The influence of task complexity factors on decision-making performance
Objective: This study aims to understand the impact of complexity factors on human decision-making performance in an economic problem (the Knapsack Problem), which is analogous to numerous situations individuals encounter daily related to the practices of administrators and managers. Methodology: Th...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | Brasil |
| Institución: | Universidade Federal de Santa Maria (UFSM) |
| Repositorio: | Revista de Administração da UFSM |
| Idioma: | inglés |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/89667 |
| Acceso en línea: | https://periodicos.ufsm.br/reaufsm/article/view/89667 |
| Access Level: | acceso abierto |
| Palabra clave: | Complexity Optimization problem Knapsack Problem Decision-making Complexidade Problema de otimização Tomada de decisão |
| Sumario: | Objective: This study aims to understand the impact of complexity factors on human decision-making performance in an economic problem (the Knapsack Problem), which is analogous to numerous situations individuals encounter daily related to the practices of administrators and managers. Methodology: The research employed an experimental design with 41 participants, varying the number of items and two metrics of computational complexity: "input size" and "instance correlation". Performance was assessed by measuring optimization performance, relative performance, response time (RT), and confidence. Results: The findings reveal a significant impact from manipulating the number of items, leading to a decrease in optimization performance and confidence, alongside an increase in RT. A phase transition was observed in relative performance, where participants managed increases from 5 to 6 items despite longer task completion times; however, this compensation was no longer feasible at 7 items. The input size measure was significantly associated with all dependent variables, explaining 51.84% of the variation in RT. Practical Implications: These results can be applied in areas involving complex decision-making, such as resource allocation and logistical planning. The research contributes to understanding human cognitive limitations, enabling the development of strategies that reduce errors and cognitive overload. |
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