Skip to main content

Neuroscience Behavioral Studies for Modulation of the FITradeoff Method

Part of the Lecture Notes in Business Information Processing book series (LNBIP,volume 454)

Abstract

It has been claimed in the literature that decision-making methods have not been modulated (transformed) by results obtained in behavioral studies as much as has been expected and that further modulation would be an important advancement in decision-making. This paper summarizes the modulation provided by the Flexible and Interactive Tradeoff (FITradeoff) method from behavioral studies performed using neuroscience tools. Modulations of the FITradeoff method have been conducted in two ways: modulations in the preference modelling process and modulations in the FITradeoff Decision Support System (DSS). For modulation in FITradeoff preference modeling, several recommendations were provided to support analysts during their advising process with decision-makers. For modulation in the FITradeoff DSS, several improvements were implemented in the design of the DSS. The modulation of the FITradeoff method was supported by neuroscience experiments. These experiments investigated decision-makers’ (DMs) behavior when they interacted with a holistic evaluation and elicitation by decomposition in the FITradeoff method. The modulation of the FITradeoff method promoted the inclusion of some features through the combination of the two paradigms of preference modeling, completely transforming the decision-making process, and its DSS.

Keywords

  • Modulation
  • FITradeoff method
  • Preference modeling
  • Decision support system
  • Behavioral studies

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   59.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Korhonen, P., Wallenius, J.: Behavioral issues in MCDM: neglected research questions. In: Multicriteria Analysis, pp. 412–422. Springer, Heidelberg (1997). https://doi.org/10.1007/978-3-642-60667-0_39

  2. Wallenius, J., Dyer, J.S., Fishburn, P.C., Steuer, R.E., Zionts, S., Deb, K.: Multiple criteria decision making, multiattribute utility theory: recent accomplishments and what lies ahead. Manage. Sci. 54(7), 1336–1349 (2008)

    CrossRef  Google Scholar 

  3. Wallenius, H., Wallenius, J.: Implications of world mega trends for MCDM research. In: Ben Amor, S., de Almeida, A., de Miranda, J., Aktas, E. (eds.) Advanced Studies in Multi-Criteria Decision Making. Series in Operations Research, 1st ed., pp. 1–10. Chapman and Hall/CRC, New York (2020)

    Google Scholar 

  4. Zhao, Y., Zhao, X., Wang, L., Chen, Y., Zhang, X.: Does elicitation method matter? Behavioral and neuroimaging evidence from capacity allocation game. Prod. Oper. Manag. 25(5), 919–934 (2016)

    CrossRef  Google Scholar 

  5. Smith, D.V., Huettel, S.: Decision neuroscience: neuroeconomics. Wiley Interdisc. Rev. Cogn. Sci. 1(6), 854–871 (2010)

    Google Scholar 

  6. Tikidji-Hamburyan, R.A., Kropat, E., Weber, G.-W.: Preface: operations research in neuroscience II. Ann. Oper. Res. 289, 1–4 (2020)

    Google Scholar 

  7. Glimcher, P.W., Rustichini, A.: Neuroeconomics: the consilience of brain and decision. Science 5695, 447–452 (2004)

    CrossRef  Google Scholar 

  8. Fehr, E., Camerer, C.F.: Social neuroeconomics: the neural circuitry of social preferences. Trends Cogn. Sci. 11(10), 419–427 (2007)

    CrossRef  Google Scholar 

  9. Khushaba, R.N.: Consumer neuroscience: assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Syst. Appl. 40(9), 3803–3812 (2013)

    CrossRef  Google Scholar 

  10. Morin, C.: Neuromarketing: the new science of consumer behavior. Society 48(2), 131–135 (2011)

    Google Scholar 

  11. Riedl, R., Davis, F.D., Hevner, A.R.: Towards a NeuroIS research methodology: intensifying the discussion on methods, tools, and measurement. J. Assoc. Inf. Syst. 15(10) (2014)

    Google Scholar 

  12. Dimoka, A., Pavlou, P.A., Davis, F.D.: Neuro-IS: the potential of cognitive neuroscience for information systems research. In: 28th International Conference on Information Systems, Proceedings, Toulon, França, pp. 1–20 (2007)

    Google Scholar 

  13. de Almeida, A.T., Almeida, J.A., Costa, A.P.C.S., Almeida-Filho, A.T.: A new method for elicitation of criteria weights in additive models: flexible and interactive tradeoff. Eur. J. Oper. Res. 250(1), 179–191 (2016)

