Computational Foundations for Strategic Coopetition: Formalizing Sequential Interaction and Reciprocity

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Abstract:Strategic coopetition in multi-stakeholder systems requires understanding how cooperation persists through time without binding contracts. This technical report extends computational foundations for strategic coopetition to sequential interaction dynamics, bridging conceptual modeling (i* framework) with game-theoretic reciprocity analysis. We develop: (1) bounded reciprocity response functions mapping partner deviations to finite conditional responses, (2) memory-windowed history tracking capturing cognitive limitations over k recent periods, (3) structural reciprocity sensitivity derived from interdependence matrices where behavioral responses are amplified by structural dependencies, and (4) trust-gated reciprocity where trust modulates reciprocity responses. The framework applies to both human stakeholder interactions and multi-agent computational systems. Comprehensive validation across 15,625 parameter configurations demonstrates robust reciprocity effects, with all six behavioral targets exceeding thresholds: cooperation emergence (97.5%), defection punishment (100%), forgiveness dynamics (87.9%), asymmetric differentiation (100%), trust-reciprocity interaction (100%), and bounded responses (100%). Empirical validation using the Apple iOS App Store ecosystem (2008-2024) achieves 43/51 applicable points (84.3%), reproducing documented cooperation patterns across five ecosystem phases. Statistical significance confirmed at p < 0.001 with Cohen's d = 1.57. This report concludes the Foundations Series (TR-1 through TR-4) adopting uniaxial treatment where agents choose cooperation levels along a single continuum. Companion work on interdependence (arXiv:2510.18802), trust (arXiv:2510.24909), and collective action (arXiv:2601.16237) has been prepublished. Extensions Series (TR-5 through TR-8) introduces biaxial treatment where cooperation and competition are independent dimensions.*

Comments: 81 pages, 19 figures. Fourth technical report in research program; should be read with companion arXiv:2510.18802, arXiv:2510.24909, and arXiv:2601.16237. Adapts and extends complex actor material from Pant (2021) doctoral dissertation, University of Toronto

Subjects:

Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Computer Science and Game Theory (cs.GT); Software Engineering (cs.SE)

Cite as: arXiv:2604.01240 [cs.MA]

(or arXiv:2604.01240v1 [cs.MA] for this version)

https://doi.org/10.48550/arXiv.2604.01240

arXiv-issued DOI via DataCite

Submission history

From: Vik Pant [view email] [v1] Sun, 29 Mar 2026 19:16:20 UTC (83 KB)