Mustafa Doğan


Academic PositionsLecturer (Assistant Professor) in Economics, University of East Anglia, School of Economics, 2024 -Assistant Professor of Economics, Istanbul Technical University, Department of Economics, 2021 - 2024Postdoctoral Associate, MIT Sloan School of Management, 2019 - 2020Postdoctoral Associate, Carnegie Mellon University, Heinz College,  2017 - 2019
EducationPh.D. Economics, University of Pennsylvania, 2017 Dissertation: Essays on Dynamic Incentive Design Advisor: Prof. George J. MailathM.Sc. Mathematical Economics and Econometrics, Toulouse School of Economics, 2010B.Sc. Mathematics, Koç University, 2009B.A. Economics, Koç University, 2009
Research Interests Applied Economic Theory, Dynamic Incentive Design, Mechanism Design, Information Economics.
Published and Accepted Papers

Working Papers
“Designing Information to Screen”A welfare-maximizing planner needs to allocate two objects to two agents without using monetary transfers. The agents are initially uninformed and they learn about their preferences through a process dynamically controlled by the planner. The planner, by designing the flow of information, can shape the evolution of private information in a way that facilitates screening. I show that, it is indeed possible to implement the efficient allocation with probabil- ity arbitrarily close to 1 by devising a two-stage disclosure rule. The signal in the initial stage provides agents information about their preference intensi- ties while concealing their preferred objects until the second stage. The initial signal exhibits “disassortative coupling” to ensure incentive compatibility. Arbitrarily large number of such signals establish efficiency in the limit.
“On-demand Service Sharing and Collective Dynamic Pricing” (with Alexandre Jacquillat)We design a dynamic pricing and allocation mechanism for an on-demand platform that can serve customers individually, or together via pooling. We consider an environment with two agents who arrive stochastically on the platform, are time-sensitive, are heterogeneous, and hold private information. Pooling can be leveraged by the platform to (i) serve both agents simultaneously at no extra cost, but also to (ii) tailor levels of service and prices for discriminatory purposes. The platform’s problem is formulated as a continuous- time dynamic program that maximizes expected discounted profits, subject to incentive compatibility and individual rationality constraints. Results show that the optimal pricing and allocation rule features interdependencies across both agents and over time—we thus refer to it as collective dynamic pricing. Specifically, the optimal policy sheds light on two competing effects of pooling. When both agents are present, pooling has a positive effect by enabling the platform to serve agents that would have otherwise remained unserved. But pooling has a negative effect at the time of the first agent’s arrival, and can induce wasteful waiting—that is, a deliberate delay in service provision but a subsequent failure to leverage the pooling opportunity. We also show that, in the presence of pooling, service timing can be used as a discriminatory lever—in contrast with the classical results on dynamic monopoly pricing under commitment. We extend the model in instances where the platform faces capacity restrictions, and where pooling imposes externalities. 
“Dynamic Incentives for Self-Monitoring”This paper studies a dynamic information acquisition problem within a regulation framework. Each period, an agent (he) would like to undertake a new project, which may cause social harm. Before undertaking the projects, he may subject them into an unobservable and costly self-monitoring process, which may only reveal "bad news" about them in a verifiable manner. There are no monetary transfers; instead, the regulator (she) uses future regulatory behavior for incentive provision. When the regulator has full commitment power, the regulator can induce costly self-monitoring and revelation of "bad news" in the initial phase of the optimal policy. During this phase, the agent is promised a higher continuation utility (in the form of future regulatory approval) each time he discloses the bad news. If the regulator internalizes self-monitoring costs, the agent is either blacklisted or whitelisted in the long run. When she does not internalize these costs, blacklisting is replaced by a temporary probation state, and whitelisting becomes the unique long run outcome. This result suggests that whitelisting, which may seem to be a form of regulatory capture, may instead be a consequence of the optimal policy. When the regulator has limited commitment power in that she can only commit to policies with non-negative aggregate measures at any point in time, the results change remarkably. If the expected social harm of a project is higher than its economic benefits, whitelisting disappears. In this case, if the regulator does not internalize the self-monitoring costs, the policy never reaches a stable outcome and fluctuates over time. 
“Product Upgrades and Posted Prices” This paper studies the dynamic pricing problem of a durable good monopolist with commitment power, when a new version of the good is expected at some point in the future. The new version of the good is superior to the existing one, bringing a higher flow utility to consumers. When the arrival of the new version of the product is a stationary stochastic process, the corresponding optimal price path is shown to be constant for both versions of the good, hence there is no delay on purchases and time is not used to discriminate over buyers, which is in line with the literature. However, if the arrival of the new version occurs at a commonly known deterministic date, then the price path may decrease over time, resulting in delayed purchases. For both arrival processes, posted prices is a sub-optimal selling mechanism. The optimal one involves bundling of both versions of the good and selling them only together, which can easily be implemented by selling the initial version of the good with a replacement guarantee.
Teaching ExperienceInstructor Introduction to Microeconomics, UPenn        Introduction to Microeconomics, ITU        Introduction to Economics, ITU Game Theory, ITU Microeconomics I (Graduate), ITU Microeconomics II (Graduate), ITU Industrial Organization, UEA 

    Teaching Assistant Microeconomic Theory (Graduate), University of Pennsylvania  Intermediate Microeconomics, University of Pennsylvania Intermediate Macroeconomics, University of Pennsylvania Game Theory, University of Pennsylvania Business Economics and Public Policy, Wharton Business School Introduction to Economics, Wharton Business School
Academic Referee Activities  Management Science The Economic Journal National Science Foundation, USA
Honors and Awards University Fellowship, Ph.D. in Economics, University of Pennsylvania Eiffel Scholarship, France