Projects and Grants per year
Grants and Contracts Details
Description
This project explores multi-disciplinary synergistic research at the interface between Behavioral Economics, Computer
Science, Mathematics, Political Science, and Psychology. The senior personnel are three Computer Scientists (Judy
Goldsmith and Nicholas Mattei, University of Kentucky, and Francesca Rossi, University of Padova, Italy) and a
Psychologist (Michel Regenwetter, University of Illinois at Urbana-Champaign). All four scholars have extensive prior
experience in interdisciplinary research. The team proposes to link its different perspectives on consensus methods
into a novel and more comprehensive analysis of robust aggregation. The project places particular emphasis on the
CP-net representation of conditional preferences, and the role of CP-nets in human decision-making behavior.
The project consists of three major themes, each with two specific aims.
• INDIVIDUAL AND COLLECTIVE CHOICE VIA CP-NETS
- The project considers, through laboratory experiments, probabilistic modeling and quantitative statistical data
analysis, whether people exhibit preferences in a form that can be modeled by CP-nets.
- The team extends ideas from aggregation in Bayesian networks (models of conditional probabilities) to aggregation
in CP-nets (models of conditional preferences). The projects investigates measures of closeness of
CP-nets; algorithms for aggregating CP-nets, and systems for reaching and supporting approximate consensus,
based on individual preferences in the form of CP-nets.
• ROBUST AGGREGATION
- The team investigates, for rating, ranking, or partial ranking ballots, how statistical inference from noisy data
relates to or interacts with strategic manipulability and bribery. Empirically, they consider how each perspective
impacts behavioral social choice analyses of, e.g., twelve American Psychological Association election data sets
with tens of thousands of voters for five candidates.
- The project extends computational social choice on voting systems, and the manipulation thereof, from preferences
expressed as ratings, rankings, or subsets to preferences expressed as CP-nets.
• BEHAVIORAL SOCIAL CHOICE CASE STUDY ON A LARGE NETFLIX DATA SET
- The team uses a publicly released Netflix movie rating database with millions of ratings of tens of thousands
of movies by hundreds of thousands of viewers to compare the outcomes under competing aggregation methods
using real data sets of various sizes extracted from the database.
- The team uses the same Netflix challenge database to investigate and empirically characterize voting scenarios
that are especially susceptible or especially resilient to strategic manipulation, as a function of the number of
voters and the number of choice options.
Intellectual Merit of the Proposed Activity. This project creates new, innovative knowledge by inter-connecting
state of the art approaches to decision making research in computer science (artificial intelligence, computational social
choice), psychology, political science, and neighboring disciplines. The multi-disciplinary approach specifically crossfertilizes
decision science disciplines that are traditionally segregated depending on their focus on individual versus
collective choice, normative rational versus descritive behavioral, analytical versus computational approaches, and
qualitative versus quantitative approaches.
Broader Impacts from the Proposed Activity. The project impacts science and society on several levels. Making
election outcomes resilient to probabilistic perturbations in ballot casting, vote counting, and to strategic manipulation
is very important to many aspects of a healthy society. This applies across many levels and types of governance, from
academic/professional to political elections. More generally, improving processes of consensus formation is relevant
to many facets of private, business, and government life. The project connects the disciplines of computational social
choice and psychology in new ways by linking the study of computational aspects of voting and manipulation to
the study of actual human preferences in a formal laboratory setting. It will also develop a psychologically and
statistically valid set of benchmark elections based on the Netflix challenge data, which will enable large-scale analysis
of voting patterns. This contributes significantly to the scientific infrastructure for analytic studies of social choice in
psychology, political science, statistics, and computer science. Moreover, it will impact both research and education,
since graduate students in these disciplines will be able to learn tools and research methods from the other disciplines
involved in the project. This will prepare them for a more fruitful use of their techniques and tools in their respective
fields, and ultimately prepare them to function in an increasingly multi-disciplinary world.
The team aims to disseminate results as broadly as possible, e.g., via symposium proposals on robust aggregation
at international conferences in multiple disciplines related to the decision sciences. All four scholars have experience
in organizing workshops and symposia on preferences and/or social choice.
Status | Finished |
---|---|
Effective start/end date | 9/1/12 → 8/31/14 |
Funding
- National Science Foundation: $89,328.00
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Projects
- 2 Finished
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Restricted Scope for REU Supplement: Robust Aggregation of Preferences
Goldsmith, J. (PI)
5/3/13 → 8/31/14
Project: Research project