How can noisy perception solve the institution bootstrapping problem?
Uncertainty, bias and the institution bootstrapping problem
May 1, 2025
https://arxiv.org/pdf/2504.21579This paper explores the "institution bootstrapping problem"—the challenge of establishing a cooperative institution (like a shared resource management system) when individual participation is only advantageous after a critical mass of participants exists. It proposes that cognitive biases, like misperceiving the existence of an institution or the cost of non-participation, can actually facilitate the formation of institutions by lowering the required threshold of initial cooperators.
Key points for LLM-based multi-agent systems:
- Bounded rationality: Perfect rationality assumptions in traditional game theory may not hold for real-world agents, including LLMs. Incorporating biases and uncertainty, inherent in LLMs, can lead to more realistic and potentially more cooperative outcomes.
- Noise benefits: Even unbiased "noise" (e.g., individual variations in perception) in a multi-agent LLM system can paradoxically improve cooperation by creating asymmetries that favor institution formation. Diversity in LLM agents, therefore, could be beneficial.
- Bootstrap problem: LLMs, like human agents, can get stuck in non-cooperative equilibria. This research suggests ways to overcome this, such as introducing biases or diversity in LLM agents.
- Social Simulation: The proposed game-theoretic model combined with the Moran process provides a framework to simulate and analyze LLM-based multi-agent interactions, taking into account both individual and population level dynamics.
- Emergence: The paper highlights the importance of considering bounded rationality and stochasticity in designing LLM-based multi-agent systems. These seemingly negative factors can potentially be leveraged to enhance the emergence of beneficial collective behaviors.