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
The inland fisheries sector is central for subsistence in many regions worldwide. The exploitation of fish resources is expected to increase along with the growing human population, with underlying conservation issues in regions with high biodiversity value. The small fishery of the Maroni River, French Guiana, is a hotspot of biodiversity and endemism where resource depletion is suspected. We surveyed 754 boat landings in seven villages located in the upper half of the watershed, representing > 6,300 fish during the study period (November 2013 -September 2014. Fishers used canoes with outboard engines almost exclusively (75%) and fished within 32 km of their villages. Most fish were caught in trammel nets (81%); the 20 most-landed species represented more than 87% of catches. Depending on the village, daily catches and biomass averaged 6-14 fish and 1.7-13 kg per boat landing, respectively. Seven control sites located outside of the fishing grounds were fished to identify potential differences in catch per unit effort and fish size. Per 100 m2 of trammel net, mean catches ranged from 4-13 and 8-29 fish in the villages and control sites, respectively, while fish biomass ranged from 0.9-4 and 3.2-7 kg in villages and control sites, respectively. For all species combined, fish caught at control sites were bigger than those landed in villages. This difference was significant for nine of the most-landed species. Differences in fishing techniques and fish catches between villages illustrated the gradual disappearance of the ancestral subsistence fishing. Our results support indications that the fish community in the upper Maroni River is harvested intensively, address the issue of sustainability of the fishery there, and call attention to the need to conserve the river's remarkable biodiversity.
International audienceCognitive map is a qualitative decision model which is frequently used in social science and decision making applications. This model allows to easily organize individuals’ judgments, thinking or beliefs about a given problem in a graphical representation containing different concepts and influences between them. However, reasoning on this model presents some limits and remains a difficult task. For example, cognitive maps donot model uncertainty within the variables, and only deductive reasoning (predicting an effect given a cause) is possible. In this paper, we show how to translate the knowledge represented in cognitive maps in the form of arguments and attack relations among them. In particular, given a decision problem, a cognitive map was first built by eliciting knowledge from experts and then transforming it in a weighted argumentation framework (WAFfor short) for ensuring efficient reasoning. Another contribution of this paper concerns enriching the WAF obtained from a given cognitive map for dealing with dynamics through the consideration of a varying set of observations
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