We study actual bidding behavior when a new auction format gets introduced into the marketplace. More specifically, we investigate this question using a novel dataset on internet display advertising auctions that exploits a staggered adoption by different publishers (sellers) of first-price auctions (FPAs), instead of the traditional second-price auctions (SPAs). Event study regression estimates indicate that, immediately after the auction format change, the revenue per sold impression (price) jumped considerably for the treated publishers relative to the control publishers, ranging from 35% to 75% of the pre-treatment price level of the treatment group. Further, we observe that in later auction format changes the increase in the price levels under FPAs relative to price levels under SPAs dissipates over time, reminiscent of the celebrated revenue equivalence theorem. A possible interpretation of these facts is initially insufficient bid shading after the format change rather than an immediate shift to a new Bayesian Nash equilibrium. The gradual decrease in prices can be interpreted as the result of bidders' learning to shade their bids. We also present suggestive evidence that bidders' sophistication may have impacted their response to the auction format change. Our work constitutes one of the first field studies on bidders' responses to auction format changes, providing an important complement to theoretical model predictions. As such, it provides valuable information to auction designers when considering the implementation of different formats.

Two-sided marketplace platforms often run experiments (or A/B tests) to test the effect of an intervention before launching it platform-wide. A typical approach is to randomize users into a treatment group, which receives the intervention, and a control group, which does not. The platform then compares the performance in the two groups to estimate the effect if the intervention were launched to everyone. We focus on two common experiment types, where the platform randomizes users either on the supply side or on the demand side. For these experiments, it is known that the resulting estimates of the treatment effect are typically biased: individuals in the market compete with each other, which creates interference and leads to a biased estimate. Here, we observe that economic interactions (competition among demand and supply) lead to statistical phenomenon (biased estimates).

We develop an analytical framework to study experimental design in two-sided marketplaces. Many of these experiments exhibit interference, where an intervention applied to one market participant influences the behavior of another participant. This interference leads to biased estimates of the treatment effect of the intervention. We develop a stochastic market model and associated mean field limit to capture dynamics in such experiments and use our model to investigate how the performance of different designs and estimators is affected by marketplace interference effects. Platforms typically use two common experimental designs: demand-side “customer” randomization ([Formula: see text]) and supply-side “listing” randomization ([Formula: see text]), along with their associated estimators. We show that good experimental design depends on market balance; in highly demand-constrained markets, [Formula: see text] is unbiased, whereas [Formula: see text] is biased; conversely, in highly supply-constrained markets, [Formula: see text] is unbiased, whereas [Formula: see text] is biased. We also introduce and study a novel experimental design based on two-sided randomization ([Formula: see text]) where both customers and listings are randomized to treatment and control. We show that appropriate choices of [Formula: see text] designs can be unbiased in both extremes of market balance while yielding relatively low bias in intermediate regimes of market balance. This paper was accepted by David Simchi-Levi, revenue management and market analytics.

We study the classic sequential screening problem under ex-post participation constraints. Thus the seller is required to satisfy buyers' ex-post participation constraints. A leading example is the online display advertising market, in which publishers frequently cannot use up-front fees and instead use transaction-contingent fees.

We study the dynamic mechanism design problem of a seller who repeatedly auctions independent items over a discrete time horizon to buyers who face a cumulative budget constraint. A driving motivation behind our model is the emergence of real-time bidding markets for online display advertising in which such budgets are prevalent. We assume the seller has a strong form of limited commitment: she commits to the rules of the current auction but cannot commit to those of future auctions. We show that the celebrated Myersonian approach that leverages the envelope theorem fails in this setting, and therefore, characterizing the dynamic optimal mechanism seems intractable. Despite these challenges, we derive and characterize a near-optimal dynamic mechanism. To do so, we show that the Myersonian approach is recovered in a corresponding fluid continuous time model in which the time interval between consecutive items becomes negligible. Then we leverage this approach to characterize the optimal dynamic direct-revelation mechanism, highlighting novel incentives at play in settings with buyers’ budget constraints and seller’s limited commitment. We show through a combination of theoretical and numerical results that the optimal mechanism arising from the fluid continuous time model approximately satisfies incentive compatibility for the buyers and is approximately sequentially rational for the seller in the original discrete time model. Supplemental material is available at https://doi.org/10.1287/opre.2018.1830 .

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.

Ad Exchanges are emerging Internet markets where advertisers may purchase display ad placements, in real-time and based on specific viewer information, directly from publishers via a simple auction mechanism. Advertisers join these markets with a prespecified budget and participate in multiple second-price auctions over the length of a campaign. This paper studies the competitive landscape that arises in Ad Exchanges and the implications for publishers' decisions.

The main advantage of a procurement combinatorial auction (CA) is that it allows suppliers to express cost synergies through package bids. However, bidders can also strategically take advantage of this flexibility, by discounting package bids and "inflating'" bid prices for single-items, even in the absence of cost synergies; the latter behavior can hurt the performance of the auction. It is an empirical question whether allowing package bids and running a CA improves performance in a given setting. In this paper, we develop a structural estimation approach for large-scale first-price CAs to estimate the firms' cost structure using bidding data, and we use these estimates to evaluate the performance of the auction. To overcome the computational difficulties arising from the large number of bids observed in large-scale CAs, we propose a novel simplified model of bidders' behavior based on pricing package characteristics. This simplified model uses markup restrictions that are parsimonious yet sufficiently flexible to capture strategic markup adjustments that can reduce the performance of CAs. We apply our method to the Chilean school meals auction, in which the government procures half a billion dollars' worth of meal services every year and bidders submit thousands of package bids. Our estimates suggest that bidders' cost synergies are economically significant in this application, and the current CA mechanism achieves high allocative efficiency and a reasonable procurement cost. We believe this is the first paper in the literature that empirically shows that a CA performs well in practice.

We propose a mean-field approximation that dramatically reduces the computational complexity of solving stochastic dynamic games. We pro- vide conditions that guarantee our method approximates an equilibrium as the number of agents grow. We then derive a performance bound to assess how well the approximation performs for any given number of agents. We apply our method to an important class of problems in ap- plied microeconomics. We show with numerical experiments that we are able to greatly expand the set of economic problems that can be analyzed computationally.