Süleyman Kerimov I am a final year Ph.D. student in Operations Research at Stanford University, where I am fortunate to be advised by Itai Ashlagi and Itai Gurvich. I received my B.S. in Mathematics from Bilkent University in 2016. I am interested in market design, matching and applied probability. I use tools from economics, mathematics and operations research to design and analyze policies for marketplaces to achieve efficient outcomes. My thesis focuses on frictions that arise due to stochasticity and liquidity in matching markets. Here is my CV. You can contact me at kerimov at stanford dot edu.

Research

• (Job Market Paper) On the Optimality of Greedy Policies in Dynamic Matching, with Itai Ashlagi and Itai Gurvich, Major Revision at Operations Research. [pdf] [SSRN]
  • Abstract

    We study centralized dynamic matching markets with finitely many agent types and heterogeneous match values. A network topology
    describes the pairs of agent types that can form a match and the value generated from each match. A matching policy is hindsight optimal
    if the policy can (nearly) maximize the total value simultaneously at all times. We find that suitably designed greedy policies are hindsight
    optimal in two-way matching networks. This implies that there is essentially no positive externality from having agents waiting to form
    future matches. We first show that the greedy longest-queue policy with a minor variation is hindsight optimal. Importantly, the policy is
    greedy relative to a residual network, which includes only non-redundant matches with respect to the static optimal matching rates. Moreover,
    when the residual network is acyclic (e.g., as in two-sided networks), we prescribe a greedy static priority policy that is also hindsight optimal.
    The priority order of this policy is robust to arrival rate perturbations that do not alter the residual network. Hindsight optimality is closely
    related to the lengths of type-specific queues. Queue-lengths cannot be smaller (in expectation) than \(O(1/\epsilon)\), where \(\epsilon\) is the general position gap
    that quantifies the stability in the network. The greedy longest-queue policy achieves this lower bound.

  
  
• Dynamic Matching: Characterizing and Achieving Constant Regret, with Itai Ashlagi and Itai Gurvich, Major Revision at Management Science. [pdf] [SSRN]
  • Abstract

    We study how to optimally match agents in a dynamic market with heterogeneous match values. A network topology determines the feasible
    matches in the market. We consider networks that are two-sided when all matches include two agents, or acyclic otherwise. An inherent trade-off
    arises between generating short- and long-term value. We find that when the network satisfies a general position condition, this trade-off is limited,
    and a simple periodic clearing policy (nearly) maximizes the total value simultaneously at all times. Central to our results is the general position gap,
    \(\epsilon\), which quantifies the stability or the imbalance in the network. No policy can achieve a regret that is lower than the order of \(1/\epsilon\) at all times. This
    lower bound is achieved by a policy, which periodically resolves a natural LP, provided that the delay between periods is of the order of \(1/\epsilon\).
    Examples illustrate the necessity of some delay in periodic clearing policies.



• Scrip Systems with Minimal Availability, with Itai Ashlagi, working paper.
  • Appeared as an extended abstract in the 15th Conference on Web and Internet Economics (WINE 2019).
  • Abstract

    In economies without monetary transfers, scrip systems serve an alternative to sustain cooperation, improve efficiency and mitigate free riding.
    This paper considers a marketplace, in which at each time period, one agent requests a service, one agent provides the service, and a unit of artificial
    currency is used to pay for service provision. We ask whether agents can sustain cooperation when the market is thin, in the sense that only few
    agents are available to provide the requested service. To study this problem, we analyze the stability of the scrip distribution assuming that among
    the available agents, the one with the minimum amount of scrips is selected to provide service. When exactly one random agent is available to
    provide service, the scrip distribution is unstable, since the number of scrips each agent has behaves like a simple random walk in one dimension.
    However, already when just two random agents are available to provide service, the scrip distribution is stable, in the sense that agents do not deviate
    much from their initial endowment, with high probability. This suggests that even with minimal liquidity in the market, cooperation can be sustained by
    balancing service provisions among agents. We further explore cases, in which agents request and become available to provide service at different rates,
    and generalize our positive results to the case, in which the request and availability rates of each agent are equal. Our theory builds on the literature on
    the power of two choices paradigm and load balancing problems. Finally, our results suggest that scrip systems can lead to efficient outcomes in kidney
    exchange platforms by sustaining cooperation between hospitals.

Teaching

• MS&E 220: Probabilistic Analysis (Summer 2019)
• MS&E 232: Introduction to Game Theory and Market Design (Winter 2019)
• MS&E 260: Introduction to Operations Management (Autumn 2017, Autumn 2018, Winter 2020)