Evaluation of Payoff Matrices for Non-Cooperative Games via Processing Binary Expert Estimations
A problem of evaluating the non-cooperative game model is considered in the paper. The evaluation is understood in the sense of obtaining the game payoff matrices whose entries are single-point values. Experts participating in the estimation procedure make their judgments on all the game situations...
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doaj-8d5ced904fb84d8aa50e0e66c066c0382021-04-02T03:02:53ZengSciendoInformation Technology and Management Science2255-90942016-12-01191101510.1515/itms-2016-0004itms-2016-0004Evaluation of Payoff Matrices for Non-Cooperative Games via Processing Binary Expert EstimationsRomanuke Vadim0Khmelnitskiy National University, 11 Institutskaya Str., 29016, Khmelnitskiy, UkraineA problem of evaluating the non-cooperative game model is considered in the paper. The evaluation is understood in the sense of obtaining the game payoff matrices whose entries are single-point values. Experts participating in the estimation procedure make their judgments on all the game situations for every player. A form of expert estimations is suggested. The form is of binary type, wherein the expert’s judgment is either 1 or 0. This type is the easiest to be implemented in social networks. For most social networks, 1 can be a “like” (the currently evaluated situation is advantageous), and 0 is a “dislike” (disadvantageous). A method of processing expert estimations is substantiated. Two requirements are provided for obtaining disambiguous payoff averages along with the clustered payoff matrices.http://www.degruyter.com/view/j/itms.2016.19.issue-1/itms-2016-0004/itms-2016-0004.xml?format=INTEstimation procedureexpert’s binary judgmentnon-cooperative gamepayoff averagespayoff matrice evaluation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Romanuke Vadim |
spellingShingle |
Romanuke Vadim Evaluation of Payoff Matrices for Non-Cooperative Games via Processing Binary Expert Estimations Information Technology and Management Science Estimation procedure expert’s binary judgment non-cooperative game payoff averages payoff matrice evaluation |
author_facet |
Romanuke Vadim |
author_sort |
Romanuke Vadim |
title |
Evaluation of Payoff Matrices for Non-Cooperative Games via Processing Binary Expert Estimations |
title_short |
Evaluation of Payoff Matrices for Non-Cooperative Games via Processing Binary Expert Estimations |
title_full |
Evaluation of Payoff Matrices for Non-Cooperative Games via Processing Binary Expert Estimations |
title_fullStr |
Evaluation of Payoff Matrices for Non-Cooperative Games via Processing Binary Expert Estimations |
title_full_unstemmed |
Evaluation of Payoff Matrices for Non-Cooperative Games via Processing Binary Expert Estimations |
title_sort |
evaluation of payoff matrices for non-cooperative games via processing binary expert estimations |
publisher |
Sciendo |
series |
Information Technology and Management Science |
issn |
2255-9094 |
publishDate |
2016-12-01 |
description |
A problem of evaluating the non-cooperative game model is considered in the paper. The evaluation is understood in the sense of obtaining the game payoff matrices whose entries are single-point values. Experts participating in the estimation procedure make their judgments on all the game situations for every player. A form of expert estimations is suggested. The form is of binary type, wherein the expert’s judgment is either 1 or 0. This type is the easiest to be implemented in social networks. For most social networks, 1 can be a “like” (the currently evaluated situation is advantageous), and 0 is a “dislike” (disadvantageous). A method of processing expert estimations is substantiated. Two requirements are provided for obtaining disambiguous payoff averages along with the clustered payoff matrices. |
topic |
Estimation procedure expert’s binary judgment non-cooperative game payoff averages payoff matrice evaluation |
url |
http://www.degruyter.com/view/j/itms.2016.19.issue-1/itms-2016-0004/itms-2016-0004.xml?format=INT |
work_keys_str_mv |
AT romanukevadim evaluationofpayoffmatricesfornoncooperativegamesviaprocessingbinaryexpertestimations |
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1724174138782777344 |