PMID:20838871

Dynamic response and transfer function of social systems: A neuro-inspired model of collective human activity patterns.

PubMed

Lymperopoulos, Ilias N

2017-10-01

The interaction of social networks with the external environment gives rise to non-stationary activity patterns reflecting the temporal structure and strength of exogenous influences that drive social dynamical processes far from an equilibrium state. Following a neuro-inspired approach, based on the dynamics of a passive neuronal membrane, and the firing rate dynamics of single neurons and neuronal populations, we build a state-of-the-art model of the collective social response to exogenous interventions. In this regard, we analyze online activity patterns with a view to determining the transfer function of social systems, that is, the dynamic relationship between external influences and the resulting activity. To this end, first we estimate the impulse response (Green's function) of collective activity, and then we show that the convolution of the impulse response with a time-varying external influence field accurately reproduces empirical activity patterns. To capture the dynamics of collective activity when the generating process is in a state of statistical equilibrium, we incorporate into the model a noisy input convolved with the impulse response function, thus precisely reproducing the fluctuations of stationary collective activity around a resting value. The outstanding goodness-of-fit of the model results to empirical observations, indicates that the model explains human activity patterns generated by time-dependent external influences in various socio-economic contexts. The proposed model can be used for inferring the temporal structure and strength of external influences, as well as the inertia of collective social activity. Furthermore, it can potentially predict social activity patterns. Copyright © 2017 Elsevier Ltd. All rights reserved.

Computer Technology and Social Issues.

ERIC Educational Resources Information Center

Garson, G. David

Computing involves social issues and political choices. Issues such as privacy, computer crime, gender inequity, disemployment, and electronic democracy versus "Big Brother" are addressed in the context of efforts to develop a national public policy for information technology. A broad range of research and case studies are examined in an…

Ndarts

NASA Technical Reports Server (NTRS)

Jain, Abhinandan

2011-01-01

Ndarts software provides algorithms for computing quantities associated with the dynamics of articulated, rigid-link, multibody systems. It is designed as a general-purpose dynamics library that can be used for the modeling of robotic platforms, space vehicles, molecular dynamics, and other such applications. The architecture and algorithms in Ndarts are based on the Spatial Operator Algebra (SOA) theory for computational multibody and robot dynamics developed at JPL. It uses minimal, internal coordinate models. The algorithms are low-order, recursive scatter/ gather algorithms. In comparison with the earlier Darts++ software, this version has a more general and cleaner design needed to support a larger class of computational dynamics needs. It includes a frames infrastructure, allows algorithms to operate on subgraphs of the system, and implements lazy and deferred computation for better efficiency. Dynamics modeling modules such as Ndarts are core building blocks of control and simulation software for space, robotic, mechanism, bio-molecular, and material systems modeling.

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

DOE PAGES

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine; ...

2017-12-15

This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \\real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns,more » emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.« less

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine

This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \\real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns,more » emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.« less

Dynamic Flood Vulnerability Mapping with Google Earth Engine

NASA Astrophysics Data System (ADS)

Tellman, B.; Kuhn, C.; Max, S. A.; Sullivan, J.

2015-12-01

Satellites capture the rate and character of environmental change from local to global levels, yet integrating these changes into flood exposure models can be cost or time prohibitive. We explore an approach to global flood modeling by leveraging satellite data with computing power in Google Earth Engine to dynamically map flood hazards. Our research harnesses satellite imagery in two main ways: first to generate a globally consistent flood inundation layer and second to dynamically model flood vulnerability. Accurate and relevant hazard maps rely on high quality observation data. Advances in publicly available spatial, spectral, and radar data together with cloud computing allow us to improve existing efforts to develop a comprehensive flood extent database to support model training and calibration. This talk will demonstrate the classification results of algorithms developed in Earth Engine designed to detect flood events by combining observations from MODIS, Landsat 8, and Sentinel-1. Our method to derive flood footprints increases the number, resolution, and precision of spatial observations for flood events both in the US, recorded in the NCDC (National Climatic Data Center) storm events database, and globally, as recorded events from the Colorado Flood Observatory database. This improved dataset can then be used to train machine learning models that relate spatial temporal flood observations to satellite derived spatial temporal predictor variables such as precipitation, antecedent soil moisture, and impervious surface. This modeling approach allows us to rapidly update models with each new flood observation, providing near real time vulnerability maps. We will share the water detection algorithms used with each satellite and discuss flood detection results with examples from Bihar, India and the state of New York. We will also demonstrate how these flood observations are used to train machine learning models and estimate flood exposure. The final stage of our comprehensive approach to flood vulnerability couples inundation extent with social data to determine which flood exposed communities have the greatest propensity for loss. Specifically, by linking model outputs to census derived social vulnerability estimates (Indian and US, respectively) to predict how many people are at risk.

Aeroelastic analysis of bridge girder section using computer modeling

DOT National Transportation Integrated Search

2001-05-01

This report describes the numerical simulation of wind flow around bridges using the Finite Element Method (FEM) and the principles of Computational Fluid Dynamics (CFD) and Computational Structural Dynamics (CSD). Since, the suspension bridges are p...

The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction.

PubMed

Casey, M

1996-08-15

Recurrent neural networks (RNNs) can learn to perform finite state computations. It is shown that an RNN performing a finite state computation must organize its state space to mimic the states in the minimal deterministic finite state machine that can perform that computation, and a precise description of the attractor structure of such systems is given. This knowledge effectively predicts activation space dynamics, which allows one to understand RNN computation dynamics in spite of complexity in activation dynamics. This theory provides a theoretical framework for understanding finite state machine (FSM) extraction techniques and can be used to improve training methods for RNNs performing FSM computations. This provides an example of a successful approach to understanding a general class of complex systems that has not been explicitly designed, e.g., systems that have evolved or learned their internal structure.

Generalized dynamic engine simulation techniques for the digital computer

NASA Technical Reports Server (NTRS)

Sellers, J.; Teren, F.

1974-01-01

Recently advanced simulation techniques have been developed for the digital computer and used as the basis for development of a generalized dynamic engine simulation computer program, called DYNGEN. This computer program can analyze the steady state and dynamic performance of many kinds of aircraft gas turbine engines. Without changes to the basic program, DYNGEN can analyze one- or two-spool turbofan engines. The user must supply appropriate component performance maps and design-point information. Examples are presented to illustrate the capabilities of DYNGEN in the steady state and dynamic modes of operation. The analytical techniques used in DYNGEN are briefly discussed, and its accuracy is compared with a comparable simulation using the hybrid computer. The impact of DYNGEN and similar all-digital programs on future engine simulation philosophy is also discussed.

Generalized dynamic engine simulation techniques for the digital computer

NASA Technical Reports Server (NTRS)

Sellers, J.; Teren, F.

1974-01-01

Recently advanced simulation techniques have been developed for the digital computer and used as the basis for development of a generalized dynamic engine simulation computer program, called DYNGEN. This computer program can analyze the steady state and dynamic performance of many kinds of aircraft gas turbine engines. Without changes to the basic program DYNGEN can analyze one- or two-spool turbofan engines. The user must supply appropriate component performance maps and design-point information. Examples are presented to illustrate the capabilities of DYNGEN in the steady state and dynamic modes of operation. The analytical techniques used in DYNGEN are briefly discussed, and its accuracy is compared with a comparable simulation using the hybrid computer. The impact of DYNGEN and similar all-digital programs on future engine simulation philosophy is also discussed.

Generalized dynamic engine simulation techniques for the digital computers

NASA Technical Reports Server (NTRS)

Sellers, J.; Teren, F.

