Neuroscience experiment applied to investigate decision-maker behavior in the tradeoff elicitation procedure

Roselli, Lucia Reis Peixoto; Pereira, Leydiana de Sousa; da Silva, Anderson Lucas Carneiro de Lima; de Almeida, Adiel Teixeira; Morais, Danielle Costa; Costa, Ana Paula Cabral Seixas

doi:10.1007/s10479-019-03394-w

S.I.: OR in Neuroscience II
Published: 24 September 2019

Neuroscience experiment applied to investigate decision-maker behavior in the tradeoff elicitation procedure

Annals of Operations Research volume 289, pages 67–84 (2020)Cite this article

533 Accesses
14 Citations
Metrics details

Abstract

The Tradeoff Elicitation Procedure is a Multi-Criteria Decision Making/Aiding method which is responsible for eliciting scaling constants and presents a robust axiomatic structure. As to its axiomatic structure, this procedure requires the decision-maker to identify the exact indifference point which induces a large number of inconsistencies in the process. In order to evaluate Decision Maker behavior in the Tradeoff elicitation and explore inconsistency in this process, a Neuroscience experiment was conducted using neuro tools, such as an Eye Tracking and an Electroencephalography (EEG). The experiment was applied in a sample of 52 management engineering students. After the data were collected, analyses were developed in order to suggest decision-makers’ behavior in the steps of this procedure. In summary, the responses of the pupils are increased during the process indicating a cognitive effort, and EEG data confirmed this result considering frontal alpha asymmetry and theta power in the frontal electrodes as variables for analysis.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price includes VAT (USA)
Tax calculation will be finalised during checkout.

