Individual mobility patterns have a first-order impact on many operational decisions, ranging from facility location decisions to the optimization of transit systems. In the past, obtaining data on individual mobility patterns was a challenging task. However, in recent years, such data have been extensively collected via mobile phones. Moreover, various data providers have made these data available at scale, enabling decision makers to leverage them for large-scale analysis.

Experimentation is prevalent in online marketplaces and social networks to assess the effectiveness of new market intervention. In an experiment, the platform exposes a (randomized) group of targeted users to the new feature or, equivalently, assigns each user to either the treatment or the control group. The platform then uses the resulting outcomes to estimate the new feature's total market effect, i.e., the difference in total user outcomes if the feature is introduced to the entire market.

We study information design problems where the designer controls information about a state and the receiver is privately informed about his preferences. The receiver's action set is general and his preferences depend linearly on the state. We show that to optimally screen the receiver, the designer can use a menu of "laminar partitional" signals. These signals partition the states such that the same message is sent in each partition element and the convex hulls of any two partition elements are either nested or have an empty intersection. Furthermore, each state is either perfectly revealed or lies in an interval in which at most n+2 different messages are sent, where n is the number of receiver types. In the finite action case an optimal menu can be obtained by solving a finite-dimensional convex program. Along the way we shed light on the solutions of optimization problems over distributions subject to a mean-preserving contraction constraint and additional constraints which might be of independent interest.

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.

Under full substitutability of preferences, it has been shown that a competitive equilibrium exists in trading networks, and is equivalent (after a restriction to equilibrium trades) to (chain) stable outcomes. In this paper, we formulate the problem of finding an efficient outcome as a generalized submodular flow problem on a suitable network. Equivalence with seemingly weaker notions of stability follows directly from the optimality conditions, in particular the absence of improvement cycles in the flow problem. Our formulation yields strongly polynomial algorithms for finding competitive equilibria in trading networks, and testing (chain) stability.

In this paper, we consider the design of the day-ahead market for the smart electrical grid. Consumers with flexible demand and generator companies participate in the market to settle on their load and supply schedules, respectively. The market is operated by an Independent System Operator (ISO) whose purpose is to maximize social welfare while keeping load and supply balanced in the electricity network. We develop two distributed pricing algorithms that achieve optimum welfare. The first algorithm yields time-dependent market prices under convexity assumptions on utility and cost functions and the second algorithm yields bundle prices for arbitrary utility and cost functions. In both algorithms, flexible consumers and generator companies simply determine their own schedules based on the prices updated by the ISO at each iteration. We show that the participation of flexible demand in the day-ahead market reduces supply volatility, which would be present when flexible demand does not take part in price setting procedure.

We study power control in a multi-cell CDMA wireless system whereby self-interested users share a common spectrum and interfere with each other. Our objective is to design a power control scheme that achieves a (near) optimal power allocation with respect to any predetermined network objective (such as the maximization of sum-rate, or some fairness criterion). To obtain this, we introduce the potential-game approach that relies on approximating the underlying noncooperative game with a "close" potential game, for which prices that induce an optimal power allocation can be derived. We use the proximity of the original game with the approximate game to establish through Lyapunov-based analysis that natural user-update schemes (applied to the original game) converge within a neighborhood of the desired operating point, thereby inducing near-optimal performance in a dynamical sense. Additionally, we demonstrate through simulations that the actual performance can in practice be very close to optimal, even when the approximation is inaccurate. As a concrete example, we focus on the sum-rate objective, and evaluate our approach both theoretically and empirically.