1975-01-01

Recently advanced simulation techniques have been developed for the digital computer and used as the basis for development of a generalized dynamic engine simulation computer program, called DYNGEN. This computer program can analyze the steady state and dynamic performance of many kinds of aircraft gas turbine engines. Without changes to the basic program, DYNGEN can analyze one- or two-spool turbofan engines. The user must supply appropriate component performance maps and design point information. Examples are presented to illustrate the capabilities of DYNGEN in the steady state and dynamic modes of operation. The analytical techniques used in DYNGEN are briefly discussed, and its accuracy is compared with a comparable simulation using the hybrid computer. The impact of DYNGEN and similar digital programs on future engine simulation philosophy is also discussed.

Residential Mobility, Technology and Social Ties

ERIC Educational Resources Information Center

Shklovski, Irina A.

2007-01-01

Humans are fundamentally social creatures and our social relationships depend on communication to survive. It is not surprising that communication is the most popular use of the Internet. Researchers have examined computer-mediated communication since the early 1980's, yet, the precise role of computer-mediated communication in growth,…

Wikipedia Writing as Praxis: Computer-Mediated Socialization of Second-Language Writers

ERIC Educational Resources Information Center

King, Brian W.

2015-01-01

This study explores the writing of Wikipedia articles as a form of authentic writing for learners of English in Hong Kong. Adopting "Second Language Socialization and Language Learning & Identity" approaches to language learning inquiry, it responds to an identified shortage of research on computer-mediated language socialization.…

Social Contagion, Adolescent Sexual Behavior, and Pregnancy: A Nonlinear Dynamic EMOSA Model.

ERIC Educational Resources Information Center

Rodgers, Joseph Lee; Rowe, David C.; Buster, Maury

1998-01-01

Expands an existing nonlinear dynamic epidemic model of onset of social activities (EMOSA), motivated by social contagion theory, to quantify the likelihood of pregnancy for adolescent girls of different sexuality statuses. Compares five sexuality/pregnancy models to explain variance in national prevalence curves. Finds that adolescent girls have…

A Dynamic Social Feedback System to Support Learning and Social Interaction in Higher Education

ERIC Educational Resources Information Center

Thoms, Brian

2011-01-01

In this research, we examine the design, construction, and implementation of a dynamic, easy to use, feedback mechanism for social software. The tool was integrated into an existing university's online learning community (OLC). In line with constructivist learning models and practical information systems (IS) design, the feedback system provides…

Applying Structural Systems Thinking to Frame Perspectives on Social Work Innovation

ERIC Educational Resources Information Center

Stringfellow, Erin J.

2017-01-01

Objective: Innovation will be key to the success of the Grand Challenges Initiative in social work. A structural systems framework based in system dynamics could be useful for considering how to advance innovation. Method: Diagrams using system dynamics conventions were developed to link common themes across concept papers written by social work…

Using Psychodynamic Interaction as a Valuable Source of Information in Social Research

ERIC Educational Resources Information Center

Schmidt, Camilla

2012-01-01

This article will address the issue of using understandings of psychodynamic interrelations as a means to grasp how social and cultural dynamics are processed individually and collectively in narratives. I apply the two theoretically distinct concepts of inter- and intrasubjectivity to gain insight into how social and cultural dynamics are…

Dynamics and computation in functional shifts

NASA Astrophysics Data System (ADS)

Namikawa, Jun; Hashimoto, Takashi

2004-07-01

We introduce a new type of shift dynamics as an extended model of symbolic dynamics, and investigate the characteristics of shift spaces from the viewpoints of both dynamics and computation. This shift dynamics is called a functional shift, which is defined by a set of bi-infinite sequences of some functions on a set of symbols. To analyse the complexity of functional shifts, we measure them in terms of topological entropy, and locate their languages in the Chomsky hierarchy. Through this study, we argue that considering functional shifts from the viewpoints of both dynamics and computation gives us opposite results about the complexity of systems. We also describe a new class of shift spaces whose languages are not recursively enumerable.

Metrics for comparing dynamic earthquake rupture simulations

USGS Publications Warehouse

Barall, Michael; Harris, Ruth A.

2014-01-01

Earthquakes are complex events that involve a myriad of interactions among multiple geologic features and processes. One of the tools that is available to assist with their study is computer simulation, particularly dynamic rupture simulation. A dynamic rupture simulation is a numerical model of the physical processes that occur during an earthquake. Starting with the fault geometry, friction constitutive law, initial stress conditions, and assumptions about the condition and response of the near‐fault rocks, a dynamic earthquake rupture simulation calculates the evolution of fault slip and stress over time as part of the elastodynamic numerical solution (Ⓔ see the simulation description in the electronic supplement to this article). The complexity of the computations in a dynamic rupture simulation make it challenging to verify that the computer code is operating as intended, because there are no exact analytic solutions against which these codes’ results can be directly compared. One approach for checking if dynamic rupture computer codes are working satisfactorily is to compare each code’s results with the results of other dynamic rupture codes running the same earthquake simulation benchmark. To perform such a comparison consistently, it is necessary to have quantitative metrics. In this paper, we present a new method for quantitatively comparing the results of dynamic earthquake rupture computer simulation codes.

Neural Computations in a Dynamical System with Multiple Time Scales.

PubMed

Mi, Yuanyuan; Lin, Xiaohan; Wu, Si

2016-01-01

Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions.

The coordination dynamics of social neuromarkers.

PubMed

Tognoli, Emmanuelle; Kelso, J A Scott

2015-01-01

Social behavior is a complex integrative function that entails many aspects of the brain's sensory, cognitive, emotional and movement capacities. Its neural processes are seldom simultaneous but occur according to precise spatiotemporal choreographies, manifested by the coordination of their oscillations within and between brains. Methods with good temporal resolution can help to identify so-called "neuromarkers" of social function and aid in disentangling the dynamical architecture of social brains. In our ongoing research, we have used dual-electroencephalography (EEG) to study neuromarker dynamics during synchronic interactions in which pairs of subjects coordinate behavior spontaneously and intentionally (social coordination) and during diachronic transactions that require subjects to perceive or behave in turn (action observation, delayed imitation). In this paper, after outlining our dynamical approach to the neurophysiological basis of social behavior, we examine commonalities and differences in the neuromarkers that are recruited for both kinds of tasks. We find the neuromarker landscape to be task-specific: synchronic paradigms of social coordination reveal medial mu, alpha and the phi complex as contributing neuromarkers. Diachronic tasks recruit alpha as well, in addition to lateral mu rhythms and the newly discovered nu and kappa rhythms whose functional significance is still unclear. Social coordination, observation, and delayed imitation share commonality of context: in each of our experiments, subjects exchanged information through visual perception and moved in similar ways. Nonetheless, there was little overlap between their neuromarkers, a result that hints strongly of task-specific neural mechanisms for social behavior. The only neuromarker that transcended both synchronic and diachronic social behaviors was the ubiquitous alpha rhythm, which appears to be a key signature of visually-mediated social behaviors. The present paper is both an entry point and a challenge: much work remains to determine the nature and scope of recruitment of other neuromarkers, and to create theoretical models of their within- and between-brain dynamics during social interaction.

The coordination dynamics of social neuromarkers

PubMed Central

Tognoli, Emmanuelle; Kelso, J. A. Scott

2015-01-01

Social behavior is a complex integrative function that entails many aspects of the brain’s sensory, cognitive, emotional and movement capacities. Its neural processes are seldom simultaneous but occur according to precise spatiotemporal choreographies, manifested by the coordination of their oscillations within and between brains. Methods with good temporal resolution can help to identify so-called “neuromarkers” of social function and aid in disentangling the dynamical architecture of social brains. In our ongoing research, we have used dual-electroencephalography (EEG) to study neuromarker dynamics during synchronic interactions in which pairs of subjects coordinate behavior spontaneously and intentionally (social coordination) and during diachronic transactions that require subjects to perceive or behave in turn (action observation, delayed imitation). In this paper, after outlining our dynamical approach to the neurophysiological basis of social behavior, we examine commonalities and differences in the neuromarkers that are recruited for both kinds of tasks. We find the neuromarker landscape to be task-specific: synchronic paradigms of social coordination reveal medial mu, alpha and the phi complex as contributing neuromarkers. Diachronic tasks recruit alpha as well, in addition to lateral mu rhythms and the newly discovered nu and kappa rhythms whose functional significance is still unclear. Social coordination, observation, and delayed imitation share commonality of context: in each of our experiments, subjects exchanged information through visual perception and moved in similar ways. Nonetheless, there was little overlap between their neuromarkers, a result that hints strongly of task-specific neural mechanisms for social behavior. The only neuromarker that transcended both synchronic and diachronic social behaviors was the ubiquitous alpha rhythm, which appears to be a key signature of visually-mediated social behaviors. The present paper is both an entry point and a challenge: much work remains to determine the nature and scope of recruitment of other neuromarkers, and to create theoretical models of their within- and between-brain dynamics during social interaction. PMID:26557067

Are Cloud Environments Ready for Scientific Applications?

