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The Institution Bootstrapping Problem and Some Counter-Intuitive Solutions

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Institutions are rule systems that play a critical role in enabling communities to manage common-pool resources (e.g., grazing lands, fisheries, or even Minecraft servers) and help them avoid antisocial outcomes by regulating individual selfinterest. Prior research suggests institutions emerge when universal participation yields greater collective benefits than noncooperation [3], [6]. However, a fundamental issue arises: Individuals typically perceive participation as advantageous only after an institution is established, creating a paradox—how can institutions form if no one will join before a critical mass exists? We term this conundrum the institution bootstrapping problem and propose that misperception—specifically, agents' erroneous belief that an institution already exists—could resolve this paradox. To achieve this, we integrate welldocumented psychological phenomena—including cognitive biases and perceptual noise—into a game-theoretic framework, demonstrating how these factors collectively mitigate the bootstrapping problem [1].
Original languageEnglish
Title of host publication2025 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages134-135
Number of pages2
ISBN (Electronic)9798331502157
ISBN (Print)9798331502164
DOIs
Publication statusPublished - 11 Nov 2025
Event2025 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) - Tokyo, Japan
Duration: 29 Sept 20253 Oct 2025

Conference

Conference2025 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)
Country/TerritoryJapan
CityTokyo
Period29/09/253/10/25

Keywords

  • Uncertainty
  • Noise
  • Psychology
  • Fisheries
  • Servers
  • Game theory
  • Distributed computing
  • Multi-agent systems

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