We study sequential procurement auctions where the sellers are provided with a ``best and final offer'' (BAFO) strategy. This strategy allows each seller $i$ to effectively ``freeze'' their price while remaining active in the auction, and it signals to the buyer, as well as all other sellers, that seller $i$ would reject any price lower than that. This is in contrast to prior work, e.g., on descending auctions, where the options provided to each seller are to either accept a price reduction or reject it and drop out. As a result, the auctions that we consider induce different extensive form games and our goal is to study the subgame perfect equilibria of these games. We focus on settings involving multiple sellers who have full information regarding each other's cost (i.e., the minimum price that they can accept) and a single buyer (the auctioneer) who has no information regarding these costs. Our main result shows that the auctions enhanced with the BAFO strategy can guarantee efficiency in every subgame perfect equilibrium, even if the buyer's valuation function is an arbitrary monotone function. This is in contrast to prior work which required that the buyer's valuation satisfies restrictive properties, like gross substitutes, to achieve efficiency. We then also briefly analyze the seller's cost in the subgame perfect equilibria of these auctions and we show that even if the auctions all return the same outcome, the cost that they induce for the buyer can vary significantly.

Online platforms and regulators face a continuing problem of designing effective evaluation metrics. While tools for collecting and processing data continue to progress, this has not addressed the problem of "unknown unknowns", or fundamental informational limitations on part of the evaluator. To guide the choice of metrics in the face of this informational problem, we turn to the evaluated agents themselves, who may have more information about how to measure their own outcomes. We model this interaction as an agency game, where we ask: "When does an agent have an incentive to reveal the observability of a metric to their evaluator?" We show that an agent will prefer to reveal metrics that differentiate the most difficult tasks from the rest, and conceal metrics that differentiate the easiest. We further show that the agent can prefer to reveal a metric "garbled" with noise over both fully concealing and fully revealing. This indicates an economic value to privacy that yields Pareto improvement for both the agent and evaluator. We demonstrate these findings on data from online rideshare platforms.

We study revenue variance in the sale of k homogeneous items to risk-neutral, unit-demand bidders with independent private values. Although the Revenue Equivalence Theorem implies that standard auctions generate the same expected revenue, the distribution of revenue differs across mechanisms. Prior work shows that, in single-item environments with ex-post individual rationality (IR), the first-price auction minimizes revenue variance. We show that this result is fragile. Under interim IR, the optimality of the first-price auction breaks down in asymmetric single-item settings, and we characterize the variance-minimizing mechanisms for any implementable allocation rule in this environment. In multi-item symmetric regular environments with interim IR, we construct a mechanism that implements the efficient allocation and guarantees constant revenue while maintaining non-negative payments. Under ex-post IR, we show that revenue variance can be reduced relative to winner-pays-bid formats by introducing negative correlations in payments. Nevertheless, we show that the variance ranking between the winner-pays-bid auction and the uniform (k+1)-st price auction is maintained in multi-unit settings.

This article uses precise and novel data on country-level Cloud IaaS and PaaS revenue to measure the intensive margin of technology diffusion across countries and within countries over time. We horse race diffusion models and find that cloud diffusion exhibits both Log-Log and Logistic Growth patterns. We use cross validation on nearly 100 features to determine what correlates with cross-country differences. We find that increases in features impacting Gross Domestic Product, Internet Connectivity, and Human Capital are associated with increases in intensity of cloud adoption. We finally compare the relative impacts of these variables using a random coefficients model. Although correlative, our algorithmic research design motivates data-driven hypothesis generation and further causal work regarding how policymakers can encourage more cloud computing adoption and technology adoption more broadly.

Google Maps uses current and historical traffic trends to provide routes to drivers. In this paper, we use microscopic traffic simulation to quantify the improvements to both travel time and CO$_2$ emissions from Google Maps real-time navigation. A case study in Salt Lake City shows that Google Maps users are, on average, saving 1.7% of CO$_2$ emissions and 6.5% travel time. If we restrict to the users for which Google Maps finds a different route than their original route, the average savings are 3.4% of CO$_2$ emissions and 12.5% of travel time. These results are based on traffic conditions observed during the Covid-19 pandemic. As congestion gradually builds back up to pre-pandemic levels, it is expected to lead to even greater savings in emissions.

In the classical secretary problem, one attempts to find the maximum of an unknown and unlearnable distribution through sequential search. In many real-world searches, however, distributions are not entirely unknown and can be learned through experience. To investigate learning in such settings, we conduct a large-scale behavioral experiment in which people search repeatedly from fixed distributions in a “repeated secretary problem.” In contrast to prior investigations that find no evidence for learning in the classical scenario, in the repeated setting we observe substantial learning resulting in near-optimal stopping behavior. We conduct a Bayesian comparison of multiple behavioral models, which shows that participants’ behavior is best described by a class of threshold-based models that contains the theoretically optimal strategy. Fitting such a threshold-based model to data reveals players’ estimated thresholds to be close to the optimal thresholds after only a small number of games. This paper was accepted by Yuval Rottenstreich, judgment and decision making.

We study a new model of complementary valuations, which we call "proportional complementarities.'' In contrast to common models, such as hypergraphic valuations, in our model, we do not assume that the extra value derived from owning a set of items is independent of the buyer's base valuations for the items. Instead, we model the complementarities as proportional to the buyer's base valuations, and these proportionalities are known market parameters. Our goal is to design a simple pricing scheme that, for a single buyer with proportional complementarities, yields approximately optimal revenue. We define a new class of mechanisms where some number of items are given away for free, and the remaining items are sold separately at inflated prices. We find that the better of such a mechanism and selling the grand bundle earns a 12-approximation to the optimal revenue for pairwise proportional complementarities. This confirms the intuition that items should not be sold completely separately in the presence of complementarities. In the more general case, a buyer has a maximum of proportional positive hypergraphic valuations, where a hyperedge in a given hypergraph describes the boost to the buyer's value for item i given by owning any set of items T in addition. The maximum-out-degree of such a hypergraph is d, and k is the positive rank of the hypergraph. For valuations given by these parameters, our simple pricing scheme is an O(min{d,k})-approximation.

