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199
Complex Preferences for Answer Set Optimization
, 2004
"... preference description language PDL . This language allows us to combine qualitative and quantitative, penalty based preferences in a flexible way. This makes it possible to express complex preferences which are needed in many realistic optimization settings. We show that several preference hand ..."
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preference description language PDL . This language allows us to combine qualitative and quantitative, penalty based preferences in a flexible way. This makes it possible to express complex preferences which are needed in many realistic optimization settings. We show that several preference handling methods described in the literature are special cases of our approach. We also demonstrate that PDL expressions can be compiled to logic programs which can be used as tester programs in a generateandimprove method for finding optimal answer sets.
The corporate objective revisited
 Organization Science
, 2004
"... The stock market convulsions and corporate scandals of 2001 and 2002 have reignited debates on the purposes of the corporation, in particular, the goal of shareholder value maximization. We revisit the debates, reexamine the traditional rationales, and develop a set of new arguments for why the pre ..."
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The stock market convulsions and corporate scandals of 2001 and 2002 have reignited debates on the purposes of the corporation, in particular, the goal of shareholder value maximization. We revisit the debates, reexamine the traditional rationales, and develop a set of new arguments for why the preferred objective function for the corporation must unambiguously continue to be the one that says ‘maximize shareholder value. ’ We trace the origins of the debates from the late nineteenth century, their implications for accepted law and practice of corporate governance in the United States, and their reflection in shareholder versus stakeholder views in the organization studies literature and contractarian versus communitarian views in the legal literature. We address in detail possible critiques of the shareholder value maximization view. While we recognize certain boundary constraints to our arguments, we conclude that the issues raised by such critiques and constraints are not unique to the shareholder value maximization view, but will exist even if the firm is managed on behalf of nonshareowning stakeholders. We are grateful for comments from the journal reviewers, and from Michael Bradley, Anthony Frost, Vijay
A Maximum Likelihood Approach towards Aggregating Partial Orders
 PROCEEDINGS OF THE TWENTYSECOND INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
"... In many of the possible applications as well as the theoretical models of computational social choice, the agents ’ preferences are represented as partial orders. In this paper, we extend the maximum likelihood approach for defining “optimal ” voting rules to this setting. We consider distributions ..."
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In many of the possible applications as well as the theoretical models of computational social choice, the agents ’ preferences are represented as partial orders. In this paper, we extend the maximum likelihood approach for defining “optimal ” voting rules to this setting. We consider distributions in which the pairwise comparisons/incomparabilities between alternatives are drawn i.i.d. We call such models pairwiseindependent models and show that they correspond to a class of voting rules that we call pairwise scoring rules. This generalizes rules such as Kemeny and Borda. Moreover, we show that Borda is the only pairwise scoring rule that satisfies neutrality, when the outcome space is the set of all alternatives. We then study which voting rules defined for linear orders can be extended to partial orders via our MLE model. We show that any weakly neutral outcome scoring rule (including any ranking/candidate scoring rule) based on the weighted majority graph can be represented as the MLE of a weakly neutral pairwiseindependent model. Therefore, all such rules admit natural extensions to profiles of partial orders. Finally, we propose a specific MLE model πk for generating a set of k winning alternatives, and study the computational complexity of winner determination for the MLE of πk.
The Unavailable Candidate Model: A DecisionTheoretic View of Social Choice
"... One of the fundamental problems in the theory of social choice is aggregating the rankings of a set of agents (or voters) into a consensus ranking. Rank aggregation has found application in a variety of computational contexts. However, the goal of constructing a consensus ranking rather than, say, a ..."
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One of the fundamental problems in the theory of social choice is aggregating the rankings of a set of agents (or voters) into a consensus ranking. Rank aggregation has found application in a variety of computational contexts. However, the goal of constructing a consensus ranking rather than, say, a single outcome (or winner) is often left unjustified, calling into question the suitability of classical rank aggregation methods. We introduce a novel model which offers a decisiontheoretic motivation for constructing a consensus ranking. Our unavailable candidate model assumes that a consensus choice must be made, but that candidates may become unavailable after voters express their preferences. Roughly speaking, a consensus ranking serves as a compact, easily communicable representation of a decision policy that can be used to make choices in the face of uncertain candidate availability. We use this model to define a principled aggregation method that minimizes expected voter dissatisfaction with the chosen candidate. We give exact and approximation algorithms for computing optimal rankings and provide computational evidence for the effectiveness of a simple greedy scheme. We also describe strong connections to popular voting protocols such as the plurality rule and the Kemeny consensus, showing specifically that Kemeny produces optimal rankings in the unavailable candidate model under certain conditions.
