Explore the vast range of titles available.

School choice programs seek to give students the option to choose their school but also close an opportunity gap. To be fair in the assignment of students, it is usually argued that the assignment of students to schools should be stable. This second concern is usually expressed in terms of proportions. As an example, in 1989, the city of White Plains, New York, required each school to have the same proportions of Blacks, Hispanics, and “others,” a term that includes Whites and Asians. Satisfying both these concerns at the same time is difficult. Prior work replaces the proportions by numbers related to the capacity of school, but this assumes each school is operating at full capacity, which is often not the case. In this paper, we treat such proportionality constraints as soft but provide ex post guarantees on how well the constraints are satisfied while preserving stability. The problem of finding stable matches that meet distributional concerns is usually formulated by imposing side constraints whose “right-hand sides” are absolute numbers specified before the preferences or number of agents on the “proposing” side are known. In many cases, it is more natural to express the relevant constraints as proportions. We treat such constraints as soft but provide ex post guarantees on how well the constraints are satisfied while preserving stability. Our technique requires an extension of Scarf’s lemma, which is of independent interest.

We study the stochastic matching problem with the goal of finding a maximum matching in a graph whose edges are unknown but can be accessed via queries. This is a special case of stochastic k -cycle packing, in which the problem is to find a maximum packing of cycles, each of which exists with some probability. We provide polynomial-time adaptive and nonadaptive algorithms that provably yield a near-optimal solution, using a number of edge queries that is linear in the number of vertices. We are especially interested in kidney exchange, with which pairs of patients with end-stage renal failure and their willing but incompatible donors participate in a mechanism that performs compatibility tests between patients and donors and swaps the donors of some patients so that a large number of patients receive compatible kidneys. Because of the significant cost of performing compatibility tests, currently, kidney exchange programs perform at most one compatibility test per patient. Our theoretical results applied to kidney exchange show that, by increasing the number of compatibility tests performed per patient from one to a larger constant, we effectively get the full benefit of exhaustive testing at a fraction of the cost. We show, on both generated and real data from the UNOS nationwide kidney exchange, that even a small number of nonadaptive edge queries per vertex results in large gains in expected successful matches.

Assigning refugee families to different locations in a host country is a timely challenge due to the global refugee crisis. This paper studies how to assign refugees to different locations while accommodating various distributional constraints. The social planner seeks to find a constrained efficient assignment with respect to refugees’ preferences over different locations. As Pareto-efficient assignments may differ significantly in the number of assigned refugees, a key challenge is to match as many refugees as possible. The paper generalizes the well-known serial dictatorship (SD) and probabilistic serial (PS) mechanisms for assigning indivisible objects to agents to accommodate distributional constraints. The new generalizations of SD and PS maintain their desirable properties while satisfying the distributional constraints with a small error. Both mechanisms assign at least the same number of students as the optimum fractional assignment. We generalize the serial dictatorship (SD) and probabilistic serial (PS) mechanism for assigning indivisible objects (seats in a school) to agents (students) to accommodate distributional constraints. Such constraints are motivated by equity considerations. Our generalization of SD maintains several of its desirable properties, including strategyproofness, Pareto optimality, and computational tractability, while satisfying the distributional constraints with a small error. Our generalization of the PS mechanism finds an ordinally efficient and envy-free assignment while satisfying the distributional constraint with a small error. We show, however, that no ordinally efficient and envy-free mechanism is also weakly strategyproof. Both of our algorithms assign at least the same number of students as the optimum fractional assignment.

The role of forward contracts in mitigating market power is an important and recurring theme in oligopolistic industries, such as electricity and gas. In “On the Inefficiency of Forward Markets in Leader–Follower Competition,” D. Cai, A. Agarwal, and A. Wierman study the impact of forward contracting in situations where firms have capacity constraints and heterogeneous production lead times. The authors introduce a model that combines forward contracting and leader–follower competition and explicitly characterize the equilibrium outcomes with and without forward contracting. They show that the impact of forward markets is delicate—it may mitigate market power or create opportunities for market manipulation. In particular, leader–follower interactions due to heterogeneous production lead times may cause forward markets to be inefficient, even when there are a large number of followers. In fact, symmetric equilibria do not necessarily exist due to differences in market power among leaders and followers. Motivated by electricity markets, this paper studies the impact of forward contracting in situations where firms have capacity constraints and heterogeneous production lead times. We consider a model with two types of firms—leaders and followers—that choose production at two different times. Followers choose productions in the second stage but can sell forward contracts in the first stage. Our main result is an explicit characterization of the equilibrium outcomes. Classic results on forward contracting suggest that it can mitigate market power in simple settings; however, the results in this paper show that the impact of forward markets in this setting is delicate—forward contracting can enhance or mitigate market power. In particular, our results show that leader–follower interactions created by heterogeneous production lead times may cause forward markets to be inefficient, even when there are a large number of followers. In fact, symmetric equilibria do not necessarily exist due to differences in market power among the leaders and followers.

