Community

Néill Sweeney Member since: Monday, June 22, 2015

Ph.D.

For my Ph.D. thesis, I developed a system to play poker.
I’m interested to see whether a similar approach can be applied to agent based models.

Liliana Perez Member since: Thursday, October 11, 2018 Full Member

B.Eng, Geomatics, Distrital University, Colombia, MSc., Geography, UPTC, Colombia, Ph.D., Geography, Simon Fraser University, Canada

My initial training was in cadastre and geodesy (B.Eng from the Distrital University, UD, Colombia). After earning my Master’s degree in Geography (UPTC, Colombia) in 2003, I worked for the “José Benito Vives de Andreis” marine and coastal research institute (INVEMAR) and for the International Center for Tropical Agriculture (CIAT). Three years later, in 2006, I left Colombia to come to Canada, where I began a PhD in Geography with a specialization in modelling complex systems at Simon Fraser University (SFU), under the direction of Dr. Suzana Dragicevic (SAMLab). In my dissertation I examined the topic of spatial and temporal modelling of insect epidemics and their complex behaviours. After obtaining my PhD in 2011, I began postdoctoral studies at the University of British Columbia (2011) and the University of Victoria (2011-2013), where I worked on issues concerning the spatial and temporal relationships between changes in indirect indicators of biodiversity and climate change.

I am an Associate Professor in the Department of Geography at the University of Montreal. My research interests center around the incorporation of artificial intelligence and machine learning techniques into the development Agent-Based Models to solve complex socio-ecological problems in different kind of systems, such as urban, forest and wetland ecosystems.

The core of my research projects aim to learn more about spatial and temporal interactions and relationships driving changes in our world, by focusing on the multidisciplinary nature of geographical information science (GIScience) to investigate the relationships between ecological processes and resulting spatial patterns. I integrate spatial analysis and modeling approaches from geographic information science (GIScience) together with computational intelligence methods and complex systems approaches to provide insights into complex problems such as climate change, landscape ecology and forestry by explicitly representing phenomena in their geographic context.

Specialties: Agent-based modeling, GIScience, Complex socio-environmental systems, Forestry, Ecology

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.

Geerten Hengeveld Member since: Thursday, January 24, 2019 Full Member

Wageningen University & Research
Currently researcher modelling at Biometris, Wageningen University & Research

modelling,
ecology, foraging, forestry, forest management, water management
scenario analysis
agent based modelling
individual based modelling
model quality, model evaluation, model governance

Geo Kocheril Member since: Tuesday, October 01, 2019

Research fellow, PhD Candidate (University of Kassel)

Energy system transiton modelling * stakeholder and market modelling, governance and policy modelling, * agent-based modelling (ABM), optimisation, * model coupling, open and integrative modelling framework, * open source, S4F

Javed Ali Member since: Monday, December 17, 2018 Full Member

Flood Risk Management, Coupled Human-Natural System Modelling, Socio-hydrological Modelling, Agent-Based Modelling, Human Behaviour Modelling, Agent-Based Social Simulation, Hydrological and Hydraulic Modeling, Geographic Information Systems (GIS), Mapping, Risk Modelling and Risk Visualization, Disaster Risk Reduction

Valentina Guleva Member since: Monday, November 14, 2016

Modeling and simulation of complex systems, particularly, interbank networks; economic models and critical phenomena modeling

Mauro Eidi Assano Member since: Monday, November 05, 2012

Msc Computer Science, MBA

Distributed computing modeling, multi-agent computing models, economic and financial models, healthcare chronic disease models

Volker Grimm Member since: Wednesday, July 18, 2007 Full Member Reviewer

Volker Grimm currently works at the Department of Ecological Modelling, Helmholtz-Zentrum für Umweltforschung. Volker does research in ecology and biodiversity research.

How to model it: Ecological models, in particular simulation models, often seem to be formulated ad hoc and only poorly analysed. I am therefore interested in strategies and methods for making ecological modelling more coherent and efficient. The ultimate aim is to develop preditive models that provide mechanstic understanding of ecological systems and that are transparent and structurally realistic enough to support environmental decision making.

Pattern-oriented modelling: This is a general strategy of using multiple patterns observed in real systems as multiple criteria for chosing model structure, selecting among alternative submodels, and inversely determining entire sets of unknown model parameters.

Individual-based and agent-based modelling: For many, if not most, ecological questions individual-level aspects can be decisive for explaining system-level behavior. IBM/ABMs allow to represent individual heterogeneity, local interactions, and/or adaptive behaviour

Ecological theory and concepts: I am particularly interested in exploring stability properties like resilience and persistence.

Modelling for ecological applications: Pattern-oriented modelling allows to develop structurally realistic models, which can be used to support decision making and the management of biodiversity and natural resources. Currently, I am involved in the EU project CREAM, where a suite of population models is developed for pesticide risk assessment.

Standards for model communication and formulation: In 2006, we published a general protocol for describing individual- and agent-based models, called the ODD protocol (Overview, Design concepts, details). ODD turned out to be more useful (and needed) than we expected.

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