Problem definition: In many matching markets, some agents are fully flexible, whereas others only accept a subset of jobs. For example, ride-sharing drivers can specify on the platform the destinations that they are willing to accept. Conventional wisdom suggests reserving flexible agents, but this can backfire; anticipating higher matching chances, agents may misreport as specialized, reducing overall matches. We ask how platforms can design simple matching policies that remain effective when agents act strategically. Methodology/results: We model job allocation as a bipartite matching queueing system and analyze equilibrium throughput performance under different policies when agents choose which queue to join. We show that flexibility reservation is optimal under full information but can perform poorly with private information, sometimes substantially worse than random assignment. To address this, we propose a new policy-flexibility reservation with fallback-that guarantees robust performance across settings without requiring precise knowledge of system parameters or agent utility functions. Managerial implications: Our results underscore the importance of accounting for strategic reporting in the design of matching policies; the proposed fallback policy both preserves flexibility and exploits latent flexibility when explicitly flexible agents are exhausted. Its simplicity and parameter-free nature also make it practical to implement in platforms such as ride-sharing and affordable housing allocation.

In ridesharing platforms such as Uber and Lyft, it is observed that drivers sometimes collaboratively go offline when the price is low, and then return after the price has risen due to the perceived lack of supply. This collective strategy leads to cyclic fluctuations in prices and available drivers, resulting in poor reliability and social welfare. We study a continuous time, non-atomic model and prove that such online/offline strategies may form a Nash equilibrium among drivers, but lead to a lower total driver payoff if the market is sufficiently dense. Further, we show how to set price floors that effectively mitigate the emergence and impact of price cycles.

Ridesharing platforms match riders and drivers, using dynamic pricing to balance supply and demand. The origin-based "surge pricing", however, does not take into consideration market conditions at trip destinations, leading to inefficient driver flows in space and incentivizes drivers to strategize. In this work, we introduce the Iterative Network Pricing mechanism, addressing a main challenge in the practical implementation of optimal origin-destination (OD) based prices, that the model for rider demand is hard to estimate. Assuming that the platform's surge algorithm clears the market for each origin in real-time, our mechanism updates the OD-based price adjustments week-over-week, using only information immediately observable during the same time window in the prior weeks. For stationary market conditions, we prove that our mechanism converges to an outcome that is approximately welfare-optimal. Using data from the City of Chicago, we illustrate (via simulation) the iterative updates under our mechanism for morning rush hours, demonstrating substantial welfare improvements despite significant fluctuations of market conditions from early 2019 through the end of 2020.

We study the matching of jobs to workers in a queue, e.g. a ridesharing platform dispatching drivers to pick up riders at an airport. Under FIFO dispatching, the heterogeneity in trip earnings incentivizes drivers to cherry-pick, increasing riders' waiting time for a match and resulting in a loss of efficiency and reliability. We first present the direct FIFO mechanism, which offers lower-earning trips to drivers further down the queue. The option to skip the rest of the line incentivizes drivers to accept all dispatches, but the mechanism would be considered unfair since drivers closer to the head of the queue may have lower priority for trips to certain destinations. To avoid the use of unfair dispatch rules, we introduce a family of randomized FIFO mechanisms, which send declined trips gradually down the queue in a randomized manner. We prove that a randomized FIFO mechanism achieves the first best throughput and the second best revenue in equilibrium. Extensive counterfactual simulations using data from the City of Chicago demonstrate substantial improvements of revenue and throughput, highlighting the effectiveness of using waiting times to align incentives and reduce the variability in driver earnings.

Motivated in part by online marketplaces such as ridesharing and freelancing platforms, we study two-sided matching markets where agents are heterogeneous in their compatibility with different types of jobs: flexible agents can fulfill any job, whereas each specialized agent can only be matched to a specific subset of jobs. When the set of jobs compatible with each agent is known, the full-information first-best throughput (i.e. number of matches) can be achieved by prioritizing dispatch of specialized agents as much as possible. When agents are strategic, however, we show that such aggressive reservation of flexible capacity incentivizes flexible agents to pretend to be specialized. The resulting equilibrium throughput could be even lower than the outcome under a baseline policy, which does not reserve flexible capacity, and simply dispatches jobs to agents at random. To balance matching efficiency with agents' strategic considerations, we introduce a novel robust capacity reservation policy (RCR). The RCR policy retains a similar structure to the first best policy, but offers additional and seemingly incompatible edges along which jobs can be dispatched. We show a Braess' paradox-like result, that offering these additional edges could sometimes lead to worse equilibrium outcomes. Nevertheless, we prove that under any market conditions, and regardless of agents' strategies, the proposed RCR policy always achieves higher throughput than the baseline policy. Our work highlights the importance of considering the interplay between strategic behavior and capacity allocation policies in service systems.

