Social Computing, Behavioral-Cultural Modeling and Prediction

In the context of modernization and development, the complex adaptive systems framework can help address the coupling of macro social constraint and opportunity with individual agency. Combining system dynamics and agent based modeling, we formalize the Human Development (HD) perspective with a system of asymmetric, coupled nonlinear equations empirically validated from World Values Survey (WVS) data, capturing the core qualitative logic of HD theory. Using a simple evolutionary game approach, we fuse endogenously derived individual socio-economic attribute changes with Prisoner’s Dilemma spatial intra-societal economic transactions. We then explore a new human development dynamics (HDD) model behavior via quasi-global simulation methods to explore economic development, cultural plasticity, social and political change.

[1]  M. F. Luce,et al.  Separate Neural Mechanisms Underlie Choices and Strategic Preferences in Risky Decision Making , 2009, Neuron.

[2]  A. Tversky,et al.  Advances in prospect theory: Cumulative representation of uncertainty , 1992 .

[3]  John Conlisk,et al.  Three Variants on the Allais Example , 1989 .

[4]  K. Stanovich,et al.  On the relative independence of thinking biases and cognitive ability. , 2008, Journal of personality and social psychology.

[5]  B Cazelles,et al.  Using the Kalman filter and dynamic models to assess the changing HIV/AIDS epidemic. , 1997, Mathematical biosciences.

[6]  Deltcho Valtchanov,et al.  Restorative Effects of Virtual Nature Settings , 2010, Cyberpsychology Behav. Soc. Netw..

[7]  Alex Pentland,et al.  Stealing Reality: When Criminals Become Data Scientists (or Vice Versa) , 2011, IEEE Intelligent Systems.

[8]  Clayton Fink,et al.  From push brooms to prayer books: Social media and social networks during the London riots , 2013 .

[9]  Kevin G. Stanley,et al.  Leveraging H1N1 infection transmission modeling with proximity sensor microdata , 2012, BMC Medical Informatics and Decision Making.

[10]  James N. Druckman,et al.  The Implications of Framing Effects for Citizen Competence , 2001 .

[11]  Simo Särkkä,et al.  Parameter estimation in stochastic differential equations with Markov chain Monte Carlo and non-linear Kalman filtering , 2012, Computational Statistics.

[12]  A. Vespignani,et al.  Competition among memes in a world with limited attention , 2012, Scientific Reports.

[13]  Paul S. Carlin,et al.  Is the Allais paradox robust to a seemingly trivial change of frame , 1990 .

[14]  Eric Gilbert,et al.  Predicting tie strength with social media , 2009, CHI.

[15]  M. Kalmijn,et al.  Intermarriage and homogamy: causes, patterns, trends. , 1998, Annual review of sociology.

[16]  Peggy Wu,et al.  Can polite computers produce better human performance , 2010, AFFINE '10.

[17]  Wang Framing Effects: Dynamics and Task Domains , 1996, Organizational behavior and human decision processes.

[18]  Fei-Yue Wang,et al.  Toward a Revolution in Transportation Operations: AI for Complex Systems , 2008, IEEE Intelligent Systems.

[19]  Valerie F Reyna,et al.  Fuzzy‐Trace Theory, Risk Communication, and Product Labeling in Sexually Transmitted Diseases , 2003, Risk analysis : an official publication of the Society for Risk Analysis.

[20]  Jonathan P. West,et al.  Workplace Relations: Friendship Patterns and Consequences (According to Managers) , 2002 .

[21]  Victor S. Johnston,et al.  Perceived social context and risk preference: A re‐examination of framing effects in a life‐death decision problem , 1995 .

[22]  A. Barrat,et al.  An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices , 2013, BMC Infectious Diseases.

[23]  Alex Pentland,et al.  Incremental Learning with Accuracy Prediction of Social and Individual Properties from Mobile-Phone Data , 2011, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

[24]  Ramesh Nallapati,et al.  Link-PLSA-LDA: A New Unsupervised Model for Topics and Influence of Blogs , 2021, ICWSM.

[25]  Deborah E. Gibbons,et al.  Friendship and Advice Networks in the Context of Changing Professional Values , 2004 .

[26]  M. Gabriela M. Gomes,et al.  A Bayesian Framework for Parameter Estimation in Dynamical Models , 2011, PloS one.

[27]  A. Tversky,et al.  The framing of decisions and the psychology of choice. , 1981, Science.

[28]  David Buckeridge,et al.  Estimated epidemiologic parameters and morbidity associated with pandemic H1N1 influenza , 2010, Canadian Medical Association Journal.

[29]  Scott Counts,et al.  Taking It All In? Visual Attention in Microblog Consumption , 2021, ICWSM.

[30]  Thomas L. Griffiths,et al.  The Author-Topic Model for Authors and Documents , 2004, UAI.

[31]  Maria A. Kazandjieva,et al.  A high-resolution human contact network for infectious disease transmission , 2010, Proceedings of the National Academy of Sciences.

[32]  Katarzyna Wac,et al.  Getting closer: an empirical investigation of the proximity of user to their smart phones , 2011, UbiComp '11.

[33]  Hans Lind,et al.  A note on the robustness of a classical framing result , 1992 .

