Modeling Decision-Making with Will for Cooperation in Social Dilemmas

TL;DR

This paper introduces a 'will' mechanism in agents that persistently pursue goals, acting as boundary constraints to promote cooperation in social dilemmas, outperforming traditional utility maximization.

Contribution

It formalizes 'willed agents' as a new class that enhances cooperation by strategically constraining rational re-evaluation, demonstrated through dynamical analysis and multi-agent simulations.

Findings

Willed agents accelerate convergence in social dilemmas.

Heterogeneous will strength promotes cooperation.

Agents suspending rational re-evaluation outperform continuous optimizers.

Abstract

Standard rational actor models often attribute cooperation failures in social dilemmas to insufficient incentives, overlooking the destabilizing effects of continuous utility maximization. To address this, we propose a framework of ``will" defined as a mechanism that persistently pursues goals while ignoring local cost-benefit fluctuations. We formalize the Willed Agents as potential minimizers, distinguishing them from cumulative utility maximization. Dynamical analysis of infinite population demonstrates that willed agents shrink the feasible state space, acting as boundary constraints that accelerate convergence in canonical social dilemmas. Through multi-agent simulations in a spatiotemporal Stag Hunt Game, we show that willed agents function as ``cooperation catalysts", enabling groups to surmount high-risk thresholds where purely utility maximization fails. We find that heterogeneous will strength promotes cooperation, and that agents who autonomously suspend rational re-evaluation can significantly outperform continuous optimizers. These findings suggest that successful cooperation relies on the cognitive capacity to strategically constrain calculation.