TY - GEN
T1 - Modeling Individual Tacit Coordination Abilities
AU - Mizrahi, Dor
AU - Laufer, Ilan
AU - Zuckerman, Inon
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Previous experiments in tacit coordination games hinted that some people are more successful in achieving coordination than others, although the variability in this ability has not yet been examined before. With that in mind, the overarching aim of our study is to model and describe the variability in human decision-making behavior in the context of tacit coordination games. To do so we first conducted a large-scale experiment to collect behavioral data, modeled the decision-making behavior, and characterize their observed variability. We then used the proposed model by predicting the individual coordination ability of a player based on its constructed strategic profile model and demonstrated that there is a direct and significant relationship between the player’s model and its coordination ability. Understanding the differences in individual’s tacit coordination abilities as well as their unique strategic profiles will allow us to better predict human’s behavior in tacit coordination scenarios and consequently construct better algorithms for human-machine interactions.
AB - Previous experiments in tacit coordination games hinted that some people are more successful in achieving coordination than others, although the variability in this ability has not yet been examined before. With that in mind, the overarching aim of our study is to model and describe the variability in human decision-making behavior in the context of tacit coordination games. To do so we first conducted a large-scale experiment to collect behavioral data, modeled the decision-making behavior, and characterize their observed variability. We then used the proposed model by predicting the individual coordination ability of a player based on its constructed strategic profile model and demonstrated that there is a direct and significant relationship between the player’s model and its coordination ability. Understanding the differences in individual’s tacit coordination abilities as well as their unique strategic profiles will allow us to better predict human’s behavior in tacit coordination scenarios and consequently construct better algorithms for human-machine interactions.
KW - Cognitive modeling
KW - Decision making
KW - Tacit coordination games
UR - http://www.scopus.com/inward/record.url?scp=85078544068&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-37078-7_4
DO - 10.1007/978-3-030-37078-7_4
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AN - SCOPUS:85078544068
SN - 9783030370770
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 29
EP - 38
BT - Brain Informatics - 12th International Conference, BI 2019, Proceedings
A2 - Liang, Peipeng
A2 - Goel, Vinod
A2 - Shan, Chunlei
T2 - 12th International Conference on Brain Informatics, BI 2019
Y2 - 13 December 2019 through 15 December 2019
ER -