Game-Specific and Player-Specific Knowledge Combine to Drive Transfer of Learning Between Games of Strategic Interaction

Michael G. Collins; Ion Juvina; Kevin A. Gluck

doi:10.1007/978-3-319-39931-7_18

Game-Specific and Player-Specific Knowledge Combine to Drive Transfer of Learning Between Games of Strategic Interaction

Michael G. Collins, Ion Juvina, Kevin A. Gluck

Psychology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Trust in others transfers between games of strategic interaction (e.g., iterated Prisoner’s Dilemma– PD and Chicken Game – CG). This transfer of trust represents knowledge acquired about the other player (player-specific knowledge), carrying over from one situation to another, which is separate from what was learned about the previous game (game-specific knowledge). We examine how the transfer of both player-specific and game-specific knowledge informs one’s decisions when interacting with a new player. In this paper, we present the experimental design of an upcoming study, where participants will sequentially play two games of strategic interaction (PD & CG) with the same or a different computerized confederate agent. In addition to the experimental design, we present model predictions, using a previously published computational cognitive model of trust dynamics. The model predicts transfer of learning effects in both conditions and larger effects when interacting with the same agent.

Original language	English
Title of host publication	Social, Cultural, and Behavioral Modeling
Editors	Nathaniel Osgood, Kevin S. Xu, David Reitter, Dongwon Lee
Publisher	Springer Verlag
Pages	186-195
Number of pages	10
ISBN (Electronic)	978-3-319-39930-0
ISBN (Print)	9783319399300
DOIs	https://doi.org/10.1007/978-3-319-39931-7_18
State	Published - 2016
Event	9th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2016 - Washington, United States Duration: Jun 28 2016 → Jul 1 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9708 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2016
Country/Territory	United States
City	Washington
Period	6/28/16 → 7/1/16

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Keywords

Behavioral game theory
Model predictions
Multiple agent interaction
Strategic interaction
Trust dynamics

Disciplines

Psychology
Social and Behavioral Sciences

Access to Document

10.1007/978-3-319-39931-7_18License: Unspecified

Cite this

Collins, M. G., Juvina, I., & Gluck, K. A. (2016). Game-Specific and Player-Specific Knowledge Combine to Drive Transfer of Learning Between Games of Strategic Interaction. In N. Osgood, K. S. Xu, D. Reitter, & D. Lee (Eds.), Social, Cultural, and Behavioral Modeling (pp. 186-195). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9708 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-39931-7_18

Game-Specific and Player-Specific Knowledge Combine to Drive Transfer of Learning Between Games of Strategic Interaction. / Collins, Michael G.; Juvina, Ion; Gluck, Kevin A.
Social, Cultural, and Behavioral Modeling. ed. / Nathaniel Osgood; Kevin S. Xu; David Reitter; Dongwon Lee. Springer Verlag, 2016. p. 186-195 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9708 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Collins, MG, Juvina, I & Gluck, KA 2016, Game-Specific and Player-Specific Knowledge Combine to Drive Transfer of Learning Between Games of Strategic Interaction. in N Osgood, KS Xu, D Reitter & D Lee (eds), Social, Cultural, and Behavioral Modeling. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9708 LNCS, Springer Verlag, pp. 186-195, 9th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2016, Washington, United States, 6/28/16. https://doi.org/10.1007/978-3-319-39931-7_18

Collins MG, Juvina I, Gluck KA. Game-Specific and Player-Specific Knowledge Combine to Drive Transfer of Learning Between Games of Strategic Interaction. In Osgood N, Xu KS, Reitter D, Lee D, editors, Social, Cultural, and Behavioral Modeling. Springer Verlag. 2016. p. 186-195. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-39931-7_18

Collins, Michael G. ; Juvina, Ion ; Gluck, Kevin A. / Game-Specific and Player-Specific Knowledge Combine to Drive Transfer of Learning Between Games of Strategic Interaction. Social, Cultural, and Behavioral Modeling. editor / Nathaniel Osgood ; Kevin S. Xu ; David Reitter ; Dongwon Lee. Springer Verlag, 2016. pp. 186-195 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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AB - Trust in others transfers between games of strategic interaction (e.g., iterated Prisoner’s Dilemma– PD and Chicken Game – CG). This transfer of trust represents knowledge acquired about the other player (player-specific knowledge), carrying over from one situation to another, which is separate from what was learned about the previous game (game-specific knowledge). We examine how the transfer of both player-specific and game-specific knowledge informs one’s decisions when interacting with a new player. In this paper, we present the experimental design of an upcoming study, where participants will sequentially play two games of strategic interaction (PD & CG) with the same or a different computerized confederate agent. In addition to the experimental design, we present model predictions, using a previously published computational cognitive model of trust dynamics. The model predicts transfer of learning effects in both conditions and larger effects when interacting with the same agent.

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Game-Specific and Player-Specific Knowledge Combine to Drive Transfer of Learning Between Games of Strategic Interaction

Abstract

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Disciplines

Access to Document

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Cite this