Individual strategic profiles in tacit coordination games

Mizrahi, Dor; Laufer, Ilan; Zuckerman, Inon

doi:10.1080/0952813x.2020.1721572

Cited by 9 publications

(15 citation statements)

References 31 publications

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“…The adaptive agent also outperformed the human players (see Figure 2 and Figure 3 ). This advantage of the adaptive agent may be explained by previous findings showing that there is a large range of tacit coordination ability levels among human players (e.g., [ 24 , 39 , 40 , 41 ]). Therefore, it is not surprising that the adaptive agent, which makes decisions that are dictated by a probabilistic model, outperforms the human players comprising strong but also weak and mediocre coordinators.…”

Section: Discussionmentioning

confidence: 93%

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: 93%

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

Mizrahi

Zuckerman

Laufer

2020

Sensors

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“…The findings of our study suggest several avenues for future research. For example, previous studies have shown that various features such as culture [ 44 , 45 ], social value orientation [ 46 , 47 ], strategic profile [ 48 , 49 ], and loss aversion [ 50 , 51 ] might bias decision-making in tacit coordination games. Therefore, it will be interesting to investigate the effect of these parameters on EEG indices.…”

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 variation in the level of Schelling’s competence might be explained by the propensity of applying multiple salient selection rules. The weighted combination of the selection rules was denoted as a strategic profile [ 15 , 16 , 19 ]. Each strategic profile reflects the subjective preferences of individual players regarding a set of heuristics or prominent and salient selection rules [ 15 , 16 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…The weighted combination of the selection rules was denoted as a strategic profile [ 15 , 16 , 19 ]. Each strategic profile reflects the subjective preferences of individual players regarding a set of heuristics or prominent and salient selection rules [ 15 , 16 , 19 ]. Thus, a strategic profile may be regarded as a weighted combination of the different selection rules utilized by a specific player across different game instances to achieve a successful coordination.…”

Section: Introductionmentioning

confidence: 99%

“…To that end, we have conducted a large-scale tacit coordination experiment to collect behavioral data. We have first described the variability in individual coordination ability (iCA) [ 16 , 17 ] among the players, detected the predominant selection rules (e.g., spatial proximity) that players have utilized, and finally modeled the relationship between iCA and the individual strategic profiles [ 16 ].…”

Section: Introductionmentioning

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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Individual strategic profiles in tacit coordination games

Cited by 9 publications

References 31 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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