arXiv subjects

Hongyao Ma

Publications and source records attributed to Hongyao Ma.

12 recordsLinked to original sources

arXiv2026

Sequential Offering in On-Demand Platforms: On the Optimality of Greedy Ranking

On-demand platforms face the fundamental challenge of fulfilling time-sensitive jobs with independent workers who may decline offers. To minimize delays and unfulfilled jobs, platforms frequently raise the offered wage sequentially following each rejection. However, the interaction between these dynamic price adjustments and the specific sequence in which workers are approached has been overlooked. In particular, if the best-suited workers (e.g., closest to the job) are also ranked earliest in the sequence, then those workers would see the lowest offered wages and may decline, leading to poor system outcomes where less-suited workers end up seeing the raised wages and accepting the job. We study the sequential offering problem to maximize expected welfare or platform profit by jointly optimizing the ranking of workers and the pricing trajectory. Surprisingly, our main result establishes that if the reservation wage distribution exhibits a non-increasing and convex density function (e.g., Uniform, Exponential), welfare is maximized by greedy ranking and wages optimized via backward induction. For arbitrary distributions, we prove that greedy ranking achieves a tight $n/(2n - 1)$ fraction of the prophet benchmark. Numerical results for settings beyond the distributional assumptions find welfare losses well below those allowed by the universal guarantee, even in families where greedy is provably suboptimal. This suggests that rather than sending initial "low ball'' offers to worse matches, platforms should stick with greedy ranking and optimize the wage offerings by appropriately taking the continuation value of the downstream offers into consideration.

cs.GT↗

arXiv2025

Potential-Based Greedy Matching for Dynamic Delivery Pooling

We study the dynamic pooling of multiple orders into a single trip, a strategy widely adopted by online delivery platforms. When an order has to be dispatched, the platform must determine which (if any) of the available orders to pool it with, weighing the immediate efficiency gains against the uncertain, differential benefits of holding each order for future pooling opportunities. In this paper, we demonstrate the effectiveness of using the delivery distance as a proxy for opportunity cost via a potential-based greedy algorithm (PB). The algorithm is simple, pooling each departing job with the available job that maximizes the immediate savings in travel distance minus "half its delivery distance", which we call the potential of the available job. Theoretically, we show that PB achieves vanishing worst-case regret per job as market density increases, whereas a naive greedy policy suffers constant regret. We further show that the potential approximates the true opportunity cost of dispatching a job, in a stochastic setting with sufficient density. Finally, we conduct extensive numerical experiments on both synthetic data and real-world data from the Meituan platform. Despite being forecast-agnostic, PB consistently outperforms greedy heuristics that rely on historical data. Moreover, PB achieves performance comparable to computationally-intensive batching heuristics, which themselves also benefit from incorporating the potential to further improve their performance or drastically reduce computational costs.

cs.DS↗

arXiv2023

Iterative Network Pricing for Ridesharing Platforms

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.

cs.GT↗

arXiv2022

Price Cycles in Ridesharing Platforms

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.

cs.GT↗

arXiv2021

Randomized FIFO Mechanisms

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.

cs.GT↗

arXiv2020

Matching Queues, Flexibility and Incentives

Problem definition: In many matching markets, some agents are fully flexible, while others only accept a subset of jobs. For example, ridesharing drivers can specify on the platform the destinations 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 ridesharing and affordable housing allocation.

cs.GT↗

arXiv2020

Penalty Bidding Mechanisms for Allocating Resources and Overcoming Present Bias

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.

cs.GT↗

arXiv2019

Ridesharing with Driver Location Preferences

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.

cs.MA↗

arXiv2018

Contingent Payment Mechanisms for Resource Utilization

We introduce the problem of assigning resources to improve their utilization. The motivation comes from settings where agents have uncertainty about their own values for using a resource, and where it is in the interest of a group 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 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. CP(W) is not dominated for expected welfare by any other mechanism, and second, amongst mechanisms that always allocate the resource and have a simple indirect structure, CP(W) strictly dominates every other mechanism. The special case with no upper bound on penalty, the contingent second-price mechanism, maximizes utilization. We extend the mechanisms to assign multiple, heterogeneous resources, and present a simulation study of the welfare properties of these mechanisms.

cs.GT↗

arXiv2018

Social Choice with Non Quasi-linear Utilities

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 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 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.

cs.GT↗

arXiv2018

Spatio-Temporal Pricing for Ridesharing Platforms

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 with the flexibility to decide how to work will nevertheless choose to accept their dispatched trips, rather than drive to another area or wait for higher prices or a better trip. In this work, we propose a complete information model that is simple yet rich enough to incorporate spatial imbalance and temporal variations in supply and demand -- conditions that lead to market failures in today's platforms. We 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. From any history onward, the equilibrium outcome of the STP mechanism is welfare-optimal, envy-free, individually rational, budget balanced, and core-selecting. We also prove the impossibility of achieving the same economic properties in a dominant-strategy equilibrium. Simulation results show that the STP mechanism can achieve substantially improved social welfare and earning equity than a myopic mechanism.

cs.GT↗

arXiv2017

Contract Design for Energy Demand Response

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, 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 in by SCE, the DR-VCG mechanism achieves higher participation, increased reliability, and significantly reduced total expenses.

cs.GT↗