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<p><b>Abstract</b>—Analytical techniques are generally inadequate for dealing with causal interrelationships among a set of individual and social concepts. Usually, causal maps are used to cope with this type of interrelationships. However, the classical view of causal maps is based on an intuitive view with ad hoc rules and no precise semantics of the primitive concepts, nor a sound formal treatment of relations between concepts. In this paper, we solve this problem by proposing a formal model for causal maps with a precise semantics based on relation algebra and the software tool, <ss>CM-RELVIEW</ss>, in which it has been implemented. Then, we investigate the issue of using this tool in multiagent environments by explaining through different examples <it>how</it> and <it>why</it> this tool is useful for the following aspects: 1) the reasoning on agents' subjective views, 2) the qualitative distributed decision making, and 3) the organization of agents considered as a holistic approach. For each of these aspects, we focus on the computational mechanisms developed within <ss>CM-RELVIEW</ss> to support it.</p>
Decision support, cognitive maps, knowledge base management, tools and supports, causal maps, agent and multiagent systems.

B. Chaib-draa, "Causal Maps: Theory, Implementation, and Practical Applications in Multiagent Environments," in IEEE Transactions on Knowledge & Data Engineering, vol. 14, no. , pp. 1201-1217, 2002.
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