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WO2014199411A1 - System for evaluating an applicability of reflexive game theory, method for evaluating an applicability of rgt - Google Patents

System for evaluating an applicability of reflexive game theory, method for evaluating an applicability of rgt Download PDF

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WO2014199411A1
WO2014199411A1 PCT/JP2013/003679 JP2013003679W WO2014199411A1 WO 2014199411 A1 WO2014199411 A1 WO 2014199411A1 JP 2013003679 W JP2013003679 W JP 2013003679W WO 2014199411 A1 WO2014199411 A1 WO 2014199411A1
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answer
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rgt
yes
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Sergey TARASENKO
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NEC Corp
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Definitions

  • the present invention relates to a technology of evaluating an applicability of Reflexive Game Theory.
  • Reflexive Game Theory enables prediction of group members (subjects) decisions. This analysis is based on only relationships (conflict or alliance) between subjects (individuals, teams or work groups, companies, etc.) and their mutual influences (see Non-Patent Documents 1 and 2).
  • the general schema of conventional algorithm for RGT inference is presented in Fig.1.
  • Implementation example of RGT inference is presented in Figs. 2.
  • the groups of subjects are presented in the form of fully connected graphs. The graph can have dashed-line ribs (conflict relationships) and solid-line ribs (alliance relationships).
  • the stages of RGT analysis (inference) based on symbolic computations are: 1) input group structure as pair-wise relations between subjects (module 11); 2) input subjects mutual influences (module 12); 3) check graphs decomposability (module 13); 4) construct a polynomial (module 14); 5) perform Diagonal Form Transformation(DFT)( module 15); 6) build decision equations (module 16); 7) perform transformation of the decision equation into canonical form (TDECF) to solve decision equation, obtain templates of decision intervals for each subject (module 17); 8) input mutual influences into the templates to get possible decisions (module 18).
  • DFT Diagonal Form Transformation
  • TECF canonical form
  • RGT The Reflexive Game Theory
  • RGT is based on subconscious psychological algorithms, which take into account opinion of each member of a group. These algorithms can be can be overridden by conscious behavior: some person considers only his/her opinion and do not listen to others. In other word, person's mindset is fixed to a particular options(Fixed Opinion - FO). In this case RGT cannot be used; 2) RGT inference is based on the group structure. Therefore if person does not consider his/herself to be a part of a group (Group Belonging - GB), then RGT is not applicable; 3) RGT does not provide a clear way to understand relations between subjects (Pair-wise Relationship - PwR). Because relations are used for RGT inference, incorrectly defined relations will cause incorrect predictions results. In some cases, people can deliberately lie about their relations.
  • the present invention has been accomplished in consideration of the above-mentioned problems, and an object of the present invention is to provide a technology of solving the above-mentioned problems, namely, a technology of evaluating an applicability of reflexive game theory. Therefore it will becomes easy to apply RGT to a computer program.
  • the present invention for solving the above-mentioned problems is a system for evaluating an applicability of Reflexive Game Theory (RGT), comprising: an inapplicable subject detection means that detects a subject to whom RGT is inapplicable; and an applicability judgment means that judges that RGT is applicable, in the case that said inapplicable subject detection means detects no such inapplicable subject.
  • RGT Reflexive Game Theory
  • the present invention for solving the above-mentioned problems is a device for detecting a subject to whom Reflexive Game Theory (RGT) is inapplicable, comprising: a Yes/No question provision means that provides a Yes/No question about a condition which is required for application of RGT; a Yes/No answer input means that inputs a Yes /No answer to said Yes/No question; a Yes/No answer detection means that detects said Yes/ No answer; an open question provision means that provides an open question which is associated with said Yes/No question and which relates to a hypothetical situation close to the real one, in the case that said Yes/No answer detection means detects a Yes answer; a free answer input means that inputs a free text answer to said open question; a consistency analysis means that analyzes consistency between the answer to the open question and the answer to the Yes/No question; and an applicable subject judgment means that judges that RGT is applicable to the subject, in the case that said consistency analysis means reaches a judgment of consistency.
  • RGT Reflexive Game Theory
  • the present invention for solving the above-mentioned problems is a method for evaluating an applicability of Reflexive Game Theory (RGT), comprising the steps of: detecting a subject to whom RGT is inapplicable; and judging that RGT is applicable, in the case that said no such inapplicable subject is detected.
  • RGT Reflexive Game Theory
  • the present invention makes it possible to detect a subject to whom RGT is inapplicable.
  • the present invention makes it possible to verify true pairwise relationships
  • the present invention provides the inapplicable subject with countermeasures for alteration of the mindset. It allow the system to improve the applicability of RGT.
  • Fig. 1 is the general schema of conventional algorithm for RGT inference.
  • Fig. 2 is implementation example of RGT inference.
  • Fig. 3 is the general schema of the system.
  • Fig. 4 is first example of CEI schema.
  • Fig. 5 is second example of CEI schema.
  • Fig. 6 is third example of CEI schema.
  • Fig. 7 is fourth example of CEI schema.
  • Fig. 8 is flowchart of basic operation on the system.
  • Fig. 9 is flowchart of operation for inapplicable subject detection.
  • Fig. 10 is operation for pairwise relationship verification.
  • Fig. 11 is the example operation of CEI.
  • Fig. 12 is the detailed analysis of the free text description.
  • the present invention includes two main technical ideas.
  • First idea is to detect a subject to whom RGT is inapplicable (for example, fixed opinion , lack of group belonging).
  • Second idea is to verify true pairwise relationships.
  • the consistency of the "YES" answer with description of actions in hypothetical situation can be evaluated as follows: the description of hypothetical situation is compared with expected result.
  • the expected result is represented as a structure with key elements.
  • the analyzer of description is searching for match between the key elements of the expected results and the content of the description.
  • NLP Natural Language Processing
  • the question is "Do you consider yourself to be a member of your unit?". The answer is "YES”. Next a person is asked a question “What do you think about electricity saving in your unit?". The answer is "I think that people waste a lot of electricity by not switching off the light or other power greedy devices like air-conditioners, TVs, etc. They should be more economical.” The expected result is that a person will speak about his/her unit as one team. But the person says “people” (they, excluding me) instead of "we” (all people, including me).
  • the main idea of this invention is to check whether positive ("YES” answer) statement about a certain state of mindset. It is confirmed by actual performance in hypothetical situation close to real one, which is described as an answer to the open question. We refer to this idea as a “consistency evaluation idea” (CEI).
  • CEI also can be applied to verify true pairwise relationships (PwR).
  • PwR in RGT are considered to be either alliance or conflict (friend or enemy).
  • alliance it is possible that two people can find consensus/compromise.
  • conflict two people cannot find a consensus with each other. This is the most important information, which is used in RGT.
  • subject A has stated that he is in alliance with subject B. This means that subject A is willing to negotiate with subject B to find consensus. To check whether it is true, it is possible to ask subject A open question like: "Suppose you have discussion with subject B. Some of your arguments are not accepted by subject B. On the other hand, you cannot accept some arguments provided by subject B. Please explain your next steps in discussion.”
  • the general schema of the system is presented in Fig. 3.
  • the system includes PwR part (module 21a,22,25,26), FO part (module 21b,23b,24b), GB part (module 21c,23c,24c) and applicability judgment part (module 27,28,29), RTG inference part (module 211) and countermeasures provision part (module 210).
  • PwR is the abbreviation for pair-wise relationships.
  • FO is the abbreviation for fixed opinion.
  • GB is the abbreviation for group belonging.
  • FO1 Is it true that you prefer to do the things you have to do rather than the things you like?
  • FO2 Even if you have a different opinion, will you follow a group decision ?
  • FO:3 Can you understand your own mistake?
  • the input of answers is performed in modules 21b.
  • the answers from module 21b are processed by modules 23b.
  • Module 23b analyzes the mindset of each subject about the fixed opinion. Module 23a evaluates the consistency between the answers to Y/N and open questions.
  • the output of module 23b is stored in a data storage module 24b, which contains the verified mindset for each subject.
  • the verified mindset is represented in a form of a table. This table (table1) contains three columns. Table.1
  • the first column contains the subject identifier.
  • the second column contains the question marker.
  • the third column contains verified answers to the corresponding questions.
  • the verified answers are coded with 2D binary vector.
  • the first component of the vector corresponds to the answer to the Y/N question: if subject answered "YES”, this is coded with 1 value; the answer "NO” is coded with 0 value.
  • the second component is consistency measure: if the result of CEI is "consistency”, then consistency measure equals 1, or 0 otherwise.
  • Module 23b outputs such table to data storage module 24b.
  • Example of an Answer Table (table.2) is presented below. Table.2
  • GB1 Do you consider yourself to be a part of a group?
  • GB2 Do you understand that you are a part of the company?
  • the input of answers is performed in modules 21c.
  • the answers from module 21c are processed by modules 23c.
  • Module 23c analyzes the mindset about the group belonging. Module 23c evaluates the consistency between the answers to Y/N and open questions. The output of module 23c has the same structure as output of module 23b (refer to table1). Module 23c outputs a table (like table.2) to data storage module 24c.
  • PwR1 Do you consider person A to be your friend?
  • PwR2 Do you consider person A to be your enemy?
  • PwR3 Do you think person A and person B are friends?
