Abstract. In the geosciences, recent attention has been paid to the influence of uncertainty on expert decision making. When making decisions under conditions of uncertainty, people tend to employ heuristics (rules of thumb) based on experience, relying on their prior knowledge and beliefs to intuitively guide choice. Over 50 years of decision making research in cognitive psychology demonstrates that heuristics can lead to less-than-optimal decisions, collectively referred to as biases. For example, a geologist who confidently interprets ambiguous data as representative of a familiar category form their research (e.g., strike slip faults for expert in extensional domains) is exhibiting the availability bias, which occurs when people make judgments based on what is most dominant or accessible in memory. Given the important social and commercial implications of many geoscience decisions, there is a need to develop effective interventions for removing or mitigating decision bias. In this paper, we summarize the key insights from decision making research about how to reduce bias and review the literature on debiasing strategies. First, we define an optimal decision, since improving decision making requires having a standard to work towards. Next, we discuss the cognitive mechanisms underlying decision biases and describe three biases that have been shown to influence geoscientists decision making (availability bias, framing bias, anchoring bias). Finally, we review existing debiasing strategies that have applicability in the geosciences, with special attention given to those strategies that make use of information technology and artificial intelligence (AI). We present two case studies illustrating different applications of intelligent systems for the debiasing of geoscientific decision making, where debiased decision making is an emergent property of the coordinated and integrated processing of human-AI collaborative teams.
- Award ID(s):
- 1839705
- NSF-PAR ID:
- 10189131
- Date Published:
- Journal Name:
- Solid Earth
- Volume:
- 10
- Issue:
- 5
- ISSN:
- 1869-9529
- Page Range / eLocation ID:
- 1469 to 1488
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/se-10-1469-2019
