Agent-based Model Selection Framework for Complex Adaptive Systems

Abstract

Human-initiated land-use and land-cover change is the most significant single factor behind global climate change. Since climate change affects human, animal and plant populations alike, and the effects are potentially disastrous and irreversible, it is equally important to understand the reasons behind land-use decisions as it is to understand their consequences. Empirical observations and controlled experimentation are not usually feasible methods for studying this change. Therefore, scientists have resorted to computer modeling, and use other complementary approaches, such as household surveys and field experiments, to add depth to their models. The computer models are not only used in the design and evaluation of environmental programs and policies, but they can be used to educate land-owners about sustainable land management practices. Therefore, it is critical which model the decision maker trusts. Computer models can generate seemingly plausible outcomes even if the generating mechanism is quite arbitrary. On the other hand, with excess complexity the model may become incomprehensible, and proper tweaking of the parameter values may make it produce any results the decision maker would like to see. The lack of adequate tools has made it difficult to compare and choose between alternative models of land-use and land-cover change on a fair basis. Especially if the candidate models do not share a single dimension, e.g., a functional form, a criterion for selecting an appropriate model, other than its face value, i.e., how well the model behavior confirms to the decision maker's ideals, may be hard to find. Due to the nature of the class of models, existing model selection methods are not applicable either. In this dissertation I propose a pragmatic method, based on algorithmic coding theory, for selecting among alternative models of land-use and land-cover change. I demonstrate the method's adequacy using both artificial and real land-cover data in multiple experimental conditions with varying error functions and initial conditions.