  • the input of answers is performed in modules 21a.
  • the answers from module 21a are processed by modules 22.
  • Module 22 verifies true pairwise relationships. Module 22 evaluates the consistency between the answers to Friend/Enemy and open questions. The output of module 22 has the same structure as output of module 23b (refer to table1). Module 22 outputs table.3 (same as table.2) to data storage module 25.
  • Module 26 transforms the table of module 25. Module 26 corrects false pairwise relationships and induces true pairwise relationships. The detail about verification of PwR is mentioned later.
  • Module 26 outputs the table for each subject. These tables about verified pair-wise relationships are used as input into RGT inference module 211.
  • Applicability judgment part includes module 27,28,29.
  • Module 27 judges whether RGT is applicable from the point of view of FO. Thus, module 27 checks if any binary vector in the third column contains the 0 value. If at least one 0 value is located, then module 27 outputs "no" signal 0 value. This means inapplicable of RGT. Otherwise module 27 outputs "Yes" signal 1 value.
  • module 28 judges whether RGT is applicable from the point of view of GB. Thus, module 28 checks if any binary vector in the third column contains the 0 value. If at least one 0 value is located, then module 27 outputs "no" signal 0 value. This means inapplicable of RGT. Otherwise module 27 outputs "Yes" signal 1 value.
  • Module 29 received input signals from modules 27,28. Module 29 output "yes” signal (1 value) to module 211., only if both of modules27,28 output "yes” signal. This means applicable of RGT.
  • Module 211 received the signals from module 26 and module 29. Module 211 executes RGT inference.
  • module 27 or module 28 If at least one of module 27 or module 28 outputs "no" signal, then application of RGT is impossible.
  • the signal from either of modules 27 and 28 launches module 210.
  • Module 210 processes the tables in module 24b or 24b and detects an inapplicable subject.
  • Module 210 provides a measure to overcome the problems (countermeasures). The detail about provision of countermeasures is mentioned later.
  • CEI schema (Detail schema of CEI) First example of CEI schema is presented in Fig. 4. First example is CEI schema regarding a single pair (a single mindset) of Y/N and open questions for a single subject.
  • Module 31 takes a question from list 32 of selected Y/N (yes/no) questions.
  • Module 12 stores Y/N questions with question marker. User gives “YES” or “NO” answers to the question.
  • Module 33 detects Yes or No answer. After the user has answered to the single question, module 31 sends the answer to module 33. Module 33 checks the answer to the question.
  • Corresponding problem type (FO, GB%) about application of RGT is automatically added into Answer Table 311 by module 310 (refer to table.2). In table.2, the first component of the third column shows the answer to the Y/N question.
  • Module 34 has two inputs from module 33 and module 31. When Module 34 receives a question marker from module 31, it sends question marker to module 35.
  • Module 35 is a database of open questions about problem types. Each question in module 32 is associated with open question in module 35 by means of question marker. After module 35 receives the question marker from module 34, it sends back a set of open questions associated with this particular question marker.
  • Module 34 randomly selects a single open question and sends this question to module 36.
  • the open question is organized in a way to request free text description of action, which user would take in a hypothetical situation close to real.
  • Module 36 is a dialog interface with user. User input free text answer to the open question. Module 36 outputs free text description to storage module 37. Then open text description in module 37 is processed by module 38.
  • Module 38 compares the content of the free text description with some template (general semantic pattern).
  • the general semantic pattern contains a set of statement and is pre-defined for each type of problem about reflexivity.
  • Module 38 calculates a level of consistency (LC) between the free text description and the template(general semantic pattern).
  • module 38 sends LC value to module 39.
  • LC level of consistency
  • Module 310 receives four inputs from modules 31, 33, 39 and 319.
  • Module 31 sends Question Marker ( second column of table.2 ) to module 310.
  • Module 33 sends Y/N answer ( first component of third column ) to module 310.
  • Module 39 sends the consistency measure ( second component of third column ) to module 310.
  • Module 319 sends subject ID ( first column of table.2 ) to module 310.
  • Module 310 outputs the Answer Table to data storage module 311. Next, the Answer Table stored in the module 311 is propagated to the module 318. Module 318 terminates the entire procedure and outputs the Answer Table.
  • Second example of CEI schema is presented in Fig. 5.
  • Second example is CEI schema regarding a multiple pairs (multiple mindsets) of Y/N and open questions for a single subject. This schema inherits all the elements from schema of First example. This schema has an additional module 313, which monitors whether all the questions have been processed.
  • module 32 contains several questions, which are selected one by one by module 31.
  • the procedure is same as first example, except that the module 39 sends signal to module 313.
  • Module 313 checks whether all questions have been processed.
  • module 313 send No signal to module 31. The procedure of CEI will restart. If all the questions are processed, module 313 sends Yes signal to the module 318. Module 318 terminates entire procedure and outputs Answer Table from module 311.
  • CEI schema Regarding a multiple pairs (multiple mindsets) of Y/N and open questions for multiple subjects. This schema aims to deal with multiple subjects. Therefore, along with modules in second example, new modules 320,321 are introduced.
  • module 319 is the input module.
  • variables Num and I are set.
  • Variable Num represents the total number of subjects in a group, while variable I is used as a counting variable.
  • Module 320 checks if value of variable I is less than value of variable Num. If it is true, it sends a signal to module 321, which increment value of variable I by 1. After that, module 321 sends "start signal" to module 31 and the processing begins.
  • Module 321 also send value of I variable to module 310.
  • Value of variable I is used by module 310 as subject ID.
  • Module 311 is a storage module, which contains an Answer Table, corresponding to subject I.
  • Module 322 stores overall data of all the Answer Tables for all Num subjects.
  • Module 31 The procedure of module 31 between 313 is same as second example.
  • Module 313 outputs the "YES" signal to module 320.
  • Module 320 checks whether all the subjects have been processed.
  • CEI schema is presented in Fig. 7.
  • Fourth example is CEI schema.
  • CEI schema with counter measures regarding a multiple pairs (multiple mindsets) of Y/N and open questions for multiple subjects. This schema aims to provide counter measure to change mindset for each problem for each subject. It inherits the structure of third example schema. This schema has new modules 312,314,315.
  • Module 314 takes an Answer Table as an input from module 311. Module 314 checks whether the second component of third column of Answer Table is 0. If it is true, this means that the answer given the by the subject I to Y/N question and open question are inconsistent. Thus the subject I is inapplicable for RGT. Further Module 314 checks whether the first component of third column of Answer Table is 0. If it is true, this means that the subject accepts own problem. Thus the subject I is inapplicable for RGT.
  • module 314 sends Question Maker to module 312.
  • Module 312 is a database of counter measures to change the mindset.
  • Module 312 returns a description of counter measure, which corresponds to the Question Marker to Module 314.
  • Module 314 adds the description of counter measures for subject I to data storage module 315.
  • the application of RGT is possible only in the case, if the list of Counter Measures for each subject is empty, i.e., module 323 contains no information. (Detail about verification of PwR)
  • Module 26 converts table.3 to table.4.
  • the cells of the table contain 2D binary vectors.
  • the first component of the vector corresponds to relationship which the subject stated: if subject A considers subject B as an enemy (subjects A and B are in conflict), this is coded with 0 value; if subject A considers subject B as friend (subjects A and B are in alliance), this is coded with 1 value.
  • the second component of the 2D binary vector corresponds to consistency measurement (result of CEI): in case judgment of inconsistency (false), this is coded with 0 value; in case judgment of consistency (true), this is coded with 1 value.
  • binary vector ⁇ 0,1 ⁇ should be interpreted as the subject considers another subject as his enemy (first component is 0) and this is consistent with free text description (second component is 1).
  • Module 26 corrects false pairwise relationships according to the following rule (table.5). If the value of second component is 1 (true), then it writes the same value of the first component ; If the value of second component is 0 (false), then it writes the opposite value of the first component. 1 is opposite to 0 and vice versa.
  • the rule is XNOR gate. Table.5
  • Module 26 generates the intermediate processing table(table.6) according to above rule. Table.6
  • Module 26 induces true pairwise relationships.
  • Module 26 generates table.7A,7B,7C for each subject based on table.6.
  • the relationship table contains N/A marks along main diagonals. Other cells of relationship tables contain verified pair-wise relationships given by a particular subject.
  • table.7A indicates that subject A considers subjects A and B are in alliance (friend). Further, table.7C indicates that subject C considers subjects A and B are in conflict (enemy).
  • Module 26 outputs the table for each subject. These tables about verified pair-wise relationships are used as input into RGT inference module 211.
  • the system detects a subject to whom RGT is inapplicable from the point of view of FO.
  • the system utilizes CEI to detect an inapplicable subject of FO (Step 10) .
  • the system detects a subject to whom RGT is inapplicable from the point of view of GB.
  • the system utilizes CEI to detect an inapplicable subject of GB (Step 20) .
  • the system judges that RGT is applicable, in the case that said no such inapplicable subject is detected(Step 30).
  • the system verifies whether a plurality of pairwise relationship are true.
  • the system utilizes CEI to verify PwR (Step 40).
  • the system executes RGT inference in the case it reaches a applicable judgment of RGT.
  • the system uses true PwR (Step 50).
  • the system provides a Yes/No question about a condition which is required for application of RGT (Step 11).
