O impacto de fatores da complexidade da tarefa na performance na tomada de decisão
DOI:
https://doi.org/10.5902/1983465989667Palavras-chave:
Complexidade, Problema de otimização, Knapsack Problem, Tomada de decisãoResumo
Objetivo: Este estudo tem como objetivo compreender o impacto de fatores da complexidade na performance na tomada de decisão humana em um problema econômico (Knapsack Problem), correlato de inúmeras situações enfrentadas diariamente pelos indivíduos relacionadas à prática de administradores e gestores.
Metodologia: A pesquisa empregou um delineamento experimental com 41 participantes, variando o número de itens e duas métricas de complexidade computacional, "input size" e "instance correlation". Foi avaliada a performance por meio da mensuração do desempenho de otimização, desempenho relativo, RT e confiança.
Resultados: Os resultados evidenciam um impacto robusto da manipulação do número de itens, resultando na redução do desempenho de otimização e confiança e aumento do RT. Encontrou-se uma transição de fase para o desempenho relativo, na qual os sujeitos suportaram aumentos de 5 para 6 itens em detrimento de realizar a tarefa em maior tempo, no entanto, para 7 itens tal compensação não foi mais possível. A medida input size associou-se significativamente com todas as variáveis dependentes, sendo capaz de explicar 51,84% da variação RT.
Implicações práticas: Esses resultados podem ser aplicados em áreas que envolvem decisões complexas, como alocação de recursos e planejamento logístico, tendo em vista que a pesquisa contribui para o entendimento de limitações cognitivas humanas, possibilitando o desenvolvimento de abordagens que reduzam erros e sobrecarga cognitiva.
Downloads
Referências
Adomavicius, G., Curley, S. P., Gupta, A., & Sanyal, P. (2020). How decision complexity affects outcomes in combinatorial auctions. Production and Operations Management, 29(11), 2579-2600. https://doi.org/10.1111/poms.13249 DOI: https://doi.org/10.1111/poms.13249
Aitchison, L., Bang, D., Bahrami, B., & Latham, P. E. (2015). Doubly Bayesian analysis of confidence in perceptual decision-making. PLoS computational biology, 11(10), e1004519. DOI: https://doi.org/10.1371/journal.pcbi.1004519
Bossaerts, P., & Murawski, C. (2017). Computational Complexity and Human Decision-Making. Trends in Cognitive Sciences, 21(12), 917–929. https://doi.org/10.1016/j.tics.2017.09.005 DOI: https://doi.org/10.1016/j.tics.2017.09.005
Brus, J., Aebersold, H., Grueschow, M., & Polania, R. (2021). Sources of confidence in value-based choice. Nature Communications, 12(1). https://doi.org/10.1038/s41467-021-27618-5 DOI: https://doi.org/10.1038/s41467-021-27618-5
Burns, N. R., Lee, M. D., & Vickers, D. (2006). Are Individual Differences in Performance on Perceptual and Cognitive Optimization Problems Determined by General Intelligence? The Journal of Problem Solving, 1(1), 5–19. https://doi.org/10.7771/1932-6246.1003 DOI: https://doi.org/10.7771/1932-6246.1003
Campbell, D. J. (1988). Task Complexity: A Review and Analysis. Academy of Management Review, 13(1), 40–52. https://doi.org/10.5465/amr.1988.4306775 DOI: https://doi.org/10.2307/258353
Carruthers, S. E. (2015). The Role of the Goal in Problem Solving Hard Computational Problems: Do People Really Optimize?
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Routledge.
Dörner, D., & Funke, J. (2017). Complex problem solving: What it is and what it is not. Frontiers in Psychology, 8 (jul), 1–11. https://doi.org/10.3389/fpsyg.2017.01153 DOI: https://doi.org/10.3389/fpsyg.2017.01153
Double, K. S., & Birney, D. P. (2018). Reactivity to confidence ratings in older individuals performing the latin square task. Metacognition and Learning, 13(3), 309–326. https://doi.org/10.1007/s11409-018-9186-5 DOI: https://doi.org/10.1007/s11409-018-9186-5
Erdfelder, E., FAul, F., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149 DOI: https://doi.org/10.3758/BRM.41.4.1149
Franco, J. P., Doroc, K., Yadav, N., Bossaerts, P., & Murawski, C. (2022). Task-independent metrics of computational hardness predict human cognitive performance. https://doi.org/10.1101/2021.04.25.441300 DOI: https://doi.org/10.1101/2021.04.25.441300
Franco, J. P., Yadav, N., Bossaerts, P., & Murawski, C. (2021). Generic properties of a computational task predict human effort and performance. Journal of Mathematical Psychology, 104. https://doi.org/10.1016/j.jmp.2021.102592 DOI: https://doi.org/10.1016/j.jmp.2021.102592
Galy, E., Cariou, M., & Mélan, C. (2012). What is the relationship between mental workload factors and cognitive load types? International Journal of Psychophysiology, 83(3), 269–275. https://doi.org/10.1016/j.ijpsycho.2011.09.023 DOI: https://doi.org/10.1016/j.ijpsycho.2011.09.023
Gavas, R. D., Tripathy, S. R., Chatterjee, D., & Sinha, A. (2018). Cognitive load and metacognitive confidence extraction from pupillary response. Cognitive Systems Research, 52, 325–334. https://doi.org/10.1016/j.cogsys.2018.07.021 DOI: https://doi.org/10.1016/j.cogsys.2018.07.021
Hidalgo-herrero, M., Rabanal, P., Rodríguez, I., & Rubio, F. (2013). Comparing Problem Solving Strategies for NP-hard. 124, 1–25. https://doi.org/10.3233/FI-2013-822 DOI: https://doi.org/10.3233/FI-2013-822
Klepsch, M., & Seufert, T. (2020). Understanding instructional design effects by differentiated measurement of intrinsic, extraneous, and germane cognitive load. Em Instructional Science (Vol. 48, Número 1). Springer Netherlands. https://doi.org/10.1007/s11251-020-09502-9 DOI: https://doi.org/10.1007/s11251-020-09502-9
Lempert, K. M., Chen, Y. L., & Fleming, S. M. (2015). Relating pupil dilation and metacognitive confidence during auditory decision-making. PLoS ONE, 10(5). https://doi.org/10.1371/journal.pone.0126588 DOI: https://doi.org/10.1371/journal.pone.0126588
Liu, P., & Li, Z. (2012). Task complexity: A review and conceptualization framework. International Journal of Industrial Ergonomics, 42(6), 553–568. https://doi.org/10.1016/j.ergon.2012.09.001 DOI: https://doi.org/10.1016/j.ergon.2012.09.001
Mackinnon, A. J., & Wearing, A. J. (1980). Complexity and decision making. Behavioral Science, 25(4), 285–296. https://doi.org/10.1002/bs.3830250405 DOI: https://doi.org/10.1002/bs.3830250405
Mangion, K. L. H. (2017). Human cognitive performance: a neurophysiological assessment of the impact that reverse assessment priming has on mental workload, performance and cognitive efficiency during transient information processing. University of Southern Queensland.
