國立虎尾科技大學 |

Individual Behavioral Modeling Across Games of Strategy.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Individual Behavioral Modeling Across Games of Strategy./
作者:	Fields, Logan.
面頁冊數:	1 online resource (108 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
Contained By:	Dissertations Abstracts International85-10B.
標題:	Psychology. -
電子資源:	click for full text (PQDT)
ISBN:	9798382316178

Individual Behavioral Modeling Across Games of Strategy.
Fields, Logan.

Individual Behavioral Modeling Across Games of Strategy. - 1 online resource (108 pages)

Source: Dissertations Abstracts International, Volume: 85-10, Section: B.

Thesis (Ph.D.)--University of South Florida, 2024.

Includes bibliographical references

An individual's actions in a particular environment and with specified resources can reveal their decision-making tendencies and patterns, and by analyzing the variations in cognitive traits among individuals, it may be possible to identify trends that can foretell their future behaviors. This can be a powerful tool in various fields, including cognitive modeling, player analytics, computer security, and threat detection. Collectible card games are a fruitful test space for studying cognitive differences in decision-making, as they can have clearly defined and replicable environments and large player bases. As such, in this work, I explore the potential of using two virtual collectible card games, Legends of Code and Magic and Hearthstone, to distinguish individual players based on their gameplay and predict the future actions of a player whose behavior is already known. However, the task of defining and fixing environments within a game is nontrivial. Additionally, it would be na¨ive to assume we could reliably obtain enough behavioral information about an individual from any singular strategic game to make decisive predictions about their cognitive traits. As such, this work also explores the ability to identify cognitive traits across multiple strategic tasks, using the commonly studied Iowa Gambling Task and Blackjack. The results indicate that, within strategic games, the maximum possible environmental information is necessary to reach full capability in identifying players and predicting their future actions. Additionally, making predictions about a player's future behavior by using individualized models is much more accurate than relying on generalized data from many players. Finally, some cognitive traits, namely confidence and working memory, may be transferable between the Iowa Gambling Task and Blackjack.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382316178Subjects--Topical Terms:

555998
Psychology.
Subjects--Index Terms:

BlackjackIndex Terms--Genre/Form:

554714
Electronic books.

Individual Behavioral Modeling Across Games of Strategy.
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520 $a An individual's actions in a particular environment and with specified resources can reveal their decision-making tendencies and patterns, and by analyzing the variations in cognitive traits among individuals, it may be possible to identify trends that can foretell their future behaviors. This can be a powerful tool in various fields, including cognitive modeling, player analytics, computer security, and threat detection. Collectible card games are a fruitful test space for studying cognitive differences in decision-making, as they can have clearly defined and replicable environments and large player bases. As such, in this work, I explore the potential of using two virtual collectible card games, Legends of Code and Magic and Hearthstone, to distinguish individual players based on their gameplay and predict the future actions of a player whose behavior is already known. However, the task of defining and fixing environments within a game is nontrivial. Additionally, it would be na¨ive to assume we could reliably obtain enough behavioral information about an individual from any singular strategic game to make decisive predictions about their cognitive traits. As such, this work also explores the ability to identify cognitive traits across multiple strategic tasks, using the commonly studied Iowa Gambling Task and Blackjack. The results indicate that, within strategic games, the maximum possible environmental information is necessary to reach full capability in identifying players and predicting their future actions. Additionally, making predictions about a player's future behavior by using individualized models is much more accurate than relying on generalized data from many players. Finally, some cognitive traits, namely confidence and working memory, may be transferable between the Iowa Gambling Task and Blackjack.
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