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Learning Actors in Spatial Planning: Incorporating Bayesian Networks in an Agent Based Model

Agent Based Models (ABMs) have been extensively applied to simulate social-spatial processes for land use change and spatial planning (Agarwal et al., 2002; Bousquet and Le Page, 2004; Parker et al., 2003; Sengupta and Sieber, 2007). In Agent Based Modelling, a social process is modelled as a collection of autonomous decision making entities called agents. Each agent individually assesses its environment and reasons over it. The reasoning about the state of the environment and other agents often follows a reactive architecture according to a set of rules defined through some sort of logic. However, social processes based on cooperation, conflicts and trust, like the ones found in interactive spatial planning, require a different reasoning that better accounts for the complexity of individual and group behaviour (Agarwal et al., 2002). Decision making in spatial planning often includes a variety of issues which are relevant to multiple actors. Actors may have different beliefs about the environment, have different preferences and may follow different strategies when making decisions (Parker et al., 2008). Often actors do not fully reveal their holdings and beliefs.