References

Aricò, P., Borghini, G., Di Flumeri, G., Colosimo, A., Bonelli, S., Golfetti, A., et al. (2016). Adaptive automation triggered by EEG-based mental workload index: a passive brain-computer interface application in realistic air traffic control environment. Frontiers in Human Neuroscience. https://doi.org/10.3389/fnhum.2016.00539.
Article Google Scholar
Belton, V., & Stewart, T. (2002). Multiple criteria decision analysis. Dordrecht: Kluwer Academic Publishers.
Book Google Scholar
Berčík, J., Horská, E., Wang, R. W., & Chen, Y. C. (2016). The impact of parameters of store illumination on food shopper response. Appetite,106, 101–109.
Article Google Scholar
Binda, P., Pereverzeva, M., & Murray, S. (2014). Pupil size reflects the focus of feature-based attention. Journal of Neurophysiology. https://doi.org/10.1152/jn.00FLF502.2014.
Article Google Scholar
Choi, Y., Park, J., & Shin, D. (2017). A semi-supervised inattention detection method using biological signal. Annals of Operations Research,258(1), 59–78. https://doi.org/10.1007/s10479-017-2406-6.
Article Google Scholar
Davidson, R. J., Ekman, P., Saron, C. D., Senulis, J. A., & Friesen, W. V. (1990). Approach-withdrawal and cerebral asymmetry: emotional expression and brain physiology: I. Journal of Personality and Social Psychology,58(2), 330.
Article Google Scholar
de Almeida, A., Cavalcante, C., Alencar, M., Ferreira, R., de Almeida-Filho, A., & Garcez, T. (2015). Multicriteria and multi-objective models for risk, reliability and maintenance decision analysis (Vol. 231)., International series in operations research and management science New York: Springer.
Book Google Scholar
de Almeida, A. & Roselli, L. (2017). Visualization for decision support in FITradeoff method: Exploring its evaluation with cognitive neuroscience. In I. Linden, C. Liu & C. Colot (Eds.), Decision Support Systems VII. Data, Information and Knowledge Visualization in Decision Support Systems. LNBIP 282 (pp. 61–73). https://doi.org/10.1007/978-3-319-57487-5_5.
Google Scholar
de Almeida, A. T., de Almeida, J. A., Costa, A. P. C. S., & de Almeida-Filho, A. T. (2016). A new method for elicitation of criteria weights in additive models: Flexible and interactive tradeoff. European Journal of Operational Research,250(1), 179–191. https://doi.org/10.1016/j.ejor.2015.08.058.
Article Google Scholar
Dimoka, A., Davis, F. D., Gupta, A., Pavlou, P. A., Banker, R. D., Dennis, A. R., et al. (2012). On the use of neurophysiological tools in IS research: Developing a research agenda for NeuroIS. MIS Quarterly. https://doi.org/10.2307/41703475.
Article Google Scholar
Fiedler, S., & Glöckner, A. (2012). The dynamics of decision making in risky choice: an eye-tracking analysis. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2012.00335.
Article Google Scholar
Figueira, J., Greco, S., & Ehrgott, M. (2005). Multiple criteria decision analysis: State of the art surveys. Berlin: Springer.
Book Google Scholar
Fischer, N. L., Peres, R., & Fiorani, M. (2018). Frontal alpha asymmetry and theta oscillations associated with information sharing intention. Frontiers in Behavioral Neuroscience. https://doi.org/10.3389/fnbeh.2018.00166.
Article Google Scholar
Hakimi, S., & Hare, T. A. (2015). Enhanced neural responses to imagined primary rewards predict reduced monetary temporal discounting. Journal of Neuroscience. https://doi.org/10.1523/JNEUROSCI.1863-15.2015.
Article Google Scholar
Hammerschmidt, W., Kagan, I., Kulke, L., & Schacht, A. (2018). Implicit reward associations impact face processing: Time-resolved evidence from event-related brain potentials and pupil dilations. Neuroimage. https://doi.org/10.1016/j.neuroimage.2018.06.055.
Article Google Scholar
Hermens, D. F., Soei, E. X., Clarke, S. D., Kohn, M. R., Gordon, E., & Williams, L. M. (2005). Resting EEG theta activity predicts cognitive performance in attention-deficit hyperactivity disorder. Pediatric Neurology. https://doi.org/10.1016/j.pediatrneurol.2004.11.009.
Article Google Scholar
Hügelschäfer, S., & Achtziger, A. (2017). Reinforcement, rationality, and intentions: How robust is automatic reinforcement learning in economic decision making? Journal of Behavioral Decision Making.,20(4), 913–932. https://doi.org/10.1002/bdm.2008.
Article Google Scholar
Keeney, R., & Raiffa, H. (1976). Decisions with multiple objectives—Preferences, and value tradeoffs. New York: Wiley.
Google Scholar
Khushaba, R. N., Wise, C., Kodagoda, S., Louviere, J., Kahn, B. E., & Townsend, C. (2013). Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2012.12.095.
Article Google Scholar
Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Research Review. https://doi.org/10.1016/s0165-0173(98)00056-3.
Article Google Scholar
Kropat, E., Tikidji-Hamburyan, R. A., & Weber, G. W. (2017). Operations research in neuroscience. Annals of Operations Research,258(1), 1–4. https://doi.org/10.1007/s10479-017-2633-x.
Article Google Scholar
Lieberman, M. D. (2007). Social Cognitive Neuroscience: a review of core process. Annual Review Psychology. https://doi.org/10.1146/annurev.psych.58.110405.085654.
Article Google Scholar
Lin, C. T., Chuang, C. H., Kerick, S., Mullen, T., Jung, T. P., Ko, L. W., et al. (2016). Mind-wandering tends to occur under low perceptual demands during driving. Scientific Reports. https://doi.org/10.1038/srep21353.
Article Google Scholar
Müller-Putz, G. R., Riedl, R., & Wriessnegger, S. C. (2015). Electroencephalography (EEG) as a research tool in the information systems discipline: foundations, measurement, and applications. CAIS,37, 46.
Article Google Scholar
Özmen, A. (2016). Robust optimization of spline models and complex regulatory networks: Theory methods and applications. Berlin: Springer. https://doi.org/10.1007/978-3-319-30800-5.
Book Google Scholar
Papesh, M. H., & Goldinger, S. D. (2014). Pupil dilation reveals cognitive effort, and cognitive effortless. International Journal of Psychophysiology. https://doi.org/10.1016/j.ijpsycho.2014.08.626.
Article Google Scholar
Park, S. Q., Kahnt, T., Rieskamp, J., & Heekeren, H. R. (2011). Neurobiology of value integration: when value impacts valuation. Journal of Neuroscience. https://doi.org/10.1523/JNEUROSCI.4973-10.2011.
Article Google Scholar
Pineda, P. J. G., Liou, J. J., Hsu, C. C., & Chuang, Y. C. (2018). An integrated MCDM model for improving airline operational and financial performance. Journal of Air Transport Management,68, 103–117.
Article Google Scholar
Poudel, G. R., Bhattarai, A., Dickinson, D. L., & Drummond, S. (2017). Neural correlates of decision-making during a Bayesian choice task. NeuroReport. https://doi.org/10.1097/WNR.0000000000000730.
Article Google Scholar
Ramsøy, T. Z., Skov, M., Christensen, M. K., & Stahlhut, C. (2018). Frontal brain asymmetry and willingness to pay. Frontiers in neuroscience,12, 138.
Article Google Scholar
Rasoulzadeh, V., Erkus, E. C., Yogurt, T. A., Ulusoy, I., & Zergeroğlu, S. A. (2017). A comparative stationarity analysis of EEG signals. Annals of Operations Research,258(1), 133–157. https://doi.org/10.1007/s10479-016-2187-3.
Article Google Scholar
Reznik, S. J., & Allen, J. J. B. (2018). Frontal asymmetry as a mediator and moderator of emotion: an updated review. Psychophysiology. https://doi.org/10.1111/psyp.12965.
Article Google Scholar
Roselli, L. R. P., Frej, E. A. & de Almeida, A. T. (2018). Neuroscience experiment for graphical visualization in the FITradeoff decision support system. In Y. Chen., G. Kersten., R. Vetschera., & H. Xu (Eds.), Group Decision and Negotiation in an Uncertain World. GDN 2018. Lecture Notes in Business Information Processing (Vol. 315).
Savku, E., & Weber, G.-W. (2018). A stochastic maximum principle for a Markov regime-switching jump-diffusion model with delay and an application to finance. Journal of Optimization Theory and Applications,179(2), 696–721. https://doi.org/10.1007/s10957-017-1159-3.
Article Google Scholar
Shen, K. Y., Hu, S. K., & Tzeng, G. H. (2017). Financial modeling and improvement planning for the life insurance industry by using a rough knowledge based hybrid MCDM model. Information Sciences,375, 296–313. https://doi.org/10.1016/j.ins.2016.09.055.
Article Google Scholar
Van der Wel, P., & van Steenbergen, H. (2018). Pupil dilation as an index of effort in cognitive control tasks: A review. Psychonomic Bulletin & Review. https://doi.org/10.3758/s13423-018-1432-y.
Article Google Scholar
Wang, L., Chu, J., & Wu, J. (2007). Selection of optimum maintenance strategies based on a fuzzy analytic hierarchy process. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2006.08.005.
Article Google Scholar
Wascher, E., Rasch, B., Sänger, J., Hoffmann, S., Schneider, D., Rinkenauer, G., et al. (2014). Frontal theta activity reflects distinct aspects of mental fatigue. Biological Psychology. https://doi.org/10.1016/j.biopsycho.2013.11.010.
Article Google Scholar
Weber, M., & Borcherding, K. (1993). Behavioral influences on weight judgments in multi-attribute decision making. European Journal of Operational Research. https://doi.org/10.1016/0377-2217(93)90318-H.
Article Google Scholar
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 Systems with Applications. https://doi.org/10.1016/j.eswa.2013.06.040.
Article Google Scholar

