Simulating Sequential Decision-Making Process of Base- Agent Actions in a Multi Agent-Based Economic Landscape (MABEL) Model
نویسندگان
چکیده
In this paper, we present the use of sequential decision-making process simulations for base agents in our multi-agent based economic landscape (MABEL) model. The sequential decision-making process described here is a data-driven Markov-Decision Problem (MDP) integrated with stochastic properties. Utility acquisition attributes in our model are generated for each time step of the simulation. We illustrate the basic components of such a process in MABEL, with respect to land-use change. We also show how geographic information systems (GIS), socioeconomic data, a Knowledge-Base, and a market-model are integrated into MABEL. A Rule-based Maximum Expected Utility acquisition is used to as a constraint optimization problem. The optimal policy of base-agents’ decision making in MABEL is one that maximizes the differences between expected utility and average expected rewards of agent actions. Finally, we present a procedural representation of extracting optimal agent policies from socio-economic data using Belief Networks (BN’s). A sample simulation of
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