Applications in Agent-Based Economics

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The aim of this thesis is to explore ways of modelling adaptive agents in Agent-based Computational Economics (ACE) models. A general reinforcement learning framework is developed and implemented in a simulation system. This system is used to implement three models of increasing complexity in two different economic domains. One of these domains form iterative games in which agents meet repeatedly and interact. In an experimental labour market, it is shown how statistical discrimination can be generated simply by the learning algorithm used. The results resemble actual patterns of observed human behaviour in laboratory settings. A second model treats strategic network formation. The main contribution in this area is to show how agent-based modelling helps to analyse non-linearity that is introduced when assumptions of perfect information and full rationality are relaxed. The other domain has a Health Economics background. The aim here is to provide insights of how the approach might be useful in real-world applications. For this, a general model of primary care is developed, and the implications of different consumer behaviour patterns (based on the learning features introduced before) analysed.