Modeling Socioecological Dynamics

Agent Based Models

The dynamic complexity that characterizes interactions between humans and the natural environment, has intersected over the past century with increasingly rapid population growth, urbanization, and technological development to make human society an important driver of environmental changes that threaten to exceed our abilities to adapt using traditional means. This makes it imperative that we find better ways to track these global socio-ecological systems (or ‘socioecosystems’), and anticipate their social and natural consequences.

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Late 20th Century advances in information technology offer powerful new tools to assist us in understanding—and hopefully even managing—these coupled social and natural systems. In this context, Agent Based Models (ABMs) have recently emerged as a promising cybertool to study the dynamics of complex human and biological systems, integrating individual perceptions and behaviors in the contexts behavioral ecology, game theory of decision-making, and geospatial representations of the world. While ABMs are much discussed and are rapidly becoming perceived as a requirement for cutting-edge research centering on human-environmental interaction, most social and natural scientists still have a limited awareness of their potential, and the experimental nature of most ABM platforms prevents them from being readily accessible to researchers.

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Examples of ABMs

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click image to go to the CoMSES Net Model Library

CoMSES Network

I direct the the Network for Computational Modeling in Social and Ecological Sciences (CoMSES Net), an international community of researchers, educators, and professionals with a common goal of improving the way we develop, share, and use computational models (including ABMs). CoMSES Net provides diverse resource for model based research, including the Computational Model Library, discussion forums, employment opportunities, and educational materials. The CoMSES CoRe Center for open science and reproducibility in scientific computation is a 'big data spoke' node in the National Science Foundation's national big data infrastructure network, with prior support from the NSF Digging Into Data, Coupled Natural and Human Systems, and Human Social Dynamics programs (grants BCS-623162, GEO-0909394, SMA-1430411, and IIS-1658584).

My Published Models

Diet breadth model from Optimal Foraging Theory (Human Behavioral Ecology)
Patch choice model from Optimal Foraging Theory (Human Behavioral Ecology)
Formation of Lithic Assemblages v. 1
Hominin ecodynamics v.1
Hominin Ecodynamics v.1.1 (update for perception and interaction)
Hominin ecodynamics v.2
Swidden farming by individual households
Swidden farming by individual households version 2
Swidden Farming Version 2.0
MedLanD Modeling Laboratory v.1

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