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 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.

Enhancing EEG-based attachment style prediction: unveiling the impact of feature domains

“…Our approach aligns with current trends in neuroscience and psychology, where ensemble machine-learning methods have shown effectiveness in interpreting complex neural patterns ( Rahman et al, 2022 ; Li et al, 2023 ). Our study is focused on examining a broad range of EEG features, totaling 45 in number, which encompass elements from the time domain ( Al-Fahoum and Al-Fraihat, 2014 ; Zuckerman et al, 2022 , 2023b ), frequency-based analyses ( Mizrahi et al, 2022a , b , 2023a ), and complexity measures ( Sheehan et al, 2018 ; Ramadoss et al, 2022 ; Mizrahi et al, 2023b ). The aim is to utilize these features to predict whether an individual has a secure or insecure attachment style and to assess the specific contribution of each feature to this prediction.…”

“…However, to ensure a comprehensive exploration of EEG patterns in attachment styles, all 45 features were included in the current analysis. Our present study adopts a predictive modeling approach, contrasting with the comparative focus of our earlier work ( Mizrahi et al, 2023a ; Zuckerman et al, 2023a , b ). By using the XGBoost model, we aim to predict individual attachment styles based on the EEG features, moving beyond simple comparisons to a more nuanced understanding of the data.…”