Computeraided proofs of Arrow’s and other impossibility theorems
 Artificial Intelligence
, 2009
"... Arrow’s Impossibility Theorem is one of the landmark results in social choice theory. Over the years since the theorem was proved in 1950, quite a few alternative proofs have been put forward. In this paper, we propose yet another alternative proof of the theorem. The basic idea is to use induction ..."
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Arrow’s Impossibility Theorem is one of the landmark results in social choice theory. Over the years since the theorem was proved in 1950, quite a few alternative proofs have been put forward. In this paper, we propose yet another alternative proof of the theorem. The basic idea is to use induction to reduce the theorem to the base case with 3 alternatives and 2 agents and then use computers to verify the base case. This turns out to be an effective approach for proving other impossibility theorems such as Sen’s and MullerSatterthwaite’s theorems as well. Furthermore, we believe this new proof opens an exciting prospect of using computers to discover similar impossibility or even possibility results.
A complexityofstrategicbehavior comparison between Schulze’s rule and ranked pairs
 In Proc. of 26th AAAI Conference on AI
, 2012
"... Schulze’s rule and ranked pairs are two Condorcet methods that both satisfy many natural axiomatic properties. Schulze’s rule is used in the elections of many organizations, including ..."
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Cited by 16 (0 self)
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Schulze’s rule and ranked pairs are two Condorcet methods that both satisfy many natural axiomatic properties. Schulze’s rule is used in the elections of many organizations, including
Bargaining power in marriage: Earnings, wage rates and household production. Working Paper
, 2005
"... together with an analysis of the role of joint taxation in family bargaining, was presented at ..."
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together with an analysis of the role of joint taxation in family bargaining, was presented at
An Efficient Reduction of Ranking to Classification
, 2007
"... This paper describes an efficient reduction of the learning problem of ranking to binary classification. The reduction is randomized and guarantees a pairwise misranking regret bounded by that of the binary classifier, improving on a recent result of Balcan et al. (2007) which ensures only twice tha ..."
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This paper describes an efficient reduction of the learning problem of ranking to binary classification. The reduction is randomized and guarantees a pairwise misranking regret bounded by that of the binary classifier, improving on a recent result of Balcan et al. (2007) which ensures only twice that upperbound. Moreover, our reduction applies to a broader class of ranking loss functions, admits a simple proof, and the expected time complexity of our algorithm in terms of number of calls to a classifier or preference function is also improved from Ω(n 2) to O(n log n). In addition, when the top k ranked elements only are required (k ≪ n), as in many applications in information extraction or search engine design, the time complexity of our algorithm can be further reduced to O(k log k+n). Our reduction and algorithm are thus practical for realistic applications where the number of points to rank exceeds several thousands. Much of our results also extend beyond the bipartite case previously studied. To further complement them, we also derive lower bounds for any deterministic reduction of ranking to binary classification, proving that randomization is necessary to achieve our reduction guarantees. 1
Health utility indices and equity considerations
 J Health Econ
, 1997
"... The aim of this paper is to propose methods that incorporate quity concerns into cost utility analysis. The focus of the paper is on QALYs. but the results apply to health utility indices in general. Two interpretations ofQALYs are considered: QALYs as (yon Neunmnn Morgenstem) utilities and QALYs as ..."
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Cited by 13 (0 self)
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The aim of this paper is to propose methods that incorporate quity concerns into cost utility analysis. The focus of the paper is on QALYs. but the results apply to health utility indices in general. Two interpretations ofQALYs are considered: QALYs as (yon Neunmnn Morgenstem) utilities and QALYs as measures of health. A justification is provided for aggregating consistently.scaled "'QALYs as utilities " over individuals. The conditions underlying unweighted aggregation f QALYs are identified. These conditions exclude two common types of equity concern. Algorithms are proposed that take into account equity concerns and that are relatively e~y to apply. JEL clas.sificati+m: 1!0 Keyword~: Equity: Uiility tbzory; S~ia! c.hoice theory.: He',,dth: Cost utility analysis 1.
Are There Any Nicely Structured Preference Profiles Nearby?
 PROCEEDINGS OF THE TWENTYTHIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
"... We investigate the problem of deciding whether a given preference profile is close to a nicely structured preference profile of a certain type, as for instance singlepeaked, singlecaved, singlecrossing, valuerestricted, bestrestricted, worstrestricted, mediumrestricted, or groupseparable profi ..."
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Cited by 12 (2 self)
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We investigate the problem of deciding whether a given preference profile is close to a nicely structured preference profile of a certain type, as for instance singlepeaked, singlecaved, singlecrossing, valuerestricted, bestrestricted, worstrestricted, mediumrestricted, or groupseparable profiles. We measure this distance by the number of voters or alternatives that have to be deleted so as to reach a nicely structured profile. Our results classify all considered problem variants with respect to their computational complexity, and draw a clear line between computationally tractable (polynomial time solvable) and computationally intractable (NPhard) questions.