  • the system inputs a Yes/No answer to Yes/No question (Step 12).
  • the system detects Yes/No answer (Step 13).
  • the system provides an open question which is associated with Yes/No question and which relates to a hypothetical situation close to the real one, in the case that it detects Yes answer (Step 14).
  • the system inputs a free text answer to the open question (Step 15).
  • the system analyzes consistency between the answer to the open question and the answer to the Yes/No question (Step 16).
  • the system judges that RGT is applicable to the subject, in the case that it reaches a judgment of consistency. Otherwise, it judges the inapplicable subject (Step 17) .
  • the system provides the inapplicable subject with countermeasures for alteration of mindset (Step 18).
  • the system provides an alternative question that a subject consider estimated subjects to be friend/enemy (Step 41).
  • the system inputs a friend/enemy answer to the alternative question (Step 42).
  • the system detects the friend/enemy answer (Step 43).
  • the system provides an open question which is associated with the friend/enemy question and which relates to a hypothetical situation close to the real one (Step 44).
  • the system inputs a free text answer to the open question (Step 45).
  • the system analyzes consistency between the answer to the open question and the answer to the friend/enemy question (Step 46).
  • the system judges that the pairwise relationship is true, in the case of consistency judgment. It judges that the pairwise relationship is false , in the case of inconsistency judgment (Step 47).
  • the system corrects false pairwise relationship and induces true pairwise relationships(Step 48).
  • Example operation of CEI The example operation of consistency evaluation idea(CEI) in the case of a single pair of questions for a single subject is presented in Fig.11 (refer to Fig.4).
  • the question " Do you consider yourself as a member of a group?” is provided from module 32.
  • the user inputs answer "YES” in the module 31.
  • the Category marker for this question is "Group Belonging (GB)”.
  • Module 34 receives a signal and question marker "GB1" from module 33.
  • Module 34 selects a set of questions, which correspond to the question marker, from module 35 andselects randomly a single question. The selected question is "What do you think about electricity saving in your team?". This question is send to module 36.
  • the output of the module 36 is a free text description of user's opinion stored in module 37.
  • the free text description is "I think people waste a lot of electricity by not switching off the light and other power greedy devices like air-conditioners, plasma TVs, etc. They should be more economical".
  • Module 38 receives input from module 37 and outputs LC between the user's answer and template of the answer.
  • the detailed analysis of the free text description is presented in Fig. 12.
  • the free text is compared with a particular template.
  • template contains three key statements: key statements1:speaker should associate him/herself with other people as on intact team; key statements2:speaker should show his/her desire to be involved into finding solution of the problem; key statements3:speaker should show his/her social responsibility.
  • the key elements are statements, which should be found in the free text answer, if person's social reflexivity (GB) level is high.
  • the variables a1, a2 and a3 correspond to key statements 1, 2 and 3.
  • Each variable ai takes value 1, if the corresponding statement is found in the free text, and 0 otherwise.
  • Module 310 also receives Question Marker GB1 from module 31.
  • Module 310 adds Question Marker GB1 to Answer Table of subject Tanaka, which is stored in module 311.
  • the GQS can be used to analyze and verify pair-wise relationships.
  • the entire schema corresponds to the functions of modules 21a, 22 and 25.
  • the GQS outputs a set of list of problems (inconsistencies between stated relationships and actual behavior in hypothetical situation) for each subject in the form of the data stored in module 25.
  • GQS can be used to analyze and verify subjects' mindset about fixed opinion.
  • GQS corresponds to modules 21b, 23b and 24b.
  • GQS outputs a set of list of problems for each subject in the form of the data stored in module 24b.
  • GQS can be used to analyze and verify the group belonging (whether a subject considers him/herself to be a group member).
  • GQS corresponds to modules 21c, 23b and 24b.
  • GQS outputs a set of list of problems for each subject in the form of the data stored in module 24c.
  • a system for evaluating an applicability of Reflexive Game Theory comprising: an inapplicable subject detection means that detects a subject to whom RGT is inapplicable; and an applicability judgment means that judges that RGT is applicable, in the case that said inapplicable subject detection means detects no such inapplicable subject.
  • RGT Reflexive Game Theory
  • said inapplicable subject detection means includes: a Yes/No question provision means that provides a Yes/No question about a condition which is required for application of RGT; a Yes/No answer input means that inputs a Yes/No answer to said Yes/No question; a Yes/No answer detection means that detects said Yes/No answer; an open question provision means that provides an open question which is associated with said Yes/No question and which relates to a hypothetical situation close to the real one, in the case that said Yes/No answer detection means detects a Yes answer; a free answer input means that inputs a free text answer to said open question; a consistency analysis means that analyzes consistency between the answer to the open question and the answer to the Yes/No question; and an applicable subject judgment means that judges that RGT is applicable to the subject, in the case that said consistency analysis means reaches a judgment of consistency.
  • the system for evaluating the applicability of RGT wherein said consistency analysis means extracts key elements in the answer to the open question, and reaches a judgment of consistency, in the case that the number of key elements is greater than or equal to a threshold value.
  • the system for evaluating the applicability of RGT wherein: said system comprises a plurality of said inapplicable subject detection means, and said applicability judgment means judges that RGT is applicable, in the case that all of said inapplicable subject detection means detect no such inapplicable subject.
  • said inapplicable subject detection means further includes: a countermeasures provision means that provides countermeasures for alteration of said mindset, in the case that said applicable subject judgment means judges that RGT is not applicable to the subject.
  • the system for evaluating the applicability of RGT further comprising: a pairwise relationship verification means that verifies whether a plurality of pairwise relationship are true, wherein said pairwise relationship verification means includes: an alternative question provision means that provides an alternative question that a subject consider estimated subjects to be friend/enemy ; a friend/enemy answer input means that inputs a friend/enemy answer to said alternative question; a friend/enemy answer detection means that detects said friend/enemy answer; an open question provision means that provides an open question which is associated with said friend/enemy question and which relates to a hypothetical situation close to the real one; a free answer input means that inputs a free text answer to said open question; a consistency analysis means that analyzes consistency between the answer to the open question and the answer to the friend/enemy question; a true/false judgment means that judges that the pairwise relationship is true, in the case that said consistency analysis means reaches a judgment of consistency , and the pairwise relationship is false , in the case that said
  • a device for detecting a subject to whom Reflexive Game Theory (RGT) is inapplicable comprising: a Yes/No question provision means that provides a Yes/No question about a condition which is required for application of RGT; a Yes/No answer input means that inputs a Yes /No answer to said Yes/No question; a Yes/No answer detection means that detects said Yes/ No answer; an open question provision means that provides an open question which is associated with said Yes/No question and which relates to a hypothetical situation close to the real one, in the case that said Yes/No answer detection means detects a Yes answer; a free answer input means that inputs a free text answer to said open question; a consistency analysis means that analyzes consistency between the answer to the open question and the answer to the Yes/No question; and an applicable subject judgment means that judges that RGT is applicable to the subject, in the case that said consistency analysis means reaches a judgment of consistency.
  • RGT Reflexive Game Theory
  • a device for verifying whether a plurality of pairwise relationship are true comprising: an alternative question provision means that provides an alternative question that a subject consider estimated subjects to be friend/enemy ; a friend/enemy answer input means that inputs a friend/enemy answer to said alternative question; a friend/enemy answer detection means that detects said friend/enemy answer; an open question provision means that provides an open question which is associated with said friend/enemy question and which relates to a hypothetical situation close to the real one; a free answer input means that inputs a free text answer to said open question; a consistency analysis means that analyzes consistency between the answer to the open question and the answer to the friend/enemy question; a true/false judgment means that judges that the pairwise relationship is true, in the case that said consistency analysis means reaches a judgment of consistency , and the pairwise relationship is false , in the case that said consistency analysis means reaches a judgment of inconsistency; and a correction means that corrects the pairwise relationship , in
  • a method for evaluating an applicability of Reflexive Game Theory comprising the steps of: detecting a subject to whom RGT is inapplicable; and judging that RGT is applicable, in the case that said no such inapplicable subject is detected.
  • the method for evaluating the applicability of RGT wherein said step of inapplicable subject detection includes the step of: providing a Yes/No question about a condition which is required for application of RGT; inputting a Yes/No answer to said Yes/No question; detecting said Yes/No answer; providing an open question which is associated with said Yes/No question and which relates to a hypothetical situation close to the real one, in case of detecting a Yes answer; inputting a free text answer to said open question; analyzing consistency between the answer to the open question and the answer to the Yes/No question; and judging that RGT is applicable to the subject, in case of consistency.
  • step of consistency analysis is to extract key elements in the answer to the open question, and reach a judgment of consistency, in the case that the number of key elements is greater than or equal to a threshold value.
  • the method for evaluating the applicability of RGT comprising: a plurality step of detecting said inapplicable subject a step of said judging that RGT is applicable, in the case that no such inapplicable subject is detected ,in all of said inapplicable subject detection step.
  • said inapplicable subject detection step further includes a step of : providing the inapplicable subject with countermeasures for alteration of said mindset.
