arXiv subjects

David C. Parkes

Publications and source records attributed to David C. Parkes.

88 records · Page 5Linked to original sources

arXiv2016

A Statistical Decision-Theoretic Framework for Social Choice

In this paper, we take a statistical decision-theoretic viewpoint on social choice, putting a focus on the decision to be made on behalf of a system of agents. In our framework, we are given a statistical ranking model, a decision space, and a loss function defined on (parameter, decision) pairs, and formulate social choice mechanisms as decision rules that minimize expected loss. This suggests a general framework for the design and analysis of new social choice mechanisms. We compare Bayesian estimators, which minimize Bayesian expected loss, for the Mallows model and the Condorcet model respectively, and the Kemeny rule. We consider various normative properties, in addition to computational complexity and asymptotic behavior. In particular, we show that the Bayesian estimator for the Condorcet model satisfies some desired properties such as anonymity, neutrality, and monotonicity, can be computed in polynomial time, and is asymptotically different from the other two rules when the data are generated from the Condorcet model for some ground truth parameter.

cs.AI↗

arXiv2015

Incentive-Compatible Experimental Design

We consider the design of experiments to evaluate treatments that are administered by self-interested agents, each seeking to achieve the highest evaluation and win the experiment. For example, in an advertising experiment, a company wishes to evaluate two marketing agents in terms of their efficacy in viral marketing, and assign a contract to the winner agent. Contrary to traditional experimental design, this problem has two new implications. First, the experiment induces a game among agents, where each agent can select from multiple versions of the treatment it administers. Second, the action of one agent -- selection of treatment version -- may affect the actions of another agent, with the resulting strategic interference complicating the evaluation of agents. An incentive-compatible experiment design is one with an equilibrium where each agent selects its natural action, which is the action that would maximize the performance of the agent if there was no competition (e.g., expected number of conversions if agent was assigned the contract). Under a general formulation of experimental design, we identify sufficient conditions that guarantee incentive-compatible experiments. These conditions rely on the existence of statistics that can estimate how agents would perform without competition, and their use in constructing score functions to evaluate the agents. In the setting with no strategic interference, we also study the power of the design, i.e., the probability that the best agent wins, and show how to improve the power of incentive-compatible designs. From the technical side, our theory uses a range of statistical methods such as hypothesis testing, variance-stabilizing transformations and the Delta method, all of which rely on asymptotics.

stat.ME↗

arXiv2014

Capturing Variation and Uncertainty in Human Judgment

The well-studied problem of statistical rank aggregation has been applied to comparing sports teams, information retrieval, and most recently to data generated by human judgment. Such human-generated rankings may be substantially different from traditional statistical ranking data. In this work, we show that a recently proposed generalized random utility model reveals distinctive patterns in human judgment across three different domains, and provides a succinct representation of variance in both population preferences and imperfect perception. In contrast, we also show that classical statistical ranking models fail to capture important features from human-generated input. Our work motivates the use of more flexible ranking models for representing and describing the collective preferences or decision-making of human participants.

cs.IR↗

arXiv2014

Long-term causal effects of economic mechanisms on agent incentives

Economic mechanisms administer the allocation of resources to interested agents based on their self-reported types. One objective in mechanism design is to design a strategyproof process so that no agent will have an incentive to misreport its type. However, typical analyses of the incentives properties of mechanisms operate under strong, usually untestable assumptions. Empirical, data-oriented approaches are, at best, under-developed. Furthermore, mechanism/policy evaluation methods usually ignore the dynamic nature of a multi-agent system and are thus inappropriate for estimating long-term effects. We introduce the problem of estimating the causal effects of mechanisms on incentives and frame it under the Rubin causal framework \citep{rubin74, rubin78}. This raises unique technical challenges since the outcome of interest (agent truthfulness) is confounded with strategic interactions and, interestingly, is typically never observed under any mechanism. We develop a methodology to estimate such causal effects that using a prior that is based on a strategic equilibrium model. Working on the domain of kidney exchanges, we show how to apply our methodology to estimate causal effects of kidney allocation mechanisms on hospitals' incentives. Our results demonstrate that the use of game-theoretic prior captures the dynamic nature of the kidney exchange multiagent system and shrinks the estimates towards long-term effects, thus improving upon typical methods that completely ignore agents' strategic behavior.

