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Rubens de Almeida Zimbres Member since: Tue, Aug 02, 2022 at 12:22 AM Full Member

Sr Machine Learning Engineer, Google Developer Expert in Cloud and Machine Learning. CompTIA Security+, AWS certified Machine Learning specialty.

Generative AI, LLMs, Multi-Agent Modeling, Agent-Based Modeling, Cellular Automata, Graph Networks, Deep Learning, Social Sciences

Tarik Hadzibeganovic Member since: Tue, Aug 09, 2022 at 06:09 PM

Tarik Hadzibeganovic is a complex systems researcher and cognitive scientist interested in all challenging topics of mathematical and computational modeling, in both basic and applied sciences. His particular focus has been on several open questions in evolutionary game theory, behavioral mathematical epidemiology, sociophysics, network theory, and episodic memory research. When addressing these questions, he combines mathematical, statistical, and agent-based modeling methods with laboratory behavioral experiments and Big Data analytics.

Davood Qorbani Member since: Mon, Oct 24, 2022 at 01:39 PM

Ph.D. Fellow, MPhil

model-based policy analysis; system dynamics; agent-based modeling

David Earnest Member since: Sat, Mar 13, 2010 at 03:46 PM Full Member

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.

Jean-Pierre Müller Member since: Thu, Mar 30, 2023 at 08:50 AM Full Member

Ph.D., Computer science, Institut National Polytechnique de Grenoble, France., HDR, Université de Montpellier, France.

1987-1989: assistant professor at the Neuchâtel University (Switzerland)
1990-2001: full professor at the Neuchâtel University (Switzerland): artificial intelligence & software engineering
2001- : senior researcher at CIRAD in the unit “Gestion des Ressources et Environnement” (GREEN) and from 2021 “Savoirs ENvironnement Sociétés” (UMR SENS)

Former professor at the University of Neuchatel in Switzerland and now senior researcher at CIRAD in France, I am doing research on artificial intelligence since 1984. Having begun with logic programming, I naturally applied logics and its extensions (i.e. modal logics of various sorts) to specify agent behaviour. Since 1987, I moved both to embedded intelligence (using mobile robots) and multi-agent systems applied, in particular, to job-shop scheduling and complex system simulation and design. Since 2001, I exclusively work on modelling and simulation of socio-ecosystems in a multidisciplinary team on renewable resources management (GREEN). I am focusing on modelling complex systems in a multi-disciplinary (economist, agronomist, sociologists, geographers, etc.) and multi-actor (stakeholders, decision makers) setting. It includes:
- representing multiple points of view at various scales and levels on a complex socio-ecosystem, using ontologies and contexts
- representing the dynamics of such systems in a variety of formalisms (differential equations, automata, rule-based systems, cognitive models, etc.)
- mapping these representations into a simulation formalism (an extension of DEVS) for running experiments and prospective analysis.
This research is instantiated within a modelling and simulation platform called MIMOSA (http://mimosa.sourceforge.net). The current applications are the assessment of the sustainability of management transfer to local communities of the renewable ressources and the dynamics of agro-biodidversity through networked exchanges.

Elham Bakhshianlamouki Member since: Tue, May 02, 2023 at 01:07 PM Full Member

Integrated Water resource management
Integrated coastal management
Complex socio-biophysical modelling
Computational social modelling
Agent-Based modelling
Participatory modelling
System Dynamics modelling

Amir Hajimirzajan Member since: Mon, May 29, 2023 at 06:26 PM Full Member

Amir Hajimirzajan Ph.D. of Industrial Engineering

Operations Management Production Planning Optimization Agribusiness Management Agent Based Modeling Complex Systems Biology Agent Based Intelligent Systems Complex Systems Complex Adaptive Systems Complex System Optimization, Optimization-simulation models.

Atiyah Elsheikh Member since: Wed, May 31, 2023 at 04:56 PM

  • Julia language
  • Agent-based modeling
  • Systems Modeling
  • ODEs / DAEs

Wolfram Barfuss Member since: Thu, Aug 10, 2023 at 12:41 PM

Hi. I’m Wolf. I’m the Argelander (Tenure-Track Assistant) Professor for Integrated System Modeling for Sustainability Transitions at the University of Bonn, Germany.

We reshape human-environment modeling to identify critical leverage points for sustainability transitions.

Cooperation at scale – in which large collectives of intelligent actors in complex environments seek ways to improve their joint well-being – is critical for a sustainable future, yet unresolved.

To move forward with this challenge, we develop a mathematical framework of collective learning, bridging ideas from complex systems science, multi-agent reinforcement learning, and social-ecological resilience.

Displaying 10 of 262 results agent clear search

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