We study the matching of jobs to workers waiting in a queue, for example a ridesharing platform dispatching drivers to pick up riders at an airport. Under FIFO dispatching, the heterogeneity in earnings from different trips incentivizes drivers to cherrypick, increasing riders' waiting times for a match, and resulting in poor reliability for riders, low average earnings for drivers, and a loss of throughput and revenue for the platform. Simple fixes by limiting dispatching transparency or drivers' flexibility are neither desirable nor fully effective. Optimal origin-destination based prices are incentive aligned in theory, but are hard to implement in practice due to operational constraints.

The second price auction has been the prevalent auction format used by advertising exchanges because of its simplicity and desirable incentive properties. However, even with an optimized choice of reserve prices, this auction is not revenue optimal when the bidders are heterogeneous and their valuation distributions differ significantly. In order to optimize the revenue of advertising exchanges, we propose an auction format called the boosted second price auction, which assigns a boost value to each bidder. The auction favors bidders with higher boost values and allocates the item to the bidder with the highest boosted bid. We propose a data-driven approach to optimize boost values using the previous bids of the bidders. Our analysis of auction data from Google's online advertising exchange shows that the boosted second price auction with data-optimized boost values outperforms the second price auction and empirical Myerson auction by up to 6% and 3%, respectively.

We consider the problem of multi-product dynamic pricing, in a contextual setting, for a seller of differentiated products. In this environment, the customers arrive over time and products are described by high-dimensional feature vectors. Each customer chooses a product according to the widely used Multinomial Logit (MNL) choice model and her utility depends on the product features as well as the prices offered. The seller a-priori does not know the parameters of the choice model but can learn them through interactions with customers. The seller's goal is to design a pricing policy that maximizes her cumulative revenue. This model is motivated by online marketplaces such as Airbnb platform and online advertising. We measure the performance of a pricing policy in terms of regret, which is the expected revenue loss with respect to a clairvoyant policy that knows the parameters of the choice model in advance and always sets the revenue-maximizing prices. We propose a pricing policy, named M3P, that achieves a T-period regret of O(log(Td)(√(T)+d log(T))) under heterogeneous price sensitivity for products with features of dimension d. We also use tools from information theory to prove that no policy can achieve worst-case T-regret better than Ω(√(T)).

Ride-hailing marketplaces like Uber and Lyft use dynamic pricing, often called surge, to balance the supply of available drivers with the demand for rides. We study pricing mechanisms for such marketplaces from the perspective of drivers, presenting the theoretical foundation that has informed the design of Uber's new additive driver surge mechanism. We present a dynamic stochastic model to capture the impact of surge pricing on driver earnings and their strategies to maximize such earnings. In this setting, some time periods (surge) are more valuable than others (non-surge), and so trips of different time lengths vary in the induced driver opportunity cost. First, we show that multiplicative surge, historically the standard on ride-hailing platforms, is not incentive compatible in a dynamic setting. We then propose a structured, incentive-compatible pricing mechanism. This closed-form mechanism has a simple form and is well-approximated by Uber's new additive surge mechanism. Finally, through both numerical analysis and real data from a ride-hailing marketplace, we show that additive surge is more incentive compatible in practice than is multiplicative surge.

We study the pricing problem faced by a firm that sells a large number of products, described via a wide range of features, to customers that arrive over time. Customers independently make purchasing decisions according to a general choice model that includes products features and customers' characteristics, encoded as $d$-dimensional numerical vectors, as well as the price offered. The parameters of the choice model are a priori unknown to the firm, but can be learned as the (binary-valued) sales data accrues over time. The firm's objective is to maximize its revenue. We benchmark the performance using the classic regret minimization framework where the regret is defined as the expected revenue loss against a clairvoyant policy that knows the parameters of the choice model in advance, and always offers the revenue-maximizing price. This setting is motivated in part by the prevalence of online marketplaces that allow for real-time pricing. We assume a structured choice model, parameters of which depend on $s_0$ out of the $d$ product features. Assuming that the market noise distribution is known, we propose a dynamic policy, called Regularized Maximum Likelihood Pricing (RMLP) that leverages the (sparsity) structure of the high-dimensional model and obtains a logarithmic regret in $T$. More specifically, the regret of our algorithm is of $O(s_0 \log d \cdot \log T)$. Furthermore, we show that no policy can obtain regret better than $O(s_0 (\log d + \log T))$. {In addition, we propose a generalization of our policy to a setting that the market noise distribution is unknown but belongs to a parametrized family of distributions. This policy obtains regret of $O(\sqrt{(\log d)T})$. We further show that no policy can obtain regret better than $\Omega(\sqrt{T})$ in such environments.}

