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- W3194176324 abstract "We study maximum entropy mechanisms of information exchange between agents modeled by neural networks and macroscopic states of a society of such agents in a few situations. Mathematical quantification of surprise, distrust of other agents and confidence about its opinion emerge as essential ingredients in entropy based learning dynamics. Learning is shown to be driven by surprises, i.e. receptor agent is confronted with concurring opinion of a distrusted agent or with a trusted agent's disagreeing opinion. Attribution of blame for surprise derives from measures of distrust of receiver towards emitter agent and receiver's confidence about its own opinion. The dynamics proceeds by changes of mainly one or other: receptor opinion about issue or distrust about emitter. A society with $N$ agents exchanging binary opinions about a set of issues show rich behavior which depend on complexity of agenda. For small sets society reaches a steady state polarized into antagonistic factions, where balanced norms such as the friend of an is an enemy are strictly satisfied. For larger sets of issues, societies can persist for a long time in spin-glass like states. There are two types of frustration: ideological and affective, with dynamical annealing properties depending on complexity of set of questions under discussion, leading to lack of sharply defined parties for long transients." @default.
- W3194176324 created "2021-08-30" @default.
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- W3194176324 date "2021-08-14" @default.
- W3194176324 modified "2023-09-27" @default.
- W3194176324 title "Frustration, glassy behavior and dynamical annealing in societies of Neural Networks" @default.
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