    CrossRef  MathSciNet  Google Scholar 

  14. de Almeida, A.T., Frej, E.A., Roselli, L.R.P.: Combining holistic and decomposition paradigms in preference modeling with the flexibility of FITradeoff. CEJOR 29(1), 7–47 (2021). https://doi.org/10.1007/s10100-020-00728-z

    CrossRef  MathSciNet  MATH  Google Scholar 

  15. Kilgour, D.M., Eden, C.: Handbook of Group Decision and Negotiation: Advances in Group Decision and Negotiation, vol. 4. Springer, Cham (2010). https://doi.org/10.1007/978-90-481-9097-3

  16. de Almeida, A., Rosselli, L., Costa Morais, D., Costa, A.: Neuroscience tools for behavioural studies in group decision and negotiation. In: Kilgour, D.M., Eden, C. (eds.) Handbook of Group Decision and Negotiation, 1st edn., pp. 1–24. Springer International Publishing, Dordrecht, Netherlands (2020)

    Google Scholar 

  17. von Neumam, J., Morgenstern, O.: Theory of Games and Economic Behavioral, 3rd edn. Princeton University Press, Princeton (1953)

    Google Scholar 

  18. Raiffa, H.: The Art and Science of Negotiation: How to Resolve Conflicts and Get the Best Out of Bargaining. Harvard University Press, Cambridge (1982)

    Google Scholar 

  19. Schmid, A., Schoop, M.: Gamification of electronic negotiation training: effects on motivation, behaviour and learning. Group Decis. Negot., 1–33 (2022)

    Google Scholar 

  20. Roszkowska, E., Kersten, G.E., Wachowicz, T.: The impact of negotiators’ motivation on the use of decision support tools in preparation for negotiations. Int. Trans. Oper. Res. (2021)

    Google Scholar 

  21. Engin, A., Vetschera, R.: Information representation in decision making: the impact of cognitive style and depletion effects. Decis. Support Syst. 103, 94–103 (2017)

    CrossRef  Google Scholar 

  22. Vetschera, R.: Preference structures and negotiator behavior in electronic negotiations. Decis. Support Syst. 44(1), 135–146 (2007)

    CrossRef  Google Scholar 

  23. Hunt, L.T., Dolan, R.J., Behrens, T.E.: Hierarchical competitions subserving multi-attribute choice. Nat. Neurosci. 17(11), 1613–1622 (2014)

    CrossRef  Google Scholar 

  24. Nermend, K.: The implementation of cognitive neuroscience techniques for fatigue evaluation in participants of the decision-making process. In: Nermend, K., Łatuszyńska, M. (eds.) Neuroeconomic and Behavioral Aspects of Decision Making. SPBE, pp. 329–339. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62938-4_21

    CrossRef  Google Scholar 

  25. Özerol, G., Karasakal, E.: A parallel between regret theory and outranking methods for multicriteria decision making under imprecise information. Theor. Decis. 65(1), 45–70 (2008)

    CrossRef  Google Scholar 

  26. Chuang, H., Lin, C., Chen, Y.: Exploring the triple reciprocity nature of organizational value cocreation behavior using multicriteria decision making analysis. Math. Problems Eng. 2015, 1–15 (2015)

    Google Scholar 

  27. Trepel, C., Fox, C.R., Poldrack, R.A.: Prospect theory on the brain? Toward a cognitive neuroscience of decision under risk. Cogn. Brain Res. 23(1), 34–50 (2005)

    Google Scholar 

  28. Barberis, N., Xiong, W.: What drives the disposition effect? An analysis of a long‐standing preference‐based explanation. J. Finan. 64(2), 751–784 (2009)

    Google Scholar 

  29. Keeney, R.L., Raiffa, H.: Decisions with Multiple Objectives: Preferences, and Value Tradeoffs. Wiley, New York (1976)

    MATH  Google Scholar 

  30. Frej, E.A., de Almeida, A.T., Costa, A.P.C.S.: Using data visualization for ranking alternatives with partial information and interactive tradeoff elicitation. Oper. Res. Int. J. 19(4), 909–931 (2019). https://doi.org/10.1007/s12351-018-00444-2

    CrossRef  Google Scholar 

  31. Kang, T.H.A., Frej, E.A., de Almeida, A.T.: Flexible and interactive tradeoff elicitation for multicriteria sorting problems. Asia Pac. J. Oper. Res. 37, 2050020 (2020)

    CrossRef  MathSciNet  Google Scholar 

  32. Frej, E.A., Ekel, P., de Almeida, A.T.: A benefit-to-cost ratio based approach for portfolio selection under multiple criteria with incomplete preference information. Inf. Sci. 545, 487–498 (2021)