Billions of dollars worth of display advertising are sold via contracts and deals. This paper presents a formal study of preferred deals, a new generation of contracts for selling online advertisement, that generalize the traditional reservation contracts; these contracts are suitable for advertisers with advanced targeting capabilities. We propose a constant-factor approximation algorithm for maximizing the revenue that can be obtained from these deals. We show, both theoretically and via data analysis, that deals, with appropriately chosen minimum-purchase guarantees, can yield significantly higher revenue than auctions. We evaluate our algorithm using data from Google's ad exchange platform. Our algorithm obtains about 90% of the optimal revenue where the second-price auction, even with personalized reserve, obtains at most 52% of the benchmark.

Ad exchange platforms connect online publishers and advertisers and facilitate the sale of billions of impressions every day. We study these environments from the perspective of a publisher who wants to find the profit-maximizing exchange in which to sell his inventory. Ideally, the publisher would run an auction among exchanges. However, this is not usually possible due to practical business considerations. Instead, the publisher must send each impression to only one of the exchanges, along with an asking price. We model the problem as a variation of the multi-armed bandits problem in which exchanges (arms) can behave strategically in order to maximizes their own profit. We propose e mechanisms that find the best exchange with sub-linear regret and have desirable incentive properties.

Data marketplaces provide platforms for management of large data sets. The data markets are rapidly growing, yet the pricing strategies for data and data analytics are not yet well-understood. In this paper, we explore some of the pricing schemes applicable to data marketplaces in the context of transportation traffic data. This includes historical and real-time freeway and arterial congestion data. We investigate pricing raw sensor data vs. processed information (e.g, prediction of traffic patterns or route planning services) and show that, under natural assumptions, the raw data should be priced higher than processed information.

We analyze several distributed, continuous time protocols for a fair allocation of bandwidths to flows in a network (or resources to agents). Our protocols converge to an allocation that is a logarithmic approximation, simultaneously, to all canonical social welfare functions (i.e., functions that are symmetric, concave, and nondecreasing). These protocols can be started in an arbitrary state. Although a similar protocol was known before, it only applied to the simple bandwidth allocation problem, and its stability and convergence time were not understood. In contrast, our protocols also apply to the more general case of Leontief utilities, where each user may place a different requirement on each resource. Furthermore, we prove that our protocols converge in polynomial time. The best convergence time we prove is O ( n log nc MAX a MAX / c MIN a MIN ), where n is the number of agents in the network, c MAX and c MIN are the maximum and minimum capacity of the links, and a max , a min are respectively the largest and smallest Leontief coefficients. This time is achieved by a simple Multiplicative Increase, Multiplicative Decrease (MIMD) protocol that had not been studied before in this setting. We also identify combinatorial properties of these protocols that may be useful in proving stronger convergence bounds. The final allocations by our protocols are supported by usage-sensitive dual prices that are fair in the sense that they shield light users of a resource from the impact of heavy users. Thus, our protocols can also be thought of as efficient distributed schemes for computing fair prices.

Motivated by the availability of real-time data on customer characteristics, we consider the problem of personalizing the assortment of products to each arriving customer. For an arriving customer of type z, the company must decide, in real-time, on the assortment of products to offer. Given the offered assortment, the customers make choices on which products to buy, if any, according to a general choice model that is specific to each customer type. Our goal is to develop a revenue-maximizing policy that determines the assortment to offer to each arriving customer, taking into account the customer type and the current inventories.

