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Title: Simulating Sequential Decision-Making Process of Base-Agent Actions in a Multi Agent-Based Economic Landscape (MABEL) Model

Citation Type: Working Paper

Publication Year: 2005

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Abstract: 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 (BNs). A sample simulation of MABEL, as it is coded in the SWARM modeling environment, is presented. We conclude with a discussion of future work that is planned.

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Authors: Pijanowski, Bryan C.; Lei, Zhen; Alexandridis, Konstantinos T.

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Institution: Purdue

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Data Collections: IPUMS USA

Topics: Other

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