Advertisement

Decision neuroscience for improving data visualization of decision support in the FITradeoff method

  • Lucia Reis Peixoto Roselli
  • Adiel Teixeira de AlmeidaEmail author
  • Eduarda Asfora Frej
Original paper
  • 57 Downloads

Abstract

Multi-criteria decision making/aiding problems are very common in everyday life in society. Nevertheless, some difficulties appear when such problems arise and visualization may facilitate this process. Neuroscience deals with the study of the neural system and has had increasing relevance for several areas of knowledge, including multi-criteria decision making/aiding, as it adds to the understanding of human behavior and the decision process. Using neuroscience tools to aid improving data visualization is becoming increasingly relevant, since this is an important issue for decision-making. Therefore, this study seeks to use neuroscience in order to investigate how decision makers evaluate the graphical visualization in FITradeoff method. In this context, a neuroscience experiment using eye-tracking was developed, the main purpose of which was to improve the FITradeoff decision support system and, moreover, to provide information for the analyst about the application of graphical visualization in multi-criteria decision making/aiding problems. The experiment was applied using graduate and postgraduate management engineering students. This paper presents the main results obtained from the experiments, and also an analysis of these results.

Keywords

Decision neuroscience Multicriteria decision making/aiding MCDM/A Eye-tracking FITradeoff Decision support system 

Notes

Acknowledgements

This study was partially sponsored by the Brazilian Research Council (CNPq) for which the authors are most grateful.

