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原文連結
論文資訊
- 類型：已發表論文
- 日期：2020-06-01
摘要
A 社會 system is susceptible to perturbation when its collective properties depend sensitively on a few pivotal components. Using the 資訊 geometry of minimal models from 統計 physics, we develop an approach to identify pivotal components to which coarse-grained, or aggregate, properties are sensitive. As an example, we introduce our approach on a reduced toy model with a median voter who always votes in the majority. The sensitivity of majority-minority divisions to changing voter behaviour pinpoints the unique role of the median. More generally, the sensitivity identifies pivotal components that precisely determine collective outcomes generated by a complex 網絡 of interactions. Using perturbations to target pivotal components in the models, we analyse datasets from political voting, finance and
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