NASA Astrophysics Data System (ADS)

Mehrotra, P.; Shackleford, K.

2011-12-01

Cloud computing environments are becoming widely available both in the commercial and government sectors. They provide flexibility to rapidly provision resources in order to meet dynamic and changing computational needs without the customers incurring capital expenses and/or requiring technical expertise. Clouds also provide reliable access to resources even though the end-user may not have in-house expertise for acquiring or operating such resources. Consolidation and pooling in a cloud environment allow organizations to achieve economies of scale in provisioning or procuring computing resources and services. Because of these and other benefits, many businesses and organizations are migrating their business applications (e.g., websites, social media, and business processes) to cloud environments-evidenced by the commercial success of offerings such as the Amazon EC2. In this paper, we focus on the feasibility of utilizing cloud environments for scientific workloads and workflows particularly of interest to NASA scientists and engineers. There is a wide spectrum of such technical computations. These applications range from small workstation-level computations to mid-range computing requiring small clusters to high-performance simulations requiring supercomputing systems with high bandwidth/low latency interconnects. Data-centric applications manage and manipulate large data sets such as satellite observational data and/or data previously produced by high-fidelity modeling and simulation computations. Most of the applications are run in batch mode with static resource requirements. However, there do exist situations that have dynamic demands, particularly ones with public-facing interfaces providing information to the general public, collaborators and partners, as well as to internal NASA users. In the last few months we have been studying the suitability of cloud environments for NASA's technical and scientific workloads. We have ported several applications to multiple cloud environments including NASA's Nebula environment, Amazon's EC2, Magellan at NERSC, and SGI's Cyclone system. We critically examined the performance of the applications on these systems. We also collected information on the usability of these cloud environments. In this talk we will present the results of our study focusing on the efficacy of using clouds for NASA's scientific applications.

Dynamic Evolution Model Based on Social Network Services

NASA Astrophysics Data System (ADS)

Xiong, Xi; Gou, Zhi-Jian; Zhang, Shi-Bin; Zhao, Wen

2013-11-01

Based on the analysis of evolutionary characteristics of public opinion in social networking services (SNS), in the paper we propose a dynamic evolution model, in which opinions are coupled with topology. This model shows the clustering phenomenon of opinions in dynamic network evolution. The simulation results show that the model can fit the data from a social network site. The dynamic evolution of networks accelerates the opinion, separation and aggregation. The scale and the number of clusters are influenced by confidence limit and rewiring probability. Dynamic changes of the topology reduce the number of isolated nodes, while the increased confidence limit allows nodes to communicate more sufficiently. The two effects make the distribution of opinion more neutral. The dynamic evolution of networks generates central clusters with high connectivity and high betweenness, which make it difficult to control public opinions in SNS.

Model implementation for dynamic computation of system cost

NASA Astrophysics Data System (ADS)

Levri, J.; Vaccari, D.

The Advanced Life Support (ALS) Program metric is the ratio of the equivalent system mass (ESM) of a mission based on International Space Station (ISS) technology to the ESM of that same mission based on ALS technology. ESM is a mission cost analog that converts the volume, power, cooling and crewtime requirements of a mission into mass units to compute an estimate of the life support system emplacement cost. Traditionally, ESM has been computed statically, using nominal values for system sizing. However, computation of ESM with static, nominal sizing estimates cannot capture the peak sizing requirements driven by system dynamics. In this paper, a dynamic model for a near-term Mars mission is described. The model is implemented in Matlab/Simulink' for the purpose of dynamically computing ESM. This paper provides a general overview of the crew, food, biomass, waste, water and air blocks in the Simulink' model. Dynamic simulations of the life support system track mass flow, volume and crewtime needs, as well as power and cooling requirement profiles. The mission's ESM is computed, based upon simulation responses. Ultimately, computed ESM values for various system architectures will feed into an optimization search (non-derivative) algorithm to predict parameter combinations that result in reduced objective function values.

Disease dynamics in a dynamic social network

NASA Astrophysics Data System (ADS)

Christensen, Claire; Albert, István; Grenfell, Bryan; Albert, Réka

2010-07-01

We develop a framework for simulating a realistic, evolving social network (a city) into which a disease is introduced. We compare our results to prevaccine era measles data for England and Wales, and find that they capture the quantitative and qualitative features of epidemics in populations spanning two orders of magnitude. Our results provide unique insight into how and why the social topology of the contact network influences the propagation of the disease through the population. We argue that network simulation is suitable for concurrently probing contact network dynamics and disease dynamics in ways that prior modeling approaches cannot and it can be extended to the study of less well-documented diseases.

The relationship between playing computer or video games with mental health and social relationships among students in guidance schools, Kermanshah.

PubMed

Reshadat, S; Ghasemi, S R; Ahmadian, M; RajabiGilan, N

2014-01-09

Computer or video games are a popular recreational activity and playing them may constitute a large part of leisure time. This cross-sectional study aimed to evaluate the relationship between playing computer or video games with mental health and social relationships among students in guidance schools in Kermanshah, Islamic Republic of Iran, in 2012. Our total sample was 573 students and our tool was the General Health Questionnaire (GHQ-28) and social relationships questionnaires. Survey respondents reported spending an average of 71.07 (SD 72.1) min/day on computer or video games. There was a significant relationship between time spent playing games and general mental health (P < 0.04) and depression (P < 0.03). There was also a significant difference between playing and not playing computer or video games with social relationships and their subscales, including trans-local relationships (P < 0.0001) and association relationships (P < 0.01) among all participants. There was also a significant relationship between social relationships and time spent playing games (P < 0.02) and its dimensions, except for family relationships.

Seeing the System: Dynamics and Complexity of Technology Integration in Secondary Schools

ERIC Educational Resources Information Center

Howard, Sarah K.; Thompson, Kate

2016-01-01

This paper introduces system dynamics modeling to understand, visualize and explore technology integration in schools, through the development of a theoretical model of technology-related change in teachers' practice. Technology integration is a dynamic social practice, within the social system of education. It is difficult, if not nearly…

Computer aided analysis and optimization of mechanical system dynamics

NASA Technical Reports Server (NTRS)

Haug, E. J.

1984-01-01

The purpose is to outline a computational approach to spatial dynamics of mechanical systems that substantially enlarges the scope of consideration to include flexible bodies, feedback control, hydraulics, and related interdisciplinary effects. Design sensitivity analysis and optimization is the ultimate goal. The approach to computer generation and solution of the system dynamic equations and graphical methods for creating animations as output is outlined.

A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements.

