We study resale platforms, an emerging type of online marketplaces in developing countries. Resale platforms are designed for individuals (resellers) to sell products to others as opposed to buying for themselves, enabling them to supplement their income by earning a margin on the transactions they generate. One challenge these platforms face is that competition among resellers may emerge as more of them join the platform, as their social circles increasingly overlap.

Two-sided platforms rely on their recommendation algorithms to help their visitors successfully find a match. However, on platforms such as VolunteerMatch - which has facilitated tens of millions of connections between volunteers and nonprofits - a sizable fraction of website traffic arrives directly to a nonprofit's volunteering page via an external link, thus bypassing the platform's recommendation algorithm. We study how such platforms should account for this external traffic in the design of their recommendation engines, given the goal of maximizing the total number of successful matches. We model the platform's problem as a special case of online matching with stochastic rewards, where (using VolunteerMatch as a motivating example) volunteers arrive sequentially and (probabilistically) match with one opportunity, each of which has finite need for volunteers. In our framework, external traffic is interested only in their targeted opportunity; in contrast, internal traffic may be interested in many opportunities, and the platform's online algorithm selects which opportunity to recommend. In evaluating the performance of different algorithms, we take a worst-case analysis approach, yet we refine the notion of the competitive ratio by parameterizing it based on the amount of external traffic. After demonstrating the shortcomings of a commonly-used algorithm which is optimal in the absence of external traffic, we introduce a new algorithm - Adaptive Capacity (AC) - which accounts for matches differently based on whether they originate from internal or external traffic. We establish a lower bound on AC's competitive ratio that is increasing in the amount of external traffic, and we compare our lower bound to a parameterized upper bound on the competitive ratio of any online algorithm. We find that (in certain parameter regimes) AC is near-optimal regardless of the amount of external traffic, even though it does not know this amount a priori. Our analysis utilizes a path-based, pseudo-rewards approach, which we further generalize to settings where the platform can recommend a ranked set of opportunities. Beyond our theoretical results, we demonstrate the strong performance of AC in a case study motivated by VolunteerMatch data.

Motivated by online labor markets, we consider the online assortment optimization problem faced by a two-sided matching platform that hosts a set of suppliers waiting to match with a customer. Arriving customers are shown an assortment of suppliers, and may choose to issue a match request to one of them. After spending some time on the platform, each supplier reviews all the match requests he has received and, based on his preferences, he chooses whether to match with a customer or to leave unmatched. We study how platforms should design online assortment algorithms to maximize the expected number of matches in such two-sided settings. We show that, when suppliers do not immediately accept/reject match requests, our problem is fundamentally different from standard (one-sided) assortment problems, where customers choose over a set of products. We establish that a simple greedy algorithm is 1/2-competitive against an optimal clairvoyant algorithm that knows in advance the full sequence of customers' arrivals. However, unlike related online assortment problems, no randomized algorithm can achieve a better competitive ratio, even in asymptotic regimes. To advance beyond this general impossibility, we consider structured settings where suppliers' preferences are described by the Multinomial Logit and Nested Logit choice models. We develop specialized balancing algorithms, which we call preference-aware, that leverage general information about the suppliers' choice models. In certain settings, the resulting competitive ratios are provably larger than the standard "barrier" of 1-1/e in the adversarial arrival model. Our results suggest that the shape and timing of suppliers' choices play critical roles in designing online two-sided assortment algorithms.

We explore the welfare implications of data tracking technologies that enable firms to collect consumer data and potentially use it for price discrimination. The model we develop centers around two features: first, competition between firms and, second, consumers' level of sophistication. Our baseline environment features a firm that can collect information about the consumers it transacts with in a duopoly market, which it can then use in a second monopoly market. We characterize and compare the equilibrium outcomes in three settings of interest: (i) an economy with myopic consumers, who, when making purchase decisions, do not internalize the fact that firms have the ability to track their behavior and use this information in future transactions, (ii) an economy with forward-looking consumers, who take into account the implications of data tracking when determining their actions, and (iii) an economy where no data tracking technologies are used either due to technological or regulatory constraints.

Motivated by our collaboration with a major US online dating company, we study how a platform should dynamically select the set of potential partners to show to each user in each period to maximize the expected number of matches in a time horizon, considering that a match is formed only after two users like each other, possibly in different periods. Increasing match rates is a prevalent objective among online platforms. We provide insights on how to leverage users? preferences and behavior towards this end. Our proposed algorithm was piloted by our collaborator in major cities in the US. We introduce a model of a dynamic matching market mediated by a platform. The platform hosts a set of users and must decide, in each period, what subset of profiles to show to each user to maximize the overall expected number of matches. Each period, users log in with some time-dependent probability and, conditional on logging in, observe a set of profiles-an assortment-that satisfies the constraints imposed by the platform. Then, users decide whether to like or not like each profile in their assortment based on their preferences. If two users mutually like each other, possibly in different periods, a match is generated. Our goal is to find an algorithm to maximize the total expected number of matches generated by the platform over an entire time horizon. We show that the platform's problem is computationally hard.