Meloso, D., Copic, J., & Bossaerts, P. (2009). Promoting intellectual discovery: Patents versus markets. Science, 323(5919), 1335–1339. https://doi.org/10.1126/science.1158624 DOI: https://doi.org/10.1126/science.1158624
Miller, G. A. (1956). The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev, 63(2), 81–97. https://doi.org/10.1177/001088049003100202 DOI: https://doi.org/10.1037/h0043158
Murawski, C., & Bossaerts, P. (2016). How Humans Solve Complex Problems: The Case of the Knapsack Problem. Scientific Reports, 6(September), 1–10. https://doi.org/10.1038/srep34851 DOI: https://doi.org/10.1038/srep34851
Payne, J. W. (1976). Task complexity and contingent processing in decision making: An information search and protocol analysis. Organizational Behavior and Human Performance, 16(2), 366–387. https://doi.org/10.1016/0030-5073(76)90022-2 DOI: https://doi.org/10.1016/0030-5073(76)90022-2
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1988). Adaptive strategy selection in decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14(3), 534–552. https://doi.org/10.1037/0278-7393.14.3.534 DOI: https://doi.org/10.1037//0278-7393.14.3.534
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1991). Consumer decision making. Em Handbook of consumer behaviour (p. 50–84). Prentice-Hall.
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker. Cambridge university press. DOI: https://doi.org/10.1017/CBO9781139173933
Pizlo, Z., & Zhen Li. (2005). Solving combinatorial problems: The 15-puzzle. Memory & Cognition, 33(6), 1069–1084. DOI: https://doi.org/10.3758/BF03193214
Ross, N. D. F., Johns, M. B., Keedwell, E. C., & Savic, D. A. (2019). Human-evolutionary problem solving through gamification of a bin-packing problem. GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion, 1465–1473. https://doi.org/10.1145/3319619.3326871 DOI: https://doi.org/10.1145/3319619.3326871
Simon, H. A. (1962). The Architecture of Complexity Proceedings of the American Philosophical Society Vol. 106.
Simon, H. A. (1990). Invariants of human behavior. Annual review of psychology, 41(1), 1-20. DOI: https://doi.org/10.1146/annurev.ps.41.020190.000245
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1016/0364-0213(88)90023-7 DOI: https://doi.org/10.1016/0364-0213(88)90023-7
Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive Architecture and Instructional Design: 20 Years Later. Educational Psychology Review, 31(2), 261–292. https://doi.org/10.1007/s10648-019-09465-5 DOI: https://doi.org/10.1007/s10648-019-09465-5
Tversky, A., & Kahneman, D. (1981). The Framing of Decisions and the Psychology of Choice. Science, 211(4481), 453–458. https://doi.org/10.1126/science.7455683 DOI: https://doi.org/10.1126/science.7455683
Wei, Z., & Wang, X. J. (2015). Confidence estimation as a stochastic process in a neurodynamical system of decision making. Journal of neurophysiology, 114(1), 99-113. https://doi.org/10.1152/jn.00793.2014 DOI: https://doi.org/10.1152/jn.00793.2014
Wood, R. E. (1986). Task Complexity: Definition of the Construct. Organizational behavior and human decision processes, 37, 60–82. DOI: https://doi.org/10.1016/0749-5978(86)90044-0
Zagermann, J., Pfeil, U., & Reiterer, H. (2016). Measuring cognitive load using eye tracking technology in visual computing. ACM International Conference Proceeding Series, 24-October, 78–85. https://doi.org/10.1145/2993901.2993908 DOI: https://doi.org/10.1145/2993901.2993908
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Copyright (c) 2025 Carolina Schneider Bender, Mauri Leodir Löbler, Eliete dos Reis Lehnhart
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
Até 2023 os direitos autorais eram transferidos pelos autores para a ReA/UFSM. A partir de 2024 os autores dos artigos publicados pela ReA/UFSM mantêm os direitos autorais de seus trabalhos.
A revista adota a licença Creative Commons (CC-BY 4.0), que permite que os artigos sejam reutilizados e distribuídos sem restrições, desde que o trabalho original seja devidamente citado.