    Google Scholar 

  33. Frej, E.A., Roselli, L.R.P., Araújo de Almeida, J., de Almeida, A.T.: A multicriteria decision model for supplier selection in a food industry based on FITradeoff method. Math. Probl. Eng. 2017, 1–9 (2017)

    CrossRef  MathSciNet  Google Scholar 

  34. Santos, I.M., Roselli, L.R.P., da Silva, A.L.G., Alencar, L.H.: A supplier selection model for a wholesaler and retailer company based on FITradeoff multicriteria method. Math. Probl. Eng. 2020, 8796282 (2020)

    Google Scholar 

  35. Dell’Ovo, M., Oppio, A., Capolongo, S.: Decision Support System for the Location of Healthcare Facilities Sit Health Evaluation Tool. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50173-0

  36. e Silva, L.C., Daher, S.D.F.D., Santiago, K.T.M., Costa, A.P.C.S.: Selection of an integrated security area for locating a state military police station based on MCDM/A method. In: IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy, pp. 1530–1534, October 2019

    Google Scholar 

  37. Camilo, D.G.G., de Souza, R.P., Frazão, T.D.C., da Costa Junior, J.F.: Multi-criteria analysis in the health area: selection of the most appropriate triage system for the emergency care units in natal. BMC Med. Inform. Decis. Mak. 20(1), 1–16 (2020)

    CrossRef  Google Scholar 

  38. Shukla, S.: A fitradeoff approach for assessment and understanding of patient adherence behavior. In: Value in Health, vol. 20, no. 5, pp. A322. Elsevier Science Inc., New York, May 2017

    Google Scholar 

  39. de Morais Correia, L.M.A., da Silva, J.M.N., dos Santos Leite, W.K., Lucas, R.E.C., Colaço, G.A.: A multicriteria decision model to rank workstations in a footwear industry based on a FITradeoff-ranking method for ergonomics interventions. Oper. Res., 1–37 (2021)

    Google Scholar 

  40. Pergher, I., Frej, E.A., Roselli, L.R.P., de Almeida, A.T.: Integrating simulation and FITradeoff method for scheduling rules selection in job-shop production systems. Int. J. Prod. Econ. 227, 107669 (2020)

    CrossRef  Google Scholar 

  41. Silva, M.M., de Gusmão, A.P.H., de Andrade, C.T.A., Silva, W.: The integration of VFT and FITradeoff multicriteria method for the selection of WCM projects. In: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 6–9 October, Bari, Italy, pp. 1513–1517 (2019)

    Google Scholar 

  42. Carrillo, P.A.A., Roselli, L.R.P., Frej, E.A., de Almeida, A.T.: Selecting an agricultural technology package based on the flexible and interactive tradeoff method. Ann. Oper. Res., 1–16 (2018)

    Google Scholar 

  43. Lima, E.S., Viegas, R.A., Costa, A.P.C.S.: A multicriteria method based approach to the BPMM selection problem. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, Canada, pp. 3334–3339, October 2017

    Google Scholar 

  44. de Gusmao, A.P.H., Pereira Medeiros, C.: A model for selecting a strategic information system using the FITradeoff. Math. Probl. Eng. 2016(2), 1–7 (2016)

    CrossRef  Google Scholar 

  45. Fossile, D.K., Frej, E.A., da Costa, S.E.G., de Lima, E.P., de Almeida, A.T.: Selecting the most viable renewable energy source for Brazilian ports using the FITradeoff method. J. Clean. Prod. 260, 121107 (2020)

    CrossRef  Google Scholar 

  46. Kang, T.H.A., Júnior, A.M.D.C.S., de Almeida, A.T.: Evaluating electric power generation technologies: a multicriteria analysis based on the FITradeoff method. Energy 165, 10–20 (2018)

    CrossRef  Google Scholar 

  47. de Macedo, P.P., de Miranda Mota, C.M., Sola, A.V.H.: Meeting the Brazilian energy efficiency law: a flexible and interactive multicriteria proposal to replace non-efficient motors. Sustain. Cities Soc. 41, 822–832 (2018)

    CrossRef  Google Scholar 

  48. Monte, M.B.S., Morais, D.C.: A decision model for identifying and solving problems in an urban water supply system. Water Resour. Manage 33(14), 4835–4848 (2019)

    CrossRef  Google Scholar 

  49. da Silva, A.L.C.D.L., Costa, A.P.C.S., de Almeida, A.T.: Exploring cognitive aspects of FITradeoff method using neuroscience tools. Ann. Oper. Res., 1–23 (2021)