From skipped exercise classes to last-minute cancellation of dentist appointments, underutilization of reserved resources abounds. Likely reasons include uncertainty about the future, further exacerbated by present bias. In this paper, we unite resource allocation and commitment devices through the design of contingent payment mechanisms, and propose the two-bid penalty-bidding mechanism. This extends an earlier mechanism proposed by Ma et al. (2019), assigning the resources based on willingness to accept a no-show penalty, while also allowing each participant to increase her own penalty in order to counter present bias. We establish a simple dominant strategy equilibrium, regardless of an agent's level of present bias or degree of "sophistication". Via simulations, we show that the proposed mechanism substantially improves utilization and achieves higher welfare and better equity in comparison with mechanisms used in practice and mechanisms that optimize welfare in the absence of present bias.

Problem definition: Agents in online marketplaces (such as ridesharing and freelancing platforms) are often strategic, and heterogeneous in their compatibility with different types of jobs: fully flexible agents can fulfill any job, whereas specialized agents can only complete specific subsets of jobs. Convention wisdom suggests reserving agents that are more flexible whenever possible, however this may incentivize agents to pretend to be more specialized, leading to loss in matches. We focus on designing a practical matching policy that performs well in a strategic environment. Methodology/results: We model the allocation of jobs to agents as a matching queue, and analyze the equilibrium performance of various matching policies when agents are strategic and report their own types. We show that reserving flexibility naively can backfire, to the extent that the equilibrium throughput can be arbitrarily bad compared to a policy which simply dispatches jobs to agents at random. To balance matching efficiency with agents' strategic considerations, we propose a new policy dubbed flexibility reservation with fallback and show that it enjoys robust performance. Managerial implications: Our work highlights the importance of considering agent strategic behavior when designing matching policies in online platforms and service systems. The robust performance guarantee, along with the parameter-free nature of our proposed policy makes it easy to implement in practice. We illustrate how this policy is implemented in the driver destination product of major ridesharing platforms.

We introduce the problem of assigning resources to improve their utilization, for settings where agents have uncertainty about their own values for using a resource, and where it is in the interest of the society or the planner that resources be used and not wasted. Done in the right way, improved utilization maximizes social welfare-balancing the utility of a high value but unreliable agent with the group's preference that resources be used. We introduce the family of contingent payment mechanisms (CP), which may charge an agent contingent on use (a penalty). A CP mechanism is parameterized by a maximum penalty, and has a simple dominant-strategy equilibrium. Under a set of axiomatic properties, we establish welfare-optimality for the special case CP(W), with CP instantiated for a maximum penalty equal to societal value W for utilization. The special case with no upper bound on penalty, the contingent secondprice mechanism, maximizes utilization. We extend the mechanisms to assign multiple, heterogeneous resources, and present a simulation study of the welfare properties of these mechanisms.

Ridesharing platforms match drivers and riders to trips, using dynamic prices to balance supply and demand. A challenge is to set prices that are appropriately smooth in space and time, so that drivers will choose to accept their dispatched trips, rather than drive to another area or wait for higher prices or a better trip. We work in a complete information, discrete time, multi-period, multi-location model, and introduce the Spatio-Temporal Pricing (STP) mechanism. The mechanism is incentive-aligned, in that it is a subgame-perfect equilibrium for drivers to always accept their trip dispatches. The mechanism is also welfare-optimal, envy-free, individually rational, budget balanced and core-selecting in equilibrium from any history onward. The proof of incentive alignment makes use of the M♯ concavity of minimum cost flow objectives. We also give an impossibility result, that there can be no dominant-strategy mechanism with the same economic properties. Simulation results suggest that the STP mechanism can achieve significantly higher social welfare than a myopic pricing mechanism.

We study revenue-optimal pricing and driver compensation in ridesharing platforms when drivers have heterogeneous preferences over locations. If a platform ignores drivers' location preferences, it may make inefficient trip dispatches; moreover, drivers may strategize so as to route towards their preferred locations. In a model with stationary and continuous demand and supply, we present a mechanism that incentivizes drivers to both (i) report their location preferences truthfully and (ii) always provide service. In settings with unconstrained driver supply or symmetric demand patterns, our mechanism achieves (full-information) first-best revenue. Under supply constraints and unbalanced demand, we show via simulation that our mechanism improves over existing mechanisms and has performance close to the first-best.

