Predicting focal point solution in divergent interest tacit coordination games

Mizrahi, Dor; Laufer, Ilan; Zuckerman, Inon

doi:10.1080/0952813x.2021.1974953

Cited by 10 publications

(7 citation statements)

References 58 publications

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“…This finding is compatible with the level-k theory, which defines picking as level k = 0 and coordination as level k > 0 [59][60][61][62]. In previous studies, we have shown that considerable variability exits in behavioral coordination ability exists between players [23][24][25]30,[63][64][65][66]. In the current study, we have shown that as coordination ability increases the TBR which reflects cognitive load, decreases.…”

Section: Discussionsupporting

confidence: 90%

“…First, it would be interesting to examine TBR magnitude modulations in other contexts not related to tacit coordination, such as resource allocation task (e.g., [30,74,75]) and optimal stopping problems (e.g., [76,77]). Second, our previous studies have shown that the cultural background of players as well as their individual preferences may greatly affect the strategies players choose in coordination games [25,65,78]. For example, it was previously shown that highly prosocial participants and highly individualistic ones are fast responders (either they cooperate or defect, respectively) [79].…”

Section: Discussionmentioning

confidence: 99%

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An Electrophysiological Model for Assessing Cognitive Load in Tacit Coordination Games

Laufer

Mizrahi

Zuckerman

2022

Sensors

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Previously, it was shown that some people are better coordinators than others; however, the relative weight of intuitive (system 1) versus deliberate (system 2) modes of thinking in tacit coordination tasks is still not resolved. To address this question, we have extracted an electrophysiological index, the theta-beta ratio (TBR), from the Electroencephalography (EEG) recorded from participants while they were engaged in a semantic coordination task. Results have shown that individual coordination ability, game difficulty and response time are each positively correlated with cognitive load. These results suggest that better coordinators rely more on complex thought process and on more deliberate thinking while coordinating. The model we have presented may be used for the assessment of the depth of reasoning individuals engage in when facing different tasks requiring different degrees of allocation of resources. The findings as well as future research directions are discussed.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

An Electrophysiological Model for Assessing Cognitive Load in Tacit Coordination Games

Laufer

Mizrahi

Zuckerman

2022

Sensors

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“…To that end, in future studies, it is recommended to utilize inverse-problem techniques such as LORETA [ 55 , 56 ]. Finally, behavioral and electrophysiological data of human agents (e.g., [ 2 , 9 , 57 , 58 , 59 ]) gained from these studies might aid in constructing brain–computer interfaces as well as autonomous agents. In this study, we used transfer learning when the training set for the embedding network was a general set of images (ImageNet) that did not include EEG signals.…”

Section: Discussionmentioning

confidence: 99%

Level-K Classification from EEG Signals Using Transfer Learning

Mizrahi

Zuckerman

Laufer

2021

Sensors

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Tacit coordination games are games in which communication between the players is not allowed or not possible. In these games, the more salient solutions, that are often perceived as more prominent, are referred to as focal points. The level-k model states that players’ decisions in tacit coordination games are a consequence of applying different decision rules at different depths of reasoning (level-k). A player at Lk=0 will randomly pick a solution, whereas a Lk≥1 player will apply their strategy based on their beliefs regarding the actions of the other players. The goal of this study was to examine, for the first time, the neural correlates of different reasoning levels in tacit coordination games. To that end, we have designed a combined behavioral-electrophysiological study with 3 different conditions, each resembling a different depth reasoning state: (1) resting state, (2) picking, and (3) coordination. By utilizing transfer learning and deep learning, we were able to achieve a precision of almost 100% (99.49%) for the resting-state condition, while for the picking and coordination conditions, the precision was 69.53% and 72.44%, respectively. The application of these findings and related future research options are discussed.

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“…The focal points that were modeled in our study are based on spatial properties (e.g., closeness, equality, and accession). Consequently, we expect the model we have constructed to be applicable to other contexts where focal points are based on spatial cues, e.g., “Bargaining Table” [ 14 , 35 , 40 , 48 ] and “Moving Discs” [ 18 ]. However, in other cases, where focal points are based on non-spatial features (e.g., the “word Selection” task based on semantic meaning [ 18 , 24 , 42 , 49 ]) our model should be modified accordingly.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, game theory lacks the ability to capture cognitive heuristics that people apply when solving coordination problem, such as symmetry, proximity, or extremeness [ 12 , 13 ]. Therefore, experiments in behavioral game theory try to fill this gap by constructing cognitive models describing decision-making heuristics (e.g., [ 14 – 18 ]).…”

Section: Introductionmentioning

confidence: 99%

Modeling and predicting individual tacit coordination ability

2022

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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.

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Predicting focal point solution in divergent interest tacit coordination games

Cited by 10 publications

References 58 publications

An Electrophysiological Model for Assessing Cognitive Load in Tacit Coordination Games

An Electrophysiological Model for Assessing Cognitive Load in Tacit Coordination Games

Level-K Classification from EEG Signals Using Transfer Learning

Modeling and predicting individual tacit coordination ability

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