cs.GT↗

arXiv2014

Truthful Prioritization Schemes for Spectrum Sharing

We design a protocol for dynamic prioritization of data on shared routers such as untethered 3G/4G devices. The mechanism prioritizes bandwidth in favor of users with the highest value, and is incentive compatible, so that users can simply report their true values for network access. A revenue pooling mechanism also aligns incentives for sellers, so that they will choose to use prioritization methods that retain the incentive properties on the buy-side. In this way, the design allows for an open architecture. In addition to revenue pooling, the technical contribution is to identify a class of stochastic demand models and a prioritization scheme that provides allocation monotonicity. Simulation results confirm efficiency gains from dynamic prioritization relative to prior methods, as well as the effectiveness of revenue pooling.

cs.GT↗

arXiv2013

Preference Elicitation For General Random Utility Models

This paper discusses {General Random Utility Models (GRUMs)}. These are a class of parametric models that generate partial ranks over alternatives given attributes of agents and alternatives. We propose two preference elicitation scheme for GRUMs developed from principles in Bayesian experimental design, one for social choice and the other for personalized choice. We couple this with a general Monte-Carlo-Expectation-Maximization (MC-EM) based algorithm for MAP inference under GRUMs. We also prove uni-modality of the likelihood functions for a class of GRUMs. We examine the performance of various criteria by experimental studies, which show that the proposed elicitation scheme increases the precision of estimation.

cs.AI↗

arXiv2013

Expressiveness and Robustness of First-Price Position Auctions

Since economic mechanisms are often applied to very different instances of the same problem, it is desirable to identify mechanisms that work well in a wide range of circumstances. We pursue this goal for a position auction setting and specifically seek mechanisms that guarantee good outcomes under both complete and incomplete information. A variant of the generalized first-price mechanism with multi-dimensional bids turns out to be the only standard mechanism able to achieve this goal, even when types are one-dimensional. The fact that expressiveness beyond the type space is both necessary and sufficient for this kind of robustness provides an interesting counterpoint to previous work on position auctions that has highlighted the benefits of simplicity. From a technical perspective our results are interesting because they establish equilibrium existence for a multi-dimensional bid space, where standard techniques break down. The structure of the equilibrium bids moreover provides an intuitive explanation for why first-price payments may be able to support equilibria in a wider range of circumstances than second-price payments.

cs.GT↗

arXiv2012

Viewpoint: Journals for Certification, Conferences for Rapid Dissemination

The publication culture in Computer Science is different from that of all other disciplines. Whereas other disciplines focus on journal publication, the standard practice in CS has been to publish in a conference and then (sometimes) publish a journal version of the conference paper. We discuss the role of journal publication in CS. Indeed, it is through publication in selective, leading conferences that the quality of CS research is typically assessed.

cs.DL↗

arXiv2012

Random Utility Theory for Social Choice

Random utility theory models an agent's preferences on alternatives by drawing a real-valued score on each alternative (typically independently) from a parameterized distribution, and then ranking the alternatives according to scores. A special case that has received significant attention is the Plackett-Luce model, for which fast inference methods for maximum likelihood estimators are available. This paper develops conditions on general random utility models that enable fast inference within a Bayesian framework through MC-EM, providing concave loglikelihood functions and bounded sets of global maxima solutions. Results on both real-world and simulated data provide support for the scalability of the approach and capability for model selection among general random utility models including Plackett-Luce.

cs.MA↗

arXiv2012

Payment Rules through Discriminant-Based Classifiers

In mechanism design it is typical to impose incentive compatibility and then derive an optimal mechanism subject to this constraint. By replacing the incentive compatibility requirement with the goal of minimizing expected ex post regret, we are able to adapt statistical machine learning techniques to the design of payment rules. This computational approach to mechanism design is applicable to domains with multi-dimensional types and situations where computational efficiency is a concern. Specifically, given an outcome rule and access to a type distribution, we train a support vector machine with a special discriminant function structure such that it implicitly establishes a payment rule with desirable incentive properties. We discuss applications to a multi-minded combinatorial auction with a greedy winner-determination algorithm and to an assignment problem with egalitarian outcome rule. Experimental results demonstrate both that the construction produces payment rules with low ex post regret, and that penalizing classification errors is effective in preventing failures of ex post individual rationality.

cs.GT↗

arXiv2012

Models for Truthful Online Double Auctions

Online double auctions (DAs) model a dynamic two-sided matching problem with private information and self-interest, and are relevant for dynamic resource and task allocation problems. We present a general method to design truthful DAs, such that no agent can benefit from misreporting its arrival time, duration, or value. The family of DAs is parameterized by a pricing rule, and includes a generalization of McAfee's truthful DA to this dynamic setting. We present an empirical study, in which we study the allocative-surplus and agent surplus for a number of different DAs. Our results illustrate that dynamic pricing rules are important to provide good market efficiency for markets with high volatility or low volume.