[34]  Chong Wang,et al.  Collaborative topic modeling for recommending scientific articles , 2011, KDD.

[35]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[36]  Peter Wallensteen,et al.  Armed Conflicts, 1946–2012 , 2013 .

[37]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[38]  Yuan Tian,et al.  Comparison between Individual-based and Aggregate Models in the context of Tuberculosis Transmission , 2011 .

[39]  Irwin P. Levin,et al.  Risk taking, frame of reference, and characterization of victim groups in AIDS treatment decisions , 1990 .

[40]  V. Jansen,et al.  Modelling the influence of human behaviour on the spread of infectious diseases: a review , 2010, Journal of The Royal Society Interface.

[41]  David Lazer,et al.  Inferring friendship network structure by using mobile phone data , 2009, Proceedings of the National Academy of Sciences.

[42]  Scott Highhouse,et al.  Perspectives, Perceptions, and Risk-Taking Behavior , 1996 .

[43]  Anton Kühberger,et al.  Risky Choice Framing: Task Versions and a Comparison of Prospect Theory and Fuzzy- Trace Theory , 2009 .

[44]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[45]  Kristina Lerman,et al.  Modeling Social Annotation: A Bayesian Approach , 2008, TKDD.

[46]  V. Reyna A new intuitionism: Meaning, memory, and development in Fuzzy-Trace Theory. , 2012, Judgment and decision making.

[47]  Daniel J. Brass,et al.  Network Analysis in the Social Sciences , 2009, Science.

[48]  Monica Chiogna,et al.  Hierarchical space-time modelling of epidemic dynamics: an application to measles outbreaks , 2004 .

[49]  V. Reyna,et al.  Risk Taking Under the Influence: A Fuzzy-Trace Theory of Emotion in Adolescence. , 2008, Developmental review : DR.

[50]  Yan Zhang,et al.  Exaggerated, mispredicted, and misplaced: when "it's the thought that counts" in gift exchanges. , 2012, Journal of experimental psychology. General.

[51]  Michael C. Frank,et al.  Verbal interference suppresses exact numerical representation , 2011, Cognitive Psychology.

[52]  James E. Rauch,et al.  Bandwidth and Echo: Trust, Information, and Gossip in Social Networks , 2001 .

[53]  Peter Schoo,et al.  Infiltrating Critical Infrastructures with Next-Generation Attacks W32.Stuxnet as a Showcase Threat , 2010 .

[54]  Steven B. Andrews,et al.  Power, Social Influence, and Sense Making: Effects of Network Centrality and Proximity on Employee Perceptions. , 1993 .

[55]  Hazhir Rahmandad,et al.  Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models , 2004, Manag. Sci..

[56]  Kevin G. Stanley,et al.  Temporal aggregation impacts on epidemiological simulations employing microcontact data , 2012, BMC Medical Informatics and Decision Making.

[57]  Bayesian Parameter Estimation of System Dynamics Models Using Markov Chain Monte Carlo Methods: An Informal Introduction , 2013 .

[58]  Keith E. Stanovich,et al.  Individual differences in framing and conjunction effects , 1998 .

[59]  Alex Pentland,et al.  How Many Makes a Crowd? On the Evolution of Learning as a Factor of Community Coverage , 2012, SBP.

[60]  Vinod Venkatraman,et al.  Strategic control in decision‐making under uncertainty , 2012, The European journal of neuroscience.

[61]  Monica Chiogna,et al.  Dynamic generalized linear models with application to environmental epidemiology , 2002 .

[62]  Mark S. Granovetter The Strength of Weak Ties , 1973, American Journal of Sociology.

[63]  Arthur Gelb,et al.  Applied Optimal Estimation , 1974 .

[64]  James N. Druckman,et al.  Evaluating framing effects , 2001 .

[65]  Kazuhisa Takemura,et al.  Influence of Elaboration on the Framing of Decision , 1994 .

[66]  Ronald A. Rensink,et al.  TO SEE OR NOT TO SEE: The Need for Attention to Perceive Changes in Scenes , 1997 .

[67]  Balachander Krishnamurthy,et al.  On the leakage of personally identifiable information via online social networks , 2009, CCRV.

[68]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[69]  Alex Pentland,et al.  Composite Social Network for Predicting Mobile Apps Installation , 2011, AAAI.

[70]  Eric R. Stone,et al.  Risk communication: absolute versus relative expressions of low-probability risks , 1994 .

[71]  M. Keeling The implications of network structure for epidemic dynamics. , 2005, Theoretical population biology.

[72]  Ee-Peng Lim,et al.  Generative Models for Item Adoptions Using Social Correlation , 2013, IEEE Transactions on Knowledge and Data Engineering.

[73]  D. Kahneman,et al.  Attention and Effort , 1973 .

[74]  Kristina Lerman,et al.  Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks , 2010, ICWSM.

[75]  William Rand,et al.  Differential Adaptive Diffusion: Understanding Diversity and Learning Whom to Trust in Viral Marketing , 2011, ICWSM.

[76]  鈴木 聡 Media Equation 研究の背景と動向 , 2011 .

[77]  Simon Cauchemez,et al.  new approach to characterising infectious disease transmission dynamics from entinel surveillance : Application to the Italian 2009 – 2010 A / H 1 N 1 influenza andemic , 2012 .