Computational Foundations for Strategic Coopetition: Formalizing Collective Action and Loyalty
Vik Pant, Eric Yu

TL;DR
This paper develops a formal framework for understanding strategic coopetition and collective action in multi-agent teams, incorporating loyalty dynamics and validating through extensive experiments and a real-world case study.
Contribution
It introduces loyalty-moderated utility functions and team cohesion mechanisms, extending computational models to analyze collective action and loyalty effects in multi-agent systems and human teams.
Findings
Loyalty effects increase median effort by 15.04x
Achieved 96.5% baseline free-riding detection
Validated framework with Apache HTTP Server case study
Abstract
Mixed-motive multi-agent settings are rife with persistent free-riding because individual effort benefits all members equally, yet each member bears the full cost of their own contribution. Classical work by Holmstr\"om established that under pure self-interest, Nash equilibrium is universal shirking. While i* represents teams as composite actors, it lacks scalable computational mechanisms for analyzing how collective action problems emerge and resolve in coopetitive settings. This technical report extends computational foundations for strategic coopetition to team-level dynamics, building on companion work formalizing interdependence/complementarity (arXiv:2510.18802) and trust dynamics (arXiv:2510.24909). We develop loyalty-moderated utility functions with two mechanisms: loyalty benefit (welfare internalization plus intrinsic contribution satisfaction) and cost tolerance (reduced…
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Taxonomy
TopicsBusiness Strategy and Innovation · Evolutionary Game Theory and Cooperation · Innovation, Sustainability, Human-Machine Systems
