Cognitive and Motivational Biases in Decision and Risk Analysis

Behavioral decision research has demonstrated that judgments and decisions of ordinary people and experts are subject to numerous biases. Decision and risk analysis were designed to improve judgments and decisions and to overcome many of these biases. However, when eliciting model components and parameters from decisionmakers or experts, analysts often face the very biases they are trying to help overcome. When these inputs are biased they can seriously reduce the quality of the model and resulting analysis. Some of these biases are due to faulty cognitive processes; some are due to motivations for preferred analysis outcomes. This article identifies the cognitive and motivational biases that are relevant for decision and risk analysis because they can distort analysis inputs and are difficult to correct. We also review and provide guidance about the existing debiasing techniques to overcome these biases. In addition, we describe some biases that are less relevant because they can be corrected by using logic or decomposing the elicitation task. We conclude the article with an agenda for future research.     

Managing Wildfire Events: Risk‐Based Decision Making Among a Group of Federal Fire Managers

Managing wildfire events to achieve multiple management objectives involves a high degree of decision complexity and uncertainty, increasing the likelihood that decisions will be informed by experience‐based heuristics triggered by available cues at the time of the decision. The research reported here tests the prevalence of three risk‐based biases among 206 individuals in the USDA Forest Service with authority to choose how to manage a wildfire event (i.e., line officers and incident command personnel). The results indicate that the subjects exhibited loss aversion, choosing the safe option more often when the consequences of the choice were framed as potential gains, but this tendency was less pronounced among those with risk seeking attitudes. The subjects also exhibited discounting, choosing to minimize short‐term over long‐term risk due to a belief that future risk could be controlled, but this tendency was less pronounced among those with more experience. Finally, the subjects, in particular those with more experience, demonstrated a status quo bias, choosing suppression more often when their reported status quo was suppression. The results of this study point to a need to carefully construct the decision process to ensure that the uncertainty and conflicting objectives inherent in wildfire management do not result in the overuse of common heuristics. Individual attitudes toward risk or an agency culture of risk aversion may counterbalance such heuristics, whereas increased experience may lead to overconfident intuitive judgments and a failure to incorporate new and relevant information into the decision.     

Re-appraising Managers' Perceptual Errors: A Behavioural Decision-Making Perspective

Findings reported by Mezias and Starbuck (2003), indicating that managers have inaccurate perceptions of their organization and its environment, are appraised in the context of work from the field of behavioural decision-making (BDM) on heuristics and biases. Drawing on theory, research and criticisms concerning calibration of human judgement, continuous versus discrete judgements and the adequacy of the normative solutions used, we identify issues that require further clarification and suggest lines of research to address them.     

Subjective machines: Probabilistic risk assessment based on deep learning of soft information

For several years machine learning methods have been proposed for risk classification. While machine learning methods have also been used for failure diagnosis and condition monitoring, to the best of our knowledge, these methods have not been used for probabilistic risk assessment. Probabilistic risk assessment is a subjective process. The problem of how well machine learning methods can emulate expert judgments is challenging. Expert judgments are based on mental shortcuts, heuristics, which are susceptible to biases. This paper presents a process for developing natural language-based probabilistic risk assessment models, applying deep learning algorithms to emulate experts’ quantified risk estimates. This allows the risk analyst to obtain an a priori risk assessment when there is limited information in the form of text and numeric data. Universal sentence embedding (USE) with gradient boosting regression (GBR) trees trained over limited structured data presented the most promising results. When we apply these models’ outputs to generate survival distributions for autonomous systems’ likelihood of loss with distance, we observe that for open water and ice shelf operating environments, the differences between the survival distributions generated by the machine learning algorithm and those generated by the experts are not statistically significant.     

