Lilia Rejeb

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Economic markets are complex systems. They are characterized by a large and dynamic population of firms. To deal with this complexity, we propose an adaptive multiagent system which models a set of firms in competition with each other within a shared market. The firms are represented by agents; each firm is represented by an adaptive agent. We show the(More)
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(More)
In this paper, we present a multi-agent system which models a set of firms in competition with each other within a shared market. We present a firm model and underline the limits of the economic models which represent only firms without considering the organizational forms. We then propose to integrate the organizational forms. We introduce a model of(More)
During the last decades, Learning Classifier Systems have known many advancements that were highlighting their potential to resolve complex problems. Despite the advantages offered by these algorithms, it is important to tackle other aspects such as the uncertainty to improve their performance. In this paper, we present a new Learning Classifier System(More)