Reproduce “Strategic Analysis of Dissent and Self-Censorship” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Joshua J. Daymude, Robert Axelrod, Stephanie Forrest
Researchers from Arizona State University, the University of Michigan, and the Santa Fe Institute developed a computational model to analyze strategic interactions between a population of individuals and an adaptive authority regarding dissent and self-censorship. The model reveals that while a "draconian policy" can theoretically suppress all dissent, a population's collective boldness significantly influences an adaptive authority's ability and speed to achieve widespread self-censorship, with higher boldness often preventing or delaying suppression.
Key insights
- Individual behavior categorizes into compliance, self-censorship, or defiance, with the type of punishment (uniform vs. proportional) significantly altering the conditions under which these behaviors occur.
- A theoretical "draconian policy" (zero tolerance, perfect surveillance, sufficiently high severity) exists that can completely suppress all dissent with zero punishment costs, acting as a Nash equilibrium.
- Under imperfect surveillance, proportional punishment can counter-intuitively lead to defiance for individuals with intermediate desired dissent, while those with very high desired dissent may still partially self-censor.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.