AI Bibliography
AI Bibliography |
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Betz, G., Hamann, M., Mchedlidze, T., & von Schmettow, S. (2019). Applying argumentation to structure and visualize multi-dimensional opinion spaces. Argument & Computation, 10(1), 23–40. |
| Resource type: Journal Article BibTeX citation key: Betz2019 View all bibliographic details  |
Categories: Artificial Intelligence, Cognitive Science, Complexity Science, Computer Science, Data Sciences, Decision Theory, General, Military Science Subcategories: Big data, Chaos theory, Decision making, Deep learning, Game theory, Human decisionmaking, Informatics, Psychology of human-AI interaction, Situational cognition, Strategy, Systems theory Creators: Betz, Hamann, Mchedlidze, von Schmettow Publisher: Collection: Argument & Computation |
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This paper presents OpMAP: a tool for visualizing large scale, multi-dimensional opinion spaces as geographic maps. OpMAP represents opinions as labelings on a structured deductive argumentation framework. It uses probabilistic degrees of justification and Bayesian coherence measures to calculate how strongly any two opinions cohere with each other. The opinion sample is, accordingly, represented as a weighted graph, a so-called opinion graph, with opinion vectors serving as nodes and coherence values as edge weights. OpMAP partitions the nodes of the opinion graph by using clustering methods. Finally, the graph is visualized as a geographic map using a method based on a particular (e.g., force-directed) layout.
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