Abstract
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, the availability bias occurs when people make judgments based on what is most dominant or accessible in memory; geoscientists who have spent the past several months studying strike-slip faults will have this terrain most readily available in their mind when interpreting new seismic data. 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 outline 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 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, wherein debiased decision-making is an emergent property of the coordinated and integrated processing of human–AI collaborative teams.
Highlights
When Grove Karl Gilbert wrote about the development of a “guessing faculty” in The Inculcation of Scientific Method by Example (1886), he was one of the first to highlight the value of understanding how geoscientists resolve epistemic uncertainty during judgment and decision-making
In experiment 1, participants of varying levels of expertise were asked to make decisions regarding hazardous waste storage, flood protection, and volcano monitoring. These problems were presented in a format similar to the disease outbreak problem by Tversky and Kahneman (1981): for the waste storage problem, the positive frame described the probability of safe storage and the negative frame described the probability of an accidental spill; for the flood protection problem, the positive frame described the probability the protection would succeed and the negative frame described the probability it would fail; and for the volcano monitoring problem, the positive frame described the probability the volcano would remain dormant and the negative frame described the probability of an eruption
To illustrate the value of this digital nudging approach in geoscience research, we discuss two case studies that represent different applications of intelligent systems for the geosciences that are presently in practice: the first case study addresses the use of unmanned aerial vehicles (UAVs or “drones”) to collect new field data, and the second addresses the use of software for geologic interpretation of seismic image data
Summary
When Grove Karl Gilbert wrote about the development of a “guessing faculty” in The Inculcation of Scientific Method by Example (1886), he was one of the first to highlight the value of understanding how geoscientists resolve epistemic uncertainty during judgment and decision-making. We outline the key insights from judgment and decision-making research about how to reduce bias and review the literature on debiasing strategies. We explain how dual-process theories can account for three specific decision biases: the availability bias, framing bias, and anchoring bias We focus on these three biases because their influence has been well-documented in the geoscience literature. Special attention is given to debiasing strategies that make use of information technology and artificial intelligence (AI) when modifying the decision maker or environment. We believe that these technologies offer the opportunity to overcome some of the cognitive constraints that result in biased strategies and hold the greatest promise of successful application in the geosciences
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