  • the method for evaluating the applicability of RGT further comprising a step of : verifying whether a plurality of pairwise relationship are true, wherein said pairwise relationship verification step includes steps of : providing an alternative question that a subject consider estimated subjects to be friend/enemy; inputting a friend/enemy answer to said alternative question; detecting said friend/enemy answer; providing an open question which is associated with said friend/enemy question and which relates to a hypothetical situation close to the real one; inputting a free text answer to said open question; analyzing consistency between the answer to the open question and the answer to the friend/enemy question; judging that the pairwise relationship is true, in case of said consistency , and the pairwise relationship is false , in case of said inconsistency; and correcting said false pairwise relationship.
  • a program for evaluating a reflexivity causing a computer to execute : an inapplicable subject detection process that detects a subject to whom RGT is inapplicable; and an applicability judgment process that judges that RGT is applicable, in the case that said inapplicable subject detection means detects no such inapplicable subject.
  • said inapplicable subject detection process includes: a Yes/No question provision process that provides a Yes/No question about a condition which is required for application of RGT; a Yes/No answer input process that inputs a Yes/No answer to said Yes/No question; a Yes/No answer detection process that detects said Yes/No answer; an open question provision process that provides an open question which is associated with said Yes/No question and which relates to a hypothetical situation close to the real one, in case of detecting a Yes answer; a free answer input process that inputs a free text answer to said open question; a consistency analysis process that analyzes consistency between the answer to the open question and the answer to the Yes/No question; and an applicable subject judgment process that judges that RGT is applicable to the subject, in case of consistency.
  • the program for evaluating the applicability of RGT wherein said consistency analysis process is to extract key elements in the answer to the open question, and reach a judgment of consistency, in the case that the number of key elements is greater than or equal to a threshold value.
  • the program for evaluating the applicability of RGT wherein said inapplicable subject detection process is to detect a subject who has a fixed opinion.
  • the program for evaluating the applicability of RGT wherein said inapplicable subject detection process is to detect a subject who lacks a mindset related to group belonging.
  • the program for evaluating the applicability of RGT causing a computer to execute : a plurality of said inapplicable subject detection process, and said applicability judgment process to judge RGT is applicable, in the case that no such inapplicable subject is detected ,in all of said inapplicable subject detection process.
  • the program for evaluating the applicability of RGT wherein said inapplicable subject detection process further includes: a countermeasures provision process that provides countermeasures for alteration of said mindset, in the case that said applicable subject judgment means judges that RGT is not applicable to the subject.
  • the program for evaluating the applicability of RGT causing a computer to further execute: a pairwise relationshipverification process that verifies whether a plurality of pairwise relationship are true, wherein said pairwise relationship verification process includes: an alternative question provision process that provides an alternative question that a subject consider estimated subjects to be friend/enemy ; a friend/enemy answer input process that inputs a friend/enemy answer to said alternative question; a friend/enemy answer detection process that detects said friend/enemy answer; an open question provision process that provides an open question which is associated with said friend/enemy question and which relates to a hypothetical situation close to the real one; a free answer input process that inputs a free text answer to said open question; a consistency analysis means that analyzes consistency between the answer to the open question and the answer to the friend/enemy question; a true/false judgment process that judges that the pairwise relationship is true, in case of said consistency, and the pairwise relationship is false , in case of said inconsistency; and
  • the present invention is used as screening before RGT inference. Non-expert users will become able to treat RGT by using a computer.

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Abstract

The system evaluates the applicability of Reflexive Game Theory (RGT). It includes PwR part (21a,22,25,26), FO part(21b,23b,24b), GB part (21c,23c,24c) and applicability judgment part (27,28,29),RTG inference part (211) and countermeasures provision part (210). FO part detects an inapplicable subject of FO. GB part detects an inapplicable subject of GB.Applicability judgment part judges that RGT is applicable, in the case that no such inapplicable subject is detected. PwR part verifies whether a plurality of pairwise relationship are true. RTG inference part executes RGT inference in the case it reaches a applicable judgment of RGT. It uses true PwR.

Description

SYSTEM FOR EVALUATING AN APPLICABILITY OF REFLEXIVE GAME THEORY, METHOD FOR EVALUATING AN APPLICABILITY OF RGT
The present invention relates to a technology of evaluating an applicability of Reflexive Game Theory.
Reflexive Game Theory (RGT) enables prediction of group members (subjects) decisions. This analysis is based on only relationships (conflict or alliance) between subjects (individuals, teams or work groups, companies, etc.) and their mutual influences (see Non-Patent Documents 1 and 2). The general schema of conventional algorithm for RGT inference is presented in Fig.1. Implementation example of RGT inference is presented in Figs. 2.
The groups of subjects are presented in the form of fully connected graphs. The graph can have dashed-line ribs (conflict relationships) and solid-line ribs (alliance relationships).
The stages of RGT analysis (inference) based on symbolic computations are:
1) input group structure as pair-wise relations between subjects (module 11);
2) input subjects mutual influences (module 12);
3) check graphs decomposability (module 13);
4) construct a polynomial (module 14);
5) perform Diagonal Form Transformation(DFT)( module 15);
6) build decision equations (module 16);
7) perform transformation of the decision equation into canonical form (TDECF) to solve decision equation, obtain templates of decision intervals for each subject (module 17);
8) input mutual influences into the templates to get possible decisions (module 18).
[NPL 1] Lefebvre, V.A Lectures on Reflexive Game Theory, Cogito-Centre, Moscow, 2009.
[NPL 2] Lefebvre, V.A Lectures on Reflexive Game Theory, Leaf & Oaks Publishers, 2010.
The Reflexive Game Theory (RGT) has been proposed to model and predict human decision making in groups. However, straight forward application of the RGT is problematic due to fundamental limitations of RGT itself. For example, three examples are listed,
1) RGT is based on subconscious psychological algorithms, which take into account opinion of each member of a group. These algorithms can be can be overridden by conscious behavior: some person considers only his/her opinion and do not listen to others. In other word, person's mindset is fixed to a particular options(Fixed Opinion - FO). In this case RGT cannot be used;
2) RGT inference is based on the group structure. Therefore if person does not consider his/herself to be a part of a group (Group Belonging - GB), then RGT is not applicable;
3) RGT does not provide a clear way to understand relations between subjects (Pair-wise Relationship - PwR). Because relations are used for RGT inference, incorrectly defined relations will cause incorrect predictions results. In some cases, people can deliberately lie about their relations.
As a result, only the experts such as psychologists were able to treat RGT. Thus it isdifficult to apply RGT to a computer program.
The present invention has been accomplished in consideration of the above-mentioned problems, and an object of the present invention is to provide a technology of solving the above-mentioned problems, namely, a technology of evaluating an applicability of reflexive game theory. Therefore it will becomes easy to apply RGT to a computer program.
The present invention for solving the above-mentioned problems is a system for evaluating an applicability of Reflexive Game Theory (RGT), comprising: an inapplicable subject detection means that detects a subject to whom RGT is inapplicable; and an applicability judgment means that judges that RGT is applicable, in the case that said inapplicable subject detection means detects no such inapplicable subject.
The present invention for solving the above-mentioned problems is a device for detecting a subject to whom Reflexive Game Theory (RGT) is inapplicable, comprising:
a Yes/No question provision means that provides a Yes/No question about a condition which is required for application of RGT;
a Yes/No answer input means that inputs a Yes /No answer to said Yes/No question; a Yes/No answer detection means that detects said Yes/ No answer;
an open question provision means that provides an open question which is associated with said Yes/No question and which relates to a hypothetical situation close to the real one, in the case that said Yes/No answer detection means detects a Yes answer;
a free answer input means that inputs a free text answer to said open question; a consistency analysis means that analyzes consistency between the answer to the open question and the answer to the Yes/No question; and
an applicable subject judgment means that judges that RGT is applicable to the subject, in the case that said consistency analysis means reaches a judgment of consistency.
The present invention for solving the above-mentioned problems is a method for evaluating an applicability of Reflexive Game Theory (RGT), comprising the steps of: detecting a subject to whom RGT is inapplicable; and judging that RGT is applicable, in the case that said no such inapplicable subject is detected.
The present invention makes it possible to detect a subject to whom RGT is inapplicable.
The present invention makes it possible to verify true pairwise relationships
So it become possible to evaluate an applicability of Reflexive Game Theory. It will becomes easy to apply RGT to a computer program. As a result, not only the experts such as psychologists , but also non-expert users will become able to treat RGT.
The present invention provides the inapplicable subject with countermeasures for alteration of the mindset. It allow the system to improve the applicability of RGT.
Fig. 1 is the general schema of conventional algorithm for RGT inference. Fig. 2 is implementation example of RGT inference. Fig. 3 is the general schema of the system. Fig. 4 is first example of CEI schema. Fig. 5 is second example of CEI schema. Fig. 6 is third example of CEI schema. Fig. 7 is fourth example of CEI schema. Fig. 8 is flowchart of basic operation on the system. Fig. 9 is flowchart of operation for inapplicable subject detection. Fig. 10 is operation for pairwise relationship verification. Fig. 11 is the example operation of CEI. Fig. 12 is the detailed analysis of the free text description.
(overview on concept)
The present invention includes two main technical ideas. First idea is to detect a subject to whom RGT is inapplicable (for example, fixed opinion , lack of group belonging). Second idea is to verify true pairwise relationships.
