Hierarchical Multi-Agent Orchestration for Automated Dispute Resolution: A Game-Theoretic Approach to Policy Adherence in Digital Wallets
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I2P122Keywords:
Digital Wallets, Multi-Agent Systems, Game Theory, Dispute Resolution, Policy Adherence, Nash Equilibrium, Reinforcement Learning, Financial Technology, Autonomous SystemsAbstract
The emergence of financial transactions has revolutionized the way financial operations are carried out given that online wallets ecosystems have rapidly grown to permit complete payments online and on various platforms. This growth has however been characterized by an ever growing volume of transaction disputes comprising of unauthorized payments, failed transactions, chargebacks and merchant-customer conflicts. The conventional dispute resolution process is very dependent on manual and centralized rule-guided systems and causes delays and inconsistencies and decreases customer confidence. These restrictions make it clear that smart, scalable, and autonomic structures are necessary which can effectively solve conflicts and at the same time enforce adherence to regulatory and organizational policies. This paper suggests a Hierarchical Multi-Agent Orchestration (HMAO) system to resolve a dispute in digital wallets through automated methods based on the idea of operating game theory to enforce the policy and strategy actions of autonomous agents. The suggested system cuts dispute resolution into hierarchical levels comprising of perception agents, negotiation agents, policy enforcement agents, and governance agents. The agents work with incomplete information and interact with other agents within a systematic environment in which the agents jointly solve conflicts. This work is novel because it incorporates game-theoretic models, especially Nash equilibrium and Stackelberg game formulations in the decision-making process of agents. This allows agents to foresee adversarial moves, maximize negotiation approaches as well as promoting equal results. There is also the mechanism of reinforcement learning that allows the policies to become dynamic and changes accordingly to the historical results of the disputes. The architecture of the system is able to support real time processing, distributed decision making as well as regulatory compliance thus applicable in the large scale digital wallet platform. Experimental analysis shows the accuracy of resolution of disputes, reduction of time at which disputes are resolved, and policy adherence to superior quality as opposed to the traditional systems. These findings demonstrate that hierarchical orchestration, together with game-theoretic rationality, has a considerable positive effect on the strength and justices of dispute resolution mechanisms. The study can help to develop self-governing financial infrastructure as it offers a scalable and smart system to handle disputes in the field of digital economies.
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