Mechanism Design with Strategic Mediators

We consider the problem of designing mechanisms that interact with strategic agents through strategic intermediaries (or mediators), and investigate the cost to society due to the mediators' strategic behavior. Selfish agents with private information are each associated with exactly one strategic mediator, and can interact with the mechanism exclusively through that mediator. Each mediator aims to optimize the combined utility of his agents, while the mechanism aims to optimize the combined utility of all agents. We focus on the problem of facility location on a metric induced by a publicly known tree. With non-strategic mediators, there is a dominant strategy mechanism that is optimal. We show that when both agents and mediators act strategically, there is no dominant strategy mechanism that achieves any approximation. We, thus, slightly relax the incentive constraints, and define the notion of a two-sided incentive compatible mechanism. We show that the $3$-competitive deterministic mechanism suggested by Procaccia and Tennenholtz (2013) and Dekel et al. (2010) for lines extends naturally to trees, and is still $3$-competitive as well as two-sided incentive compatible. This is essentially the best possible. We then show that by allowing randomization one can construct a $2$-competitive randomized mechanism that is two-sided incentive compatible, and this is also essentially tight. This result also closes a gap left in the work of Procaccia and Tennenholtz (2013) and Lu et al. (2009) for the simpler problem of designing strategy-proof mechanisms for weighted agents with no mediators on a line, while extending to the more general model of trees. We also investigate a further generalization of the above setting where there are multiple levels of mediators.Comment: 46 pages, 1 figure, an extended abstract of this work appeared in ITCS 201

Distributed Signaling Games

A recurring theme in recent computer science literature is that proper design of signaling schemes is a crucial aspect of effective mechanisms aiming to optimize social welfare or revenue. One of the research endeavors of this line of work is understanding the algorithmic and computational complexity of designing efficient signaling schemes. In reality, however, information is typically not held by a central authority, but is distributed among multiple sources (third-party "mediators"), a fact that dramatically changes the strategic and combinatorial nature of the signaling problem, making it a game between information providers, as opposed to a traditional mechanism design problem. In this paper we introduce {\em distributed signaling games}, while using display advertising as a canonical example for introducing this foundational framework. A distributed signaling game may be a pure coordination game (i.e., a distributed optimization task), or a non-cooperative game. In the context of pure coordination games, we show a wide gap between the computational complexity of the centralized and distributed signaling problems. On the other hand, we show that if the information structure of each mediator is assumed to be "local", then there is an efficient algorithm that finds a near-optimal ($5$-approximation) distributed signaling scheme. In the context of non-cooperative games, the outcome generated by the mediators' signals may have different value to each (due to the auctioneer's desire to align the incentives of the mediators with his own by relative compensations). We design a mechanism for this problem via a novel application of Shapley's value, and show that it possesses some interesting properties, in particular, it always admits a pure Nash equilibrium, and it never decreases the revenue of the auctioneer

The Relationship Between Active Learning and Workload During Clinically Relevant Simulations

Purpose: Active learning through medical simulation has been shown to improve learning outcomes when used appropriately. However, simulation can inhibit learning outcomes and learner engagement when the simulation scenario context requires a high level of workload (perceived or actual) that is inappropriate for the level of learner (Curtis 2012). This study examines the relationship between individual learner engagement, team problem solving, and perceived workload during a Simulation Based Orientation to Clinical Medicine (SBOC)

Longitudinal Changes in Psychological Resilience and Wellness During Clinical Clerkship

Purpose: An individual’s capacity to monitor, anticipate, react, and recover from stressful events defines their resilience. Resilient coping strategies have been shown to reduce stress, burnout, and improve performance in medical students (Erschans 2018, Thompson 2016, Wetzel 2018), but less is known on how resiliency changes over time across different phases of medical school or whether it is a trait. Ideally, educators would understand the periods in the curriculum where resilience building and stress reduction interventions are most needed, and most effective. The purpose of this study was to assess longitudinal changes in resiliency and the association between resiliency and wellness during the clerkship phase of medical school

Solving Cooperative Reliability Games

Cooperative games model the allocation of profit from joint actions, following considerations such as stability and fairness. We propose the reliability extension of such games, where agents may fail to participate in the game. In the reliability extension, each agent only "survives" with a certain probability, and a coalition's value is the probability that its surviving members would be a winning coalition in the base game. We study prominent solution concepts in such games, showing how to approximate the Shapley value and how to compute the core in games with few agent types. We also show that applying the reliability extension may stabilize the game, making the core non-empty even when the base game has an empty core

Entanglement Estimation in Tensor Network States via Sampling

We introduce a method for extracting meaningful entanglement measures of tensor network states in general dimensions. Current methods require the explicit reconstruction of the density matrix, which is highly demanding, or the contraction of replicas, which requires an effort exponential in the number of replicas and which is costly in terms of memory. In contrast, our method requires the stochastic sampling of matrix elements of the classically represented reduced states with respect to random states drawn from simple product probability measures constituting frames. Even though not corresponding to physical operations, such matrix elements are straightforward to calculate for tensor network states, and their moments provide the Rényi entropies and negativities as well as their symmetry-resolved components. We test our method on the one-dimensional critical XX chain and the two-dimensional toric code in a checkerboard geometry. Although the cost is exponential in the subsystem size, it is sufficiently moderate so that—in contrast with other approaches—accurate results can be obtained on a personal computer for relatively large subsystem sizes

Wild wheat : an introduction

Title from JPEG cover page (University of Missouri Digital Library, viewed Mar. 24, 2010).Includes bibliographical references (pages 132-142)