Cognitive maps are powerful graphical models for knowledge representation. They offer an easy means to express individual's judgments, thinking or beliefs about a given problem. However, drawing inferences in cognitive maps, especially when the problem is complex, may not be an easy task. The main reason of this limitation in cognitive maps is that they do not model uncertainty with the variables. Our contribution in this paper is twofold : we firstly enrich the cognitive map formalism regarding the influence relation and then we propose to built a Bayesian causal map (BCM) from the constructed cognitive map in order to lead reasoning on the problem. A simple application on a real problem is given, it concerns fishing activities.

Scientists and managers are not the only holders of knowledge regarding environmental issues: other stakeholders such as farmers or fishers do have empirical and relevant knowledge. Thus, new approaches for knowledge representation in the case of multiple knowledge sources, but still enabling reasoning, are needed. Cognitive maps and Bayesian networks constitute some useful formalisms to address knowledge representations. Cognitive maps are powerful graphical models for knowledge gathering or displaying. If they offer an easy means to express individuals judgments, drawing inferences in cognitive maps remains a difficult task. Bayesian networks are widely used for decision making processes that face uncertain information or diagnosis. But they are difficult to elicitate. To take advantage of each formalism and to overcome their drawbacks, Bayesian causal maps have been developed. In this approach, cognitive maps are used to build the network and obtain conditional probability tables. We propose here a complete framework applied on a real problem. From the different views of a group of shellfish dredgers about their activity, we derive a decision facilitating tool, enabling scenarios testing for fisheries management.qualitative modelling, cognitive maps, bayesian networks, fisher's knowledge, fisheries management, qualitative decision support 1

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