Without monetary payments, the Gibbard-Satterthwaite theorem proves that under mild requirements all truthful social choice mechanisms must be dictatorships. When payments are allowed, the Vickrey-Clarke-Groves (VCG) mechanism implements the value-maximizing choice, and has many other good properties: it is strategy-proof, onto, deterministic, individually rational, and does not make positive transfers to the agents. By Roberts’ theorem, with three or more alternatives, the weighted VCG mechanisms are essentially unique for domains with quasilinear utilities. The goal of this paper is to characterize domains of non-quasi-linear utilities where “reasonable” mechanisms (with VCG-like properties) exist. Our main result is a tight characterization of the maximal non quasi-linear utility domain, which we call the largest parallel domain. We extend Roberts’ theorem to parallel domains, and use the generalized theorem to prove two impossibility results. First, any reasonable mechanism must be dictatorial when the utility domain is quasi-linear together with any single non-parallel type. Second, for richer utility domains that still differ very slightly from quasi-linearity, every strategy-proof, onto and deterministic mechanism must be a dictatorship.

Without monetary payments, the Gibbard-Satterthwaite theorem proves that under mild requirements all truthful social choice mechanisms must be dictatorships. When payments are allowed, the Vickrey-Clarke-Groves (VCG) mechanism implements the value-maximizing choice, and has many other good properties: it is strategy-proof, onto, deterministic, individually rational, and does not not make positive transfers to the agents. By Roberts' theorem, with three or more alternatives, the weighted VCG mechanisms are essentially unique for domains with quasi-linear utilities. The goal of this paper is to characterize domains of non-quasi-linear utilities where "reasonable'' mechanisms (with VCG-like properties) exist. Our main result is a tight characterization of the maximal non quasi-linear utility domain, which we call the largest parallel domain. We extend Roberts' theorem to parallel domains, and use the generalized theorem to prove two impossibility results. First, any reasonable mechanism must be dictatorial when the type domain is quasi-linear together with any single non-parallel type. Second, for richer utility domains that still differ very slightly from quasi-linearity, every strategy-proof, onto and deterministic mechanism must be a dictatorship.

Demand-side response (DR) is emerging as a crucial technology to assure stability of modern power grids. The uncertainty about the cost agents face for reducing consumption imposes challenges in achieving reliable, coordinated response. In recent work, [Ma et al. 2016] introduce DR as a mechanism design problem and solve it for a setting where an agent has a binary preparation decision and where, contingent on preparation, the probability an agent will be able to reduce demand and the cost to do so are fixed. We generalize this model to allow uncertainty in agents' costs of responding, and also multiple levels of effort agents can exert in preparing. For both cases, the design of contingent payments now affects the probability of response. We design a new, truthful and reliable mechanism that uses a "reward-bidding" approach rather than the "penalty-bidding" approach. It has good performance when compared to natural benchmarks. The mechanism also extends to handle multiple units of demand response from each agent.

My research focuses on mechanism design for coordination when assigning resources or tasks to agents, when choosing a plan for a future event, in the presence of uncertainty, self-interest and private information. At the time of planning, each agent has uncertainty in her value in utilizing the resources, completing the tasks or attending the event at each specific times. The uncertainty would later be resolved in a future period, based on which agents decides on which actions to take. The design objective is to determine assignments, plans and also payments that may be contingent on the actions taken, in order to incentivize good outcomes. My past research include resource allocation to maximize utilization, incentivizing reliability demand side response in electric power systems, and possibilities of non-dictatorial mechanisms for the non-quasi-linear social choice problem. Looking forward, many challenges remain for the design and implementation of coordination mechanisms, including better understanding mechanism design with non-quasi-linear utilities, designing simple indirect mechanisms, exploring the effects of temporal preferences and present-bias, and experimenting to study human decisions on uncertain future events.