The Value of Performance Weights and Discussion in Aggregated Expert Judgments

In risky situations characterized by imminent decisions, scarce resources, and insufficient data, policymakers rely on experts to estimate model parameters and their associated uncertainties. Different elicitation and aggregation methods can vary substantially in their efficacy and robustness. While it is generally agreed that biases in expert judgments can be mitigated using structured elicitations involving groups rather than individuals, there is still some disagreement about how to best elicit and aggregate judgments. This mostly concerns the merits of using performance‐based weighting schemes to combine judgments of different individuals (rather than assigning equal weights to individual experts), and the way that interaction between experts should be handled. This article aims to contribute to, and complement, the ongoing discussion on these topics.     

A Quantitative Test of the Cultural Theory of Risk Perceptions: Comparison with the Psychometric Paradigm

This paper seeks to compare two frameworks which have been proposed to explain risk perceptions, namely, cultural theory and the psychometric paradigm. A structured questionnaire which incorporated elements from both approaches was administered to 129 residents of Norwich, England. The qualitative risk characteristics generated by the psychometric paradigm explained a far greater proportion of the variance in risk perceptions than cultural biases, though it should be borne in mind that the qualitative characteristics refer directly to risks whereas cultural biases are much more distant variables. Correlations between cultural biases and risk perceptions were very low, but the key point was that each cultural bias was associated with concern about distinct types of risks and that the pattern of responses was compatible with that predicted by cultural theory. The cultural approach also provided indicators for underlying beliefs regarding trust and the environment; beliefs which were consistent within each world view but divergent between them. An important drawback, however, was that the psychometric questionnaire could only allocate 32% of the respondents unequivocally to one of the four cultural types. The rest of the sample expressed several cultural biases simultaneously, or none at all. Cultural biases are therefore probably best interpreted as four extreme world views, and a mixture of qualitative and quantitative research methodologies would generate better insights into who might defend these views in what circumstances, whether there are only four mutually exclusive world views or not, and how these views are related to patterns of social solidarity, and judgments on institutional trust.     

Judgment Error and Expert Critics in Forecasting Tasks*

This paper presents an approach to elicit and critique intuitive forecasts. It attempts to retain the valid component of experts' judgments while correcting some biases. Decision science and expert system techniques were unified with the design of an expert critiquing system. A real world application of the resulting critic verifies the approach. A discussion addresses how to create a similar critic for other forecast settings.     

Heuristics Structure and Pervade Formal Risk Assessment

Lay perceptions of risk appear rooted more in heuristics than in reason. A major concern of the risk regulation literature is that such “error‐strewn” perceptions may be replicated in policy, as governments respond to the (mis)fears of the citizenry. This has led many to advocate a relatively technocratic approach to regulating risk, characterized by high reliance on formal risk and cost‐benefit analysis. However, through two studies of chemicals regulation, we show that the formal assessment of risk is pervaded by its own set of heuristics. These include rules to categorize potential threats, define what constitutes valid data, guide causal inference, and to select and apply formal models. Some of these heuristics lay claim to theoretical or empirical justifications, others are more back‐of‐the‐envelope calculations, while still more purport not to reflect some truth but simply to constrain discretion or perform a desk‐clearing function. These heuristics can be understood as a way of authenticating or formalizing risk assessment as a scientific practice, representing a series of rules for bounding problems, collecting data, and interpreting evidence (a methodology). Heuristics are indispensable elements of induction. And so they are not problematic per se, but they can become so when treated as laws rather than as contingent and provisional rules. Pitfalls include the potential for systematic error, masking uncertainties, strategic manipulation, and entrenchment. Our central claim is that by studying the rules of risk assessment qua rules, we develop a novel representation of the methods, conventions, and biases of the prior art.     

