Cognitive bias and how to improve sustainable decision making
Open accessAbstract
The rapid advances of science and technology have provided a large part of the world with all conceivable needs and comfort. However, this welfare comes with serious threats to the planet and many of its inhabitants. An enormous amount of scientific evidence points at global warming, mass destruction of bio-diversity, scarce resources, health risks, and pollution all over the world. These facts are generally acknowledged nowadays, not only by scientists, but also by the majority of politicians and citizens. Nevertheless, this understanding has caused insufficient changes in our decision making and behavior to preserve our natural resources and to prevent upcoming (natural) disasters. In the present study, we try to explain how systematic tendencies or distortions in human judgment and decision-making, known as “cognitive biases,” contribute to this situation. A large body of literature shows how cognitive biases affect the outcome of our deliberations. In natural and primordial situations, they may lead to quick, practical, and satisfying decisions, but these decisions may be poor and risky in a broad range of modern, complex, and long-term challenges, like climate change or pandemic prevention. We first briefly present the social-psychological characteristics that are inherent to (or typical for) most sustainability issues. These are: experiential vagueness, long-term effects, complexity and uncertainty, threat of the status quo, threat of social status, personal vs. community interest, and group pressure. For each of these characteristics, we describe how this relates to cognitive biases, from a neuro-evolutionary point of view, and how these evolved biases may affect sustainable choices or behaviors of people. Finally, based on this knowledge, we describe influence techniques (interventions, nudges, incentives) to mitigate or capitalize on these biases in order to foster more sustainable choices and behaviors.
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These biases stem from innate psychological tendencies and can potentially lead to misjudgments and suboptimal outcomes in patient care.
“These biases stem from innate psychological tendencies and can potentially lead to misjudgments and suboptimal outcomes in patient care [2-4].”
From Mitigating cognitive biases in clinical decision-making through multi-agent conversations using large language models: simulation study (2024) · cites this paper as [2]
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- Authors
- Korteling, J. E. (., Paradies, G. L., & Sassen-van Meer, J. P.
- Affiliations
- Netherlands Organisation for Applied Scientific Research (NL)
- Corresponding author
- Korteling, Johan. E. (Hans)
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2023-02-28
- Volume
- 14
- Pages
- 1129835-1129835
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC10071311
- OpenAlex
- W4322630742
- Cited by
- 143
- References
- 131
- Citations by year
- 2026: 45 · 2025: 60 · 2024: 33 · 2023: 5
- FWCI
- 29.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- TNO Repository (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Environmental Education and Sustainability
- Social Sciences › Sociology and Political Science › Climate Change Communication and Perception
- Keywords
- cognitive biases, sustainable decision-making, sustainable behavior, climate change, pandemic prevention, long-term thinking, uncertainty, status quo bias, group pressure, nudging, incentives, behavioral interventions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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