PubMed

Durstewitz, Daniel

2017-06-01

The computational and cognitive properties of neural systems are often thought to be implemented in terms of their (stochastic) network dynamics. Hence, recovering the system dynamics from experimentally observed neuronal time series, like multiple single-unit recordings or neuroimaging data, is an important step toward understanding its computations. Ideally, one would not only seek a (lower-dimensional) state space representation of the dynamics, but would wish to have access to its statistical properties and their generative equations for in-depth analysis. Recurrent neural networks (RNNs) are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective. Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs (PLRNNs) within the statistical framework of state space models, which accounts for noise in both the underlying latent dynamics and the observation process. The Expectation-Maximization algorithm is used to infer the latent state distribution, through a global Laplace approximation, and the PLRNN parameters iteratively. After validating the procedure on toy examples, and using inference through particle filters for comparison, the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex (ACC) obtained during performance of a classical working memory task, delayed alternation. Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance, including stimulus-selective delay activity. The estimated models were rarely multi-stable, however, but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point. In summary, the present work advances a semi-analytical (thus reasonably fast) maximum-likelihood estimation framework for PLRNNs that may enable to recover relevant aspects of the nonlinear dynamics underlying observed neuronal time series, and directly link these to computational properties.

On the Rhetorical Contract in Human-Computer Interaction.

ERIC Educational Resources Information Center

Wenger, Michael J.

1991-01-01

An exploration of the rhetorical contract--i.e., the expectations for appropriate interaction--as it develops in human-computer interaction revealed that direct manipulation interfaces were more likely to establish social expectations. Study results suggest that the social nature of human-computer interactions can be examined with reference to the…

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Federal Register 2010, 2011, 2012, 2013, 2014

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Methodology for Uncertainty Analysis of Dynamic Computational Toxicology Models

EPA Science Inventory

The task of quantifying the uncertainty in both parameter estimates and model predictions has become more important with the increased use of dynamic computational toxicology models by the EPA. Dynamic toxicological models include physiologically-based pharmacokinetic (PBPK) mode...

Amoeba-inspired nanoarchitectonic computing: solving intractable computational problems using nanoscale photoexcitation transfer dynamics.

PubMed

Aono, Masashi; Naruse, Makoto; Kim, Song-Ju; Wakabayashi, Masamitsu; Hori, Hirokazu; Ohtsu, Motoichi; Hara, Masahiko

2013-06-18

Biologically inspired computing devices and architectures are expected to overcome the limitations of conventional technologies in terms of solving computationally demanding problems, adapting to complex environments, reducing energy consumption, and so on. We previously demonstrated that a primitive single-celled amoeba (a plasmodial slime mold), which exhibits complex spatiotemporal oscillatory dynamics and sophisticated computing capabilities, can be used to search for a solution to a very hard combinatorial optimization problem. We successfully extracted the essential spatiotemporal dynamics by which the amoeba solves the problem. This amoeba-inspired computing paradigm can be implemented by various physical systems that exhibit suitable spatiotemporal dynamics resembling the amoeba's problem-solving process. In this Article, we demonstrate that photoexcitation transfer phenomena in certain quantum nanostructures mediated by optical near-field interactions generate the amoebalike spatiotemporal dynamics and can be used to solve the satisfiability problem (SAT), which is the problem of judging whether a given logical proposition (a Boolean formula) is self-consistent. SAT is related to diverse application problems in artificial intelligence, information security, and bioinformatics and is a crucially important nondeterministic polynomial time (NP)-complete problem, which is believed to become intractable for conventional digital computers when the problem size increases. We show that our amoeba-inspired computing paradigm dramatically outperforms a conventional stochastic search method. These results indicate the potential for developing highly versatile nanoarchitectonic computers that realize powerful solution searching with low energy consumption.

Information dynamics algorithm for detecting communities in networks

NASA Astrophysics Data System (ADS)

Massaro, Emanuele; Bagnoli, Franco; Guazzini, Andrea; Lió, Pietro

2012-11-01

The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network-inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method [4] by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark and on computer generated networks with known community structure. Our approach has three important features: the capacity of detecting overlapping communities, the capability of identifying communities from an individual point of view and the fine tuning the community detectability with respect to prior knowledge of the data. Finally we discuss how to use a Shannon entropy measure for parameter estimation in complex networks.

Parallel computational fluid dynamics '91; Conference Proceedings, Stuttgart, Germany, Jun. 10-12, 1991

NASA Technical Reports Server (NTRS)

Reinsch, K. G. (Editor); Schmidt, W. (Editor); Ecer, A. (Editor); Haeuser, Jochem (Editor); Periaux, J. (Editor)

1992-01-01

A conference was held on parallel computational fluid dynamics and produced related papers. Topics discussed in these papers include: parallel implicit and explicit solvers for compressible flow, parallel computational techniques for Euler and Navier-Stokes equations, grid generation techniques for parallel computers, and aerodynamic simulation om massively parallel systems.

Dynamic social networks based on movement

USGS Publications Warehouse

Scharf, Henry; Hooten, Mevin B.; Fosdick, Bailey K.; Johnson, Devin S.; London, Joshua M.; Durban, John W.

2016-01-01

Network modeling techniques provide a means for quantifying social structure in populations of individuals. Data used to define social connectivity are often expensive to collect and based on case-specific, ad hoc criteria. Moreover, in applications involving animal social networks, collection of these data is often opportunistic and can be invasive. Frequently, the social network of interest for a given population is closely related to the way individuals move. Thus, telemetry data, which are minimally invasive and relatively inexpensive to collect, present an alternative source of information. We develop a framework for using telemetry data to infer social relationships among animals. To achieve this, we propose a Bayesian hierarchical model with an underlying dynamic social network controlling movement of individuals via two mechanisms: an attractive effect and an aligning effect. We demonstrate the model and its ability to accurately identify complex social behavior in simulation, and apply our model to telemetry data arising from killer whales. Using auxiliary information about the study population, we investigate model validity and find the inferred dynamic social network is consistent with killer whale ecology and expert knowledge.

Steady-State Computation of Constant Rotational Rate Dynamic Stability Derivatives

NASA Technical Reports Server (NTRS)

Park, Michael A.; Green, Lawrence L.

2000-01-01

Dynamic stability derivatives are essential to predicting the open and closed loop performance, stability, and controllability of aircraft. Computational determination of constant-rate dynamic stability derivatives (derivatives of aircraft forces and moments with respect to constant rotational rates) is currently performed indirectly with finite differencing of multiple time-accurate computational fluid dynamics solutions. Typical time-accurate solutions require excessive amounts of computational time to complete. Formulating Navier-Stokes (N-S) equations in a rotating noninertial reference frame and applying an automatic differentiation tool to the modified code has the potential for directly computing these derivatives with a single, much faster steady-state calculation. The ability to rapidly determine static and dynamic stability derivatives by computational methods can benefit multidisciplinary design methodologies and reduce dependency on wind tunnel measurements. The CFL3D thin-layer N-S computational fluid dynamics code was modified for this study to allow calculations on complex three-dimensional configurations with constant rotation rate components in all three axes. These CFL3D modifications also have direct application to rotorcraft and turbomachinery analyses. The modified CFL3D steady-state calculation is a new capability that showed excellent agreement with results calculated by a similar formulation. The application of automatic differentiation to CFL3D allows the static stability and body-axis rate derivatives to be calculated quickly and exactly.

Computer-Assisted Face Processing Instruction Improves Emotion Recognition, Mentalizing, and Social Skills in Students with ASD.

PubMed

Rice, Linda Marie; Wall, Carla Anne; Fogel, Adam; Shic, Frederick

2015-07-01

This study examined the extent to which a computer-based social skills intervention called FaceSay was associated with improvements in affect recognition, mentalizing, and social skills of school-aged children with Autism Spectrum Disorder (ASD). FaceSay offers students simulated practice with eye gaze, joint attention, and facial recognition skills. This randomized control trial included school-aged children meeting educational criteria for autism (N = 31). Results demonstrated that participants who received the intervention improved their affect recognition and mentalizing skills, as well as their social skills. These findings suggest that, by targeting face-processing skills, computer-based interventions may produce changes in broader cognitive and social-skills domains in a cost- and time-efficient manner.

Social Studies: Application Units. Course II, Teachers. Computer-Oriented Curriculum. REACT (Relevant Educational Applications of Computer Technology).

