Abstract
This paper describes a simulation approach for modelling decision-making processes under incomplete and imperfect information in Agent-based Computational Economics (ACE). The main idea is to represent decision-making in a model-free framework that can be applied to a larger set of simulation problems, not just the domain modelled. The method translates some basic sociopsychological concepts from the bounded rationality and learning literature into an executable algorithm. In a simple example, the algorithm is applied in the domain of behavioural game theory, illustrating how the algorithm can be used to reproduce observed patterns of human behaviour.
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