Nicholas Mattei

PrefLib: A Library for Preferences http://www.preflib.org

We introduce PREFLIB: A Library for Preferences; an online resource located at http://www.preflib.org. With the emergence of computational social choice and an increased awareness of the applicability of preference reasoning techniques to areas ranging from recommendation systems to kidney exchanges, the interest in preferences has never been higher. We hope to encourage the growth of all facets of preference reasoning by establishing a centralized repository of high quality data based around simple, delimited data formats. We detail the challenges of constructing such a repository, provide a survey of the initial release of the library, and invite the community to use and help expand PREFLIB.

Bribery in voting with CP-nets

We investigate the computational complexity of finding optimal bribery schemes in voting domains where the candidate set is the Cartesian product of a set of variables and voters use CP-nets, an expressive and compact way to represent preferences. To do this, we generalize the traditional bribery problem to take into account several issues over which agents vote, and their inter-dependencies. We consider five voting rules, three kinds of bribery actions, and five cost schemes. For most of the combinations of these parameters, we find that bribery in this setting is computationally easy.

Updates and Uncertainty in CP-Nets

In this paper we present a two-fold generalization of conditional preference networks (CP-nets) that incorporates uncertainty. CP-nets are a formal tool to model qualitative conditional statements (cp-statements) about preferences over a set of objects. They are inherently static structures, both in their ability to capture dependencies between objects and in their expression of preferences over features of a particular object. Moreover, CP-nets do not provide the ability to express uncertainty over the preference statements. We present and study a generalization of CP-nets which supports changes and allows for encoding uncertainty, expressed in probabilistic terms, over the structure of the dependency links and over the individual preference relations.

A behavioral perspective on social choice

We discuss what behavioral social choice can contribute to computational social choice. An important trademark of behavioral social choice is to switch perspective away from a traditional sampling approach in the social choice literature and to ask inference questions: Based on limited, imperfect, and highly incomplete observed data, what inference can we make about social choice outcomes at the level of a population that generated those observed data? A second important consideration in theoretical and behavioral work on social choice is model dependence: How do theoretical predictions and conclusions, as well as behavioral predictions and conclusions, depend on modeling assumptions about the nature of human preferences and/or how these preferences are expressed in ratings, rankings, and ballots of various kinds? Using a small subcollection from the Netflix Prize dataset, we illustrate these notions with real movie ratings from real raters. We highlight the key roles that inference and behavioral modeling play in the analysis of such data, particularly for sparse data like the Netflix ratings. The social and behavioral sciences can provide a supportive role in the effort to develop behaviorally meaningful and robust studies in computational social choice.

Empirical evaluation of voting rules with strictly ordered preference data

The study of voting systems often takes place in the theoretical domain due to a lack of large samples of sincere, strictly ordered voting data.We derive several million elections (more than all the existing studies combined) from a publicly available data, the Netflix Prize dataset. The Netflix data is derived from millions of Netflix users, who have an incentive to report sincere preferences, unlike random survey takers. We evaluate each of these elections under the Plurality, Borda, k-Approval, and Repeated Alternative Vote (RAV) voting rules.We examine the Condorcet Efficiency of each of the rules and the probability of occurrence of Condorcets Paradox. We compare our votes to existing theories of domain restriction (e.g., single-peakedness) and statistical models used to generate election data for testing (e.g., Impartial Culture). We find a high consensus among the different voting rules; almost no instances of Condorcets Paradox; almost no support for restricted preference profiles, and very little support for many of the statistical models currently used to generate election data for testing.

Fiction as an Introduction to Computer Science Research

The undergraduate computer science curriculum is generally focused on skills and tools; most students are not exposed to much research in the field, and do not learn how to navigate the research literature. We describe how fiction reviews (and specifically science fiction) are used as a gateway to research reviews. Students learn a little about current or recent research on a topic that stirs their imagination, and learn how to search for, read critically, and compare technical papers on a topic related to their chosen science fiction book, movie, or TV show.

An English-Language Argumentation Interface for Explanation Generation with Markov Decision Processes in the Domain of Academic Advising

A Markov Decision Process (MDP) policy presents, for each state, an action, which preferably maximizes the expected utility accrual over time. In this article, we present a novel explanation system for MDP policies. The system interactively generates conversational English-language explanations of the actions suggested by an optimal policy, and does so in real time. We rely on natural language explanations in order to build trust between the user and the explanation system, leveraging existing research in psychology in order to generate salient explanations. Our explanation system is designed for portability between domains and uses a combination of domain-specific and domain-independent techniques. The system automatically extracts implicit knowledge from an MDP model and accompanying policy. This MDP-based explanation system can be ported between applications without additional effort by knowledge engineers or model builders. Our system separates domain-specific data from the explanation logic, allowing for a robust system capable of incremental upgrades. Domain-specific explanations are generated through case-based explanation techniques specific to the domain and a knowledge base of concept mappings used to generate English-language explanations.

A natural language argumentation interface for explanation generation in Markov decision processes

A Markov Decision Process (MDP) policy presents, for each state, an action, which preferably maximizes the expected reward accrual over time. In this paper, we present a novel system that generates, in real time, natural language explanations of the optimal action, recommended by an MDP while the user interacts with the MDP policy. We rely on natural language explanations in order to build trust between the user and the explanation system, leveraging existing research in psychology in order to generate salient explanations for the end user. Our explanation system is designed for portability between domains and uses a combination of domain specific and domain independent techniques. The system automatically extracts implicit knowledge from an MDP model and accompanying policy. This policy-based explanation system can be ported between applications without additional effort by knowledge engineers or model builders. Our system separates domain-specific data from the explanation logic, allowing for a robust system capable of incremental upgrades. Domain-specific explanations are generated through case-based explanation techniques specific to the domain and a knowledge base of concept mappings for our natural language model.

On the complexity of bribery and manipulation in tournaments with uncertain information

We study the computational complexity of optimal bribery and manipulation schemes for sports tournaments with uncertain information: cup; challenge or caterpillar; and round robin. Our results carry over to the equivalent voting rules: sequential pair-wise elections, cup, and Copeland, when the set of candidates is exactly the set of voters. This restriction creates new difficulties for most existing algorithms. The complexity of bribery and manipulation are well studied, almost always assuming deterministic information about votes and results. We assume that for candidates i and j the probability that i beats j and the costs of lowering each probability by fixed increments are known to the manipulators. We provide complexity analyses for cup, challenge, and round robin competitions ranging from polynomial time to np^pp. This shows that the introduction of uncertainty into the reasoning process drastically increases the complexity of bribery problems in some instances.

Mechanisms for Online Organ Matching

Matching donations from deceased patients to patients on the waiting list account for over 85% of all kidney transplants performed in Australia. We propose a simple mechanisms to perform this matching and compare this new mechanism with the more complex algorithm currently under consideration by the Organ and Tissue Authority in Australia. We perform a number of experiments using real world data provided by the Organ and Tissue Authority of Australia. We find that our simple mechanism is more efficient and fairer in practice compared to the other mechanism currently under consideration.