ERIC Educational Resources Information Center

Tecnica Education Corp., San Carlos, CA.

This book is one of a series in Course II of the Relevant Educational Applications of Computer Technology (REACT) Project. It is designed to point out to teachers two of the major applications of computers in the social sciences: simulation and data analysis. The first section contains a variety of simulation units organized under the following…

Immediate early gene activation throughout the brain is associated with dynamic changes in social context.

PubMed

Williamson, Cait M; Klein, Inbal S; Lee, Won; Curley, James P

2018-05-31

Social competence is dependent on successful processing of social context information. The social opportunity paradigm is a methodology in which dynamic shifts in social context are induced through removal of the alpha male in a dominance hierarchy, leading to rapid ascent in the hierarchy of the beta male and of other subordinate males in the social group. In the current study, we use the social opportunity paradigm to determine what brain regions respond to this dynamic change in social context, allowing an individual to recognize the absence of the alpha male and subsequently perform status-appropriate social behaviors. Replicating our previous work, we show that following removal of the alpha male, beta males rapidly ascend the social hierarchy and attain dominant status by increasing aggression towards more subordinate individuals. Analysis of patterns of Fos immunoreactivity throughout the brain indicates that in individuals undergoing social ascent, there is increased activity in regions of the social behavior network, as well as the infralimbic and prelimbic regions of the prefrontal cortex and areas of the hippocampus. Our findings demonstrate that male mice are able to respond to changes in social context and provide insight into the how the brain processes these complex behavioral changes.

Brain connectivity dynamics during social interaction reflect social network structure

PubMed Central

Schmälzle, Ralf; Brook O’Donnell, Matthew; Garcia, Javier O.; Cascio, Christopher N.; Bayer, Joseph; Vettel, Jean M.

2017-01-01

Social ties are crucial for humans. Disruption of ties through social exclusion has a marked effect on our thoughts and feelings; however, such effects can be tempered by broader social network resources. Here, we use fMRI data acquired from 80 male adolescents to investigate how social exclusion modulates functional connectivity within and across brain networks involved in social pain and understanding the mental states of others (i.e., mentalizing). Furthermore, using objectively logged friendship network data, we examine how individual variability in brain reactivity to social exclusion relates to the density of participants’ friendship networks, an important aspect of social network structure. We find increased connectivity within a set of regions previously identified as a mentalizing system during exclusion relative to inclusion. These results are consistent across the regions of interest as well as a whole-brain analysis. Next, examining how social network characteristics are associated with task-based connectivity dynamics, we find that participants who showed greater changes in connectivity within the mentalizing system when socially excluded by peers had less dense friendship networks. This work provides insight to understand how distributed brain systems respond to social and emotional challenges and how such brain dynamics might vary based on broader social network characteristics. PMID:28465434

Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation

PubMed Central

Hu, Hongzhi; Mao, Huajuan; Hu, Xiaohua; Hu, Feng; Sun, Xuemin; Jing, Zaiping; Duan, Yunsuo

2015-01-01

Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency. PMID:26609303

Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation.

PubMed

Hu, Hongzhi; Mao, Huajuan; Hu, Xiaohua; Hu, Feng; Sun, Xuemin; Jing, Zaiping; Duan, Yunsuo

2015-01-01

Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency.

Supercomputer optimizations for stochastic optimal control applications

NASA Technical Reports Server (NTRS)

Chung, Siu-Leung; Hanson, Floyd B.; Xu, Huihuang

1991-01-01

Supercomputer optimizations for a computational method of solving stochastic, multibody, dynamic programming problems are presented. The computational method is valid for a general class of optimal control problems that are nonlinear, multibody dynamical systems, perturbed by general Markov noise in continuous time, i.e., nonsmooth Gaussian as well as jump Poisson random white noise. Optimization techniques for vector multiprocessors or vectorizing supercomputers include advanced data structures, loop restructuring, loop collapsing, blocking, and compiler directives. These advanced computing techniques and superconducting hardware help alleviate Bellman's curse of dimensionality in dynamic programming computations, by permitting the solution of large multibody problems. Possible applications include lumped flight dynamics models for uncertain environments, such as large scale and background random aerospace fluctuations.

Towards Better Human Robot Interaction: Understand Human Computer Interaction in Social Gaming Using a Video-Enhanced Diary Method

NASA Astrophysics Data System (ADS)

See, Swee Lan; Tan, Mitchell; Looi, Qin En

This paper presents findings from a descriptive research on social gaming. A video-enhanced diary method was used to understand the user experience in social gaming. From this experiment, we found that natural human behavior and gamer’s decision making process can be elicited and speculated during human computer interaction. These are new information that we should consider as they can help us build better human computer interfaces and human robotic interfaces in future.

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

PubMed Central

Ayton, Ellyn; Porterfield, Katherine; Corley, Courtney D.

2017-01-01

This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in “real-time”) and forecasting (predicting the future) ILI dynamics in the 2011 – 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a) Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns. (g) Model performance improves with more tweets available per geo-location e.g., the error gets lower and the Pearson score gets higher for locations with more tweets. PMID:29244814

Forecasting influenza-like illness dynamics for military populations using neural networks and social media.

PubMed

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine; Corley, Courtney D

2017-01-01

This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in "real-time") and forecasting (predicting the future) ILI dynamics in the 2011 - 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a) Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns. (g) Model performance improves with more tweets available per geo-location e.g., the error gets lower and the Pearson score gets higher for locations with more tweets.

Social Information Links Individual Behavior to Population and Community Dynamics.

PubMed

Gil, Michael A; Hein, Andrew M; Spiegel, Orr; Baskett, Marissa L; Sih, Andrew

2018-05-07

When individual animals make decisions, they routinely use information produced intentionally or unintentionally by other individuals. Despite its prevalence and established fitness consequences, the effects of such social information on ecological dynamics remain poorly understood. Here, we synthesize results from ecology, evolutionary biology, and animal behavior to show how the use of social information can profoundly influence the dynamics of populations and communities. We combine recent theoretical and empirical results and introduce simple population models to illustrate how social information use can drive positive density-dependent growth of populations and communities (Allee effects). Furthermore, social information can shift the nature and strength of species interactions, change the outcome of competition, and potentially increase extinction risk in harvested populations and communities. Copyright © 2018 Elsevier Ltd. All rights reserved.

Empirical study on social groups in pedestrian evacuation dynamics

NASA Astrophysics Data System (ADS)

von Krüchten, Cornelia; Schadschneider, Andreas

2017-06-01

Pedestrian crowds often include social groups, i.e. pedestrians that walk together because of social relationships. They show characteristic configurations and influence the dynamics of the entire crowd. In order to investigate the impact of social groups on evacuations we performed an empirical study with pupils. Several evacuation runs with groups of different sizes and different interactions were performed. New group parameters are introduced which allow to describe the dynamics of the groups and the configuration of the group members quantitatively. The analysis shows a possible decrease of evacuation times for large groups due to self-ordering effects. Social groups can be approximated as ellipses that orientate along their direction of motion. Furthermore, explicitly cooperative behaviour among group members leads to a stronger aggregation of group members and an intermittent way of evacuation.

