Fiche du document
2013
- handle: 10670/1.cxtm1b
- halshs: halshs-00913235
- doi: 10.1016/j.geb.2013.09.006
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.geb.2013.09.006
info:eu-repo/semantics/OpenAccess
Mots-clés
Influence Anonymity Ordered weighted averaging operator Convergence Consensus Fuzzy linguistic quantifier D - Microeconomics/D.D8 - Information, Knowledge, and Uncertainty/D.D8.D85 - Network Formation and Analysis: Theory D - Microeconomics/D.D8 - Information, Knowledge, and Uncertainty/D.D8.D83 - Search • Learning • Information and Knowledge • Communication • Belief • Unawareness D - Microeconomics/D.D7 - Analysis of Collective Decision-Making/D.D7.D72 - Political Processes: Rent-Seeking, Lobbying, Elections, Legislatures, and Voting Behavior C - Mathematical and Quantitative Methods/C.C7 - Game Theory and Bargaining Theory/C.C7.C73 - Stochastic and Dynamic Games • Evolutionary Games • Repeated Games C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling/C.C6.C65 - Miscellaneous Mathematical ToolsSujets proches
Influence and resultsCiter ce document
Manuel Foerster et al., « Anonymous social influence », HAL-SHS : économie et finance, ID : 10.1016/j.geb.2013.09.006
Métriques
Partage / Export
Résumé
We study a stochastic model of influence where agents have "yes" or "no" inclinations on some issue, and opinions may change due to mutual influence among the agents. Each agent independently aggregates the opinions of the other agents and possibly herself. We study influence processes modeled by ordered weighted averaging operators, which are anonymous: they only depend on how many agents share an opinion. For instance, this allows to study situations where the influence process is based on majorities, which are not covered by the classical approach of weighted averaging aggregation. We find a necessary and sufficient condition for convergence to consensus and characterize outcomes where the society ends up polarized. Our results can also be used to understand more general situations, where ordered weighted averages are only used to some extent. Furthermore, we apply our results to fuzzy linguistic quantifiers, i.e., expressions like "most" or "at least a few".