The impact of surplus sharing on the outcomes of specific investments under negotiated transfer pricing: An agent-based simulation with fuzzy Q-learning agents
Christian Mitsch

TL;DR
This study uses agent-based simulation with fuzzy Q-learning agents to explore how surplus sharing affects specific investments under negotiated transfer pricing, revealing that non-myopic agents can optimize investments and increase firm profit.
Contribution
It introduces a novel agent-based simulation approach with fuzzy Q-learning to analyze transfer pricing decisions under bounded rationality, challenging traditional assumptions of fully rational managers.
Findings
Myopic fuzzy Q-learning agents invest as in classic hold-up problems.
Non-myopic fuzzy Q-learning agents invest optimally.
Deviations from surplus sharing rules can increase firm profit.
Abstract
This paper focuses on specific investments under negotiated transfer pricing. Reasons for transfer pricing studies are primarily to find conditions that maximize the firm's overall profit, especially in cases with bilateral trading problems with specific investments. However, the transfer pricing problem has been developed in the context where managers are fully individual rational utility maximizers. The underlying assumptions are rather heroic and, in particular, how managers process information under uncertainty, do not perfectly match with human decision-making behavior. Therefore, this paper relaxes key assumptions and studies whether cognitively bounded agents achieve the same results as fully rational utility maximizers and, in particular, whether the recommendations on managerial-compensation arrangements and bargaining infrastructures are designed to maximize headquarters'…
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Taxonomy
TopicsEconomic theories and models · Auction Theory and Applications · Corporate Finance and Governance
MethodsQ-Learning
