An Application of Agent-Based Modeling to Sustainable Natural Resource Management

Abstract

Decision-support systems are useful tools to evaluate the complexity of systems, and for predictive and policy assessment. Agent-based modeling (ABM) is a relatively new method to model complex systems characterized by the role of independent and interrelating agents. Simulations contribute to estimating and comprehending emerging behaviors that require the development of new regulations for local agents that would make incremental improvements to the system. This analysis applies this methodology to develop a beef cattle simulation model named Befergyonet, an ABM used to conduct computer simulations within a spatio-intertemporal environment. The methodology discussed in this paper is intended solely to stimulate the use of innovative computer programs to simulate complex systems as an approach to represent real world events and may be a methodological guide for readers interested in developing their own ABM. With growing interest in optimal natural resource allocation under alternative carbon sequestration, price and policy regimes coupled with climate-based uncertainty, this analysis should also serve as a useful illustration of how ABM can contribute to solutions that are more sustainable and compatible with both private and societal outcomes.