We address the question of how a monopolist should price when facing evolutionary consumers who gradually move in the direction of following their optimal strategy but may make temporary suboptimal choices. We show that under a broad generalization of the most commonly used model of evolution, the monopolist will set a path of prices such that all consumers eventually stop purchasing the monopolist's product.

It is a well-known statistical property that learning tends to slow down with each additional data point. Thus even if scale effects are important in web search, they could be important in a range that any viable entrant could easily achieve. In this paper we address these questions using browsing logs that give click-through-rates by query on two major search engines. An ideal experiment would be to fix the "query difficulty" and exogenously provide more or less historical data. We approximate the ideal experiment by finding queries that were not previously observed. Of these "new queries", some grow to be moderately popular, having 1000–2000 clicks in a calendar year. We examine ranking quality during the lifespan of the query and find statistically significant improvement on the order of 2–3% and learning faster at lower levels of data. We are careful to rule out alternate explanations for this pattern. In particular, we show that the effect is not explained by new, more relevant documents entering the landscape, rather it is mainly shifting the most relevant documents to the top of the ranking. We thus conclude they represent direct scale effects. Finally, we show that scale helps link new queries to existing queries with ample historical data by forming edges in the query document bipartite graph. This "indirect knowledge" is shown to be important for "deflating uniqueness" and improving ranking.

In the classical secretary problem, one attempts to find the maximum of an unknown and unlearnable distribution through sequential search. In many real-world searches, however, distributions are not entirely unknown and can be learned through experience. To investigate learning in such a repeated secretary problem we conduct a large-scale behavioral experiment in which people search repeatedly from fixed distributions. In contrast to prior investigations that find no evidence for learning in the classical scenario, in the repeated setting we observe substantial learning resulting in near-optimal stopping behavior. We conduct a Bayesian comparison of multiple behavioral models which shows that participants' behavior is best described by a class of threshold-based models that contains the theoretically optimal strategy. Fitting such a threshold-based model to data reveals players' estimated thresholds to be surprisingly close to the optimal thresholds after only a small number of games.

We examine the economics of demand and supply in cloud computing. The public cloud offers three main benefits to firms: 1) utilization can be scaled up or down easily; 2) capital expenditure (on-premises servers) can be converted to operating expenses, with the capital incurred by a specialist; 3) software can be ``pay-as-you-go.'' These benefits increase with the firm's ability to dynamically scale resource utilization and thus point to the need for dynamic prices to shape demand to the (short-run) fixed datacenter supply. Detailed utilization analysis reveals the large swings in utilization at the hourly, daily or weekly level are very rare at the customer level and non-existent at the datacenter level. Furthermore, few customers show volatility patterns that are excessively correlated with the market. These results explain why fixed prices currently prevail despite the seeming need for time-varying dynamics. Examining the actual CPU utilization provides a lens into the future. Here utilization varies by order half the datacenter capacity, but most firms are not dynamically scaling their assigned resources at-present to take advantage of these changes. If these gains are realized, demand fluctuations would be on par with the three classic industries where dynamic pricing is important (hotels, electricity, airlines) and dynamic prices would be essential for efficiency.

SummaryWe characterize the optimal loss functions for predicted click‐through rates in auctions for online advertising. Whereas standard loss functions such as mean squared error or log likelihood severely penalize large mispredictions while imposing little penalty on smaller mistakes, a loss function reflecting the true economic loss from mispredictions imposes significant penalties for small mispredictions and only slightly larger penalties on large mispredictions. We illustrate that when the model is misspecified using such a loss function can improve economic efficiency, but the efficiency gain is likely to be small.

This paper illustrates a method for making side payments to advertiser networks that creates an incentive for the advertiser networks to submit the second-highest bids they received to an ad exchange and simultaneously ensures that the publishers will make more money on average in the short run as a result of adopting this scheme. We also illustrate how this payment scheme affects publisher payoffs in the long run after advertisers have a chance to modify their strategies in response to the changed incentives of the mechanism.

Fraud is an ancient crime and one that annually causes hundreds of billions of dollars in losses. We develop an evolutionary theory that suggests cyclical behavior in frauds should be common. We perform a wavelet analysis of the frequencies of fraudulent and non-fraudulent offenses. Our results demonstrate that the frequencies of fraudulent offenses exhibit cyclical behavior that differs markedly from the cyclical behavior of non-fraudulent offenses.

The “Big Data” revolution is upon us: Firms are scrambling to hire a new brand of analysts dubbed “data scientists” and universities have responded to this demand by introducing data science courses into degrees ranging from computer science to business. Survey-based reports find that firms are currently spending an estimated $36 billion on storage and infrastructure and that is expected to double by 2020. Once companies are logging and storing detailed data on all their customer engagements and internal processes, what’s next? Presumably, firms are investing in big data infrastructure because they believe that it offers a positive return on investment. However, looking at the surveys and consulting reports it is unclear what are the precise use cases which will drive this positive ROI from big data.

This chapter surveys the theoretical and empirical literature on bid rigging in auctions. In particular, it reviews the theory and practice of bidding rings in one-shot auctions and in repeated auctions. The main theme is how the type of auction, whether it is first-price or second-price, sealed bid or oral, affects the incentive of bidders to collude and the way in which they collude.