News: I will be joining the Department of Economics at Boston University in July 2014 after a year as a postdoc at Caltech.
I would like to take the opportunity to (publicly) express my gratitude to my advisors as well as the many faculty at UCLA who have mentored and supported me over the past years.
Abstract: This paper studies the dynamic collaboration of a team on a project that progresses gradually over time and generates a payoff upon completion. The main result is that members of a larger team work harder than members of a smaller team if and only if the project is sufficiently far from completion. In contrast, as the project gets close to completion, the aggregate effort of a larger team can become less than that of a smaller team due to aggravated free-riding. This result provides a rationale for the formation of project teams even without mutual monitoring, peer pressure, synergies, or non-pecuniary benefits from teamwork. In addition, this result has three implications in the organization of partnerships and when a manager recruits agents into a team to undertake a project on her behalf. First, given a fixed budget, larger teams are preferable if the project is large. Second, the manager can benefit from dynamically decreasing the team size as the project approaches completion. Third, smaller teams and asymmetric compensation are preferable if the project is small.
Abstract: We consider the Retail Planning Problem in which the retailer chooses suppliers, and determines the production, distribution and inventory planning for products with uncertain demand in order to minimize total expected costs. This problem is often faced by large retail chains that carry private label products. We formulate this problem as a convex mixed integer program and show that it is strongly NP-hard. We determine a lower bound by applying a Lagrangean relaxation and show that this bound outperforms the standard convex programming relaxation, while being computationally efficient. We also establish a worst-case error bound for the Lagrangean relaxation. We then develop heuristics to generate feasible solutions. Our computational results indicate that our convex programming heuristic yields feasible solutions that are close to optimal with an average suboptimality gap at 3.4%. We also develop managerial insights for practitioners who choose suppliers, and make production, distribution and inventory decisions in the supply chain.
Abstract: We study the interaction between a group of agents who exert costly effort over time to complete a project, and a manager who chooses the objectives that must be met for her to sign off on the project. The manager has limited commitment power so that she can commit to the requirements only when the project is sufficiently close to completion. This is common in projects that involve design or quality objectives which are hard to define far in advance. The main result is that the manager has incentives to extend the project as it progresses: she is time-inconsistent. This result has three implications. First, the manager will choose a larger project if she has less commitment power. Second, if the agents receive a fraction of the project's worth upon its completion, then the manager should delegate the decision rights over the project size to the agents unless she has sufficient commitment power. Third, cultivating an insider culture so that the agents act in the interest of the entire team may aggravate the manager's commitment problem and lower profits.
Abstract: When operating in a market with heterogeneous customers, a service firm (e.g., a car rental company or a hotel) needs to manage its capacity so as to maximize its revenue. To gauge the potential demand, a service firm often allows each customer to reserve a unit of service in advance. However, to avoid the loss associated with “no-shows”, service firms may require a non-refundable deposit. To determine an optimal reservation policy with a non-refundable deposit, we consider the case in which the market is divided into four segments (high vs. low valuation and high vs. low show-up probability). When customer demand and the firm's capacity are large so that they can be approximated by continuous values, we determine the optimal reservation policy analytically, and we establish analytical conditions under which the firm should discriminate against (i.e., price out) certain customer segments. For the case when customer demand and the firm's capacity are finite so that they take on discrete values, we find that some of the insights obtained from the “continuous” case continue to hold especially when the firm's capacity is large. However, the key difference is that in the former case, the firm discriminates mostly based on customers' valuation, whereas in the latter case it discriminates mostly based on customers' show-up probability.