Uncertainty, bias and the institution bootstrapping problem
Stavros Anagnou, Christoph Salge, Peter R. Lewis
TL;DR
The paper tackles the institution bootstrapping problem: how to form cooperative institutions when early adopters face costs without guaranteed reciprocity. It relaxes perfect rationality by embedding perceptual biases and bounded uncertainty into an evolutionary game-theoretic framework, using three strategies (D, C, CM) and Moran-process dynamics to capture finite-population stochasticity. Key findings show that coarse bias, probability distortion, and especially unbiased bounded noise can lower the threshold for institutional emergence and that the effects depend on distortion shape and boundary conditions. The work highlights that human-like cognition and uncertainty can enhance cooperation, offering insights for the design of resilient multi-agent systems and collective-action frameworks under real-world cognitive constraints.
Abstract
Institutions play a critical role in enabling communities to manage common-pool resources and avert tragedies of the commons. 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. By integrating well-documented psychological phenomena, including cognitive biases, probability distortion, and perceptual noise, into a game-theoretic framework, we demonstrate how these factors collectively mitigate the bootstrapping problem. Notably, unbiased perceptual noise (e.g., noise arising from agents' heterogeneous physical or social contexts) drastically reduces the critical mass of cooperators required for institutional emergence. This effect intensifies with greater diversity of perceptions. We explain this counter-intuitive result through asymmetric boundary conditions: proportional underestimation of low-probability sanctions produces distinct outcomes compared to equivalent overestimation. Furthermore, the type of perceptual distortion, proportional versus absolute, yields qualitatively different evolutionary pathways. These findings challenge conventional assumptions about rationality in institutional design, highlighting how "noisy" cognition can paradoxically enhance cooperation. Finally, we contextualize these insights within broader discussions of multi-agent system design and collective action. Our analysis underscores the importance of incorporating human-like cognitive constraints, not just idealized rationality, into models of institutional emergence and resilience.