The Cost of Sybils, Credible Commitments, and False-Name Proof Mechanisms

Consider a mechanism that cannot observe how many players there are directly, but instead must rely on their self-reports to know how many are participating. Suppose the players can create new identities to report to the auctioneer at some cost $c$. The usual mechanism design paradigm is equivalent to implicitly assuming that $c$ is infinity for all players, while the usual Sybil attacks literature is that it is zero or finite for one player (the attacker) and infinity for everyone else (the 'honest' players). The false-name proof literature largely assumes the cost to be 0. We consider a model with variable costs that unifies these disparate streams. A paradigmatic normal form game can be extended into a Sybil game by having the action space by the product of the feasible set of identities to create action where each player chooses how many players to present as in the game and their actions in the original normal form game. A mechanism is (dominant) false-name proof if it is (dominant) incentive-compatible for all the players to self-report as at most one identity. We study mechanisms proposed in the literature motivated by settings where anonymity and self-identification are the norms, and show conditions under which they are not Sybil-proof. We characterize a class of dominant Sybil-proof mechanisms for reward sharing and show that they achieve the efficiency upper bound. We consider the extension when agents can credibly commit to the strategy of their sybils and show how this can break mechanisms that would otherwise be false-name proof

Changing the Boston School Choice Mechanism

In July 2005 the Boston School Committee voted to replace the existing Boston school choice mechanism with a deferred acceptance mechanism that simplifies the strategic choices facing parents. This paper presents the empirical case against the previous Boston mechanism, a priority matching mechanism, and the case in favor of the change to a strategy-proof mechanism. Using detailed records on student choices and assignments, we present evidence both of sophisticated strategic behavior among some parents, and of unsophisticated strategic behavior by others. We find evidence that some parents pay close attention to the capacity constraints of different schools, while others appear not to. In particular, we show that many unassigned students could have been assigned to one of their stated choices with a different strategy under the current mechanism. This interaction between sophisticated and unsophisticated players identifies a new rationale for strategy-proof mechanisms based on fairness, and was a critical argument in Boston's decision to change the mechanism. We then discuss the considerations that led to the adoption of a deferred acceptance mechanism as opposed to the (also strategy-proof) top trading cycles mechanism.

The Antitrust of Reputation Mechanisms: Institutional Economics and Concerted Refusals to Deal

An agreement among competitors to refuse to deal with another party is traditionally per se illegal under the antitrust laws. But coordinated refusals to deal are often necessary to punish wrongdoers, and thus to deter undesirable behavior that state-sponsored courts cannot reach. When viewed as a mechanism to govern transactions and induce socially desirable cooperative behavior, coordinated refusals to deal can sustain valuable reputation mechanisms. This paper employs institutional economics to understand the role of coordinated refusals to deal in merchant circles and to evaluate the economic desirability of permitting such coordinated actions among competitors. It concludes that if the objective of antitrust law is to promote economic efficiency, then per se treatment-or any heightened presumption of illegality-of reputation mechanisms with coordinated punishments is misplaced

