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020 ▼a 9780438376557
035 ▼a (MiAaPQ)AAI10811769
035 ▼a (MiAaPQ)duke:14738
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 001.5
1001 ▼a Freeman, Rupert.
24510 ▼a Eliciting and Aggregating Information for Better Decision Making.
260 ▼a [S.l.]: ▼b Duke University., ▼c 2018.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2018.
300 ▼a 276 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
500 ▼a Adviser: Vincent Conitzer.
5021 ▼a Thesis (Ph.D.)--Duke University, 2018.
520 ▼a In this thesis, we consider two classes of problems where algorithms are increasingly used to make, or assist in making, a wide range of decisions. The first class of problems we consider is the allocation of jointly owned resources among a grou
520 ▼a In the first part of the thesis, we consider shared resource allocation, where we relax two common assumptions in the fair divison literature. Firstly, we relax the assumption that goods are private, meaning that they must be allocated to only a
520 ▼a In the second part of the thesis, we consider the design of mechanisms for forecasting. We first consider a tradeoff between several desirable properties for wagering mechanisms, showing that the properties of Pareto efficiency, incentive compat
590 ▼a School code: 0066.
650 4 ▼a Artificial intelligence.
690 ▼a 0800
71020 ▼a Duke University. ▼b Computer Science.
7730 ▼t Dissertation Abstracts International ▼g 80-02B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0066
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997999 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 201812 ▼f 2019
990 ▼a ***1012033