cs.GT↗

arXiv2012

Optimal Coordinated Planning Amongst Self-Interested Agents with Private State

Consider a multi-agent system in a dynamic and uncertain environment. Each agent's local decision problem is modeled as a Markov decision process (MDP) and agents must coordinate on a joint action in each period, which provides a reward to each agent and causes local state transitions. A social planner knows the model of every agent's MDP and wants to implement the optimal joint policy, but agents are self-interested and have private local state. We provide an incentive-compatible mechanism for eliciting state information that achieves the optimal joint plan in a Markov perfect equilibrium of the induced stochastic game. In the special case in which local problems are Markov chains and agents compete to take a single action in each period, we leverage Gittins allocation indices to provide an efficient factored algorithm and distribute computation of the optimal policy among the agents. Distributed, optimal coordinated learning in a multi-agent variant of the multi-armed bandit problem is obtained as a special case.

cs.GT↗

arXiv2012

Learning and Solving Many-Player Games through a Cluster-Based Representation

In addressing the challenge of exponential scaling with the number of agents we adopt a cluster-based representation to approximately solve asymmetric games of very many players. A cluster groups together agents with a similar "strategic view" of the game. We learn the clustered approximation from data consisting of strategy profiles and payoffs, which may be obtained from observations of play or access to a simulator. Using our clustering we construct a reduced "twins" game in which each cluster is associated with two players of the reduced game. This allows our representation to be individually- responsive because we align the interests of every individual agent with the strategy of its cluster. Our approach provides agents with higher payoffs and lower regret on average than model-free methods as well as previous cluster-based methods, and requires only few observations for learning to be successful. The "twins" approach is shown to be an important component of providing these low regret approximations.

cs.GT↗

arXiv2012

Quantifying the Strategyproofness of Mechanisms via Metrics on Payoff Distributions

Strategyproof mechanisms provide robust equilibrium with minimal assumptions about knowledge and rationality but can be unachievable in combination with other desirable properties such as budget-balance, stability against deviations by coalitions, and computational tractability. In the search for maximally-strategyproof mechanisms that simultaneously satisfy other desirable properties, we introduce a new metric to quantify the strategyproofness of a mechanism, based on comparing the payoff distribution, given truthful reports, against that of a strategyproof "reference" mechanism that solves a problem relaxation. Focusing on combinatorial exchanges, we demonstrate that the metric is informative about the eventual equilibrium, where simple regretbased metrics are not, and can be used for online selection of an effective mechanism.

cs.GT↗

arXiv2011

Simplicity-Expressiveness Tradeoffs in Mechanism Design

A fundamental result in mechanism design theory, the so-called revelation principle, asserts that for many questions concerning the existence of mechanisms with a given outcome one can restrict attention to truthful direct revelation-mechanisms. In practice, however, many mechanism use a restricted message space. This motivates the study of the tradeoffs involved in choosing simplified mechanisms, which can sometimes bring benefits in precluding bad or promoting good equilibria, and other times impose costs on welfare and revenue. We study the simplicity-expressiveness tradeoff in two representative settings, sponsored search auctions and combinatorial auctions, each being a canonical example for complete information and incomplete information analysis, respectively. We observe that the amount of information available to the agents plays an important role for the tradeoff between simplicity and expressiveness.

cs.GT↗

arXiv2009

Minimal Economic Distributed Computing

In an ideal distributed computing infrastructure, users would be able to use diverse distributed computing resources in a simple coherent way, with guaranteed security and efficient use of shared resources in accordance with the wishes of the owners of the resources. Our strategy for approaching this ideal is to first find the simplest structure within which these goals can plausibly be achieved. This structure, we find, is given by a particular recursive distributive lattice freely constructed from a presumed partially ordered set of all data in the infrastructure. Minor syntactic adjustments to the resulting algebra yields a simple language resembling a UNIX shell, a concept of execution and an interprocess protocol. Persons, organizations and servers within the system express their interests explicitly via a hierarchical currency. The currency provides a common framework for treating authentication, access control and resource sharing as economic problems while also introducing a new dimension for improving the infrastructure over time by designing system components which compete with each other to earn the currency. We explain these results, discuss experience with an implementation called egg and point out areas where more research is needed.

cs.DC↗