國立虎尾科技大學 |

Measuring intrinsic quality of human decisions.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Measuring intrinsic quality of human decisions./
Author:	Biswas, Tamal Tanu.
Description:	1 online resource (161 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-04(E), Section: B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9781369185331

Measuring intrinsic quality of human decisions.
Biswas, Tamal Tanu.

Measuring intrinsic quality of human decisions. - 1 online resource (161 pages)

Source: Dissertation Abstracts International, Volume: 78-04(E), Section: B.

Thesis (Ph.D.)--State University of New York at Buffalo, 2016.

Includes bibliographical references

Human decisions are often influenced by various factors, such as risk, uncertainty, time pressure, and depth of thinking, whereas decisions by an AI agent can be effectively optimal without these limitations. The concept of depth, a well-defined term in game theory (including chess), does not have a clear formulation in decision theory. To quantify depth, we can configure an AI agent of supreme competence to 'think' at depths beyond the capability of any human, and in the process collect evaluations of decisions at various depths. We formulate a new measure called 'depth of satisficing' from analyzing the decisions made by humans along with utilizing computers' suggestions for the same set of problems. This formulation combines Herbert Simon's concept of satisficing with aspects of level- k thinking. It augments the statistical chess model of Regan and Haworth, which provides skill ratings based on the intrinsic quality of the decisions made.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369185331Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Measuring intrinsic quality of human decisions.
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100 1 $a Biswas, Tamal Tanu. $3 1188554
245 1 0 $a Measuring intrinsic quality of human decisions.
264 0 $c 2016
300 $a 1 online resource (161 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
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500 $a Source: Dissertation Abstracts International, Volume: 78-04(E), Section: B.
500 $a Adviser: Kenneth W. Regan.
502 $a Thesis (Ph.D.)--State University of New York at Buffalo, 2016.
504 $a Includes bibliographical references
520 $a Human decisions are often influenced by various factors, such as risk, uncertainty, time pressure, and depth of thinking, whereas decisions by an AI agent can be effectively optimal without these limitations. The concept of depth, a well-defined term in game theory (including chess), does not have a clear formulation in decision theory. To quantify depth, we can configure an AI agent of supreme competence to 'think' at depths beyond the capability of any human, and in the process collect evaluations of decisions at various depths. We formulate a new measure called 'depth of satisficing' from analyzing the decisions made by humans along with utilizing computers' suggestions for the same set of problems. This formulation combines Herbert Simon's concept of satisficing with aspects of level- k thinking. It augments the statistical chess model of Regan and Haworth, which provides skill ratings based on the intrinsic quality of the decisions made.
520 $a We use a large dataset from real chess tournaments and evaluations from chess programs (AI agents) of strength beyond all human players. We then seek to transfer the results to other decision-making domains in which effectively optimal judgments can be obtained from either hindsight, answer banks, or powerful AI agents. In some applications, such as multiple-choice tests, we establish an isomorphism of the underlying mathematical quantities, which induces a correspondence between our model and various measurement theories. We present and discuss results showing distinctive human traits that are not demonstrated by the computers. We further provide results toward the objective of applying the correspondence in reverse to obtain and quantify the measure of swing, difficulty, discrimination and complexity for any decision problem.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Psychology. $3 555998
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0621
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a State University of New York at Buffalo. $b Computer Science and Engineering. $3 1180201
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10163892 $z click for full text (PQDT)