Spatially explicit experiments for the exploration of land-use decision-making dynamics

We explore the special outcomes of decision-making through two laboratory-based experiments, one with a homogenous land suitability surface and another with a heterogeneous suitability surface. Subjects make resource allocation decisions on an abstract landscape and are given a monetary incentive to maximize their revenue during the experiment. These experimental results are compared with simulation output from an agent-based model run on the same abstract landscape that uses a utility-maximizing agent. The main findings are: (1) landscapes produced by subjects result in greater patchiness and more edge than the utility-maximization agent predicts; (2) there is considerable diversity in the decisions subjects make despite the relatively simple decision-making context; and (3) there is greater deviation of subject revenue from the maximum potential revenue in early rounds of the experiment compared with later rounds, demonstrating the challenge of making optimal decisions with little historical context. The findings demonstrate the value of using non-maximizing agents in agent-based models of land-cover change and the importance of acknowledging actor heterogeneity in land-change systems.