A relational model of cognitive maps

Chaib-draa, Brahim; Desharnais, Jules

doi:10.1006/ijhc.1998.0201

Cited by 17 publications

(6 citation statements)

References 25 publications

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“…Previous work on causal maps (see [1,5,11,19] for excellent surveys) has produced a variety of formalisms for representing and making inference on causal maps. As DANA aims to support policy analysts in modeling and comparing different actor perceptions, its causal maps should be represented in such a way that: 1. the analyst can differentiate between stronger and weaker causal influence; 2. the analyst can represent an actor's uncertainty regarding external influences and causal relations; 3. the impact of tactics and external influences on outcomes of interest can be inferred from a map, and compared across maps; 4. the analyst can differentiate between more and less important outcomes of interest; 5. an actor's preferred strategy can be inferred from a map (to detect conflicting views on what is good policy).…”

Section: A Choice Of Causal Mapsmentioning

confidence: 99%

“…Unless some rules for truncation are defined, a change may 'run off the scale'. DANA originally (see [4]) defined algebraic laws for ⊕ and ⊗, using tables similar to those in [5]. This led to the undesirable property that the sequence Tables 1 and 2 define the conversion functions for changes and change multipliers that are used in DANA, with the aim to preserve as much as possible the intuitive interpretation of ⊗ and ⊕, as well as their associative and commutative properties, and the property that ⊗ distributes over ⊕.…”

Section: Change and Causal Links As Multipliersmentioning

confidence: 99%

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Analysis of Multi-Actor Policy Contexts Using Perception Graphs

Bots

2007

2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'07)

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Section: A Choice Of Causal Mapsmentioning

confidence: 99%

Section: Change and Causal Links As Multipliersmentioning

confidence: 99%

Analysis of Multi-Actor Policy Contexts Using Perception Graphs

Bots

2007

2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'07)

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“…These considerations brought us to develop the formal model presented in the next section. More details on this formal model can be found elsewhere [9].…”

Section: Outline Of This Papermentioning

confidence: 99%

Causal maps: theory, implementation, and practical applications in multiagent environments

Chaib-draa

2002

IEEE Trans. Knowl. Data Eng.

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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, CM-RELVIEW, in which it has been implemented. Then, we investigate the issue of using this tool in multiagent environments by explaining through different examples how and why 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 CM-RELVIEW to support it.Index Terms-Decision support, cognitive maps, knowledge base management, tools and supports, causal maps, agent and multiagent systems. ae. The author is with the

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“…The earliest version of the Causal CM corresponds to the model proposed by Axelrod (1976), when he designed an international affairs model through the sketch of pure causal links between concepts (Peñ a & Gutiérrez, 2004a). Since them, a broad spectrum of applications has been carrying out in many fields like: Decision support (Nakamura, Iwai, & Sawaragi, 1982), Multi-Agent systems (Chaib-draa & Desharnais, 1998), virtual reality (Johns & Blake, 2001), probabilistic causality (Wellman, 1994), on line analytical process (Zhang, 2003c) and much more (Aguilar, 2005;Kitchin, 1982). Furthermore, causal CM have dealt with specific issues regarding to: Qualitative, fuzzy, uncertainty, double-side effects and rules.…”

Section: Introductionmentioning

confidence: 99%