Funding

This work was partially supported by the National Council for Scientific and Technological Development (CNPq) and by the Coordination for the Improvements of Higher Education Personnel – Brazil (CAPES).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Ares G et al (2014) Influence of rational and intuitive thinking styles on food choice: preliminary evidence from an eye-tracking study with yogurt labels. Food Qual Prefer 31:28–37.  https://doi.org/10.1016/j.foodqual.2013.07.005 CrossRefGoogle Scholar
  2. Bault N, Wydoodt P, Coricelli G (2016) Different attentional patterns for regret and disappointment: an eye-tracking study. J Behav Decis Mak 29:194–205.  https://doi.org/10.1002/bdm.1938 CrossRefGoogle Scholar
  3. Bazzazi A, Osanloo M, Karimi B (2009) Optimal open pit mining equipment selection using fuzzy multiple attribute decision making approach. Arch Min Sci 54(2):301–320Google Scholar
  4. Belton V, Stewart T (2002) Multiple criteria decision analysis. Kluwer Academic Publishers, DordrechtCrossRefGoogle Scholar
  5. Brookes VJ, DelRio VV, Ward MP (2015) Disease prioritization: what is the state of the art? Epidemiol Infect 143(14):2911–2922.  https://doi.org/10.1017/S0950268815000801 CrossRefGoogle Scholar
  6. Chai J, Liu J, Ngai E (2013) Application of decision-making techniques in supplier selection: a systematic review of literature. Expert Syst Appl 40(10):3872–3885.  https://doi.org/10.1016/j.eswa.2012.12.040 CrossRefGoogle Scholar
  7. De Almeida AT (2007) Multicriteria decision model for outsourcing contracts selection based on utility function and ELECTRE method. Comput Oper Res 34(12):3569–3574.  https://doi.org/10.1016/j.cor.2006.01.003 CrossRefGoogle Scholar
  8. De Almeida AT, Roselli LRP (2017) Visualization for decision support in FITradeoff method: exploring its evaluation with cognitive neuroscience. In: Linden I, Liu C, Colot C (eds) Decision support systems VII: data, information and knowledge visualization in decision support systems. LNBIP 282, pp 1–13.  https://doi.org/10.1007/978-3-319-57487-5_5
  9. De Almeida AT, Cavalcante C, Alencar M, Ferreira R, de Almeida-Filho AT, Garcez T (2015) Multicriteria and multi-objective models for risk, reliability and maintenance decision analysis. International Series in Operations Research & Management Science, vol 231. Springer, New YorkGoogle Scholar
  10. De Almeida AT, de Almeida J, Costa APCS, De Almeida-Filho AT (2016) A new method for elicitation of criteria weights in additive models: flexible and interactive tradeoff. Eur J Oper Res 250:179–191.  https://doi.org/10.1016/j.ejor.2015.08.058 CrossRefGoogle Scholar
  11. Demirel N, Demirel T, Deveci M, Vardar G (2017) Location selection for underground natural gas storage using Choquet integral. J Nat Gas Sci Eng 45:368–379.  https://doi.org/10.1016/j.jngse.2017.05.013 CrossRefGoogle Scholar
  12. Fehr E, Camerer CF (2007) Social neuroeconomics: the neural circuitry of social preferences. Trends Cogn Sci 11:419–427.  https://doi.org/10.1016/j.tics.2007.09.002 CrossRefGoogle Scholar
  13. Figueira J, Greco S, Ehrgott M (eds) (2005) Multiple criteria decision analysis: state of the art surveys. Springer, BerlinGoogle Scholar
  14. Glimcher PW, Rustichini A (2004) Neuroeconomics: the consilience of brain and decision. Science 5695:447–452.  https://doi.org/10.1126/science.1102566 CrossRefGoogle Scholar
  15. Goucher-Lambert K, Moss J, Cagan J (2017) Inside the mind: using neuroimaging to understand moral product preference judgments involving sustainability. J Mech Des 139:041–103.  https://doi.org/10.1115/1.4035859 CrossRefGoogle Scholar
  16. Guixeres J et al (2017) Consumer neuroscience-based metrics predict recall, liking and viewing rates in online advertising. Front Psychol 8:1808.  https://doi.org/10.3389/fpsyg.2017.01808 CrossRefGoogle Scholar
  17. Hunt LT, Dolan RJ, Behrens TE (2014) Hierarchical competitions subserving multi-attribute choice. Nat Neurosci 17:1613–1622.  https://doi.org/10.1038/nn.3836 CrossRefGoogle Scholar
  18. Kasanen E, Östermark R, Zeleny M (1991) Gestalt system of holistic graphics: new management support view of MCDM. Comput Oper Res 18(2):233–239.  https://doi.org/10.1016/0305-0548(91)90093-7 CrossRefGoogle Scholar
  19. Keeney RL, Raiffa H (1976a) Decisions with multiple objectives: preferences, and value tradeoffs. Wiley, New YorkGoogle Scholar
  20. Keeney RL, Raiffa H (1976b) Decision analysis with multiple conflicting objectives. Wiley, New YorkGoogle Scholar
