Varying Domain Representations in Hagl
Experimental game theory is an increasingly important research tool in many fields, providing insight into strategic behavior through simulation and experimentation on game theoretic models. Unfortunately, despite relying heavily on automation, this approach has not been well supported by tools. Here we present our continuing work on Hagl, a domain-specific language embedded in Haskell, intended to drastically reduce the development time of such experiments and support a highly explorative research style.
In this paper we present a fundamental redesign of the underlying game representation in Hagl. These changes allow us to better utilize domain knowledge by allowing different classes of games to be represented differently, exploiting existing domain representations and algorithms. In particular, we show how this supports analytical extensions to Hagl, and makes strategies for state-based games vastly simpler and more efficient.
KeywordsNash Equilibrium Game Theory Pareto Optimal Solution Game Tree Matrix Game
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