Cognitive Biases: Understanding and Mitigating Their Effects

Author(s)

Claudio Paya Santos , Juan José Delgado Morán ,

Download Full PDF Pages: 10-27 | Views: 17 | Downloads: 6 | DOI: 10.5281/zenodo.15412164

Volume 14 - April 2025 (04)

Abstract

Every day, people make decisions that affect who gets in, who is hired or promoted, who is punished or rewarded, and what challenges are taken on. People are influenced in these decisions by internally generated signals reflecting the past and predictions about the future. Unfortunately, no one knows whether these feelings are representative, and overwhelmingly these decisions are made in ways that violate basic principles of logical inference (Mohanani et al., 2017). Indeed, people have a variety of cognitive biases that influence the accumulation of evidence for and against their beliefs as well as the weight put on that evidence. Cognitive biases are universal human phenomena. By definition, every human being experiences cognitive biases. If the cognitive bias is of sufficient strength, it will affect that person’s behavior regardless of context or culture. However, this does not necessarily mean that every cognitive bias is not experienced, nor does it mean every cognitive bias is experienced equally by every individual. Any number of factors, including emotional triggers, social support, education, and life experience, can influence cognitive bias susceptibility. Yet in aggregate, cognitive biases have been well studied, and over a hundred distinct effects have been documented with reliable experimental results. Cognitive biases are not surprising; they are side-effects of systems developed through evolution to extract a world model from unreliable, noisy information, stable enough to be useful but flexible enough to accommodate new observations. Because they affect everyone, individually and socially, they are important in every aspect of life. The application of cognitive biases extends well beyond the fields in which they were originally studied. The investigation of how cognitive biases affect the development and maintenance of human technology is both natural and timely.

Keywords

Cognitive Biases

References

Mohanani, R., Salman, I., Turhan, B., Rodriguez, P., & Ralph, P. (2017). Cognitive Biases in Software Engineering: A Systematic Mapping Study.

Rollwage, M. & M. Fleming, S. (2021). Confirmation bias is adaptive when coupled with efficient metacognition. ncbi.nlm.nih.gov

E. Allahverdyan, A. & Galstyan, A. (2014). Opinion Dynamics with Confirmation Bias. ncbi.nlm.nih.gov

Felipe Sánchez Gómez, L. (2018). Análisis y medición del efecto anclaje en la población bogotana segmentada por variables socioeconómicas.

Dimara, E., Dragicevic, P., & Bezerianos, A. (2016). Accounting for Availability Biases in Information Visualization.

S. Nobandegani, A., da Silva Castanheira, K., Ross Otto, A., & R. Shultz, T. (2018). Over-representation of Extreme Events in Decision-Making: A Rational Metacognitive Account.

Wang, X., Zheng, L., Li, L., Zheng, Y., Sun, P., A. Zhou, F., & Guo, X. (2017). Immune to Situation: The Self-Serving Bias in Unambiguous Contexts. ncbi.nlm.nih.gov

E. Korteling, J., Brouwer, A. M., & Toet, A. (2018). A Neural Network Framework for Cognitive Bias.

Faith Harris, E. (2014). Reaching New Heights: An Examination of Cognitive Dissonance and the Attitude Toward Height and Leadership.

Nikolic, J. (2018). Biases in the Decision-Making Process and Possibilities of Overcoming Them.

Joyce, A., Risely, E., Green, C., Carey, G., & Buick, F. (2023). What Can Public Health Administration Learn from the Decision-Making Processes during COVID-19?. ncbi.nlm.nih.gov

K. Morewedge, C., Yoon, H., Scopelliti, I., W. Symborski, C., H. Korris, J., & Kassam, K. (2015). Debiasing Decisions. Improved Decision Making With A Single Training Intervention.

Adam, M. (2019). Social Cues as Digital Nudges in Information Systems Usage Contexts.

Jayles, B., Sire, C., & H. J. M Kurvers, R. (2020). Crowd control: Reducing individual estimation bias by sharing biased social information.

D. Johnson, S. & L. II Weaver, R. (1992). Groupthink and the Classroom: Changing Familiar Patterns to Encourage Critical Thought.

Osmani, J. (2017). Heuristics and Cognitive Biases: Can the Group Decision-Making Avoid Them?.

E. Plaisance, B. (2018). The Influence of Regional Stereotypes in Employee Selection.

AlKhars, M., Evangelopoulos, N., Pavur, R., & Kulkarni, S. (2019). Cognitive biases resulting from the representativeness heuristic in operations management: an experimental investigation. ncbi.nlm.nih.gov

Casal, S., DellaValle, N., Mittone, L., & Soraperra, I. (2017). Feedback and efficient behavior. ncbi.nlm.nih.gov

Barbero, Álvaro & Grosse-Wentrup, M. (2010). Biased feedback in brain-computer interfaces. ncbi.nlm.nih.gov

Celiktutan, B., Cadario, R., & K. Morewedge, C. (2024). People see more of their biases in algorithms. ncbi.nlm.nih.gov

(Hans) Korteling, J. E., Y. J. Gerritsma, J., & Toet, A. (2021). Retention and Transfer of Cognitive Bias Mitigation Interventions: A Systematic Literature Study. ncbi.nlm.nih.gov

Denmark, D., Harker, D., & McCollough, A. (2013). Interliminal design: Mitigating cognitive bias and design distortion.

Nguyen-Phuong-Mai, M. (2021). What Bias Management Can Learn From Change Management? Utilizing Change Framework to Review and Explore Bias Strategies. ncbi.nlm.nih.gov

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