  21. Khushaba RN (2013) Consumer neuroscience: assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Syst Appl 40:3803–3812.  https://doi.org/10.1016/j.eswa.2012.12.095 CrossRefGoogle Scholar
  22. Kim BE, Seligman D, Kable JW (2012) Preference reversals in decision making under risk are accompanied by changes in attention to different attributes. Front Neurosci 6:109.  https://doi.org/10.3389/fnins.2012.00109 Google Scholar
  23. Kothe CA, Makeig S (2011) Estimation of task workload from EEG data: new and current tools and perspectives. In: Engineering in Medicine and Biology Society, annual international conference of the IEEEGoogle Scholar
  24. Laeng B, Sirois S, Gredebäck G (2012) Pupillometry: a window to the preconscious? Perspect Psychol Sci 7:18–27.  https://doi.org/10.1177/1745691611427305 CrossRefGoogle Scholar
  25. Lashgari A, Yazdani-Chamzini A, Fouladgar M, Zavadskas E, Shafiee S, Abbate N (2012) Equipment selection using fuzzy multi criteria decision making model: key study of Gole Gohar iron mine. Eng Econ 23(2):125–136.  https://doi.org/10.5755/j01.ee.23.2.1544 CrossRefGoogle Scholar
  26. Miettinen K (2014) Survey of methods to visualize alternatives in multiple criteria decision-making problems. OR Spectr 36(1):3–37.  https://doi.org/10.1007/s00291-012-0297-0 CrossRefGoogle Scholar
  27. Mohr PNC, Biele G, Heekeren H (2010) Neural processing of risk. J Neurosci 30:6613–6619.  https://doi.org/10.1523/JNEUROSCI.0003-10.2010 CrossRefGoogle Scholar
  28. Morin C (2011) Neuromarketing: the new science of consumer behavior. Society 48:131–135.  https://doi.org/10.1007/s12115-010-9408-1 CrossRefGoogle Scholar
  29. Porter G, Troscianko T, Gilchrist ID (2007) Effort during visual search and counting: insights from pupillometry. Q J Exp Psychol 60:211–229.  https://doi.org/10.1080/17470210600673818 CrossRefGoogle Scholar
  30. Rangel A, Camerer C, Montague PR (2008) A framework for studying the neurobiology of value-based decision making. Nat Rev Neurosci 9:545–556.  https://doi.org/10.1038/nrn2357 CrossRefGoogle Scholar
  31. Riedl R, Davis FD, Hevne R, Alan R (2014) Towards a NeuroIS research methodology: intensifying the discussion on methods, tools, and measurement. J Assoc Inf Syst 15:IGoogle Scholar
  32. Roselli LRP, Frej EA, de Almeida AT (2018) Neuroscience experiment for graphical visualization in the FITradeoff decision support system. In: Chen Y, Kersten G, Vetschera R, Xu H (eds) Group decision and negotiation in an uncertain world. GDN 2018. Lecture notes in business information processing, vol 315. Springer, Cham, pp 56–69.  https://doi.org/10.1007/978-3-319-92874-6_5
  33. Sanfey AG, Rilling JK, Aronson JA, Nystrom LE, Cohen JP (2003) The neural basis of economic decision-making in the ultimatum game. Science 5626:1755–1758.  https://doi.org/10.1126/science.1082976 CrossRefGoogle Scholar
  34. Sharma N, Gedeon T (2012) Objective measures, sensors and computational techniques for stress recognition and classification: a survey. Comput Methods Programs Biomed 108:1287–1301.  https://doi.org/10.1016/j.cmpb.2012.07.003 CrossRefGoogle Scholar
  35. Slanzi G, Balazs J, Velásquez JD (2016) Predicting Web user click intention using pupil dilation and electroencephalogram analysis. In: Web intelligence (WI), IEEE/WIC/ACM international conference on IEEE.  https://doi.org/10.1109/WI.2016.64
  36. Smith DV, Huettel SA (2010) Decision neuroscience: neuroeconomics. Wiley Interdiscip Rev Cogn Sci 1:854–871.  https://doi.org/10.1002/wcs.73 CrossRefGoogle Scholar
  37. Sylcott B, Cagan J, Tabibnia G (2013) Understanding consumer tradeoffs between form and function through metaconjoint and cognitive neuroscience analyses. J Mech Des 135(10):101002.  https://doi.org/10.1115/1.4024975 CrossRefGoogle Scholar
  38. Wang L, Chu J, Wu J (2007) Selection of optimum maintenance strategies based on a fuzzy analytic hierarchy process. Int J Prod Econ 107(1):151–163.  https://doi.org/10.1016/j.ijpe.2006.08.005 CrossRefGoogle Scholar
  39. Weber M, Borcherding K (1993) Behavioral influences on weight judgments in multi-attribute decision making. Eur J Oper Res 67:1–12.  https://doi.org/10.1016/0377-2217(93)90318-H CrossRefGoogle Scholar
  40. Zolfani S, Aghdaie M, Derakhti A, Zavadskas E, Varzandeh M (2013) Decision making on business issues with foresight perspective; an application of new hybrid MCDM model in shopping mall locating. Expert Syst Appl 40(17):7111–7121.  https://doi.org/10.1016/j.eswa.2013.06.040 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.CDSID - Center for Decision Systems and Information DevelopmentFederal University of Pernambuco – UFPERecifeBrazil

Personalised recommendations