Model use in sustainability policy making: An experimental study

Czaika, Ellen Gail; Selin, Noelle E.

doi:10.1016/j.envsoft.2017.09.001

Cited by 15 publications

(8 citation statements)

References 17 publications

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“…A widely applied approach to operationalizing scientific knowledge is simulation modelling for decision support. This may be due to evidence that simulation models outperform expert judgement in prioritizing management actions for ecological systems (Czaika and Selin, 2017). Additionally, the application of modelling within the environmental domain point to its multiple uses, including to clarify values, explore trade-offs, and mediate conflicting perspectives (Bots and Van Daalen, 2008).…”

Section: Quantitative Analysis In Dpsir (Analytical)mentioning

confidence: 99%

Next Generation Application of Dpsir for Sustainable Policy Implementation

Carnohan¹,

Trier

Liu³

et al. 2022

SSRN Journal

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Section: Quantitative Analysis In Dpsir (Analytical)mentioning

confidence: 99%

Next Generation Application of Dpsir for Sustainable Policy Implementation

Carnohan¹,

Trier

Liu³

et al. 2022

SSRN Journal

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“…In the geosciences, recent research investigating the impact of model use on human judgment about resource management found that management outcomes were superior when student participants relied on models rather than their own experience (Holden and Ellner, 2016). Also, model use has been shown to improve sustainability policy decisions -in a role play simulation, model users evidenced better outcomes (e.g., low change in temperature, high access to electricity, and high global economy) than participants who did not use models (Czaika and Selin, 2017).…”

Section: Modifying the Decision Makermentioning

confidence: 99%

How can geologic decision making under uncertainty be improved?

Wilson¹,

Bond²,

Shipley³

2019

Preprint

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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.

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“…In situations in which expert experience is critical to predicting outcomes, that experience can become a decision input in the model. Past research has shown that such computational models outperform expert judgment in a variety of domains (Dawes et al, 1989), and even the simplest linear models that equally weight all relevant decision inputs (not taking into account historical data on inputs) can outperform expert judgments (Dawes and Corrigan, 1974). In the geosciences, recent research investigating the impact of model use on human judgment about resource management found that management outcomes were superior when student participants relied on models rather than their own experience (Holden and Ellner, 2016).…”

Section: Modifying the Decision Makermentioning

confidence: 99%

How can geologic decision-making under uncertainty be improved?

2019

View full text Add to dashboard Cite

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.

show abstract

Model use in sustainability policy making: An experimental study

Cited by 15 publications

References 17 publications

Next Generation Application of Dpsir for Sustainable Policy Implementation

Next Generation Application of Dpsir for Sustainable Policy Implementation

How can geologic decision making under uncertainty be improved?

How can geologic decision-making under uncertainty be improved?

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