The information about fixed opinion or group belonging is obtained from subjects.But it is possible that person provides incorrect information, for example, to tell a lie for some reasons. In order to avoid such situation, we suggest to use the combination of questions, which can help to verify the true state the person's mind.
We consider one example of analysis as follows.(FO)
A person is asked the question "Can you understand your own mistake?" and the answer is "YES". Then we can test person's mindset with an open question, which asks his/her behavior in a situation close to real one. We can explain him/her a hypothetical situation, in which this person collides with another person and we ask "Whom would you blame and why?".
Usually, in such situation both people have some fault, except for some extraordinary cases. If the answer is, for example, "I blame the other person, because he/she was looking in a different direction", then we can consider the answer of the person to be inconsistent with the answer to the question "Can you understand your own mistakes?". Therefore, we doubt person's "YES" answer to the question "Can you understand your own mistake?". This means that person's mindset in action is different from the stated above.
The consistency of the "YES" answer with description of actions in hypothetical situation can be evaluated as follows: the description of hypothetical situation is compared with expected result. The expected result is represented as a structure with key elements. The analyzer of description is searching for match between the key elements of the expected results and the content of the description.
In order to evaluate consistency between "YES" answers of participants and description of their behavior in hypothetical situation close to real one, we propose to use methods of Natural Language Processing (NLP). For example, approach to analysis of the text based on ontological sematics can be used. The ontological semantics provide a general description of a situation in terms of basic concepts.
In other words, the collection of basic concepts related to a particular situation represents the general pattern of this situation in a semantics space (general semantic pattern). By using semantic analysis of free text message, which describes behavior in a hypothetical situation, it is possible to build a correspondence between the words in the free text message and concepts in the general semantic pattern.
Next we consider another example of analysis (GB).
The question is "Do you consider yourself to be a member of your unit?". The answer is "YES". Next a person is asked a question "What do you think about electricity saving in your unit?". The answer is "I think that people waste a lot of electricity by not switching off the light or other power greedy devices like air-conditioners, TVs, etc. They should be more economical."
The expected result is that a person will speak about his/her unit as one team. But the person says "people" (they, excluding me) instead of "we" (all people, including me).
The person also should show the involvedness, by suggesting how he/she him/herself could participate in improving the situation. But Person only says "They should be more economical." instead of "we". The word "we" is more reasonable, when talking about the group of people and considering oneself as a part of the group. He/she shows no involvedness.
Therefore analysis of the hypothetical situation as an answer to the open question allows to verify whether person thinks differently from what he/she answers to the previous question.
The main idea of this invention is to check whether positive ("YES" answer) statement about a certain state of mindset. It is confirmed by actual performance in hypothetical situation close to real one, which is described as an answer to the open question. We refer to this idea as a "consistency evaluation idea" (CEI).
CEI also can be applied to verify true pairwise relationships (PwR).
PwR in RGT are considered to be either alliance or conflict (friend or enemy). In the case of alliance, it is possible that two people can find consensus/compromise. However, in the case of conflict two people cannot find a consensus with each other. This is the most important information, which is used in RGT.
In this case, subjects are asked a question about friend or enemy, instead of Y/N question. Verification of pair-wise relationships is done of the basis of consistency between stated relationship (friend or enemy) and answer to open question.
Assume that subject A has stated that he is in alliance with subject B. This means that subject A is willing to negotiate with subject B to find consensus.
To check whether it is true, it is possible to ask subject A open question like: "Suppose you have discussion with subject B. Some of your arguments are not accepted by subject B. On the other hand, you cannot accept some arguments provided by subject B. Please explain your next steps in discussion."
Hereinafter, the exemplary embodiments of the present invention will be illustratively explained in details by referencing the accompanied drawings.
However, the constituent element described in the following exemplary embodiments is only an exemplification, and there is no intention of limiting the technological scope of the present invention to hereto.
(General Schema of the system)
The general schema of the system is presented in Fig. 3. The system includes PwR part ( module 21a,22,25,26), FO part ( module 21b,23b,24b), GB part ( module 21c,23c,24c) and applicability judgment part ( module 27,28,29), RTG inference part (module 211) and countermeasures provision part (module 210).
PwR is the abbreviation for pair-wise relationships. FO is the abbreviation for fixed opinion. GB is the abbreviation for group belonging.
The outline of FO part will be explained.
All the subjects in a group should answer to the questions about FO as below.
FO1: Is it true that you prefer to do the things you have to do rather than the things you like?
FO2: Even if you have a different opinion, will you follow a group decision ?
FO:3 Can you understand your own mistake?
The input of answers is performed in modules 21b. The answers from module 21b are processed by modules 23b.
Module 23b analyzes the mindset of each subject about the fixed opinion. Module 23a evaluates the consistency between the answers to Y/N and open questions.
The output of module 23b is stored in a data storage module 24b, which contains the verified mindset for each subject. The verified mindset is represented in a form of a table. This table (table1) contains three columns.
Table.1
Figure JPOXMLDOC01-appb-I000001
The first column contains the subject identifier. The second column contains the question marker. The third column contains verified answers to the corresponding questions.
The verified answers are coded with 2D binary vector. The first component of the vector corresponds to the answer to the Y/N question: if subject answered "YES", this is coded with 1 value; the answer "NO" is coded with 0 value. The second component is consistency measure: if the result of CEI is "consistency", then consistency measure equals 1, or 0 otherwise.
Module 23b outputs such table to data storage module 24b. Example of an Answer Table (table.2) is presented below.
Table.2
Figure JPOXMLDOC01-appb-I000002
The outline of GB part will be explained.
All the subjects in a group should answer to the questions about GB below.
GB1: Do you consider yourself to be a part of a group?
GB2: Do you understand that you are a part of the company?
The input of answers is performed in modules 21c. The answers from module 21c are processed by modules 23c.
Module 23c analyzes the mindset about the group belonging. Module 23c evaluates the consistency between the answers to Y/N and open questions. The output of module 23c has the same structure as output of module 23b (refer to table1). Module 23c outputs a table (like table.2) to data storage module 24c.
The outline of PwR part will be explained.
All the subjects in a group should answer to the questions about PwR as below.
PwR1: Do you consider person A to be your friend?
PwR2: Do you consider person A to be your enemy?
PwR3: Do you think person A and person B are friends?
The input of answers is performed in modules 21a. The answers from module 21a are processed by modules 22.
Module 22 verifies true pairwise relationships. Module 22 evaluates the consistency between the answers to Friend/Enemy and open questions. The output of module 22 has the same structure as output of module 23b (refer to table1). Module 22 outputs table.3 (same as table.2) to data storage module 25.
Module 26 transforms the table of module 25. Module 26 corrects false pairwise relationships and induces true pairwise relationships. The detail about verification of PwR is mentioned later.
Module 26 outputs the table for each subject. These tables about verified pair-wise relationships are used as input into RGT inference module 211.
The outline of applicability judgment part will be explained. Applicability judgment part includes module 27,28,29.
The content of the module 24b is processed in module 27. Module 27 judges whether RGT is applicable from the point of view of FO. Thus, module 27 checks if any binary vector in the third column contains the 0 value. If at least one 0 value is located, then module 27 outputs "no" signal 0 value. This means inapplicable of RGT. Otherwise module 27 outputs "Yes" signal 1 value.
The content of the module 24c is processed in module 28. Module 28 judges whether RGT is applicable from the point of view of GB. Thus, module 28 checks if any binary vector in the third column contains the 0 value. If at least one 0 value is located, then module 27 outputs "no" signal 0 value. This means inapplicable of RGT. Otherwise module 27 outputs "Yes" signal 1 value.
Module 29 received input signals from modules 27,28. Module 29 output "yes" signal (1 value) to module 211., only if both of modules27,28 output "yes" signal. This means applicable of RGT.
Module 211 received the signals from module 26 and module 29. Module 211 executes RGT inference.
If at least one of module 27 or module 28 outputs "no" signal, then application of RGT is impossible. The signal from either of modules 27 and 28 launches module 210. Module 210 processes the tables in module 24b or 24b and detects an inapplicable subject. Module 210 provides a measure to overcome the problems (countermeasures). The detail about provision of countermeasures is mentioned later.
If the subject will change the mindset, The system become available again.
(Detail schema of CEI)
First example of CEI schema is presented in Fig. 4. First example is CEI schema regarding a single pair (a single mindset) of Y/N and open questions for a single subject.
Module 31 takes a question from list 32 of selected Y/N (yes/no) questions. Module 12 stores Y/N questions with question marker. User gives "YES" or "NO" answers to the question.
Module 33 detects Yes or No answer. After the user has answered to the single question, module 31 sends the answer to module 33. Module 33 checks the answer to the question.
If the answer is " YES", module 33 sends "YES" signal (value = 1) to module 34. If the answer is "NO", module 33 sends "NO" signal (value = 0) to module 310. Corresponding problem type (FO, GB...) about application of RGT is automatically added into Answer Table 311 by module 310 (refer to table.2). In table.2,the first component of the third column shows the answer to the Y/N question.
If the answer is " YES", module 33 sends "YES" signal (value = 1) to module 310 too.
If the answer is "NO", module 33 sends "consistensy" signal (value = 1) to module 310. This means that the subject accepts own problem. Therefore there is no need to check the consistency.
