Tacit coordination games are games where players are trying to select the same solution without any communication between them. Various theories have attempted to predict behavior in tacit coordination games. Until now, research combining tacit coordination games with electrophysiological measures was mainly based on spectral analysis. In contrast, EEG coherence enables the examination of functional and morphological connections between brain regions. Hence, we aimed to differentiate between different cognitive conditions using coherence patterns. Specifically, we have designed a method that predicts the class label of coherence graph patterns extracted out of multi-channel EEG epochs taken from three conditions: a no-task condition and two cognitive tasks, picking and coordination. The classification process was based on a coherence graph extracted out of the EEG record. To assign each graph into its appropriate label, we have constructed a hierarchical classifier. First, we have distinguished between the resting-state condition and the other two cognitive tasks by using a bag of node degrees. Next, to distinguish between the two cognitive tasks, we have implemented an anonymous random walk. Our classification model achieved a total accuracy value of 96.55%.

https://www.mdpi.com/1999-4893/15/4/114/pdf?version=1648287909

EEG Pattern Classification of Picking and Coordination Using Anonymous Random Walks

Achieving successful human–agent collaboration in the context of smart environments requires the modeling of human behavior for predicting people’s decisions. The goal of the current study was to utilize the TBR and the Alpha band as electrophysiological features that will discriminate between different tasks, each associated with a different depth of reasoning. To that end, we monitored the modulations of the TBR and Alpha, while participants were engaged in performing two cognitive tasks: picking and coordination. In the picking condition (low depth of processing), participants were requested to freely choose a single word out of a string of four words. In the coordination condition (high depth of processing), participants were asked to try and select the same word as an unknown partner that was assigned to them. We performed two types of analyses, one that considers the time factor (i.e., observing dynamic changes across trials) and the other that does not. When the temporal factor was not considered, only Beta was sensitive to the difference between picking and coordination. However, when the temporal factor was included, a transition occurred between cognitive effort and fatigue in the middle stage of the experiment. These results highlight the importance of monitoring the electrophysiological indices, as different factors such as fatigue might affect the instantaneous relative weight of intuitive and deliberate modes of reasoning. Thus, monitoring the response of the human–agent across time in human–agent interactions might turn out to be crucial for smooth coordination in the context of human–computer interaction.

/pdf/electrophysiological-features-to-aid-in-the-construction-of-22fkywbh.pdf

Electrophysiological Features to Aid in the Construction of Predictive Models of Human–Agent Collaboration in Smart Environments

The social value orientation (SVO) has a profound effect on the strategic decision making in economic choices and the ability to succeed in coordination games. With that in mind, in this study we wanted to examine an electrophysiological measure elicited in different resource allocation problems that affect the preferences of the player. We recorded EEG from participants while they were engaged in different allocation problems varying in the magnitude of reward and the difference size between alternative choices. We found that the theta to alpha ratio (TAR) can differentiate between individualistic and prosocial players. Specifically, individualistic players were more sensitive to the magnitude of the overall payoff (reflected by the radius size) as well as to the difference between two reward alternatives in the resource allocation task. These two variables, reward magnitude, and the difference between payoff alternatives, have significantly differentiated between the TAR levels of prosocials and proselfs (p < 0.001). For extreme differences (small or large), TAR was higher in comparison to medium sized differences. Our results demonstrated that in resource allocation games the TAR can be predicted based on the parameters of the task and the SVO category of the player (prosocial or individualistic). Specifically, an interaction was found between the attractiveness of the reward (radius) as well as the conflict between alternatives (Δ∅) and the SVO of the player at a significance level of p < 0.0001. These results highlight the importance of the SVO construct in economic decision choices varying in both reward magnitude and the proximity between alternative choices. Suggestions for future studies are discussed.

/pdf/the-effect-of-social-value-orientation-on-theta-to-alpha-2w9bycm2.pdf

The Effect of Social Value Orientation on Theta to Alpha Ratio in Resource Allocation Games

Sensitivity of Electrophysiological Patterns in Level-K States as Function of Individual Coordination Ability

Understanding how the public perceives various risks and hazards associated with our well-being and health is crucial for governments and policymakers. The present research aimed to assess the public perception of various risks and hazards associated with well-being and health. The study combined two well-known risk assessment approaches: the analytical hierarchical process (AHP) and the psychometric paradigm. Seven risk attributes were chosen from the risk perception literature to evaluate 27 risks and hazard activities using a survey questionnaire developed based on the psychometric paradigm literature. The collected data were then analyzed using the AHP to determine the priority weight for each risk attribute. The results showed that the most crucial risk attribute was voluntariness of risk, followed by chronic catastrophic and newness of risk. Furthermore, the study found that natural hazards were ranked the highest, followed by refugee influx and fire hazards. In contrast, the mobile phone was perceived as posing the lowest type of risk. Policymakers can use these findings to develop effective and sustainable risk communication strategies to help the governments to inform and educate the public about potential risks, improve coordination among agencies and stakeholders, and enhance public trust in government decision making.

/pdf/a-novel-framework-of-public-risk-assessment-using-an-1zs1i1lx.pdf

A Novel Framework of Public Risk Assessment Using an Integrated Approach Based on AHP and Psychometric Paradigm

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.

/pdf/an-electrophysiological-model-for-assessing-cognitive-load-2v09lotq.pdf

An Electrophysiological Model for Assessing Cognitive Load in Tacit Coordination Games

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.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.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.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. 

/pdf/modeling-and-predicting-individual-tacit-coordination-kovknavi.pdf

Modeling and predicting individual tacit coordination ability

https://braininformatics.springeropen.com/counter/pdf/10.1186/s40708-022-00152-w

Risk management is an ongoing process that includes several stages of mapping and identification, analysis, and evaluation, planning, and implementation to reduce risks and ensure ongoing control. Risk management along the supply chains has become more significant in recent years due to an increased complexity of the relationships between components in the chain as well as various disruptions such as climate change, COVID-19, or geo-political scenarios. The current literature alongside the increase in complexity and frequency of risk events, leads us to the single, most prominent challenge in risk management today: the auditor’s subjectivity in determining the risk levels. Simply stated, two different auditors may assess a given situation differently due to their specific history and experience. Specifically, it seems to be extremely difficult to find cases in which different auditors, working on the same organization, made the same risk assessment. With that in mind, this research aims to reduce the human subjectivity bias and reach a risk evaluation that is as objective as possible, by using the machine learning approach. For this aim the paper introduces a new risk assessment framework based on factors analysis and artificial neural network as the predictive model. We first introduced a new approach of deconstructing the risk factors into their basic elements and analyzing them as a feature vector. Next, we collected unique, real-world data of risk surveys and audit reports from 60 industrial companies of various industries (from plastic and metal factories to logistic and medical devices companies). Lastly, we constructed a neural network to predict the risk levels of operational processes in the industry. We trained our model on 42 samples and managed to achieve a R2 score of 0.9 on the test set of 18 samples. Our model was validated and managed to predict the risk accuracy with R = 0.95 in accordance with the human auditor results.

/pdf/deconstructing-risk-factors-for-predicting-risk-assessment-f3o2d9im.pdf

Inon Zuckerman

Papers

An Electrophysiological Model for Assessing Cognitive Load in Tacit Coordination Games

Modeling and predicting individual tacit coordination ability

Modeling and predicting individual tacit coordination ability

EEG Pattern Classification of Picking and Coordination Using Anonymous Random Walks

Deconstructing Risk Factors for Predicting Risk Assessment in Supply Chains Using Machine Learning