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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
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
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
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):301–320
Belton V, Stewart T (2002) Multiple criteria decision analysis. Kluwer Academic Publishers, Dordrecht
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
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
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
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
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 York
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
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
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
Figueira J, Greco S, Ehrgott M (eds) (2005) Multiple criteria decision analysis: state of the art surveys. Springer, Berlin
Glimcher PW, Rustichini A (2004) Neuroeconomics: the consilience of brain and decision. Science 5695:447–452. https://doi.org/10.1126/science.1102566
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
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
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
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
Keeney RL, Raiffa H (1976a) Decisions with multiple objectives: preferences, and value tradeoffs. Wiley, New York
Keeney RL, Raiffa H (1976b) Decision analysis with multiple conflicting objectives. Wiley, New York
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
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
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 IEEE
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
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
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
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
Morin C (2011) Neuromarketing: the new science of consumer behavior. Society 48:131–135. https://doi.org/10.1007/s12115-010-9408-1
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
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
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:I
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
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
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
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
Smith DV, Huettel SA (2010) Decision neuroscience: neuroeconomics. Wiley Interdiscip Rev Cogn Sci 1:854–871. https://doi.org/10.1002/wcs.73
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
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
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
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
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).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Roselli, L.R.P., de Almeida, A.T. & Frej, E.A. Decision neuroscience for improving data visualization of decision support in the FITradeoff method. Oper Res Int J 19, 933–953 (2019). https://doi.org/10.1007/s12351-018-00445-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12351-018-00445-1