We introduce a new framework for designing online algorithms that can incorporate additional information about the input sequence, while maintaining a reasonable competitive ratio if the additional information is incorrect. Within this framework, we present online algorithms for several problems including allocation of online advertisement space, load balancing, and facility location.

We study a duopoly setting that consists of two capacity-constrained sellers and a single buyer. The capacity of each seller is her private information. The sellers simultaneously offer menus of quantity-price contracts to the buyer. Then the buyer chooses a set of contracts to maximize his own utility. We show that under certain natural conditions there exists a pure strategy equilibrium for the sellers which defines an efficient allocation. We study the effects of asymmetry of information, using the full information setting as a benchmark. We show that the revenue is higher when the capacities obtained by the sellers are private information.

We present a simple auction mechanism which extends the second-price auction with reserve and is truthful in expectation. This mechanism is particularly effective in private value environments where the distribution of valuations are irregular. Bidders can "buy-it-now", or alternatively "take-a-chance" where the top d bidders are equally likely to win. The randomized take-a-chance allocation incentivizes high valuation bidders to buy-it-now. We show that for a large class of valuations, this mechanism achieves similar allocations and revenues as Myerson's optimal mechanism, and outperforms the second-price auction with reserve.

We study an online job scheduling problem motivated by storyboarding in web advertising, where an advertiser derives value from uninterrupted sequential access to a user surfing the web. The user ceases to browse with probability 1 – β at each step, independently. Stories (jobs) arrive online; job s has length ℓs and per-unit value vs. A value vs is obtained for every unit of the job that is scheduled consecutively without interruption, discounted for the time at which it is scheduled. Jobs can be preempted, but no further value can be derived from the residual unscheduled units of the job. We seek an online algorithm whose total reward is competitive against that of the offline scheduler that knows all jobs in advance. We consider two models based on the maximum delay that can be allowed between the arrival and scheduling of a job. In the first, a job can be scheduled anytime after its arrival; in the second a job is lost unless scheduled immediately upon arrival, preempting a currently running job if needed. The two settings correspond to two natural models of how long an advertiser retains interest in a relevant user. We show that there is, in fact, a sharp separation between what an online scheduler can achieve in these two settings. In the first setting with no deadlines, we give a natural deterministic algorithm with a constant competitive ratio against the offline scheduler. In contrast, we show that in the sharp deadline setting, no (deterministic or randomized) online algorithm can achieve better than a polylogarithmic ratio.

Most current banner advertising is sold through negotiation thereby incurring large transaction costs and possibly suboptimal allocations. We propose a new automated system for selling banner advertising. In this system, each advertiser specifies a collection of host webpages which are relevant to his product, a desired total quantity of impressions on these pages, and a maximum per-impression price. The system selects a subset of advertisers as 'winners' and maps each winner to a set of impressions on pages within his desired collection. The distinguishing feature of our system as opposed to current combinatorial allocation mechanisms is that, mimicking the current negotiation system, we guarantee that winners receive at least as many advertising opportunities as they requested or else receive ample compensation in the form of a monetary payment by the host. Such guarantees are essential in markets like banner advertising where a major goal of the advertising campaign is developing brand recognition.

We study the Cost-Per-Action or Cost-Per-Acquisition (CPA) charging scheme in online advertising. In this scheme, instead of paying per click, the advertisers pay only when a user takes a specific action (e.g. fills out a form) or completes a transaction on their websites.

We study the problem of finding approximate Nash equilibria of two player games. We show that for any 0<ε<1, there is no 1 1 + ε - approximate equilibrium with strategies of support O(log n ε2).

We study the problem of optimally allocating online advertisement space to budget-constrained advertisers. This problem was defined and studied from the perspective of worst-case online competitive analysis by Mehta et al.