Modeling and predicting individual tacit coordination ability
Abstract Background 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 descr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-02-01
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| Series: | Brain Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40708-022-00152-w |
| _version_ | 1798023849209495552 |
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| author | Dor Mizrahi Ilan Laufer Inon Zuckerman |
| author_facet | Dor Mizrahi Ilan Laufer Inon Zuckerman |
| author_sort | Dor Mizrahi |
| collection | DOAJ |
| description | Abstract Background 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. Methods In this study, we conducted a large-scale experiment to collect behavioral data, characterized the distribution of tacit coordination ability, and modeled the decision-making behavior of players. First, we measured the multimodality in the data and described it by using a Gaussian mixture model. Then, using multivariate linear regression and dimensionality reduction (PCA), we have constructed a model linking between individual strategic profiles of players and their coordination ability. Finally, we validated the predictive performance of the model by using external validation. Results We demonstrated that coordination ability is best described by a multimodal distribution corresponding to the levels of coordination ability and that there is a significant relationship between the player’s strategic profile and their coordination ability. External validation determined that our predictive model is robust. Conclusions The study provides insight into the amount of variability that exists in individual tacit coordination ability as well as in individual strategic profiles and shows that both are quite diverse. Our findings may facilitate the construction of improved algorithms for human–machine interaction in diverse contexts. Additional avenues for future research are discussed. |
| first_indexed | 2024-04-11T17:53:11Z |
| format | Article |
| id | doaj.art-5e0bde928aae48c29fad4044335752ef |
| institution | Directory Open Access Journal |
| issn | 2198-4018 2198-4026 |
| language | English |
| last_indexed | 2024-04-11T17:53:11Z |
| publishDate | 2022-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Brain Informatics |
| spelling | doaj.art-5e0bde928aae48c29fad4044335752ef2022-12-22T04:10:58ZengSpringerOpenBrain Informatics2198-40182198-40262022-02-019111410.1186/s40708-022-00152-wModeling and predicting individual tacit coordination abilityDor Mizrahi0Ilan Laufer1Inon Zuckerman2Department of Industrial Engineering and Management, Ariel UniversityDepartment of Industrial Engineering and Management, Ariel UniversityDepartment of Industrial Engineering and Management, Ariel UniversityAbstract Background 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. Methods In this study, we conducted a large-scale experiment to collect behavioral data, characterized the distribution of tacit coordination ability, and modeled the decision-making behavior of players. First, we measured the multimodality in the data and described it by using a Gaussian mixture model. Then, using multivariate linear regression and dimensionality reduction (PCA), we have constructed a model linking between individual strategic profiles of players and their coordination ability. Finally, we validated the predictive performance of the model by using external validation. Results We demonstrated that coordination ability is best described by a multimodal distribution corresponding to the levels of coordination ability and that there is a significant relationship between the player’s strategic profile and their coordination ability. External validation determined that our predictive model is robust. Conclusions The study provides insight into the amount of variability that exists in individual tacit coordination ability as well as in individual strategic profiles and shows that both are quite diverse. Our findings may facilitate the construction of improved algorithms for human–machine interaction in diverse contexts. Additional avenues for future research are discussed.https://doi.org/10.1186/s40708-022-00152-wTacit coordinationDecision-makingCognitive modeling |
| spellingShingle | Dor Mizrahi Ilan Laufer Inon Zuckerman Modeling and predicting individual tacit coordination ability Brain Informatics Tacit coordination Decision-making Cognitive modeling |
| title | Modeling and predicting individual tacit coordination ability |
| title_full | Modeling and predicting individual tacit coordination ability |
| title_fullStr | Modeling and predicting individual tacit coordination ability |
| title_full_unstemmed | Modeling and predicting individual tacit coordination ability |
| title_short | Modeling and predicting individual tacit coordination ability |
| title_sort | modeling and predicting individual tacit coordination ability |
| topic | Tacit coordination Decision-making Cognitive modeling |
| url | https://doi.org/10.1186/s40708-022-00152-w |
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