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Supporting joint human-computer judg...

University of Illinois at Urbana-Champaign.

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Supporting joint human-computer judgment under uncertainty.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Supporting joint human-computer judgment under uncertainty./
作者:	Miller, Sarah.
面頁冊數:	135 p.
附註:	Adviser: Alex Kirlik.
Contained By:	Dissertation Abstracts International70-02B.
標題:	Engineering, Industrial. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3347452
ISBN:	9781109025170

Supporting joint human-computer judgment under uncertainty.
Miller, Sarah.

Supporting joint human-computer judgment under uncertainty. - 135 p.

Adviser: Alex Kirlik.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008.

Recent high-profile failures of computational models to make accurate predictions, estimations or forecasts (e.g., risk models used on Wall Street) highlight the importance of keeping humans in the loop in a variety operational contexts, often involving judgment and decision making. The purpose of this dissertation was to design and evaluate a joint, human-computer architecture that integrates those aspects of a judgment task that can be best supported by computational models and those aspects best left to human expertise. Computational techniques are typically based on judgment or decision models that may integrate information more accurately and reliably than unaided human cognition. Yet humans may have knowledge transcending what can be formally modeled, either in principle, or because of access to information the model does not have.

ISBN: 9781109025170Subjects--Topical Terms:

626639
Engineering, Industrial.

Supporting joint human-computer judgment under uncertainty.
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500 $a Source: Dissertation Abstracts International, Volume: 70-02, Section: B, page: 1268.
502 $a Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008.
520 $a Recent high-profile failures of computational models to make accurate predictions, estimations or forecasts (e.g., risk models used on Wall Street) highlight the importance of keeping humans in the loop in a variety operational contexts, often involving judgment and decision making. The purpose of this dissertation was to design and evaluate a joint, human-computer architecture that integrates those aspects of a judgment task that can be best supported by computational models and those aspects best left to human expertise. Computational techniques are typically based on judgment or decision models that may integrate information more accurately and reliably than unaided human cognition. Yet humans may have knowledge transcending what can be formally modeled, either in principle, or because of access to information the model does not have.
520 $a Three experiments were conducted to evaluate and refine the architecture design. The experimental tasks were patterned after the popular games of fantasy baseball and fantasy football, which require competitors, near the beginning of each season, to predict the performance of professional baseball or football players over the course of that season on the basis of past performance and other relevant information. Some aspects of this predictive judgment may be best performed computationally (by linear regression models) and other aspects may be best performed by human expertise. The most substantial and challenging aspects of interface design involved how to welcome expert input on a case-by-case (player-by-player) basis, yet also provide visual guidance for, and constraint on, how these case-specific inputs should also reflect an appropriate degree of regression to the mean, or reliance on base-rate, rather than case-specific, information.
520 $a Overall, the results revealed that the integrated, human-computer architecture supported including humans into the loop without loss in judgment quality. In some cases, the joint, human-computer system outperformed either the human or the model alone. Implications and future directions are discussed.
590 $a School code: 0090.
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