Module 34 has two inputs from module 33 and module 31. When Module 34 receives a question marker from module 31, it sends question marker to module 35.
Module 35 is a database of open questions about problem types. Each question in module 32 is associated with open question in module 35 by means of question marker. After module 35 receives the question marker from module 34, it sends back a set of open questions associated with this particular question marker.
Module 34 randomly selects a single open question and sends this question to module 36. The open question is organized in a way to request free text description of action, which user would take in a hypothetical situation close to real.
Module 36 is a dialog interface with user. User input free text answer to the open question. Module 36 outputs free text description to storage module 37. Then open text description in module 37 is processed by module 38.
Module 38 compares the content of the free text description with some template (general semantic pattern). The general semantic pattern contains a set of statement and is pre-defined for each type of problem about reflexivity. Module 38 calculates a level of consistency (LC) between the free text description and the template(general semantic pattern). Next, module 38 sends LC value to module 39.
Module 39 compares LC value with preset threshold. If LC value is higher or equal to a threshold value, module 19 sends a Yes signal (value = 1) to module 310. This means consistency. If LC value is low than threshold, module 39 sends No signal (value = 0) to module 310. This means inconsistency. In table.2,the second component of the third column shows the consistency measure.
Module 310 receives four inputs from modules 31, 33, 39 and 319. Module 31 sends Question Marker ( second column of table.2 ) to module 310. Module 33 sends Y/N answer ( first component of third column ) to module 310. Module 39 sends the consistency measure ( second component of third column ) to module 310. Module 319 sends subject ID ( first column of table.2 ) to module 310.
Module 310 outputs the Answer Table to data storage module 311. Next, the Answer Table stored in the module 311 is propagated to the module 318. Module 318 terminates the entire procedure and outputs the Answer Table.
Second example of CEI schema is presented in Fig. 5. Second example is CEI schema regarding a multiple pairs (multiple mindsets) of Y/N and open questions for a single subject. This schema inherits all the elements from schema of First example. This schema has an additional module 313, which monitors whether all the questions have been processed.
In this case, module 32 contains several questions, which are selected one by one by module 31. The procedure is same as first example, except that the module 39 sends signal to module 313. Module 313 checks whether all questions have been processed.
If all the questions are not processed, module 313 send No signal to module 31. The procedure of CEI will restart. If all the questions are processed, module 313 sends Yes signal to the module 318. Module 318 terminates entire procedure and outputs Answer Table from module 311.
Third example of CEI schema is presented in Fig. 6. Third example is CEI schema regarding a multiple pairs (multiple mindsets) of Y/N and open questions for multiple subjects. This schema aims to deal with multiple subjects. Therefore, along with modules in second example, new modules 320,321 are introduced.
In this case, module 319 is the input module. Here variables Num and I are set. Variable Num represents the total number of subjects in a group, while variable I is used as a counting variable.
Module 320 checks if value of variable I is less than value of variable Num. If it is true, it sends a signal to module 321, which increment value of variable I by 1. After that, module 321 sends "start signal" to module 31 and the processing begins.
Module 321 also send value of I variable to module 310. Value of variable I is used by module 310 as subject ID. Module 311 is a storage module, which contains an Answer Table, corresponding to subject I.
Module 322 stores overall data of all the Answer Tables for all Num subjects.
The procedure of module 31 between 313 is same as second example. Module 313 outputs the "YES" signal to module 320. Module 320 checks whether all the subjects have been processed.
If condition I < Num is not satisfied, this means that I = Num, i.e., all the subjects have been processed. In this case, module 320 outputs "no" signal to module 318, flagging termination of entire procedure. Module 318 outputs content of Overall Data Storage module 322.
Fourth example of CEI schema is presented in Fig. 7. Fourth example is CEI schema. Fourth example is CEI schema with counter measures regarding a multiple pairs (multiple mindsets) of Y/N and open questions for multiple subjects. This schema aims to provide counter measure to change mindset for each problem for each subject. It inherits the structure of third example schema. This schema has new modules 312,314,315.
Module 314 takes an Answer Table as an input from module 311. Module 314 checks whether the second component of third column of Answer Table is 0.
If it is true, this means that the answer given the by the subject I to Y/N question and open question are inconsistent. Thus the subject I is inapplicable for RGT. Further Module 314 checks whether the first component of third column of Answer Table is 0. If it is true, this means that the subject accepts own problem. Thus the subject I is inapplicable for RGT.
Then module 314 sends Question Maker to module 312. Module 312 is a database of counter measures to change the mindset. Module 312 returns a description of counter measure, which corresponds to the Question Marker to Module 314. Module 314 adds the description of counter measures for subject I to data storage module 315.
The lists of counter measures for each subject I, I = 1,..., Num, are stored in Overall Storage module 323. If the Answer Table for subject I contains no 0 in third column, this means RGT is applicable to the subject I. So list of Counter Measure for subject I is empty.
Finally, module 318 outputs the Answer Tables and list of Counter Measures, if any, for each subject I, I = 1,..., Num. The application of RGT is possible only in the case, if the list of Counter Measures for each subject is empty, i.e., module 323 contains no information.
(Detail about verification of PwR)
Application CEI to verify PwR makes table.3. The detail is mentioned above. table.3. Module 25 stores table.3. Module 26 transforms the table of module 25. Module 26 corrects false pairwise relationships and induces true pairwise relationships.
Table.3
Figure JPOXMLDOC01-appb-I000003
Module 26 converts table.3 to table.4. Table.4 is substantial equivalent to table.3. Rows indicate the thinking subjects. The columns contain pairs of estimated subjects. A particular subject's estimates all pair-wise relationships. The number of columns corresponds to the number of possible pair-wise relationships (N), where N can be calculated as a number of permutations: N =n*(n-1)/2, wherein n is a number of subjects. In the case of three subjects A, B and C, the number of columns is 3. The pair of letters in the table represents the pair-wise relationship between two subjects.
Table.4
Figure JPOXMLDOC01-appb-I000004
The cells of the table contain 2D binary vectors. The first component of the vector corresponds to relationship which the subject stated: if subject A considers subject B as an enemy (subjects A and B are in conflict), this is coded with 0 value; if subject A considers subject B as friend (subjects A and B are in alliance), this is coded with 1 value. The second component of the 2D binary vector corresponds to consistency measurement (result of CEI): in case judgment of inconsistency (false), this is coded with 0 value; in case judgment of consistency (true), this is coded with 1 value.
Therefore, binary vector {0,1} should be interpreted as the subject considers another subject as his enemy (first component is 0) and this is consistent with free text description (second component is 1).
Module 26 corrects false pairwise relationships according to the following rule (table.5). If the value of second component is 1 (true), then it writes the same value of the first component ; If the value of second component is 0 (false), then it writes the opposite value of the first component. 1 is opposite to 0 and vice versa. The rule is XNOR gate.
Table.5
Figure JPOXMLDOC01-appb-I000005
Module 26 generates the intermediate processing table(table.6) according to above rule.
Table.6
Figure JPOXMLDOC01-appb-I000006
Module 26 induces true pairwise relationships. Module 26 generates table.7A,7B,7C for each subject based on table.6. The relationship table contains N/A marks along main diagonals. Other cells of relationship tables contain verified pair-wise relationships given by a particular subject.
Table.7A
Figure JPOXMLDOC01-appb-I000007
Table.7B
Figure JPOXMLDOC01-appb-I000008
Table.7C
Figure JPOXMLDOC01-appb-I000009
For example, table.7A indicates that subject A considers subjects A and B are in alliance (friend). Further, table.7C indicates that subject C considers subjects A and B are in conflict (enemy).
Module 26 outputs the table for each subject. These tables about verified pair-wise relationships are used as input into RGT inference module 211.
(Basic operation of the system )
The basic operation of the system, presented in Fig.8, will be explained. The system evaluates the applicability of Reflexive Game Theory (RGT).
The system detects a subject to whom RGT is inapplicable from the point of view of FO. The system utilizes CEI to detect an inapplicable subject of FO (Step 10) .
The system detects a subject to whom RGT is inapplicable from the point of view of GB. The system utilizes CEI to detect an inapplicable subject of GB (Step 20) .
The system judges that RGT is applicable, in the case that said no such inapplicable subject is detected(Step 30).
The system verifies whether a plurality of pairwise relationship are true. The system utilizes CEI to verify PwR (Step 40).
The system executes RGT inference in the case it reaches a applicable judgment of RGT. The system uses true PwR (Step 50).
(Operation of inapplicable subject detection )
The operation of inapplicable subject detection, presented in Fig.9, will be explained. For example, the system detects an inapplicable subject of FO or GB.
The system provides a Yes/No question about a condition which is required for application of RGT (Step 11).
The system inputs a Yes/No answer to Yes/No question (Step 12).
The system detects Yes/No answer (Step 13).
The system provides an open question which is associated with Yes/No question and which relates to a hypothetical situation close to the real one, in the case that it detects Yes answer (Step 14).
The system inputs a free text answer to the open question (Step 15).
The system analyzes consistency between the answer to the open question and the answer to the Yes/No question (Step 16).
The system judges that RGT is applicable to the subject, in the case that it reaches a judgment of consistency. Otherwise, it judges the inapplicable subject (Step 17) .
The system provides the inapplicable subject with countermeasures for alteration of mindset (Step 18).