Smoking Behavior and Friendship Formation: The Importance of Time Heterogeneity in Studying Social Network Dynamics

DTIC Science & Technology

2010-01-01

Smoking Behavior and Friendship Formation: The Importance of Time Heterogeneity in Studying Social Network Dynamics Joshua A. Lospinoso Department of...djsatchell@gmail.com Abstract—This study illustrates the importance of assessing and accounting for time heterogeneity in longitudinal social net- work...analysis. We apply the time heterogeneity model selection procedure of [1] to a dataset collected on social tie formation for university freshman in the

Natural neural projection dynamics underlying social behavior

PubMed Central

Gunaydin, Lisa A.; Grosenick, Logan; Finkelstein, Joel C.; Kauvar, Isaac V.; Fenno, Lief E.; Adhikari, Avishek; Lammel, Stephan; Mirzabekov, Julie J.; Airan, Raag D.; Zalocusky, Kelly A.; Tye, Kay M.; Anikeeva, Polina; Malenka, Robert C.; Deisseroth, Karl

2014-01-01

Social interaction is a complex behavior essential for many species, and is impaired in major neuropsychiatric disorders. Pharmacological studies have implicated certain neurotransmitter systems in social behavior, but circuit-level understanding of endogenous neural activity during social interaction is lacking. We therefore developed and applied a new methodology, termed fiber photometry, to optically record natural neural activity in genetically- and connectivity-defined projections to elucidate the real-time role of specified pathways in mammalian behavior. Fiber photometry revealed that activity dynamics of a ventral tegmental area (VTA)-to-nucleus accumbens (NAc) projection could encode and predict key features of social but not novel-object interaction. Consistent with this observation, optogenetic control of cells specifically contributing to this projection was sufficient to modulate social behavior, which was mediated by type-1 dopamine receptor signaling downstream in the NAc. Direct observation of projection-specific activity in this way captures a fundamental and previously inaccessible dimension of circuit dynamics. PMID:24949967

Responses of bat social groups to roost loss: More questions than answers

USGS Publications Warehouse

Silvis, Alexander; Abaid, Nicole; Ford, W. Mark; Britzke, Eric R.; Ortega, Jorge

2016-01-01

Though characterization of, and understanding determinants of, social structure in bats is increasing, little is known about how bat social groups respond to disturbance resulting in roost loss. Given that many species of bats roost in ephemeral or transitory resources such as plants, it is clear that bat social groups can tolerate some level of roost loss. Understanding responses of bat social groups to roost loss can provide insight into social structure that have applied conservation use. Herein, we review the existing literature on the effects of disturbance on bat social groups, and present a parameterizable agent-based model that can be used to explore the relationships among roost dynamics, population dynamics, and social behavior.

The effects of a computer skill training programme adopting social comparison and self-efficacy enhancement strategies on self-concept and skill outcome in trainees with physical disabilities.

PubMed

Tam, S F

2000-10-15

The aim of this controlled, quasi-experimental study was to evaluate the effects of both self-efficacy enhancement and social comparison training strategy on computer skills learning and self-concept outcome of trainees with physical disabilities. The self-efficacy enhancement group comprised 16 trainees, the tutorial training group comprised 15 trainees, and there were 25 subjects in the control group. Both the self-efficacy enhancement group and the tutorial training group received a 15 week computer skills training course, including generic Chinese computer operation, Chinese word processing and Chinese desktop publishing skills. The self-efficacy enhancement group received training with tutorial instructions that incorporated self-efficacy enhancement strategies and experienced self-enhancing social comparisons. The tutorial training group received behavioural learning-based tutorials only, and the control group did not receive any training. The following measurements were employed to evaluate the outcomes: the Self-Concept Questionnaire for the Physically Disabled Hong Kong Chinese (SCQPD), the computer self-efficacy rating scale and the computer performance rating scale. The self-efficacy enhancement group showed significantly better computer skills learning outcome, total self-concept, and social self-concept than the tutorial training group. The self-efficacy enhancement group did not show significant changes in their computer self-efficacy: however, the tutorial training group showed a significant lowering of their computer self-efficacy. The training strategy that incorporated self-efficacy enhancement and positive social comparison experiences maintained the computer self-efficacy of trainees with physical disabilities. This strategy was more effective in improving the learning outcome (p = 0.01) and self-concept (p = 0.05) of the trainees than the conventional tutorial-based training strategy.

The investigation of tethered satellite system dynamics

NASA Technical Reports Server (NTRS)

Lorenzini, E.

1985-01-01

The tether control law to retrieve the satellite was modified in order to have a smooth retrieval trajectory of the satellite that minimizes the thruster activation. The satellite thrusters were added to the rotational dynamics computer code and a preliminary control logic was implemented to simulate them during the retrieval maneuver. The high resolution computer code for modelling the three dimensional dynamics of untensioned tether, SLACK3, was made fully operative and a set of computer simulations of possible tether breakages was run. The distribution of the electric field around an electrodynamic tether in vacuo severed at some length from the shuttle was computed with a three dimensional electrodynamic computer code.

An efficient formulation of robot arm dynamics for control and computer simulation

NASA Astrophysics Data System (ADS)

Lee, C. S. G.; Nigam, R.

This paper describes an efficient formulation of the dynamic equations of motion of industrial robots based on the Lagrange formulation of d'Alembert's principle. This formulation, as applied to a PUMA robot arm, results in a set of closed form second order differential equations with cross product terms. They are not as efficient in computation as those formulated by the Newton-Euler method, but provide a better analytical model for control analysis and computer simulation. Computational complexities of this dynamic model together with other models are tabulated for discussion.

Modeling Potential Carbon Monoxide Exposure Due to Operation of a Major Rocket Engine Altitude Test Facility Using Computational Fluid Dynamics

NASA Technical Reports Server (NTRS)

Blotzer, Michael J.; Woods, Jody L.

2009-01-01

This viewgraph presentation reviews computational fluid dynamics as a tool for modelling the dispersion of carbon monoxide at the Stennis Space Center's A3 Test Stand. The contents include: 1) Constellation Program; 2) Constellation Launch Vehicles; 3) J2X Engine; 4) A-3 Test Stand; 5) Chemical Steam Generators; 6) Emission Estimates; 7) Located in Existing Test Complex; 8) Computational Fluid Dynamics; 9) Computational Tools; 10) CO Modeling; 11) CO Model results; and 12) Next steps.

Chinese EFL Teachers' Social Interaction and Socio-Cognitive Presence in Synchronous Computer-Mediated Communication

ERIC Educational Resources Information Center

Wu, Heping; Gao, Junde; Zhang, Weimin

2014-01-01

The present study examines the professional growth of three Chinese English teachers by analyzing their interactional patterns and their social and cognitive presence in an online community. The data from social network analysis (SNA) and content analysis revealed that computer-mediated communication (CMC) created new opportunities for teachers to…

Quantitative and Qualitative Evaluation of The Structural Designing of Medical Informatics Dynamic Encyclopedia

PubMed Central

Safdari, Reza; Shahmoradi, Leila; Hosseini-beheshti, Molouk-sadat; Nejad, Ahmadreza Farzaneh; Hosseiniravandi, Mohammad

2015-01-01

Introduction: Encyclopedias and their compilation have become so prevalent as a valid cultural medium in the world. The daily development of computer industry and the expansion of various sciences have made indispensable the compilation of electronic, specialized encyclopedias, especially the web-based ones. Materials and Methods: This is an applied-developmental study conducted in 2014. First, the main terms in the field of medical informatics were gathered using MeSH Online 2014 and the supplementary terms of each were determined, and then the tree diagram of the terms was drawn based on their relationship in MeSH. Based on the studies done by the researchers, the tree diagram of the encyclopedia was drawn with respect to the existing areas in this field, and the terms gathered were put in related domains. Findings: In MeSH, 75 preferred terms together with 249 supplementary ones were indexed. One of the informatics’ sub-branches is biomedical informatics and health which itself consists of three sub-divisions of bioinformatics, clinical informatics, and health informatics. Medical informatics which is a subdivision of clinical informatics has developed from the three fields of medical sciences, management and social sciences, and computational sciences and mathematics. Results and Discussion: Medical Informatics is created of confluence and fusion and applications of the three major scientific branches include health and biological sciences, social sciences and management sciences, computing and mathematical sciences, and according to that the structure of MeSH is weak for future development of Encyclopedia of Medical Informatics. PMID:26635440

A computer solution for the dynamic load, lubricant film thickness, and surface temperatures in spiral-bevel gears

NASA Technical Reports Server (NTRS)

Chao, H. C.; Baxter, M.; Cheng, H. S.

