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020 ▼a 9780438026742
035 ▼a (MiAaPQ)AAI10815171
035 ▼a (MiAaPQ)cornellgrad:10816
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 004
1001 ▼a Jalaly Khalilabadi, Pooya.
24510 ▼a Fairness, Learning and Efficiency in Markets with Budgeted Agents.
260 ▼a [S.l.] : ▼b Cornell University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 207 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 ▼a Adviser: Eva Tardos.
5021 ▼a Thesis (Ph.D.)--Cornell University, 2018.
520 ▼a In almost all online markets with monetary transactions, the participants have a limited budget which restricts their ability to purchase their desired commodities. Models from mechanism design, algorithm design and auction theory which study th
520 ▼a This dissertation presents a deep study of such markets with budget limited agents, using theoretical models as well as data from real world auction markets. In chapter 2, we study the problem of a budget limited buyer who wants to buy a set of
520 ▼a In chapter 3, we present a deep study of the behavior of real estate agents in the new online advertising platform provided by Zillow. We analyze behavior of the agents through time using the provided data from Zillow. We use a no-regret based a
520 ▼a In chapter 4, we show equilibria of markets with budget limited agents can be used to achieve fairness for problems of matching without money with agents who have preferences over commodities. A unit budget with artificial money is given to each
590 ▼a School code: 0058.
650 4 ▼a Computer science.
690 ▼a 0984
71020 ▼a Cornell University. ▼b Computer Science.
7730 ▼t Dissertation Abstracts International ▼g 79-10B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0058
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998160 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자