Platforms facilitating the exchange of goods and services between individuals are prevalent: one can purchase goods from others on eBay, arrange accommodation through Airbnb, and find temporary projects/workers on online labor markets such as Upwork. The majority of these markets exhibit three key features. First, the platforms do not dictate the transaction prices, i.e., buyers and sellers determine at which price the goods/services will be exchanged. Second, not all buyers or sellers on a platform are compatible. This may be due to taste differences (a buyer may be interested only in the types of goods/services a subset of the sellers offer), geographical or import/export restrictions (e.g., being able to provide services only regionally), or other sources of mismatch (e.g., a mismatch in the desired and available skills in online labor markets). Finally, buyers/sellers are heterogeneous in their valuations for goods or services they receive/provide.

Two-sided matching platforms, such as those for labor, accommodation, dating, and taxi hailing, can control and optimize over many aspects of the search for partners. To understand how the search for partners should be designed, we consider a dynamic model of search by strategic agents with costly discovery of pair-specific match value. We find that in many settings, the platform can mitigate wasteful competition in partner search via restricting what agents can see/do. For medium-sized screening costs (relative to idiosyncratic variation in utilities), the platform should prevent one side of the market from exercising choice (similar to Instant Book on Airbnb), whereas for large screening costs, the platform should centrally determine matches (similar to taxi hailing marketplaces). Surprisingly, simple restrictions can improve social welfare even when screening costs are small, and agents on each side are ex-ante homogeneous. In asymmetric markets where agents on one side have a tendency to be more selective (due to smaller screening costs or greater market power), the platform should force the more selective side of the market to reach out first, by explicitly disallowing the less selective side from doing so. This allows the agents on the less selective side to exercise more choice in equilibrium.When agents are vertically differentiated, forcing one side of the market to propose results in a significant increase in welfare even in the limit of vanishing screening costs. Furthermore, a Pareto improvement in welfare is possible in this limit: the weakest agents can be helped without hurting other agents. In addition, in this setting the platform can further boost welfare by hiding quality information.

We consider the problem faced by a procurement agency that runs an auction-type mechanism to construct an assortment of products with posted prices, from a set of differentiated products offered by strategic suppliers. Heterogeneous consumers then buy their most preferred alternative from the assortment as needed. Framework agreements (FAs), widely used in the public sector, take this form; the central government runs the initial auction and then the public organizations (hospitals, schools, etc.) buy from the selected assortment. This type of mechanism is also relevant in other contexts, such as the design of medical formularies and group buying. When evaluating the bids, the procurement agency must consider the optimal trade-off between offering a richer assortment of products for consumers versus offering less variety, hoping to engage the suppliers in a more aggressive price competition. We develop a mechanism design approach to study this problem and provide a characterization of the optimal assortments and prices.

Assignment markets involve matching with transfers, as in labor markets and housing markets. We consider a two-sided assignment market with agent types and stochastic structure similar to models used in empirical studies, and characterize the size of the core in such markets. Each agent has a randomly drawn productivity with respect to each type of agent on the other side. The value generated from a match between a pair of agents is the sum of the two productivity terms, each of which depends only on the type but not the identity of one of the agents, and a third deterministic term driven by the pair of types. We allow the number of agents to grow, keeping the number of agent types fixed. Let n be the number of agents and K be the number of types on the side of the market with more types. We find, under reasonable assumptions, that the relative variation in utility per agent over core outcomes is bounded as O*(1/n1/K), where polylogarithmic factors have been suppressed. Further, we show that this bound is tight in worst case. We also provide a tighter bound under more restrictive assumptions.

We consider the problem of reallocating indivisible objects amongst a set of agents when the preference ordering of each agent may contain indifferences. The same model, but with strict preferences, goes back to the seminal work of Shapley and Scarf in 1974. When preferences are strict, we now know that the Top-Trading Cycles (TTC) mechanism invented by Gale is Pareto efficient, strategy-proof, and finds a core allocation, and that it is the only mechanism satisfying these properties. In the extensive literature on this problem since then, the TTC mechanism has been characterized in multiple ways, establishing its central role within the class of all allocation mechanisms. The question motivating our work is the extent to which these results can be generalized to the setting with indifferences. Our main contribution is a general framework to design strategyproof mechanisms that find a Pareto optimal allocation in the weak-core. Along the way, we establish a sufficient condition for a mechanism (within a broad class of mechanisms) to be strategyproof and use this condition to design fast algorithms for finding a "good" reallocation. Our results generalize and unify two (different) mechanisms for the reallocation problem derived, independently of each other, by Manjunath and Jaramillo, and Alcalde-Unzu and Molis.