Incentive Compatible Mechanisms without Money

Mechanism design arises in environments where a set of strategic agents should achieve a common goal, but this goal may be affected by the selfish behavior of the agents. A popular tool to mitigate this impact is incentive compatibility, the design of mechanisms in such a way that strategic agents are motivated to act honestly. Many times this can be done using payments: monetary transactions can be implemented by the mechanism, which provide the agents with the right incentives to reveal their true colors. However, there are cases where such payments are not applicable for various reasons, moral, legal, or practical. In this thesis, we focus on problems where payments are prohibited, and we propose incentive compatible solutions, respecting this constraint. We concentrate on two main problems: the problem of impartial selection and the problem of truthful budget aggregation. In both problems, strategic agents need to come up with a joint decision, but their selfish behavior may lead them to highly sub-optimal solutions. Our goal is to design mechanisms providing the agents with proper incentives to act sincerely. Unfortunately, we are only able to achieve this by sacrificing the quality of the solution, in the sense that the solutions we get are not as good as the solutions we could get in an environment where the agents would not be strategic. Therefore, we compare our mechanisms with ideal, non-strategic outcomes, providing worst-case approximation guarantees. The first problem we confront, impartial selection, involves the selection of an influential member of a community of individuals. This community can be described by a directed graph, where the nodes represent the individuals and the directed edges represent nominations. The task is given this graph to select the node with the highest number of nominations. However, the community members are selfish agents; hence, their reported nominations are not trusted, and this seemingly trivial task is now challenging. Impartiality, a property requiring no single node to influence her selection probability, provides proper incentives to the agents to act honestly. Recent progress in the literature has identified impartial selection rules with optimal approximation ratios, i.e., the ratio between the maximum in-degree and the in-degree of the selected node. However, it was noted that worst-case instances are graphs with small in-degrees. Motivated by this fact, we deviate from the trend and propose the study of additive approximation: the difference between the highest number of nominations and the number of nominations of the selected member, as an alternative measure of the quality of impartial selection mechanisms. The first part of this thesis is concerned with the design of impartial selection mechanisms with small additive approximation guarantees. On the positive side, we were able to design two randomized impartial selection mechanisms with sub-linear, on the community size, additive approximation guarantees for two well-studied models in the literature. We complement our positive results by providing negative results for various cases. We continue our investigation of the impartial selection problem from another direction. Getting our inspiration from the design of auction and posted pricing mechanisms with good approximation guarantees for welfare and profit maximization, we follow up our work with an enhanced model, where we study the extent to which prior information on voters' preferences could be helpful in the design of efficient deterministic impartial selection mechanisms with good additive approximation guarantees. First, we define a hierarchy of three models of prior information, which we call the opinion poll, the a priori popularity, and the uniform models. Then, we analyze the performance of a natural mechanism that we call Approval Voting with Default and show that it achieves a sub-linear additive guarantee for opinion poll and a polylogarithmic for a priori popularity inputs. We consider the polylogarithmic bound as the leading technical contribution of this part. Finally, we complement this last result by showing that our analysis is close to tight. We then turn our attention to the truthful budget aggregation problem. In this problem, strategic voters wish to split a divisible budget among different projects by aggregating their proposals into a single budget division. Unfortunately, it is well-known that the straightforward rule that divides the budget proportionally is susceptible to manipulation. While sophisticated incentive compatible mechanisms have been proposed in the literature, their outcomes are often far from fair. To capture this loss of fairness imposed by the need for truthfulness, we propose a quantitative framework that evaluates a budget aggregation mechanism according to its worst-case distance from the proportional allocation. We study this measure in the recently proposed class of incentive compatible mechanisms, called the moving phantom}mechanisms, and we provide approximation guarantees. For two projects, we show that the well-known Uniform Phantom mechanism is optimal among all truthful mechanisms. For three projects, we propose the proportional, Piecewise Uniform mechanism that is optimal among all moving phantom mechanisms. Finally, we provide impossibility results regarding the approximability of moving phantom mechanisms, and budget aggregation mechanisms, in general

MECHANISM DESIGN WITH GENERAL UTILITIES

This thesis studies mechanism design from an optimization perspective. Our main contribution is to characterize fundamental structural properties of optimization problems arising in mechanism design and to exploit them to design general frameworks and techniques for efficiently solving the underlying problems. Not only do our characterizations allow for efficient computation, they also reveal qualitative characteristics of optimal mechanisms which are important even from a non-computational standpoint. Furthermore, most of our techniques are widely applicable to optimization problems outside of mechanism design such as online algorithms or stochastic optimization. Our frameworks can be summarized as follows. When the input to an optimization problem (e.g., a mechanism design problem) comes from independent sources (e.g., independent agents), the complexity of the problem can be exponentially reduced by (i) decomposing the problem into smaller subproblems, each one involving one input source, (ii) simultaneously optimizing the subproblems subject to certain relaxation of coupling constraints, and (iii) combining the solutions of the subproblems in a certain way to obtain an (approximately) optimal solution for the original problem. We use our proposed framework to construct optimal or approximately optimal mechanisms for several settings previously considered in the literature and to improve upon the best previously known results. We also present applications of our techniques to non-mechanism design problems such as online stochastic generalized assignment problem which itself captures online and stochastic versions of various other problems such as resource allocation and job scheduling

Better than PageRank: Hitting Time as a Reputation Mechanism

In online multi-agent systems, reputation systems are needed to distinguish between trustworthy agents and potentially malicious or unreliable agents. A good reputation system should be accurate, resistant to strategic manipulations, and computationally tractable. I experimentally analyze the accuracy and manipulation-resistance of a reputation mechanism called personalized hitting time, and present efficient algorithms for its calculation. I present an alternate definition to hitting time that is amenable to Monte Carlo estimation, and show that it is linearly equivalent to the standard definition for hitting time. I present exact and approximation algorithms for computing personalized hitting time, and I show that the approximation algorithms can obtain a highly accurate estimate of hitting time on large graphs more quickly than an exact algorithm can find an exact solution. An experimental comparison of the accuracy of six reputation systems — global and personalized PageRank, global and personalized hitting time, maximum flow, and shortest path — under strategic manipulation shows that personalized hitting time is the most accurate reputation mechanism in the presence of a moderate number of strategic agents

Behavioral constraints on the design of subgame-perfect implementation mechanisms