MEDIA MERGERS AND MEDIA BIAS WITH RATIONAL CONSUMERS

We present an economic model of media bias and media mergers. Media owners have political motives as well as profit motives, and can influence public opinion by withholding information that is pejorative to their political agenda—provided that their agenda is not too far from the political mainstream. This is true even with rational consumers who understand the media owners’ biases, because the public do not know how much information the news organizations have and so do not know when news is being withheld. In line with conventional wisdom, this problem can be undone by competition; but competition can be defeated in equilibrium by media mergers that enhance profits at the expense of the public interest. We thus derive a motive for media merger policy that is completely distinct from the motives behind conventional antitrust. While media bias may reduce the profit incentives to merge, media markets nonetheless err by being insufficiently competitive, and the consequences of merger are more severe than in other markets.     

Effective heuristics for the blocking flowshop scheduling problem with makespan minimization

The blocking flowshop scheduling problem with makespan criterion has important applications in a variety of industrial systems. Heuristics that explore specific characteristics of the problem are essential for many practical systems to find good solutions with limited computational effort. This paper first presents two simple constructive heuristics, namely weighted profile fitting (wPF) and PW, based on the profile fitting (PF) approach of McCormick et al. [Sequencing in an assembly line with blocking to minimize cycle time. Operations Research 1989;37:925-36] and the characteristics of the problem. Then, three improved constructive heuristics, called PF-NEH, wPF-NEH, and PW-NEH, are proposed by combining the PF, wPF, and PW with the enumeration procedure of the Nawaz-Enscore-Ham (NEH) heuristic [A heuristic algorithm for the m-machine, n-job flow shop sequencing problem. OMEGA-International Journal of Management Science 1983;11:91-5] in an effective way. Thirdly, three composite heuristics i.e., PF-NEH_LS, wPF-NEH_LS, and PW-NEH_LS, are developed by using the insertion-based local search method to improve the solutions generated by the constructive heuristics. Computational simulations and comparisons are carried out based on the well-known flowshop benchmarks of Taillard [Benchmarks for basic scheduling problems. European Journal of Operation Research 1993;64:278-85] that are considered as blocking flowshop instances. The results show that the presented constructive heuristics perform significantly better than the existing ones, and the proposed composite heuristics further improve the presented constructive heuristics by a considerable margin. In addition, 17 new best-known solutions for Taillard benchmarks with large scale are found by the presented heuristics.     

An Experimental Study of the Effectiveness of Three Debiasing Techniques*

Subjective probability distributions constitute an important part of the input to decision analysis and other decision aids. The long list of persistent biases associated with human judgments under uncertainy [16] suggests, however, that these biases can be translated into the elicited probabilities which, in turn, may be reflected in the output of the decision aids, potentially leading to biased decisions. This experiment studies the effectiveness of three debiasing techniques in elicitation of subjective probability distributions. It is hypothesized that the Socratic procedure [18] and the devil's advocate approach [6] [7] [31] [32] [33] [34] will increase subjective uncertainty and thus help assessors overcome a persistent bias called “overconfidence.” Mental encoding of the frequency of the observed instances into prespecified intervals, however, is expected to decrease subjective uncertainty and to help assessors better capture, mentally, the location and skewness of the observed distribution. The assessors' ratings of uncertainty confirm these hypotheses related to subjective uncertainty but three other measures based on the dispersion of the elicited subjective probability distributions do not. Possible explanations are discussed. An intriguing explanation is that debiasing may affect what some have called “second order” uncertainty. While uncertainty ratings may include this second component, the measures based on the elicited distributions relate only to “first order” uncertainty.     

Does Concern‐Driven Risk Management Provide a Viable Alternative to QRA?