1983-01-01

A computer method for determining the dynamic load between spiral bevel pinion and gear teeth contact along the path of contact is described. The dynamic load analysis governs both the surface temperature and film thickness. Computer methods for determining the surface temperature, and film thickness are presented along with results obtained for a pair of typical spiral bevel gears.

Tax authorities' interaction with taxpayers: A conception of compliance in social dilemmas by power and trust

PubMed Central

Gangl, Katharina; Hofmann, Eva; Kirchler, Erich

2015-01-01

Tax compliance represents a social dilemma in which the short-term self-interest to minimize tax payments is at odds with the collective long-term interest to provide sufficient tax funds for public goods. According to the Slippery Slope Framework, the social dilemma can be solved and tax compliance can be guaranteed by power of tax authorities and trust in tax authorities. The framework, however, remains silent on the dynamics between power and trust. The aim of the present theoretical paper is to conceptualize the dynamics between power and trust by differentiating coercive and legitimate power and reason-based and implicit trust. Insights into this dynamic are derived from an integration of a wide range of literature such as on organizational behavior and social influence. Conclusions on the effect of the dynamics between power and trust on the interaction climate between authorities and individuals and subsequent individual motivation of cooperation in social dilemmas such as tax contributions are drawn. Practically, the assumptions on the dynamics can be utilized by authorities to increase cooperation and to change the interaction climate from an antagonistic climate to a service and confidence climate. PMID:25859096

Dynamic pupillary exchange engages brain regions encoding social salience

PubMed Central

Harrison, Neil A.; Gray, Marcus A.; Critchley, Hugo D.

2008-01-01

Covert exchange of autonomic responses may shape social affective behavior, as observed in mirroring of pupillary responses during sadness processing. We examined how, independent of facial emotional expression, dynamic coherence between one's own and another's pupil size modulates regional brain activity. Fourteen subjects viewed pairs of eye stimuli while undergoing fMRI. Using continuous pupillometry biofeedback, the size of the observed pupils was varied, correlating positively or negatively with changes in participants’ own pupils. Viewing both static and dynamic stimuli activated right fusiform gyrus. Observing dynamically changing pupils activated STS and amygdala, regions engaged by non-static and salient facial features. Discordance between observed and observer's pupillary changes enhanced activity within bilateral anterior insula, left amygdala and anterior cingulate. In contrast, processing positively correlated pupils enhanced activity within left frontal operculum. Our findings suggest pupillary signals are monitored continuously during social interactions and that incongruent changes activate brain regions involved in tracking motivational salience and attentionally meaningful information. Naturalistically, dynamic coherence in pupillary change follows fluctuations in ambient light. Correspondingly, in social contexts discordant pupil response is likely to reflect divergence of dispositional state. Our data provide empirical evidence for an autonomically mediated extension of forward models of motor control into social interaction. PMID:19048432

Social costs enforce honesty of a dynamic signal of motivation.

PubMed

Ligon, Russell A; McGraw, Kevin J

2016-10-26

Understanding the processes that promote signal reliability may provide important insights into the evolution of diverse signalling strategies among species. The signals that animals use to communicate must comprise mechanisms that prohibit or punish dishonesty, and social costs of dishonesty have been demonstrated for several fixed morphological signals (e.g. colour badges of birds and wasps). The costs maintaining the honesty of dynamic signals, which are more flexible and potentially cheatable, are unknown. Using an experimental manipulation of the dynamic visual signals used by male veiled chameleons (Chamaeleo calyptratus) during aggressive interactions, we tested the idea that the honesty of rapid colour change signals is maintained by social costs. Our results reveal that social costs are an important mechanism maintaining the honesty of these dynamic colour signals-'dishonest' chameleons whose experimentally manipulated coloration was incongruent with their contest behaviour received more physical aggression than 'honest' individuals. This is the first demonstration, to the best our knowledge, that the honesty of a dynamic signal of motivation-physiological colour change-can be maintained by the social costliness of dishonesty. Behavioural responses of signal receivers, irrespective of any specific detection mechanisms, therefore prevent chameleon cheaters from prospering. © 2016 The Author(s).

Social costs enforce honesty of a dynamic signal of motivation

PubMed Central

McGraw, Kevin J.

2016-01-01

Understanding the processes that promote signal reliability may provide important insights into the evolution of diverse signalling strategies among species. The signals that animals use to communicate must comprise mechanisms that prohibit or punish dishonesty, and social costs of dishonesty have been demonstrated for several fixed morphological signals (e.g. colour badges of birds and wasps). The costs maintaining the honesty of dynamic signals, which are more flexible and potentially cheatable, are unknown. Using an experimental manipulation of the dynamic visual signals used by male veiled chameleons (Chamaeleo calyptratus) during aggressive interactions, we tested the idea that the honesty of rapid colour change signals is maintained by social costs. Our results reveal that social costs are an important mechanism maintaining the honesty of these dynamic colour signals—‘dishonest’ chameleons whose experimentally manipulated coloration was incongruent with their contest behaviour received more physical aggression than ‘honest’ individuals. This is the first demonstration, to the best our knowledge, that the honesty of a dynamic signal of motivation—physiological colour change—can be maintained by the social costliness of dishonesty. Behavioural responses of signal receivers, irrespective of any specific detection mechanisms, therefore prevent chameleon cheaters from prospering. PMID:27798310

Tax authorities' interaction with taxpayers: A conception of compliance in social dilemmas by power and trust.

PubMed

Gangl, Katharina; Hofmann, Eva; Kirchler, Erich

2015-02-01

Tax compliance represents a social dilemma in which the short-term self-interest to minimize tax payments is at odds with the collective long-term interest to provide sufficient tax funds for public goods. According to the Slippery Slope Framework, the social dilemma can be solved and tax compliance can be guaranteed by power of tax authorities and trust in tax authorities. The framework, however, remains silent on the dynamics between power and trust. The aim of the present theoretical paper is to conceptualize the dynamics between power and trust by differentiating coercive and legitimate power and reason-based and implicit trust. Insights into this dynamic are derived from an integration of a wide range of literature such as on organizational behavior and social influence. Conclusions on the effect of the dynamics between power and trust on the interaction climate between authorities and individuals and subsequent individual motivation of cooperation in social dilemmas such as tax contributions are drawn. Practically, the assumptions on the dynamics can be utilized by authorities to increase cooperation and to change the interaction climate from an antagonistic climate to a service and confidence climate.

Dynamic Target Match Signals in Perirhinal Cortex Can Be Explained by Instantaneous Computations That Act on Dynamic Input from Inferotemporal Cortex

PubMed Central

Pagan, Marino

2014-01-01

Finding sought objects requires the brain to combine visual and target signals to determine when a target is in view. To investigate how the brain implements these computations, we recorded neural responses in inferotemporal cortex (IT) and perirhinal cortex (PRH) as macaque monkeys performed a delayed-match-to-sample target search task. Our data suggest that visual and target signals were combined within or before IT in the ventral visual pathway and then passed onto PRH, where they were reformatted into a more explicit target match signal over ∼10–15 ms. Accounting for these dynamics in PRH did not require proposing dynamic computations within PRH itself but, rather, could be attributed to instantaneous PRH computations performed upon an input representation from IT that changed with time. We found that the dynamics of the IT representation arose from two commonly observed features: individual IT neurons whose response preferences were not simply rescaled with time and variable response latencies across the population. Our results demonstrate that these types of time-varying responses have important consequences for downstream computation and suggest that dynamic representations can arise within a feedforward framework as a consequence of instantaneous computations performed upon time-varying inputs. PMID:25122904

Linking body mass and group dynamics in an obligate cooperative breeder.

