東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Information Elicitation and Aggregation: Theory, Behavior, and Application.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Information Elicitation and Aggregation: Theory, Behavior, and Application./
作者:	Wang, Juntao.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	204 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29209324
ISBN:	9798819382530

Information Elicitation and Aggregation: Theory, Behavior, and Application.
Wang, Juntao.

Information Elicitation and Aggregation: Theory, Behavior, and Application. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 204 p.

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (Ph.D.)--Harvard University, 2022.

This item must not be sold to any third party vendors.

Recent decades have witnessed a broadening application of the wisdom of crowds, ranging from forecasting geopolitical events to estimating business variables to predicting the replicability of social science studies. In these applications, information is elicited from a crowd of human participants and then aggregated to generate final judgments or informed decisions. Two central problems in this process are the information elicitation problem, i.e., how we can elicit authentic and high-quality information from selfish information holders, and the information aggregation problem, i.e., how we can aggregate the noisy or even biased information that we collect into more accurate judgments or decisions. The challenges in solving the above two problems vary with the concrete application scenarios. At a high level, these scenarios can be divided into two categories, the with-verification setting and the without-verification setting. In the with-verification setting, the principal can access (historical) ground truth information to verify the information quality of each participant and design elicitation and aggregation mechanisms accordingly. In contrast, the principal cannot access such ground truth information in the without-verification setting. In this thesis, I explore and make progress on information elicitation and aggregation problems under several specific scenarios in both settings.In the with-verification setting, I study the betting scenario and propose the randomized wagering mechanisms for prediction elicitation. These mechanisms overcome an impossibility result for deterministic mechanisms that four desirable properties cannot be satisfied simultaneously. In the without-verification setting, I study both the probabilistic prediction elicitation and aggregation problems. I first extend the strictly proper scoring rules, the most prevalent information elicitation solution in the with-verification setting, into the without-verification setting and derive the surrogate scoring rules. These rules not only provide strong incentives for participants to report true beliefs but also characterize their prediction accuracy. I further develop a forecast aggregation framework using the elicitation without-verification schemes such as the surrogate scoring rules to improve the aggregation accuracy consistently. This improvement is examined and verified on a diverse set of real-world human forecasting datasets.Human behavior, involving how people understand elicitation questions, generate beliefs, and react to elicitation schemes, is overlooked in the literature on information elicitation and aggregation. This thesis also explores human behavior and its influence within this domain. In particular, I develop auction mechanisms to elicit truthful reporting of private signals when human players have bounded rationality in interdependent valuation auctions. I also conduct real-world elicitation and aggregation experiments that use laypeople to predict the replicability of social science studies. Several interesting experimental findings of laypeople's behaviors are analyzed and discussed.

ISBN: 9798819382530Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Information aggregation

Information Elicitation and Aggregation: Theory, Behavior, and Application.
LDR:04236nmm a2200349 4500 001 2351608
005 20221107090138.5
008 241004s2022 ||||||||||||||||| ||eng d
020 $a 9798819382530
035 $a (MiAaPQ)AAI29209324
035 $a AAI29209324
040 $a MiAaPQ $c MiAaPQ
100 1 $a Wang, Juntao. $3 1279109
245 1 0 $a Information Elicitation and Aggregation: Theory, Behavior, and Application.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 204 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
500 $a Advisor: Chen, Yiling.
502 $a Thesis (Ph.D.)--Harvard University, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Recent decades have witnessed a broadening application of the wisdom of crowds, ranging from forecasting geopolitical events to estimating business variables to predicting the replicability of social science studies. In these applications, information is elicited from a crowd of human participants and then aggregated to generate final judgments or informed decisions. Two central problems in this process are the information elicitation problem, i.e., how we can elicit authentic and high-quality information from selfish information holders, and the information aggregation problem, i.e., how we can aggregate the noisy or even biased information that we collect into more accurate judgments or decisions. The challenges in solving the above two problems vary with the concrete application scenarios. At a high level, these scenarios can be divided into two categories, the with-verification setting and the without-verification setting. In the with-verification setting, the principal can access (historical) ground truth information to verify the information quality of each participant and design elicitation and aggregation mechanisms accordingly. In contrast, the principal cannot access such ground truth information in the without-verification setting. In this thesis, I explore and make progress on information elicitation and aggregation problems under several specific scenarios in both settings.In the with-verification setting, I study the betting scenario and propose the randomized wagering mechanisms for prediction elicitation. These mechanisms overcome an impossibility result for deterministic mechanisms that four desirable properties cannot be satisfied simultaneously. In the without-verification setting, I study both the probabilistic prediction elicitation and aggregation problems. I first extend the strictly proper scoring rules, the most prevalent information elicitation solution in the with-verification setting, into the without-verification setting and derive the surrogate scoring rules. These rules not only provide strong incentives for participants to report true beliefs but also characterize their prediction accuracy. I further develop a forecast aggregation framework using the elicitation without-verification schemes such as the surrogate scoring rules to improve the aggregation accuracy consistently. This improvement is examined and verified on a diverse set of real-world human forecasting datasets.Human behavior, involving how people understand elicitation questions, generate beliefs, and react to elicitation schemes, is overlooked in the literature on information elicitation and aggregation. This thesis also explores human behavior and its influence within this domain. In particular, I develop auction mechanisms to elicit truthful reporting of private signals when human players have bounded rationality in interdependent valuation auctions. I also conduct real-world elicitation and aggregation experiments that use laypeople to predict the replicability of social science studies. Several interesting experimental findings of laypeople's behaviors are analyzed and discussed.
590 $a School code: 0084.
650 4 $a Computer science. $3 523869
650 4 $a Information science. $3 554358
650 4 $a Behavioral sciences. $3 529833
653 $a Information aggregation
653 $a Information elicitation
653 $a Human behavior
690 $a 0984
690 $a 0723
690 $a 0602
710 2 $a Harvard University. $b Engineering and Applied Sciences - Computer Science. $3 3184546
773 0 $t Dissertations Abstracts International $g 83-12B.
790 $a 0084
791 $a Ph.D.
792 $a 2022
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29209324