Community

Yevgeny Patarakin Member since: Tuesday, May 25, 2021 Full Member

Doctor of Sciences, Pedagogica, Moscow City Teacher Training University, Associate Professor, 2010

National Research University Higher School of Economics, Professor: Institute of Education / Department of Educational Programmes. Leading Expert: Institute of Education / Laboratory for Digital Transformation of Education - 2019 – present

2016 – present Leading Researcher at Moscow City University, Educational policies & educational practices

2018 – 2020 World Bank, Consultant. Children Learning to Code: Essential for 21st Century Human Capital
2011 - 2019 - Co-founder, chief community officer at WikiVote!

Educational network - Letopisi.org 2006 – present, Co-founder, chief community officer
Scientific project “Mobile and ubi-learning”, 2009 - 2011

ABM, wiki, NetLogo, StarLogo Nova, R, Collaboration

Nathan Rollins Member since: Wednesday, August 27, 2008 Full Member Reviewer

I am a Ph.D. student studying the interactions between external regulations and social norms in natural resource management and international development. In particular, I am looking to use mixed methods research, including ethnographic research, field experiments, and agent-based computational models to explore the sustainability of market-based interventions and their possible perverse outcomes.

Garry Sotnik Member since: Friday, April 06, 2018 Full Member Reviewer

I have a background in social science and training in modeling coupled human and natural systems, and apply both to advance our understanding of how interactions among cognitive, behavioral, social, and demographic processes influence human adaptation to climate change.

agent-based modeling, cognition

John Glass Member since: Wednesday, November 15, 2017

Ph.D., Sociology, B.A., Sociology

Interested in learning how to accurately model social power, diffusion of ideas, social exchange

Christopher Parrett Member since: Sunday, October 20, 2019 Full Member

I am a lowly civil servant moonlighting as a PhD student interested in urban informatics, Smart Cities, artificial intelligence/machine learning, all-things geospatial and temporal, advanced technologies, agent-based modeling, and social complexity… and enthusiastically trying to find a combination thereof to form a disseration. Oh… and I would like to win the lottery.

  • Applied data science (machine/deep learning applications) and computational modeling (agent-based
    modeling) in U.S. Government
  • Geographic Information Systems and analysis of dense urban environments and complex terrain
  • Complexity theory and computational organizational design of distributed enterprise teams.
  • Human Capital Management and Talent Management policy development

kianercy Member since: Wednesday, January 04, 2012

Msc. Mechanical Eng., Msc. Chemical Eng.

Adapting Agents on Evolving Networks: An evolutionary game theory approach

Mirsad Hadzikadic Member since: Thursday, January 12, 2012 Full Member Reviewer

PhD Computer Science, SMU, MPA, Harvard University

Complex adaptive systems, complexity, systems science, creativity, data mining, machine learning, economic and health systems, science education

Malik Koné Member since: Thursday, January 21, 2016

Master in mathematics and didactics

Agent Based Modeling (ABM), Agent Based Social System (ABSS), Multi-Agent Systems (MAS), Bayesian learning, Social networks Analysis (SNA), Socio ecological Dynamics.

Dominik Reusser Member since: Thursday, January 12, 2012 Full Member Reviewer

Ph.D.
  • societal transitions under climate change
  • models as learning tools
  • communication of scientific results and uncertainties to decision makers
  • efficient information processing and knowledge management in science

David Earnest Member since: Saturday, March 13, 2010 Full Member Reviewer

Ph.D. in political science (2004), M.A. in security policy studies (1994)

Two themes unite my research: a commitment to methodological creativity and innovation as expressed in my work with computational social sciences, and an interest in the political economy of “globalization,” particularly its implications for the ontological claims of international relations theory.

I have demonstrated how the methods of computational social sciences can model bargaining and social choice problems for which traditional game theory has found only indeterminate and multiple equilibria. My June 2008 article in International Studies Quarterly (“Coordination in Large Numbers,” vol. 52, no. 2) illustrates that, contrary to the expectation of collective action theory, large groups may enjoy informational advantages that allow players with incomplete information to solve difficult three-choice coordination games. I extend this analysis in my 2009 paper at the International Studies Association annual convention, in which I apply ideas from evolutionary game theory to model learning processes among players faced with coordination and commitment problems. Currently I am extending this research to include social network theory as a means of modeling explicitly the patterns of interaction in large-n (i.e. greater than two) player coordination and cooperation games. I argue in my paper at the 2009 American Political Science Association annual convention that computational social science—the synthesis of agent-based modeling, social network analysis and evolutionary game theory—empowers scholars to analyze a broad range of previously indeterminate bargaining problems. I also argue this synthesis gives researchers purchase on two of the central debates in international political economy scholarship. By modeling explicitly processes of preference formation, computational social science moves beyond the rational actor model and endogenizes the processes of learning that constructivists have identified as essential to understanding change in the international system. This focus on the micro foundations of international political economy in turn allows researchers to understand how social structural features emerge and constrain actor choices. Computational social science thus allows IPE to formalize and generalize our understandings of mutual constitution and systemic change, an observation that explains the paradoxical interest of constructivists like Ian Lustick and Matthew Hoffmann in the formal methods of computational social science. Currently I am writing a manuscript that develops these ideas and applies them to several challenges of globalization: developing institutions to manage common pool resources; reforming capital adequacy standards for banks; and understanding cascading failures in global networks.

While computational social science increasingly informs my research, I have also contributed to debates about the epistemological claims of computational social science. My chapter with James N. Rosenau in Complexity in World Politics (ed. by Neil E. Harrison, SUNY Press 2006) argues that agent-based modeling suffers from underdeveloped and hidden epistemological and ontological commitments. On a more light-hearted note, my article in PS: Political Science and Politics (“Clocks, Not Dartboards,” vol. 39, no. 3, July 2006) discusses problems with pseudo-random number generators and illustrates how they can surprise unsuspecting teachers and researchers.

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