PubMed

Ozgul, Arpat; Bateman, Andrew W; English, Sinead; Coulson, Tim; Clutton-Brock, Tim H

2014-11-01

Social and environmental factors influence key life-history processes and population dynamics by affecting fitness-related phenotypic traits such as body mass. The role of body mass is particularly pronounced in cooperative breeders due to variation in social status and consequent variation in access to resources. Investigating the mechanisms underlying variation in body mass and its demographic consequences can help elucidate how social and environmental factors affect the dynamics of cooperatively breeding populations. In this study, we present an analysis of the effect of individual variation in body mass on the temporal dynamics of group size and structure of a cooperatively breeding mongoose, the Kalahari meerkat, Suricata suricatta. First, we investigate how body mass interacts with social (dominance status and number of helpers) and environmental (rainfall and season) factors to influence key life-history processes (survival, growth, emigration and reproduction) in female meerkats. Next, using an individual-based population model, we show that the models explicitly including individual variation in body mass predict group dynamics better than those ignoring this morphological trait. Body mass influences group dynamics mainly through its effects on helper emigration and dominant reproduction. Rainfall has a trait-mediated, destabilizing effect on group dynamics, whereas the number of helpers has a direct and stabilizing effect. Counteracting effects of number of helpers on different demographic rates, despite generating temporal fluctuations, stabilizes group dynamics in the long term. Our study demonstrates that social and environmental factors interact to produce individual variation in body mass and accounting for this variation helps to explain group dynamics in this cooperatively breeding population. © 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society.

The Effects of Social Environments on Time Spent Gaming: Focusing on the Effects of Communities and Neighborhoods.

PubMed

Lim, Tee Teng; Jung, Sun Young; Kim, Eunyi

2018-04-01

This study examined the impact of community and neighborhood on time spent computer gaming. Computer gaming for over 20 hours a week was set as the cutoff line for "engaged use" of computer games. For the analysis, this study analyzed data for about 1,800 subjects who participated in the Korean Children and Youth Panel Survey. The main findings are as follows: first, structural community characteristics and neighborhood social capital affected the engaged use of computer games. Second, adolescents who reside in regions with a higher divorce rate or higher residential mobility were likely to exhibit engaged use of computer games. Third, adolescents who highly perceive neighborhood social capital exhibited lower possibility of engaged use of computer games. Based on these findings, practical implications and directions for further study are suggested.

Dynamic Transfers Of Tasks Among Computers

NASA Technical Reports Server (NTRS)

Liu, Howard T.; Silvester, John A.

1989-01-01

Allocation scheme gives jobs to idle computers. Ideal resource-sharing algorithm should have following characteristics: Dynamics, decentralized, and heterogeneous. Proposed enhanced receiver-initiated dynamic algorithm (ERIDA) for resource sharing fulfills all above criteria. Provides method balancing workload among hosts, resulting in improvement in response time and throughput performance of total system. Adjusts dynamically to traffic load of each station.

Information diffusion in structured online social networks

NASA Astrophysics Data System (ADS)

Li, Pei; Zhang, Yini; Qiao, Fengcai; Wang, Hui

2015-05-01

Nowadays, due to the word-of-mouth effect, online social networks have been considered to be efficient approaches to conduct viral marketing, which makes it of great importance to understand the diffusion dynamics in online social networks. However, most research on diffusion dynamics in epidemiology and existing social networks cannot be applied directly to characterize online social networks. In this paper, we propose models to characterize the information diffusion in structured online social networks with push-based forwarding mechanism. We introduce the term user influence to characterize the average number of times that messages are browsed which is incurred by a given type user generating a message, and study the diffusion threshold, above which the user influence of generating a message will approach infinity. We conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of use in understanding the diffusion dynamics in online social networks and also critical for advertisers in viral marketing who want to estimate the user influence before posting an advertisement.

Identifying Complex Dynamics in Social Systems: A New Methodological Approach Applied to Study School Segregation

ERIC Educational Resources Information Center

Spaiser, Viktoria; Hedström, Peter; Ranganathan, Shyam; Jansson, Kim; Nordvik, Monica K.; Sumpter, David J. T.

2018-01-01

It is widely recognized that segregation processes are often the result of complex nonlinear dynamics. Empirical analyses of complex dynamics are however rare, because there is a lack of appropriate empirical modeling techniques that are capable of capturing complex patterns and nonlinearities. At the same time, we know that many social phenomena…

Defragging Computer/Videogame Implementation and Assessment in the Social Studies

ERIC Educational Resources Information Center

McBride, Holly

2014-01-01

Students in this post-industrial technological age require opportunities for the acquisition of new skills, especially in the marketplace of innovation. A pedagogical strategy that is becoming more and more popular within social studies classrooms is the use of computer and video games as enhancements to everyday lesson plans. Computer/video games…

77 FR 33547 - Privacy Act of 1974, as Amended; Computer Matching Program (SSA/Centers for Medicare and Medicaid...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-06

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2013-06-21

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2013-08-20

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2012-04-25

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2012-02-08

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2010-04-09

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2011-03-07

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2012-04-25

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2010-10-12

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2010-09-28

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Computational fluid dynamics - The coming revolution

NASA Technical Reports Server (NTRS)

Graves, R. A., Jr.

1982-01-01

The development of aerodynamic theory is traced from the days of Aristotle to the present, with the next stage in computational fluid dynamics dependent on superspeed computers for flow calculations. Additional attention is given to the history of numerical methods inherent in writing computer codes applicable to viscous and inviscid analyses for complex configurations. The advent of the superconducting Josephson junction is noted to place configurational demands on computer design to avoid limitations imposed by the speed of light, and a Japanese projection of a computer capable of several hundred billion operations/sec is mentioned. The NASA Numerical Aerodynamic Simulator is described, showing capabilities of a billion operations/sec with a memory of 240 million words using existing technology. Near-term advances in fluid dynamics are discussed.

A FRAMEWORK FOR FINE-SCALE COMPUTATIONAL FLUID DYNAMICS AIR QUALITY MODELING AND ANALYSIS

EPA Science Inventory

Fine-scale Computational Fluid Dynamics (CFD) simulation of pollutant concentrations within roadway and building microenvironments is feasible using high performance computing. Unlike currently used regulatory air quality models, fine-scale CFD simulations are able to account rig...

Evolution and social epidemiology.

PubMed

Nishi, Akihiro

2015-11-01

Evolutionary biology, which aims to explain the dynamic process of shaping the diversity of life, has not yet significantly affected thinking in social epidemiology. Current challenges in social epidemiology include understanding how social exposures can affect our biology, explaining the dynamics of society and health, and designing better interventions that are mindful of the impact of exposures during critical periods. I review how evolutionary concepts and tools, such as fitness gradient in cultural evolution, evolutionary game theory, and contemporary evolution in cancer, can provide helpful insights regarding social epidemiology. Copyright © 2015 Elsevier Ltd. All rights reserved.

Evolutionary vaccination dynamics with internal support mechanisms

NASA Astrophysics Data System (ADS)

Tang, Guo-Mei; Cai, Chao-Ran; Wu, Zhi-Xi

2017-05-01

This paper reports internal support mechanisms (i.e., without external intervention) to enhance the vaccine coverage in the evolutionary vaccination dynamics. We present two internal support mechanisms, one is global support mechanism in which each individual pays a support cost to build up a public fund and then the public fund is divided by all vaccinated individuals, while another is local support mechanism in which each individual pays a support cost and then this support cost will be divided by its immediate vaccinated neighbors. By means of extensive computer simulations, we show that, in the same strength of support cost, the heterogeneous (local) support mechanism can encourage more people to take vaccination than the homogeneous (global) support mechanism. And then, we study the most general case that includes supporters and troublemakers together, where supporters (troublemakers) mean that the individuals join (do not join) the internal support mechanism, in the population. We surprisingly find that, in scale-free networks, the voluntary vaccination dynamics with the local support mechanism will not degrade into the original voluntary vaccination dynamics, and the vaccination level can still be effectively improved. In view of most social networks are of scale-free degree distribution, we study further in empirical networks and find that the vaccination level can still be improved in the absence of external intervention.