Download references

Acknowledgements

This study was partially sponsored by the Coordination for the Improvements of Higher Education Personnel—Brazil (CAPES) and the Brazilian Research Council (CNPq) for which the authors are most grateful.

Funding

This study was financed in part by the Coordination for the Improvements of Higher Education Personnel—Brazil (CAPES) and the Brazilian Research Council (CNPq).

Author information

Authors and Affiliations

Center for Decision Systems and Information Development (CDSID), Universidade Federal de Pernambuco, Recife, PE, Brazil
Lucia Reis Peixoto Roselli, Leydiana de Sousa Pereira, Anderson Lucas Carneiro de Lima da Silva, Adiel Teixeira de Almeida, Danielle Costa Morais & Ana Paula Cabral Seixas Costa

Authors

Lucia Reis Peixoto Roselli
View author publications
You can also search for this author in PubMed Google Scholar
Leydiana de Sousa Pereira
View author publications
You can also search for this author in PubMed Google Scholar
Anderson Lucas Carneiro de Lima da Silva
View author publications
You can also search for this author in PubMed Google Scholar
Adiel Teixeira de Almeida
View author publications
You can also search for this author in PubMed Google Scholar
Danielle Costa Morais
View author publications
You can also search for this author in PubMed Google Scholar
Ana Paula Cabral Seixas Costa
View author publications
You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Danielle Costa Morais.

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

Reprints and Permissions

About this article

Cite this article

Roselli, L.R.P., Pereira, L.S., da Silva, A.L.C.L. et al. Neuroscience experiment applied to investigate decision-maker behavior in the tradeoff elicitation procedure. Ann Oper Res 289, 67–84 (2020). https://doi.org/10.1007/s10479-019-03394-w

Download citation

Published: 24 September 2019
Issue Date: June 2020
DOI: https://doi.org/10.1007/s10479-019-03394-w

Keywords

Decision neuroscience
MCDM/A
Tradeoff elicitation procedure
Eye tracking
Electroencephalography
EEG