A Decision-Support Framework for Supply Chain Cooperation under Asymmetric Information: Integrating AHP and Repeated Game Theory for Punishment Mechanism Design

Xue Jin

doi:10.31081/dmame8220251603

Authors

Xue Jin Associate Professor, PHD, School of Public Foundation Nanjing University of Finance and Economics Hongshan College，Nanjing, China, 211300

DOI:

https://doi.org/10.31081/dmame8220251603

Keywords:

Supply Chain Collaboration, Asymmetric Information, Repeated Games, Punishment Mechanisms, Multi-Criteria Decision-Making

Abstract

Variations in knowledge among partners within a supply chain can sometimes lead to untrustworthy behaviour, undermining sustained collaboration. This study introduces a multi-criteria framework incorporating the Analytic Hierarchy Process (AHP) to develop robust punishment mechanisms in repeated game contexts, aiming to preserve cooperation when one participant possesses less information than the other. The framework conceptualises supply chains as repeated games and evaluates a range of punitive strategies with respect to their economic outcomes, associated risks, enforceability, and fairness. By employing AHP, these evaluation criteria are systematically structured and weighted, ensuring that the selected policies effectively deter opportunistic behaviour while retaining overall benefits. To address imperfect information, the model explicitly incorporates information asymmetry when analysing firm strategies. The practical applicability of this approach is demonstrated through numerical simulations and a real-world case study, highlighting improvements in supply chain stability, collective profitability, and trust among partners. Results suggest that integrating multi-criteria decision-making with game-theoretic punishment designs enables managers to mitigate risks and foster cooperative behaviour across the supply chain. Consequently, this methodology offers an enhanced tool for supply chain management and decision sciences, providing a structured, data-driven basis for aligning diverse objectives within complex organisational networks. By combining insights from repeated games with a rigorous analytical framework, the proposed approach supports more informed managerial and engineering decisions for the formulation of joint strategies.

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