Community

Mariam Kiran Member since: Friday, August 17, 2012 Full Member Reviewer

PhD Agent based modelling of economic and social systems, MSc (Eng) Advanced software engineering

Dr. Mariam Kiran is a Research Scientist at LBNL, with roles at ESnet and Computational Research Division. Her current research focuses on deep reinforcement learning techniques and multi-agent applications to optimize control of system architectures such as HPC grids, high-speed networks and Cloud infrastructures.. Her work involves optimization of QoS, performance using parallelization algorithms and software engineering principles to solve complex data intensive problems such as large-scale complex decision-making. Over the years, she has been working with biologists, economists, social scientists, building tools and performing optimization of architectures for multiple problems in their domain.

Flavio Diniz Member since: Sunday, November 15, 2020

Eletronic Engineer with specialization in Computer Science and a passion for Artificial Intelligence, Simulation, Programming, and many other tech topcis . One life is really not enough to learn and experiment all cool things that are out there. Love also learning languages: Portuguese, English, French, Italian, and German.

  • Agent-based Modeling
  • Automated Planning and Distributed Problem Solving
  • Natural Language Processing
  • Machine Learning
  • Internet of Things and Cloud-based Distributed Architectures

Sandra Schmid Member since: Saturday, March 07, 2015

Dr.

Innovation management

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.

Adam Rorabaugh Member since: Monday, May 18, 2015

PhD

Northwest Coast Archaeology
Complex Hunter-Gatherers
Social Learning
Lithic Technologies
Sedentism

Deniz Kayar Member since: Tuesday, July 24, 2018 Full Member

Machine Learning, Artifical Inteligence, Multi Agent Simulations

Sae Schatz Member since: Tuesday, November 04, 2014

Modeling and Simulation, Ph.D., Modeling and Simulation, M.S., Computer Information Technology, B.S.

Sae Schatz, Ph.D., is an applied human–systems researcher, professional facilitator, and cognitive scientist. Her work focuses on human–systems integration (HSI), with an emphasis on human cognition and learning, instructional technologies, adaptive systems, human performance assessment, and modeling and simulation (M&S). Frequently, her work seeks to enhance individual’s higher-order cognitive skills (i.e., the mental, emotional, and relational skills associated with “cognitive readiness”).

Haifeng Zhang Member since: Sunday, March 18, 2018 Full Member

Agent-based Modeling, Maching Learning, Algorithmic Marketing, Diffusion of Innovations, Online Communities

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