(Operation of pairwise relationship verification )
The operation of pairwise relationship verification, presented in Fig.10, will be explained.
The system provides an alternative question that a subject consider estimated subjects to be friend/enemy (Step 41).
The system inputs a friend/enemy answer to the alternative question (Step 42).
The system detects the friend/enemy answer (Step 43).
The system provides an open question which is associated with the friend/enemy question and which relates to a hypothetical situation close to the real one (Step 44).
The system inputs a free text answer to the open question (Step 45).
The system analyzes consistency between the answer to the open question and the answer to the friend/enemy question (Step 46).
The system judges that the pairwise relationship is true, in the case of consistency judgment. It judges that the pairwise relationship is false , in the case of inconsistency judgment (Step 47).
The system corrects false pairwise relationship and induces true pairwise relationships(Step 48).
(Example operation of CEI )
The example operation of consistency evaluation idea(CEI) in the case of a single pair of questions for a single subject is presented in Fig.11 (refer to Fig.4).
First, the question " Do you consider yourself as a member of a group?" is provided from module 32. The user inputs answer "YES" in the module 31. The Category marker for this question is "Group Belonging (GB)".
Module 33 detects answer "YES" and sends the signal (value=1) to module 34. Module 34 receives a signal and question marker "GB1" from module 33. Module 34 selects a set of questions, which correspond to the question marker, from module 35 andselects randomly a single question. The selected question is "What do you think about electricity saving in your team?". This question is send to module 36.
A user input free answer to the question in module 36. The output of the module 36 is a free text description of user's opinion stored in module 37. In this case, the free text description is "I think people waste a lot of electricity by not switching off the light and other power greedy devices like air-conditioners, plasma TVs, etc. They should be more economical".
Module 38 receives input from module 37 and outputs LC between the user's answer and template of the answer. The detailed analysis of the free text description is presented in Fig. 12. The free text is compared with a particular template. In this example, template contains three key statements:
key statements1:speaker should associate him/herself with other people as on intact team;
key statements2:speaker should show his/her desire to be involved into finding solution of the problem;
key statements3:speaker should show his/her social responsibility.
Here the key elements are statements, which should be found in the free text answer, if person's social reflexivity (GB) level is high. The variables a1, a2 and a3 correspond to key statements 1, 2 and 3. Each variable ai takes value 1, if the corresponding statement is found in the free text, and 0 otherwise.
In this case, a1, a2 and a3 equal 0, because none of the statements has been found in the free text, and LC value is 0. The threshold value corresponds to the number of statements (threshold = 3). Therefore, module 39 sends " inconsistency " signal (value=0) to module 310. Module 310 also receives Question Marker GB1 from module 31. Module 310 adds Question Marker GB1 to Answer Table of subject Tanaka, which is stored in module 311.
(Grand Question Schema )
The schema presented in Fig. 5 (Grand Question Schema - GQS) can be used for two purposed within the general schema of invention.
First, the GQS can be used to analyze and verify pair-wise relationships. In such case, the entire schema corresponds to the functions of modules 21a, 22 and 25. The GQS outputs a set of list of problems (inconsistencies between stated relationships and actual behavior in hypothetical situation) for each subject in the form of the data stored in module 25.
Second, GQS can be used to analyze and verify subjects' mindset about fixed opinion. In this case, GQS corresponds to modules 21b, 23b and 24b. GQS outputs a set of list of problems for each subject in the form of the data stored in module 24b.
Third, GQS can be used to analyze and verify the group belonging (whether a subject considers him/herself to be a group member). In this case, GQS corresponds to modules 21c, 23b and 24b. GQS outputs a set of list of problems for each subject in the form of the data stored in module 24c.
(Supplementary note)
Further, the content of the above-mentioned exemplary embodiments can be expressed as follows.
A system for evaluating an applicability of Reflexive Game Theory (RGT), comprising:
an inapplicable subject detection means that detects a subject to whom RGT is inapplicable; and
an applicability judgment means that judges that RGT is applicable, in the case that said inapplicable subject detection means detects no such inapplicable subject.
The system for evaluating the applicability of RGT, wherein said inapplicable subject detection means includes:
a Yes/No question provision means that provides a Yes/No question about a condition which is required for application of RGT;
a Yes/No answer input means that inputs a Yes/No answer to said Yes/No question;
a Yes/No answer detection means that detects said Yes/No answer;
an open question provision means that provides an open question which is associated with said Yes/No question and which relates to a hypothetical situation close to the real one, in the case that said Yes/No answer detection means detects a Yes answer;
a free answer input means that inputs a free text answer to said open question;
a consistency analysis means that analyzes consistency between the answer to the open question and the answer to the Yes/No question; and
an applicable subject judgment means that judges that RGT is applicable to the subject, in the case that said consistency analysis means reaches a judgment of consistency.
The system for evaluating the applicability of RGT, wherein
said consistency analysis means extracts key elements in the answer to the open question, and reaches a judgment of consistency, in the case that the number of key elements is greater than or equal to a threshold value.
The system for evaluating the applicability of RGT, wherein
said inapplicable subject detection means detects a subject who has a fixed opinion.
The system for evaluating the applicability of RGT, wherein
said inapplicable subject detection means detects a subject who lacks a mindset related to group belonging.
The system for evaluating the applicability of RGT, wherein:
said system comprises a plurality of said inapplicable subject detection means, and
said applicability judgment means judges that RGT is applicable, in the case that all of said inapplicable subject detection means detect no such inapplicable subject.
The system for evaluating the applicability of RGT, wherein said inapplicable subject detection means further includes:
a countermeasures provision means that provides countermeasures for alteration of said mindset, in the case that said applicable subject judgment means judges that RGT is not applicable to the subject.
The system for evaluating the applicability of RGT, further comprising:
a pairwise relationship verification means that verifies whether a plurality of pairwise relationship are true, wherein
said pairwise relationship verification means includes:
an alternative question provision means that provides an alternative question that a subject consider estimated subjects to be friend/enemy ;
a friend/enemy answer input means that inputs a friend/enemy answer to said alternative question;
a friend/enemy answer detection means that detects said friend/enemy answer;
an open question provision means that provides an open question which is associated with said friend/enemy question and which relates to a hypothetical situation close to the real one;
a free answer input means that inputs a free text answer to said open question;
a consistency analysis means that analyzes consistency between the answer to the open question and the answer to the friend/enemy question;
a true/false judgment means that judges that the pairwise relationship is true, in the case that said consistency analysis means reaches a judgment of consistency , and the pairwise relationship is false , in the case that said consistency analysis means reaches a judgment of inconsistency; and
a correction means that corrects the pairwise relationship , in the case that said true/false judgment means judges the pairwise relationship is false.
A device for detecting a subject to whom Reflexive Game Theory (RGT) is inapplicable, comprising:
a Yes/No question provision means that provides a Yes/No question about a condition which is required for application of RGT;
a Yes/No answer input means that inputs a Yes /No answer to said Yes/No question;
a Yes/No answer detection means that detects said Yes/ No answer;
an open question provision means that provides an open question which is associated with said Yes/No question and which relates to a hypothetical situation close to the real one, in the case that said Yes/No answer detection means detects a Yes answer;
a free answer input means that inputs a free text answer to said open question;
a consistency analysis means that analyzes consistency between the answer to the open question and the answer to the Yes/No question; and
an applicable subject judgment means that judges that RGT is applicable to the subject, in the case that said consistency analysis means reaches a judgment of consistency.
A device for verifying whether a plurality of pairwise relationship are true, comprising:
an alternative question provision means that provides an alternative question that a subject consider estimated subjects to be friend/enemy ;
a friend/enemy answer input means that inputs a friend/enemy answer to said alternative question;
a friend/enemy answer detection means that detects said friend/enemy answer;
an open question provision means that provides an open question which is associated with said friend/enemy question and which relates to a hypothetical situation close to the real one;
a free answer input means that inputs a free text answer to said open question;
a consistency analysis means that analyzes consistency between the answer to the open question and the answer to the friend/enemy question;
a true/false judgment means that judges that the pairwise relationship is true, in the case that said consistency analysis means reaches a judgment of consistency , and the pairwise relationship is false , in the case that said consistency analysis means reaches a judgment of inconsistency; and
a correction means that corrects the pairwise relationship , in the case that said true/false judgment means judges the pairwise relationship is false.
A method for evaluating an applicability of Reflexive Game Theory (RGT), comprising the steps of:
detecting a subject to whom RGT is inapplicable; and
judging that RGT is applicable, in the case that said no such inapplicable subject is detected.
The method for evaluating the applicability of RGT, wherein said step of inapplicable subject detection includes the step of:
providing a Yes/No question about a condition which is required for application of RGT;
inputting a Yes/No answer to said Yes/No question;
detecting said Yes/No answer;
providing an open question which is associated with said Yes/No question and which relates to a hypothetical situation close to the real one, in case of detecting a Yes answer;
inputting a free text answer to said open question;
analyzing consistency between the answer to the open question and the answer to the Yes/No question; and
judging that RGT is applicable to the subject, in case of consistency.
The method for evaluating the applicability of RGT, wherein
said step of consistency analysis is to extract key elements in the answer to the open question, and reach a judgment of consistency, in the case that the number of key elements is greater than or equal to a threshold value.
