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Award Abstract #1350598

CAREER: A Broad Synthesis of Artificial Intelligence and Social Choice

NSF Org: IIS
Div Of Information & Intelligent Systems
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Initial Amendment Date: February 12, 2014
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Latest Amendment Date: February 9, 2016
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Award Number: 1350598
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Award Instrument: Continuing grant
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Program Manager: Hector Munoz-Avila
IIS Div Of Information & Intelligent Systems
CSE Direct For Computer & Info Scie & Enginr
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Start Date: February 15, 2014
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End Date: January 31, 2019 (Estimated)
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Awarded Amount to Date: $314,057.00
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Investigator(s): Ariel Procaccia arielpro@cs.cmu.edu (Principal Investigator)
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Sponsor: Carnegie-Mellon University
5000 Forbes Avenue
PITTSBURGH, PA 15213-3815 (412)268-9527
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NSF Program(s): ROBUST INTELLIGENCE,
ALGORITHMIC FOUNDATIONS
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Program Reference Code(s): 1045, 7796, 7932, 7495
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Program Element Code(s): 7495, 7796

ABSTRACT

Social choice theory is the field that studies the aggregation of individual preferences toward a collective choice. While the artificial intelligence (AI) community has so far played a dominant role in the study of the computational aspects of social choice, the interaction between core AI paradigms and social choice theory has been surprisingly limited.

This project is enhancing the interaction between the two fields through a synthesis of social choice with the following AI areas: (i) decision making under uncertainty, by building on models studied in AI to create new ways to model, analyze, and make decisions in environments where preferences are dynamically changing; (ii) multiagent systems, by studying settings where agents randomly vote over multiple states, and investigating the connection between normative properties and system performance; and finally (iii) machine learning, by employing insights about strategic behavior under structured preferences, developed in the social choice literature, in order to design regression learning algorithms that discourage strategic manipulation.

An overarching goal of this project is to demonstrate the potential of social choice theory to AI researchers, and ultimately to establish social choice theory as a standard paradigm in AI. Equally importantly, this project is expected to increase the scope of social choice theory. Broader impacts include a new web-based voting system, which has the potential to serve and educate hundreds of thousands of users; dissemination through a new book on computational social choice; and a workshop on computational social choice, which will help set a new agenda for the field.


PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Amit Datta, Anupam Datta, Ariel D. Procaccia, and Yair Zick. "Influence in Classification via Cooperative Game Theory," IJCAI'15, 2015.

Ariel D. Procaccia and Nisarg Shah. "Is Approval Voting Optimal Given Approval Votes?," NIPS'15, 2015.

Ariel D. Procaccia and Nisarg Shah. "Optimal Aggregation of Uncertain Preferences," AAAI'16, 2016.

Ariel D. Procaccia, Nisarg Shah, and Eric Sodomka. "Ranked Voting on Social Networks," IJCAI'15, 2015.

Ariel D. Procaccia, Nisarg Shah, and Yair Zick. "Voting Rules As Error-Correcting Codes," AAAI'15, 2015.

Ariel D. Procaccia, Nisarg Shah, and Yair Zick. "Voting Rules As Error-Correcting Codes," Artificial Intelligence, v.231, 2016.

Avrim Blum, John P. Dickerson, Nika Haghtalab, Ariel D. Procaccia, Tuomas Sandholm, and Ankit Sharma. "Ignorance is Almost Bliss: Near-Optimal Stochastic Matching With Few Queries," EC'15, 2015.

David Kurokawa, Ariel D. Procaccia, and Junxing Wang. "When Can the Maximin Share Guarantee Be Guaranteed?," AAAI'16, 2016.

David Kurokawa, Ariel D. Procaccia, and Nisarg Shah. "Leximin Allocations in the Real World," EC'15, 2015.

David Kurokawa, Omer Lev, Jamie Morgenstern, and Ariel D. Procaccia. "Impartial Peer Review," IJCAI'15, 2015.


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