We study subgame-perfect implementation (SPI) mechanisms that have been proposed as a solution to incomplete contracting problems. We show that these mechanisms — which are based on off-equilibrium arbitration clauses that impose large fines for lying and the inappropriate use of arbitration — have severe behavioral constraints because the fines induce retaliation against legitimate uses of arbitration. Incorporating reciprocity preferences into the theory explains the observed behavioral patterns and helps us develop a new mechanism that is more robust and achieves high rates of truth-telling and efficiency. Our results highlight the importance of tailoring implementation mechanisms to the underlying behavioral environment

Designing and Optimizing Matching Markets

Matching market design studies the fundamental problem of how to allocate scarce resources to individuals with varied needs. In recent years, the theoretical study of matching markets such as medical residency, public housing and school choice has greatly informed and improved the design of such markets in practice. Impactful work in matching market design frequently makes use of techniques from computer science, economics and operations research to provide end–to-end solutions that address design questions holistically. In this dissertation, I develop tools for optimization in market design by studying matching mechanisms for school choice, an important societal problem that exemplifies many of the challenges in effective marketplace design. In the first part of this work I develop frameworks for optimization in school choice that allow us to address operational problems in the assignment process. In the school choice market, where scarce public school seats are assigned to students, a key operational issue is how to reassign seats that are vacated after an initial round of centralized assignment. We propose a class of reassignment mechanisms, the Permuted Lottery Deferred Acceptance (PLDA) mechanisms, which generalize the commonly used Deferred Acceptance school choice mechanism and retain its desirable incentive and efficiency properties. We find that under natural conditions on demand all PLDA mechanisms achieve equivalent allocative welfare, and the PLDA based on reversing the tie-breaking lottery during the reassignment round minimizes reassignment. Empirical investigations on data from NYC high school admissions support our theoretical findings. In this part, we also provide a framework for optimization when using the prominent Top Trading Cycles (TTC) mechanism. We show that the TTC assignment can be described by admission cutoffs, which explain the role of priorities in determining the TTC assignment and can be used to tractably analyze TTC. In a large-scale continuum model we show how to compute these cutoffs directly from the distribution of preferences and priorities, providing a framework for evaluating policy choices. As an application of the model we solve for optimal investment in school quality under choice and find that an egalitarian distribution can be more efficient as it allows students to choose schools based on idiosyncracies in their preferences. In the second part of this work, I consider the role of a marketplace as an information provider and explore how mechanisms affect information acquisition by agents in matching markets. I provide a tractable “Pandora's box” model where students hold a prior over their value for each school and can pay an inspection cost to learn their realized value. The model captures how students’ decisions to acquire information depend on priors and market information, and can rationalize a student’s choice to remain partially uninformed. In such a model students need market information in order to optimally acquire their personal preferences, and students benefit from waiting for the market to resolve before acquiring information. We extend the definition of stability to this partial information setting and define regret-free stable outcomes, where the matching is stable and each student has acquired the same information as if they had waited for the market to resolve. We show that regret-free stable outcomes have a cutoff characterization, and the set of regret-free stable outcomes is a non-empty lattice. However, there is no mechanism that always produces a regret-free stable matching, as there can be information deadlocks where every student finds it suboptimal to be the first to acquire information. In settings with sufficient information about the distribution of preferences, we provide mechanisms that exploit the cutoff structure to break the deadlock and approximately implement a regret-free stable matching

Non-Hierarchical Networks for Censorship-Resistant Personal Communication.

The Internet promises widespread access to the world’s collective information and fast communication among people, but common government censorship and spying undermines this potential. This censorship is facilitated by the Internet’s hierarchical structure. Most traffic flows through routers owned by a small number of ISPs, who can be secretly coerced into aiding such efforts. Traditional crypographic defenses are confusing to common users. This thesis advocates direct removal of the underlying heirarchical infrastructure instead, replacing it with non-hierarchical networks. These networks lack such chokepoints, instead requiring would-be censors to control a substantial fraction of the participating devices—an expensive proposition. We take four steps towards the development of practical non-hierarchical networks. (1) We first describe Whisper, a non-hierarchical mobile ad hoc network (MANET) architecture for personal communication among friends and family that resists censorship and surveillance. At its core are two novel techniques, an efficient routing scheme based on the predictability of human locations anda variant of onion-routing suitable for decentralized MANETs. (2) We describe the design and implementation of Shout, a MANET architecture for censorship-resistant, Twitter-like public microblogging. (3) We describe the Mason test, amethod used to detect Sybil attacks in ad hoc networks in which trusted authorities are not available. (4) We characterize and model the aggregate behavior of Twitter users to enable simulation-based study of systems like Shout. We use our characterization of the retweet graph to analyze a novel spammer detection technique for Shout.PhDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107314/1/drbild_1.pd