In various economic environments, people observe other people with whom they strategically interact. We can model such information-sharing relations as an information network, and the strategic interactions as a game on the network. When any two agents in the network are connected either directly or indirectly in a large network, empirical modeling using an equilibrium approach can be cumbersome, since the testable implications from an equilibrium generally involve all the players of the game, whereas a researcher's data set may contain only a fraction of these players in practice. This paper develops a tractable empirical model of linear interactions where each agent, after observing part of his neighbors' types, not knowing the full information network, uses best responses that are linear in his and other players' types that he observes, based on simple beliefs about the other players' strategies. We provide conditions on information networks and beliefs such that the best responses take an explicit form with multiple intuitive features. Furthermore, the best responses reveal how local payoff interdependence among agents is translated into local stochastic dependence of their actions, allowing the econometrician to perform asymptotic inference without having to observe all the players in the game or having to know the precise sampling process.

We consider a class of Nash games, termed as aggregative games, being played over a networked system. In an aggregative game, a player’s objective is a function of the aggregate of all the players’ decisions. Every player maintains an estimate of this aggregate, and the players exchange this information with their local neighbors over a connected network. We study distributed synchronous and asynchronous algorithms for information exchange and equilibrium computation over such a network. Under standard conditions, we establish the almost-sure convergence of the obtained sequences to the equilibrium point. We also consider extensions of our schemes to aggregative games where the players’ objectives are coupled through a more general form of aggregate function. Finally, we present numerical results that demonstrate the performance of the proposed schemes.

Managers of emerging platforms must decide what level of platform performance to invest in at each product development cycle in markets that exhibit two-sided network externalities. High performance is a selling point for consumers, but in many cases it requires developers to make large investments to participate. Abstracting from an example drawn from the video game industry, we build a strategic model to investigate the trade-off between investing in high platform performance versus reducing investment in order to facilitate third party content development. We carry out a full analysis of three distinct settings: monopoly, price-setting duopoly, and price-taking duopoly. We provide insights on the optimum investment in platform performance and demonstrate how conventional wisdom about product development may be misleading in the presence of strong cross-network externalities. In particular, we show that, contrary to the conventional wisdom about \"winner-take-all\" markets, heavily investing in the core performance of a platform does not always yield a competitive edge. We characterize the conditions under which offering a platform with lower performance but greater availability of content can be a winning strategy.

Online social networks (OSNs) are rapidly growing and have become a huge source of all kinds of global and local news for millions of users. However, OSNs are a double-edged sword. Although the great advantages they offer such as unlimited easy communication and instant news and information, they can also have many disadvantages and issues. One of their major challenging issues is the spread of fake news. Fake news identification is still a complex unresolved issue. Furthermore, fake news detection on OSNs presents unique characteristics and challenges that make finding a solution anything but trivial. On the other hand, artificial intelligence (AI) approaches are still incapable of overcoming this challenging problem. To make matters worse, AI techniques such as machine learning and deep learning are leveraged to deceive people by creating and disseminating fake content. Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed in a way to closely resemble the truth, and it is often hard to determine its veracity by AI alone without additional information from third parties. This work aims to provide a comprehensive and systematic review of fake news research as well as a fundamental review of existing approaches used to detect and prevent fake news from spreading via OSNs. We present the research problem and the existing challenges, discuss the state of the art in existing approaches for fake news detection, and point out the future research directions in tackling the challenges.

Geography and social links shape economic interactions. In industries, schools, and markets, the entire network determines outcomes. This paper analyzes a large class of games and obtains a striking result. Equilibria depend on a single network measure: the lowest eigenvalue. This paper is the first to uncover the importance of the lowest eigenvalue to economic and social outcomes. It captures how much the network amplifies agents' actions. The paper combines new tools—potential games, optimization, and spectral graph theory—to solve for all Nash and stable equilibria and applies the results to R&D, crime, and the econometrics of peer effects.

The merger of game-based approaches and Virtual Reality (VR) environments that can enhance learning and training methodologies have a very promising future, reinforced by the widespread market-availability of affordable software and hardware tools for VR-environments. Rather than passive observers, users engage in those learning environments as active participants, permitting the development of exploration-based learning paradigms. There are separate reviews of VR technologies and serious games for educational and training purposes with a focus on only one knowledge area. However, this review covers 135 proposals for serious games in immersive VR-environments that are combinations of both VR and serious games and that offer end-user validation. First, an analysis of the forum, nationality, and date of publication of the articles is conducted. Then, the application domains, the target audience, the design of the game and its technological implementation, the performance evaluation procedure, and the results are analyzed. The aim here is to identify the factual standards of the proposed solutions and the differences between training and learning applications. Finally, the study lays the basis for future research lines that will develop serious games in immersive VR-environments, providing recommendations for the improvement of these tools and their successful application for the enhancement of both learning and training tasks.