Empirical Agent-Based Modelling - Challenges and Solutions: Volume 1, The Characterisation and Parameterisation of Empirical Agent-Based Models

Agent-based modeling (ABM) is a powerful, simulation-modeling technique that has seen an uptick in the number of useful real-world applications in the last few years. In ABM, a system is modeled as a collection of autonomous decision-making entities called “agents.” Each agent individually assesses its situation and makes decisions on the basis of a set of rules. Agents may execute various behaviors appropriate for the system they represent—for example, producing, consuming, or selling. ABM is primarily used for simulating real-world systems, such as natural resource use, transportation, public health, and conflict. Decision makers increasingly demand support that covers a multitude of indicators that can only be dealt with ABM, especially those situations where human behavior is identified as a critical element. This book provides a timely overview of key ABM methodologies as well as a variety of empirical applications dealing with the most innovative approaches. It features cutting-edge research from leading academics in the social sciences, and will focus on such topics as characterizing and parameterizing human behavioral dimensions, determining the scalability of human and social variables, and validating empirical ABM models. Very little has been published in the area of empirical ABM, and this contributed volume will appeal to graduate-level students and researchers studying simulation modeling in economics, sociology, ecology, and trans-disciplinary studies, such as sustainability-related topics.