Analytical Models for Strategic Decisions in Settings with Asymmetric Information

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2019

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Abstract

This dissertation studies managerial problems involving strategic considerations under asymmetric information. Specifically, we build analytical models to investigate three problems.

The first problem relates to how riders' and drivers' behavior evolves in response to a ride-hailing platform's operational decisions, and study how it impacts the platform's performance and the social welfare. We build an evolutionary game theory model to establish two sustainable asymptotically stable equilibria of the dynamic system of the platform, one resembling a traditional taxi service while the other resembling a successful ride-hailing platform. Using this characterization, we then show how the platform could leverage operational tools at its disposal to optimize its performance. Finally, we establish that a platform can generally improve social welfare and may achieve the socially optimal state by prioritizing high-rating riders in matching under supply shortage. Our analysis highlights the importance for ride-hailing platforms to implement and strategically leverage rider ratings, and can potentially provide guidelines for improving platform performance not just with standard instruments such as price and wage adjustments, but also by making rider rating-driven adjustments into the matching procedure.

The second problem relates to managing innovation spillover risk in sourcing. In particular, when an innovator sources for an innovative product from a supplier who is also a competitor in the end market, the potential innovation spillover may be a serious concern. Will an innovation ever source from a competitor-supplier in the presence of innovation spillover? We attempt to answer this question with an emphasis on the ex-ante uncertain values of innovations, and distinguish between technical innovations which can only spill over through sourcing and non-technical innovations which can spill over through sourcing as well as in the market. We find that for both types, an innovator may strategically source from a competitor-supplier, albeit for polar-opposite motivations: for technical innovations it does so such that the latter would postpone launching the innovative product; and for non-technical innovations it does so such that the latter would immediately launch the innovative product alongside the innovator. These insights highlight the richness of and may inform sourcing decisions in the presence of innovation spillover.

The third problem relates to information acquisition and technology adoption decisions in a partnership. Using classical information acquisition and technology adoption results for a single decision-maker as a benchmark, we establish that it could be optimal for the partnership to prematurely adopt/reject the technology. Furthermore, anticipating premature decisions in a later period could trigger unraveling which leads to a series of premature decisions in earlier periods. Finally, for a given precision of the partnership's belief of the success probability of the technology, the structure of the optimal policy may be non-monotonic in the belief, due to the non-convexity and discontinuity of the associated coupled optimization problem. Thus, the presence of a partner may have a non-trivial and profound impact on the prescribed optimal information acquisition and technology adoption decisions.

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Mai, Yunke (2019). Analytical Models for Strategic Decisions in Settings with Asymmetric Information. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/18743.

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