My research is broadly situated at the interface between economics and computer science (especially artificial intelligence) and draws on concepts from multi-agent systems, planning, and game theory. I am particularly interested in mechanism design for coordination when assigning resources or tasks to agents, when choosing a plan for a future event, in the presence of uncertainty, ex-post decisions, self-interest and private information. Consider assigning time slots for a neighborhood electric vehicle charging station in a way that they are utilized by the residents. At the time of planning, each resident is uncertain about her values and availability of using the station at different times (e.g. they may get stuck in traffic so can’t arrive at the station on time, or emergency may happen so they need to drive the cars,) but has private information about their distributions. When the time comes, the values are realized and the assigned residents will then make decisions on whether to use the stations. Similar coordination problems exist when selecting consumers to prepare for reliable consumption reduction to balance supply and demand in electric power systems, or a group of students choosing a time for a project meeting. In each case, the decision is associated with an intended action (to cut consumption, and to show-up at the meeting) and the mechanism needs to elicit information about uncertain values for different alternatives, make a plan for the future along setting payments that may be contingent on people’s future actions. We seek truthful mechanisms in which people will voluntarily choose to participate. The rich private information (all possible distributions), the possibility of different payments contingent on different actions, the voluntary choice of actions the agents have and the resulting utilities that are non-quasi-linear (NQL) in payments, impose big challenges for information elicitation and incentive alignment. Moreover, the design objective depends on the outcome that is determined by the ex-post actions taken by the agents, which Appears in: Proc. of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017), S. Das, E. Durfee, K. Larson, M. Winikoff (eds.), May 8–12, 2017, São Paulo, Brazil. Copyright c © 2017, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved. goes beyond welfare or revenue maximization that are studied in standard mechanism design. My past research include resource allocation [2] with the objective of maximizing the probability of good outcomes (i.e. resources being utilized), and incentivizing reliable demand-side response in electricity grids [4, 3] where the objective is to guarantee a probabilistic constraint on good outcomes (i.e. sufficient reduction of consumption) without much disturbance of he economy or excessive payment to agents. Normally in social choice setting (such as voting) there is no money. My problem on meeting scheduling is a special case of social choice with payments and NQL utilities. I prove a negative result on the existence of non-dictatorial truthful mechanisms, and characterized NQL utility domains on which mechanisms with VCG properties continue to exist [1]. An ongoing project on bike sharing system addresses the problem of designing a truthful mechanism as a part of a larger optimization problem: how to incentivize riders to help rebalancing the bikes and make plans for truck routs at the same time, in a welfare optimal and cost effective manner. Looking forward, many challenges remain for the design and implementation of successful coordination mechanisms. Much effort is needed for understanding NQL utilities, e.g. the assignment problem without unit demand and with combinatorial values, and mechanisms for social choice with solution concepts weaker than dominant strategy. To go beyond working with homo economicus, an ongoing project explores the effect of temporal preference and present-bias in the coordination of future events and the design of commitment devices through contingent-payment mechanisms. We also plan on designing experiments to better understand how humans react to future tasks and how the behavior differ under theoretically equivalent mechanisms.

Power companies such as Southern California Edison (SCE) uses Demand Response (DR) contracts to incentivize consumers to reduce their power consumption during periods when demand forecast exceeds supply. Current mechanisms in use offer contracts to consumers independent of one another, do not take into consideration consumers' heterogeneity in consumption profile or reliability, and fail to achieve high participation. We introduce DR-VCG, a new DR mechanism that offers a flexible set of contracts (which may include the standard SCE contracts) and uses VCG pricing. We prove that DR-VCG elicits truthful bids, incentivizes honest preparation efforts, and enables efficient computation of allocation and prices. With simple fixed-penalty contracts, the optimization goal of the mechanism is an upper bound on probability that the reduction target is missed. Extensive simulations show that compared to the current mechanism deployed by SCE, the DR-VCG mechanism achieves higher participation, increased reliability, and significantly reduced total expenses.

Without monetary payments, the Gibbard-Satterthwaite theorem proves that under mild requirements all truthful social choice mechanisms must be dictatorships. When payments are allowed, the Vickrey-Clarke-Groves mechanism truthfully implements the value-maximizing choices, assuming agents’ utilities are quasi-linear in money. We study social choice with payments where utilities are non-quasi-linear. The main result of this paper is a tight characterization of the maximal non-quasi-linear utility domain, which we call the largest parallel domain, for which where there exist nondictatorial mechanisms that are strategy-proof, onto, deterministic, individually rational and that do not not make positive transfers to the agents. In particular, mechanisms satisfying the above conditions must be dictatorial when the type domain is quasi-linear together with any single non-parallel type. We then show that for richer utility domains which still differ very slightly from quasi-linearity, every strategy-proof, onto and deterministic mechanism must be a dictatorship.

The existence of truthful social choice mechanisms strongly depends on whether monetary transfers are allowed. Without payments there are no truthful, non-dictatorial mechanisms under mild requirements, whereas the VCG mechanism guarantees truthfulness along with welfare maximization when there are payments and utility is quasi-linear in money. In this paper we study mechanisms in which we can use payments but where agents have non quasi-linear utility functions. Our main result extends the Gibbard-Satterthwaite impossibility result by showing that, for two agents, the only truthful mechanism for at least three alternatives under general decreasing utilities remains dictatorial. We then show how to extend the VCG mechanism to work under a more general utility space than quasi-linear (the \"parallel domain\") and show that the parallel domain is maximal--no mechanism with the VCG properties exists in any larger domain.