This article discusses a concept of concern-driven risk management, in which qualitative expert judgments about whether concerns warrant specified risk management interventions are used in preference to quantitative risk assessment (QRA) to guide risk management decisions. Where QRA emphasizes formal quantitative assessment of the probable consequences caused by the recommended actions, and comparison to the probable consequences of alternatives, including the status quo, concern-driven risk management instead emphasizes perceived urgency or severity of the situation motivating recommended interventions. In many instances, especially those involving applications of the precautionary principle, no formal quantification or comparison of probable consequences for alternative decisions is seen as being necessary (or, perhaps, possible or desirable) prior to implementation of risk management measures. Such concern-driven risk management has been recommended by critics of QRA in several areas of applied risk management. Based on case studies and psychological literature on the empirical performance of judgment-based approaches to decision making under risk and uncertainty, we conclude that, although concern-driven risk management has several important potential political and psychological advantages over QRA, it is not clear that it performs better than (or as well as) QRA in identifying risk management interventions that successfully protect human health or achieve other desired consequences. Therefore, those who advocate replacing QRA with concern-driven alternatives, such as expert judgment and consensus decision processes, should assess whether their recommended alternatives truly outperform QRA, by the criterion of producing preferred consequences, before rejecting the QRA paradigm for practical applications.     

Capturing and Integrating Knowledge for Managing Risks in Tunnel Works

Risk‐related knowledge gained from past construction projects is regarded as potentially extremely useful in risk management. This article describes a proposed approach to capture and integrate risk‐related knowledge to support decision making in construction projects. To ameliorate the problem related to the scarcity of risks information often encountered in construction projects, Bayesian Belief Networks are used and expert judgment is elicited to augment available information. Particularly, the article provides an overview of judgment‐based biases that can appear in the elicitation of judgments for constructing Bayesian Networks and the provisos that can be made in this respect to minimize these types of bias. The proposed approach is successfully applied to develop six models for top risks in tunnel works. More than 30 tunneling experts in the Netherlands and Germany were involved in the investigation to provide information on identifying relevant scenarios than can lead to failure events associated with tunneling risks. The article has provided an illustration of the applicability of the developed approach for the case of “face instability in soft soils using slurry shields.”     

Temptation and Revealed Preference

Gul and Pesendorfer (2001) model the static behavior of an agent who ranks menus prior to the experience of temptation. This paper models the dynamic behavior of an agent whose ranking of menus itself is subject to temptation. The representation for the agent's dynamically inconsistent choice behavior views him as possessing a dynamically consistent view of what choices he “should” make (a normative preference) and being tempted by menus that contain tempting alternatives. Foundations for the model require a departure from Gul and Pesendorfer's idea that temptation creates a preference for commitment. Instead, it is hypothesized that distancing an agent from the consequences of his choices separates normative preference and temptation.     

Effects of Risk Representation and Scope on Willingness to Pay for Reduced Risks: Evidence from Tokyo Bay, Japan

When applying the contingent valuation method (CVM) in risk reduction studies, some studies report that willingness to pay (WTP) is insensitive to the magnitude of risk reduction while other studies do not. On the other hand, social-psychological research has shown that the affect heuristic biases judgments on probability, relative frequency, and risk. This article examines both magnitude (or scope) effect and affect heuristic (or representation) effect on WTP for risk reduction measures against tsunamis by introducing two experimental (i.e., absolute and relative) systems with four different representations to evaluate public behaviors in two different scenarios of risk reduction. Two common denominators (100 and 10,000) are introduced into absolute risk reduction representation (i.e., "of every 100 persons, from present 2 deaths to 1") to form different formats (i.e., "of every 10,000 persons from 200 to 100," and "of every 100,000 persons from 2,000 deaths to 1,000"). There is little evidence that WTP estimates are sufficiently sensitive to the magnitude of risk reduction, but relative risk reduction representations may be better than the absolute one given in CVM mail surveys when the risk is small. There is a statistically insignificant effect of risk reduction representations on respondent frequency, but mixed effects on the monetary values of WTP at the level of 0.05. The representation effect of absolute risk reduction on the WTP value varies with the common denominator. The larger the common denominator, the less the WTP to reduce the risk of tsunamis, and the significance probability is improved to less than the level of 0.05 when the common denominator becomes large enough. The findings suggest that improved methods are required for estimating the rates of tradeoff between fatality risk and other goods among consumers.