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

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' profit in such a setting. Based on an agent-based simulation with fuzzy Q-learning agents, it is shown that in case of symmetric marginal cost parameters, myopic fuzzy Q-learning agents invest only as much as in the classic hold-up problem, while non-myopic fuzzy Q-learning agents invest optimally. However, in scenarios with non-symmetric marginal cost parameters, a deviation from the previously recommended surplus sharing rules can lead to higher investment decisions and, thus, to an increase in the firm's overall profit.