Learning agents have to deal with the exploration-exploitation dilemma. The choice between exploration and exploitation is very difficult in dynamic systems; in particular in large scale ones such as economic systems. Recent research shows that there is neither an optimal nor a unique solution for this problem. In this paper, we propose an adaptive approach based on meta-rules to adapt the choice between exploration and exploitation. This new adaptive approach relies on the variations of the performance of the agents. To validate the approach, we apply it to economic systems and compare it to two adaptive methods: one local and one global. Herein, we adapt these two methods, which were originally proposed by Wilson, to economic systems. Moreover, we compare different exploration strategies and focus on their influence on the performance of the agents.
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