    Google Scholar 

  50. Silva, A.L.C.L; Costa, A.P.C.S.: FITradeoff decision support system: an exploratory study with neuroscience tools. In: NeuroIS Retreat 2019, Viena. NeuroIS Retreat (2019)

    Google Scholar 

  51. Roselli, L.R.P., Pereira, L., da Silva, A., de Almeida, A.T., Morais, D.C., Costa, A.P.C.S.: Neuroscience experiment applied to investigate decision-maker behavior in the tradeoff elicitation procedure. Ann. Oper. Res. 289(1), 67–84 (2019). https://doi.org/10.1007/s10479-019-03394-w

    CrossRef  Google Scholar 

  52. Roselli, L.R.P., de Almeida, A.T.: Use of the Alpha-Theta Diagram as a decision neuroscience tool for analyzing holistic evaluation in decision making. Ann. Oper. Res. (2022)

    Google Scholar 

  53. Roselli, L.R.P., de Almeida, A.T.: The use of the success-based decision rule to support the holistic evaluation process in FITradeoff. Int. Trans. Oper. Res. (2021)

    Google Scholar 

  54. Pessoa, M.E.B.T., Roselli, L.R.P., de Almeida, A.T.: A neuroscience experiment to investigate the selection decision process versus the elimination decision process in the FITradeoff method. In: EWG-DSS 7th International Conference on Decision Support System Technology. Loughborough, United Kingdom (2021)

    Google Scholar 

  55. Reis Peixoto Roselli, L., de Almeida, A.: Analysis of graphical visualizations for multi-criteria decision making in FITradeoff method using a decision neuroscience experiment. In: Moreno-Jiménez, J. M., Linden, I., Dargam, F., Jayawickrama, U. (eds.) ICDSST 2020. LNBIP, vol. 384, pp. 30–42. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46224-6_3

    CrossRef  Google Scholar 

  56. Roselli, L., de Almeida, A.: Improvements in the FITradeoff decision support system for ranking order problematic based in a behavioral study with NeuroIS tools. In: Davis, F. D., Riedl, R., vom Brocke, J., Léger, P.-M., Randolph, A. B., Fischer, T. (eds.) NeuroIS 2020. LNISO, vol. 43, pp. 121–132. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60073-0_14

    CrossRef  Google Scholar 

  57. 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(4), 933–953 (2019). https://doi.org/10.1007/s12351-018-00445-1

    CrossRef  Google Scholar 

  58. Roselli, L., Frej, E., de Almeida, A.: Neuroscience experiment for graphical visualization in the FITradeoff decision support system. In: Chen, Y., Kersten, G., Vetschera, R., Xu, H. (eds.) GDN 2018. LNBIP, vol. 315, pp. 56–69. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92874-6_5

    CrossRef  Google Scholar 

  59. Roselli, L.R.P., de Almeida, A.T.: Behavioral study for holistic evaluation in FITradeoff method: hit rate for selecting versus eliminating alternatives. In: 21th International Conference on Group Decision and Negotiation in 2021, Toronto, Canada, GDN 2021, Proceedings (2021)

    Google Scholar 

  60. Rosch, J.L., Vogel-Walcutt, J.J.: A review of eye-tracking applications as tools for training. Cogn. Technol. Work 15, 313–327 (2013)

    CrossRef  Google Scholar 

  61. Klimesch, W.: EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res. Rev. 29(2–3), 169–195 (1999)

    CrossRef  Google Scholar 

  62. Barla, S.B.: A case study of supplier selection for lean supply by using a mathematical model. Logist. Inf. Manag. 16, 451–459 (2003)

    CrossRef  Google Scholar 

Download references

Acknowledgment

This work had partial support from the Brazilian Research Council (CNPq) [grant 308531/2015-9;312695/2020-9] and the Foundation of Support in Science and Technology of the State of Pernambuco (FACEPE) [APQ-0484-3.08/17].

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lucia Reis Peixoto Roselli .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Roselli, L.R.P., de Almeida, A.T. (2022). Neuroscience Behavioral Studies for Modulation of the FITradeoff Method. In: Morais, D.C., Fang, L. (eds) Group Decision and Negotiation: Methodological and Practical Issues. GDN 2022. Lecture Notes in Business Information Processing, vol 454. Springer, Cham. https://doi.org/10.1007/978-3-031-07996-2_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-07996-2_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07995-5

  • Online ISBN: 978-3-031-07996-2

  • eBook Packages: Computer ScienceComputer Science (R0)