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

Computer Science and Game Theory: A Brief Survey

There has been a remarkable increase in work at the interface of computer science and game theory in the past decade. In this article I survey some of the main themes of work in the area, with a focus on the work in computer science. Given the length constraints, I make no attempt at being comprehensive, especially since other surveys are also available, and a comprehensive survey book will appear shortly.Comment: To appear; Palgrave Dictionary of Economic

Convergence of Learning Dynamics in Information Retrieval Games

We consider a game-theoretic model of information retrieval with strategic authors. We examine two different utility schemes: authors who aim at maximizing exposure and authors who want to maximize active selection of their content (i.e. the number of clicks). We introduce the study of author learning dynamics in such contexts. We prove that under the probability ranking principle (PRP), which forms the basis of the current state of the art ranking methods, any better-response learning dynamics converges to a pure Nash equilibrium. We also show that other ranking methods induce a strategic environment under which such a convergence may not occur

Perfect Implementation

Privacy and trust aect our strategic thinking, yet they have not been precisely modeled in mechanism design. In settings of incomplete information, traditional implementations of a normal-form mechanism - by disregarding the players' privacy, or assuming trust in a mediator - may fail to reach the mechanism's objectives. We thus investigate implementations of a new type. We put forward the notion of a perfect implementation of a normal-form mechanism M: in essence, a concrete extensive-form mechanism exactly preserving all strategic properties of M, without relying on a trusted mediator or violating the privacy of the players. We prove that any normal-form mechanism can be perfectly implemented by a verifiable mediator using envelopes and an envelope-randomizing device (i.e., the same tools used for running fair lotteries or tallying secret votes). Differently from a trusted mediator, a veriable one only performs prescribed public actions, so that everyone can verify that he is acting properly, and that he never learns any information that should remain private

Moderators, mediators and nonspecific predictors of outcome after cognitive rehabilitation of executive functions in a randomised controlled trial

Moderators, mediators and nonspecific predictors of treatment after cognitive rehabilitation of executive functions in a randomised controlled trial Objective: To explore moderators, mediators and nonspecific predictors of executive functioning after cognitive rehabilitation in a randomised controlled trial, comparing Goal Management Training (GMT) with an active psycho-educative control-intervention, in patients with chronic acquired brain injury. Methods: Seventy patients with executive dysfunction were randomly allocated to GMT (n = 33) or control (n = 37). Outcome measures were established by factor-analysis and included cognitive executive complaints, emotional dysregulation and psychological distress. Results: Higher age and IQ emerged as nonspecific predictors. Verbal memory and planning ability at baseline moderated cognitive executive complaints, while planning ability at six-month follow-up mediated all three outcome measures. Inhibitory cognitive control emerged as a unique GMT specific mediator. A general pattern regardless of intervention was identified; higher levels of self-reported cognitive—and executive–symptoms of emotional dysregulation and psychological distress at six-month follow-up mediated less improvement across outcome factors. Conclusions: The majority of treatment effects were nonspecific to intervention, probably underscoring the variables’ general contribution to outcome of cognitive rehabilitation interventions. Interventions targeting specific cognitive domains, such as attention or working memory, need to take into account the patients’ overall cognitive and emotional self-perceived functioning. Future studies should investigate the identified predictors further, and also consider other predictor candidates

Innovation attributes and managers' decisions about the adoption of innovations in organizations: A meta-analytical review

The adop­tion of in­no­va­tions has emerged as a dom­i­nant re­search topic in the man­age­ment of in­no­va­tion in or­ga­ni­za­tions, al­though in­ves­ti­ga­tions of­ten yield mixed re­sults. To help man­agers and re­searchers im­prove their ef­fec­tive­ness, the au­thors em­ployed a meta-analy­sis in­te­grated with struc­tural equa­tion mod­el­ing to an­a­lyze the as­so­ci­a­tions be­tween the at­trib­utes of in­no­va­tions, man­agers' be­hav­ioral pref­er­ences, and or­ga­ni­za­tions' in­no­va­tion adop­tion de­ci­sions in a me­di­ated-mod­er­ated frame­work. Our find­ings of­fer ev­i­dence that at­trib­utes of in­no­va­tions in­flu­ence man­agers' be­hav­ioral pref­er­ences and, con­se­quently, adop­tion de­ci­sions in or­ga­ni­za­tions. We also ob­serve the sig­nif­i­cance of the con­text in which the adop­tion de­ci­sion oc­curs as well as the re­search set­tings em­ployed by schol­ars. Fi­nally, we dis­cuss the the­o­ret­i­cal con­tri­bu­tion and prac­ti­cal im­pli­ca­tions of our meta-an­a­lyt­i­cal re­sults