The Effect of Individual Coordination Ability on Cognitive-Load in Tacit Coordination Games

Mizrahi, Dor; Laufer, Ilan; Zuckerman, Inon

doi:10.1007/978-3-030-60073-0_28

Cited by 11 publications

(13 citation statements)

References 38 publications

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“…For example, Ref. [ 48 ] found that there is a negative relationship between the player’s individual coordination ability and the theta–beta ratio, which is a measure of cognitive load. As this latter finding was found in the context of tacit coordination with common interest, it will be worthwhile exploring this relationship in the context of divergent interests as well.…”

Section: Discussionmentioning

confidence: 99%

Using a Stochastic Agent Model to Optimize Performance in Divergent Interest Tacit Coordination Games

Mizrahi

Zuckerman

Laufer

2020

Sensors

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In recent years collaborative robots have become major market drivers in industry 5.0, which aims to incorporate them alongside humans in a wide array of settings ranging from welding to rehabilitation. Improving human–machine collaboration entails using computational algorithms that will save processing as well as communication cost. In this study we have constructed an agent that can choose when to cooperate using an optimal strategy. The agent was designed to operate in the context of divergent interest tacit coordination games in which communication between the players is not possible and the payoff is not symmetric. The agent’s model was based on a behavioral model that can predict the probability of a player converging on prominent solutions with salient features (e.g., focal points) based on the player’s Social Value Orientation (SVO) and the specific game features. The SVO theory pertains to the preferences of decision makers when allocating joint resources between themselves and another player in the context of behavioral game theory. The agent selected stochastically between one of two possible policies, a greedy or a cooperative policy, based on the probability of a player to converge on a focal point. The distribution of the number of points obtained by the autonomous agent incorporating the SVO in the model was better than the results obtained by the human players who played against each other (i.e., the distribution associated with the agent had a higher mean value). Moreover, the distribution of points gained by the agent was better than any of the separate strategies the agent could choose from, namely, always choosing a greedy or a focal point solution. To the best of our knowledge, this is the first attempt to construct an intelligent agent that maximizes its utility by incorporating the belief system of the player in the context of tacit bargaining. This reward-maximizing strategy selection process based on the SVO can also be potentially applied in other human–machine contexts, including multiagent systems.

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Section: Discussionmentioning

confidence: 99%

Using a Stochastic Agent Model to Optimize Performance in Divergent Interest Tacit Coordination Games

Mizrahi

Zuckerman

Laufer

2020

Sensors

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“…For example, game board #1 displays a trial containing the set (“Water”, “Beer”, “Wine”, “Whisky”) appearing in Hebrew, respectively. Each set of words was displayed between two short vertical lines following a slide containing only the lines without the word set so that participants will focus their gaze at the center of the screen ( Figure 1 A,B) (e.g., [ 4 , 13 ]). In the first experimental condition, the task presented to the players was a picking task, i.e., participants were only required to freely pick a word out of each set of four words presented to them in each of the 12 trials.…”

Section: Methodsmentioning

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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“…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. For example, by updating the number of Gaussians which correspond to different strategic profiles extracted from the game.…”

Section: Discussionmentioning

confidence: 99%

“…Third, improved models in the framework of human–machine coordination [ 3 , 54 ] can optimize performance in diverse contexts such as intension prediction [ 55 ] and safety [ 56 ] in industry, fraud detections (e.g., [ 57 – 59 ]), as well as in other complex tasks (e.g., [ 60 ]). Fourth, it might also be worthwhile exploring whether the differences in individual coordination ability are accompanied by parallel changes in brain activation reflected by electrophysiological markers associated, for example, with cognitive load [ 24 ] or with time pressure [ 50 ]. These electrophysiological markers could then be added to our model as additional features that may improve its predictive ability.…”

Section: Discussionmentioning

confidence: 99%

“…It should be noted that the calculation was only carried out on the ten predefined games, but not on the four randomized games, since only the predefined games were kept constant across participants. iCA [ 16 , 17 , 24 ] is formally defined as follows: where i denotes the i th participant, j denotes the index of the j th co-player, N denotes the total number of participants, and t denotes the number of games in the experiments. The CF (coordination function) is defined as follows: …”

Section: Methodsmentioning

confidence: 99%

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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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The Effect of Individual Coordination Ability on Cognitive-Load in Tacit Coordination Games

Cited by 11 publications

References 38 publications

Using a Stochastic Agent Model to Optimize Performance in Divergent Interest Tacit Coordination Games

Using a Stochastic Agent Model to Optimize Performance in Divergent Interest Tacit Coordination Games

Level-K Classification from EEG Signals Using Transfer Learning

Modeling and predicting individual tacit coordination ability

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