The method for evaluating the applicability of RGT, wherein
said step of inapplicable subject detection is to detect a subject who has a fixed opinion.
The method for evaluating the applicability of RGT, wherein
said step of inapplicable subject detection to detect a subject who lacks a mindset related to group belonging.
The method for evaluating the applicability of RGT, comprising:
a plurality step of detecting said inapplicable subject
a step of said judging that RGT is applicable, in the case that no such inapplicable subject is detected ,in all of said inapplicable subject detection step.
The method for evaluating the applicability of RGT, wherein said inapplicable subject detection step further includes a step of :
providing the inapplicable subject with countermeasures for alteration of said mindset.
The method for evaluating the applicability of RGT, further comprising a step of :
verifying whether a plurality of pairwise relationship are true, wherein
said pairwise relationship verification step includes steps of :
providing an alternative question that a subject consider estimated subjects to be friend/enemy;
inputting a friend/enemy answer to said alternative question;
detecting said friend/enemy answer;
providing an open question which is associated with said friend/enemy question and which relates to a hypothetical situation close to the real one;
inputting a free text answer to said open question;
analyzing consistency between the answer to the open question and the answer to the friend/enemy question;
judging that the pairwise relationship is true, in case of said consistency , and the pairwise relationship is false , in case of said inconsistency; and
correcting said false pairwise relationship.
A program for evaluating a reflexivity, causing a computer to execute :
an inapplicable subject detection process that detects a subject to whom RGT is inapplicable; and
an applicability judgment process that judges that RGT is applicable, in the case that said inapplicable subject detection means detects no such inapplicable subject.
The program for evaluating the applicability of RGT, wherein said inapplicable subject detection process includes:
a Yes/No question provision process that provides a Yes/No question about a condition which is required for application of RGT;
a Yes/No answer input process that inputs a Yes/No answer to said Yes/No question;
a Yes/No answer detection process that detects said Yes/No answer;
an open question provision process that provides an open question which is associated with said Yes/No question and which relates to a hypothetical situation close to the real one, in case of detecting a Yes answer;
a free answer input process that inputs a free text answer to said open question;
a consistency analysis process that analyzes consistency between the answer to the open question and the answer to the Yes/No question; and
an applicable subject judgment process that judges that RGT is applicable to the subject, in case of consistency.
The program for evaluating the applicability of RGT, wherein
said consistency analysis process is to extract key elements in the answer to the open question, and reach a judgment of consistency, in the case that the number of key elements is greater than or equal to a threshold value.
The program for evaluating the applicability of RGT, wherein
said inapplicable subject detection process is to detect a subject who has a fixed opinion.
The program for evaluating the applicability of RGT, wherein
said inapplicable subject detection process is to detect a subject who lacks a mindset related to group belonging.
The program for evaluating the applicability of RGT, causing a computer to execute :
a plurality of said inapplicable subject detection process, and
said applicability judgment process to judge RGT is applicable, in the case that no such inapplicable subject is detected ,in all of said inapplicable subject detection process.
The program for evaluating the applicability of RGT, wherein said inapplicable subject detection process further includes:
a countermeasures provision process that provides countermeasures for alteration of said mindset, in the case that said applicable subject judgment means judges that RGT is not applicable to the subject.
The program for evaluating the applicability of RGT, causing a computer to further execute:
a pairwise relationshipverification process that verifies whether a plurality of pairwise relationship are true, wherein
said pairwise relationship verification process includes:
an alternative question provision process that provides an alternative question that a subject consider estimated subjects to be friend/enemy ;
a friend/enemy answer input process that inputs a friend/enemy answer to said alternative question;
a friend/enemy answer detection process that detects said friend/enemy answer;
an open question provision process that provides an open question which is associated with said friend/enemy question and which relates to a hypothetical situation close to the real one;
a free answer input process that inputs a free text answer to said open question;
a consistency analysis means that analyzes consistency between the answer to the open question and the answer to the friend/enemy question;
a true/false judgment process that judges that the pairwise relationship is true, in case of said consistency, and the pairwise relationship is false , in case of said inconsistency; and
a correction process that corrects said false pairwise relationship.
The present invention is used as screening before RGT inference. Non-expert users will become able to treat RGT by using a computer.
11-18 module (RGT inference )
21a,21b,21c larger scale module( input)
22,23b,23c larger scale module(CEI)
24b,24c,25 module(storage)
26 module(correct)
27,28,29 module(judge)
31 module (Y/N question input)
32 module (Yes-No question provision)
33 module (Yes-No answer detection)
34,35 module (open question provision)
36,37 module (free answer input)
38,39 module (consistency analysis )
210 larger scale module(countermeasure)
211 larger scale module (RGT inference )
310 module (table make)
311 module (storage)
312 module (storage)
313 module (all question check)
314 module (countermeasure)
315 module (storage)
318 module (finish)
319 module (subject ID input)
320,321 module (all subject check)
322 module (storage)
323 module (storage)

Claims (10)

  1. A system for evaluating an applicability of Reflexive Game Theory (RGT), comprising:
    an inapplicable subject detection means that detects a subject to whom RGT is inapplicable; and
    an applicability judgment means that judges that RGT is applicable, in the case that said inapplicable subject detection means detects no such inapplicable subject.
  2. The system for evaluating the applicability of RGT according to claim 1, wherein said inapplicable subject detection means includes:
    a Yes/No question provision means that provides a Yes/No question about a condition which is required for application of RGT;
    a Yes/No answer input means that inputs a Yes/No answer to said Yes/No question;
    a Yes/No answer detection means that detects said Yes/No answer;
    an open question provision means that provides an open question which is associated with said Yes/No question and which relates to a hypothetical situation close to the real one, in the case that said Yes/No answer detection means detects a Yes answer;
    a free answer input means that inputs a free text answer to said open question;
    a consistency analysis means that analyzes consistency between the answer to the open question and the answer to the Yes/No question; and
    an applicable subject judgment means that judges that RGT is applicable to the subject, in the case that said consistency analysis means reaches a judgment of consistency.
  3. The system for evaluating the applicability of RGT according to claim 2, wherein
    said consistency analysis means extracts key elements in the answer to the open question, and reaches a judgment of consistency, in the case that the number of key elements is greater than or equal to a threshold value.
  4. The system for evaluating the applicability of RGT according to claims 1 through 3, wherein
    said inapplicable subject detection means detects a subject who has a fixed opinion.
  5. The system for evaluating the applicability of RGT according to claims 1 through 3, wherein
    said inapplicable subject detection means detects a subject who lacks a mindset related to group belonging.
  6. The system for evaluating the applicability of RGT according to claims 1 through 3, wherein:
    said system comprises a plurality of said inapplicable subject detection means, and
    said applicability judgment means judges that RGT is applicable, in the case that all of said inapplicable subject detection means detect no such inapplicable subject.
  7. The system for evaluating the applicability of RGT according to claims 2 through 6, wherein said inapplicable subject detection means further includes:
    a countermeasures provision means that provides countermeasures for alteration of said mindset, in the case that said applicable subject judgment means judges that RGT is not applicable to the subject.
  8. The system for evaluating the applicability of RGT according to claims 1 through 7, further comprising:
    a pairwise relationship verification means that verifies whether a plurality of pairwise relationship are true, wherein
    said pairwise relationship verification means includes:
    an alternative question provision means that provides an alternative question that a subject consider estimated subjects to be friend/enemy ;
    a friend/enemy answer input means that inputs a friend/enemy answer to said alternative question;
    a friend/enemy answer detection means that detects said friend/enemy answer;
    an open question provision means that provides an open question which is associated with said friend/enemy question and which relates to a hypothetical situation close to the real one;
    a free answer input means that inputs a free text answer to said open question;
    a consistency analysis means that analyzes consistency between the answer to the open question and the answer to the friend/enemy question;
    a true/false judgment means that judges that the pairwise relationship is true, in the case that said consistency analysis means reaches a judgment of consistency , and the pairwise relationship is false , in the case that said consistency analysis means reaches a judgment of inconsistency; and
    a correction means that corrects the pairwise relationship , in the case that said true/false judgment means judges the pairwise relationship is false.
  9. A device for detecting a subject to whom Reflexive Game Theory (RGT) is inapplicable, comprising:
    a Yes/No question provision means that provides a Yes/No question about a condition which is required for application of RGT;
    a Yes/No answer input means that inputs a Yes /No answer to said Yes/No question;
    a Yes/No answer detection means that detects said Yes/ No answer;
    an open question provision means that provides an open question which is associated with said Yes/No question and which relates to a hypothetical situation close to the real one, in the case that said Yes/No answer detection means detects a Yes answer;
    a free answer input means that inputs a free text answer to said open question;
    a consistency analysis means that analyzes consistency between the answer to the open question and the answer to the Yes/No question; and
    an applicable subject judgment means that judges that RGT is applicable to the subject, in the case that said consistency analysis means reaches a judgment of consistency.
  10. A method for evaluating an applicability of Reflexive Game Theory (RGT), comprising the steps of:
    detecting a subject to whom RGT is inapplicable; and
    judging that RGT is applicable, in the case that said no such inapplicable subject is detected.
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EP2354967A1 (en) * 2010-01-29 2011-08-10 British Telecommunications public limited company Semantic textual analysis

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