Abstract
Defining and testing a policy on a socioeconomic system is one of the main problems addressed by agent-based modelling. While research continues to be conducted to come up with hybrid frameworks that tackle the complexity of different problems, no model explicitly integrates computational replications of multi-agent systems, particularly in dealing with partially observable situations. We show in our work how a Markov based reinforced learning and partially observable computations in the behaviour of a taxpayer agent can contribute to refining the analysis of an audit policy.
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