The new method improves group decisions and ranks alternatives by means of an ideal solution Is there such a thing as a perfect decision? Probably not, especially when it has to be taken in a group setting with many divergent opinions. New findings by principal investigator Nuria Agell, of the ESADE Research Group on Knowledge Engineering, take artificial intelligence a step further and provide answers that take us closer to reaching the best solution in group decisions.  Published in the Journal of Information Sciences, the researchers have developed a new mechanism that minimises uncertainty and balances divergence of opinions in the search of the ideal answer. "The method could be very useful in terms of resolving decision-making problems and speeding up group decisions in the coming years," said artificial intelligence expert Nuria Agell.  One of the benefits of this new method is that it does not require communication between the experts in the committee, and therefore minimises the risk of having experts who are more persuasive and may lead to final decisions that are not necessarily the best ones. "The experts don't need to meet; they just need to introduce their evaluation of alternatives into the system. This way the method ensures that consensus is reached in a more impartial manner." Capturing uncertaintyThe first step in the study was to capture the lack of precision among some of the experts and then aggregate the resulting answers for each of the alternatives presented to solve the problem. The second step was to assign linguistic labels for each of the options and measure the distance between each of the alternatives to find an artificially constructed 'ideal solution'. "The system is capable of capturing and measuring uncertain answers by using different degrees of precision and then, based on all the data collected, create the ideal alternative." Three solutions for real-case scenarios Grounded in mathematical calculations, the study analysed opinions in three fields in which group decisions are crucial: civil engineering projects; doping control laboratory accreditation; improving retailing performance.  The first real-case scenario was to choose the better of two options for the construction of a subway line in Barcelona. The method analysed all the evaluators' judgments through linguistic labels and identified the first alternative - adopting a tunnel diameter of 12m with the stations included within the tunnel - as the best solution. "This was the solution adopted by experts in the case of Barcelona underground, which highlights the accuracy of our method in this real-life case," said Professor Agell. The second case was linked to the accreditation process that laboratories involved in doping controls have to go through and which require a large panel of evaluation experts. "Reaching an agreement in the expert committee takes a long time. Our method shortened this time and proved that the level of quality was the threshold defining the maximum distance value to optimum for a laboratory to be accredited." In the third real case, the goal was to detect the features that describe a firm's performance. The case was based on a retail firm in Taiwan and examined information on 44 of the firm's features, provided by 84 expert managers. "The method led to two different rankings of the features that were ratified by an advisory committee and which demonstrated the experiment's accuracy." The method also revealed the advantages of allowing evaluators' judgments with different levels of precision and not requiring an average of the judgments. The practical results of these findings are extremely encouraging and could prove useful if applied to real-life managerial decision making. The group is already working on the next steps to make this happen: "We are currently developing web-based software to allow group decision-making meetings anytime and anywhere." Agell said that the group's next research challenge will be to explore machine-learning techniques that will allow the updating of information for decision-making and come up with a system that is capable of adapting to changes in real-world settings.

ESADE

Back to home

ESADE researchers use artificial intelligence to minimise uncertainty in group decisions

05/2019

The new method improves group decisions and ranks alternatives by means of an ideal solution


Is there such a thing as a perfect decision? Probably not, especially when it has to be taken in a group setting with many divergent opinions. New findings by principal investigator Nuria Agell, of the ESADE Research Group on Knowledge Engineering, take artificial intelligence a step further and provide answers that take us closer to reaching the best solution in group decisions. 

Published in the Journal of Information Sciences, the researchers have developed a new mechanism that minimises uncertainty and balances divergence of opinions in the search of the ideal answer. "The method could be very useful in terms of resolving decision-making problems and speeding up group decisions in the coming years," said artificial intelligence expert Nuria Agell. 

One of the benefits of this new method is that it does not require communication between the experts in the committee, and therefore minimises the risk of having experts who are more persuasive and may lead to final decisions that are not necessarily the best ones. 

"The experts don't need to meet; they just need to introduce their evaluation of alternatives into the system. This way the method ensures that consensus is reached in a more impartial manner."

Capturing uncertainty


The first step in the study was to capture the lack of precision among some of the experts and then aggregate the resulting answers for each of the alternatives presented to solve the problem. 

The second step was to assign linguistic labels for each of the options and measure the distance between each of the alternatives to find an artificially constructed 'ideal solution'. "The system is capable of capturing and measuring uncertain answers by using different degrees of precision and then, based on all the data collected, create the ideal alternative."

Three solutions for real-case scenarios


Grounded in mathematical calculations, the study analysed opinions in three fields in which group decisions are crucial: civil engineering projects; doping control laboratory accreditation; improving retailing performance. 

The first real-case scenario was to choose the better of two options for the construction of a subway line in Barcelona. The method analysed all the evaluators' judgments through linguistic labels and identified the first alternative - adopting a tunnel diameter of 12m with the stations included within the tunnel - as the best solution. "This was the solution adopted by experts in the case of Barcelona underground, which highlights the accuracy of our method in this real-life case," said Professor Agell. 

The second case was linked to the accreditation process that laboratories involved in doping controls have to go through and which require a large panel of evaluation experts. "Reaching an agreement in the expert committee takes a long time. Our method shortened this time and proved that the level of quality was the threshold defining the maximum distance value to optimum for a laboratory to be accredited." 

In the third real case, the goal was to detect the features that describe a firm's performance. The case was based on a retail firm in Taiwan and examined information on 44 of the firm's features, provided by 84 expert managers. "The method led to two different rankings of the features that were ratified by an advisory committee and which demonstrated the experiment's accuracy." The method also revealed the advantages of allowing evaluators' judgments with different levels of precision and not requiring an average of the judgments. 

The practical results of these findings are extremely encouraging and could prove useful if applied to real-life managerial decision making. The group is already working on the next steps to make this happen: "We are currently developing web-based software to allow group decision-making meetings anytime and anywhere." 

Agell said that the group's next research challenge will be to explore machine-learning techniques that will allow the updating of information for decision-making and come up with a system that is capable of adapting to changes in real-world settings.
More Knowledge
Free double hierarchy hesitant fuzzy linguistic term sets: An application on ranking alternatives in GDM
Montserrat Adell, Jordi; Xu , Zeshui; Gou , Xunjie; Agell Jan, Nria
Information Fusion
N 47, 05/2019, p. 45 - 59
Consensus, dissension and precision in group decision making by means of an algebraic extension of hesitant fuzzy linguistic term sets
Montserrat Adell, Jordi; Agell Jan, Nria; Snchez Soler, Mnica; Ruiz Vegas, Francisco Javier
Information Fusion
N 42, 07/2018, p. 1 - 11
Back to home