- Behavioural Insights
- Risk Management
- Decision Analysis
- Behavioural Economics
- Risk Communication
- Medical Decision Making
- Consumer Insurance Behaviour
Laurel Austin is an Adjunct Research Professor in Management Science. Prior to joining Ivey she was an Associate Professor of Strategic Decision Making and Risk Management at the Copenhagen Business School in Denmark. In 2015 she won the Case Centres’ Outstanding New Case Writer award for her case USCD: A cancer cluster in the literature building? As a behavioral decision theorist, Laurel’s research focuses on the study of risk perceptions and decision making processes in situations that involve risk and uncertainty. Using both qualitative and quantitative research methods she models, elicits, and analyzes influences on decision making and related risk taking and risk mitigation behaviors. Her recent work has included study of medical professional and patient decision making related to preventive medicine, influences on safety decisions and behaviors in high risk occupations, retailer decision making, and consumer insurance decisions. Past work includes research on computer supported collaborative work, adolescent risky behaviors, and risk associated with hearing-impairment in truck drivers.
- HBA Ivey Field Project
- HBA elective Decision Making and Risk Management
- MSc elective International Decisions and Risk
- Ph.D.,Management and Decision Sciences, Carnegie Mellon University
- M.S.E., Industrial and Operations Engineering, University of Michigan
- B.S., Industrial Engineering, Northwestern University
Recent Refereed Articles
2019, "Physician and Non-physician Estimates of Positive Predictive Value in Diagnostic Versus Mass Screening Mammography: An Examination of Bayesian Reasoning", Medical Decision Making, January 39(2): 108 - 118.
The same test with the same result has different positive predictive values (PPVs) for people with different pre-test probability of disease. Representative thinking theory suggests people are unlikely to realize this because they ignore or underweight prior beliefs when given new information (e.g., test results), or due to confusing test sensitivity (probability of positive test given disease) with PPV (probability of disease given positive test). This research examines whether physicians and MBAs intuitively know that PPV following positive mammography for an asymptomatic woman is less than PPV for a symptomatic woman, and if so, whether they correctly perceive the difference.
60 general practitioners and 84 MBA students were given two vignettes of women with abnormal (positive) mammography tests: one with prior symptoms (diagnostic test), the other an asymptomatic woman participating in mass screening (screening test). Respondents estimated pre-test and post-test probabilities. Sensitivity and specificity were neither provided nor elicited.
88% of GPs and 46% of MBAs considered base rates and estimated PPV in diagnosis > PPV in screening. On average, GPs estimated a 27-point difference and MBAs an 18-point difference, compared to actual of 55 or more points. 10% of GPs and 46% of MBAs ignored base rates, incorrectly assessing the two PPVs as equal.
Physicians and patients are better at intuitive Bayesian reasoning than is suggested by studies that make test accuracy values readily available to be confused with PPV. However, MBAs and physicians interpret a positive in screening as more similar to a positive in diagnosis than it is, with nearly half of MBAs and some physicians wrongly equating the two. This has implications for overdiagnosis and overtreatment.
Link(s) to publication:
Austin, L.; Reventlow, S.; Sandøe, P.; Brodersen, J.,
2013, "The Structure of Medical Decisions: Probability, Uncertainty and Risk in Five Common Choice Situations", Health, Risk & Society, April 15(1): 27 - 50.
Abstract: Increasingly, medical choices involve deciding whether to look for evidence of undetected, asymptomatic conditions, or increased risk of future conditions (i.e. screening). Those who screen at sufficiently high risk face decisions about interventions to prevent or postpone the onset of possible, but not certain, future symptomatic conditions. Other preventive decisions include whether or not to accept population-based intervention, such as vaccination. Using decision trees, we model the normative structures and associated uncertainties that underlie five medical decision situations, each of which involves assessing the probabilistic hypothesis that a person has, or will in the future have, a given symptomatic condition. The probability estimate that results from assessment becomes an input into predicting treatment benefit, with the probability of benefit decreasing as that of the symptomatic condition decreases. The five situations identified in this paper involve assessing: (1) a symptomatic patient (2) an asymptomatic individual for an undetected condition (3) an individual for risk of a future condition (4) an individual for multiple risks simultaneously (shotgun assessment) and (5) an individual for a population-based intervention. Analysis of these situations facilitates examination of intuitive probabilistic reasoning. Drawing on evidence in related literature, we discuss some implications of decision-makers imposing the wrong structure or probabilistic reasoning when making medical choices. In particular, we discuss (1) overestimation of expected benefit due to systematic underestimation of uncertainty in a given decision (2) overconfidence in probabilistic test results and (3) failure to understand the implications of cumulative probabilities when shot-gun’ testing.
Link(s) to publication:
Austin, L.; Fischhoff, B.,
2012, "Injury Prevention Risk Communication: A Mental Models Approach", Injury Prevention, November 18(2): 124 - 129.
Abstract: Individuals' decisions and behaviour can play a critical role in determining both the probability and severity of injury. Behavioural decision research studies peoples' decision-making processes in terms comparable to scientific models of optimal choices, providing a basis for focusing interventions on the most critical opportunities to reduce risks. That research often seeks to identify the 'mental models' that underlie individuals' interpretations of their circumstances and the outcomes of possible actions. In the context of injury prevention, a mental models approach would ask why people fail to see risks, do not make use of available protective interventions or misjudge the effectiveness of protective measures. If these misunderstandings can be reduced through context-appropriate risk communications, then their improved mental models may help people to engage more effectively in behaviours that they judge to be in their own best interest. If that proves impossible, then people may need specific instructions, not trusting to intuition or even paternalistic protection against situations that they cannot sufficiently control. The method entails working with domain specialists to elicit and create an expert model of the risk situation, interviewing lay people to elicit their comparable mental models, and developing and evaluating communication interventions designed to close the gaps between lay people and experts. This paper reviews the theory and method behind this research stream and uses examples to discuss how the approach can be used to develop scientifically validated context-sensitive injury risk communications.
Link(s) to publication:
Austin, L.; Fischhoff, B.,
2010, "Consumers’ Collision Insurance Decisions: A Mental Models Approach to Theory Evaluation", Journal of Risk Research, October 13(7): 895 - 911.
Abstract: Using interviews with 74 drivers, we elicit and analyze how people think about collision insurance coverage and decide whether to buy coverage, and if so, what deductible level to carry. We compare respondents’ judgments and behaviors to predictions of three models: baseline expected utility (EU) theory, which predicts that insurance is an inferior good, meaning more wealthy people buy less a modified EU model, which incorporates income constraints and suggests that property insurance is a normal good, meaning more wealthy people buy more and a mental accounting model which predicts that consumers budget income across consumption categories. The results suggest they purchase insurance as a normal good, guided by a cognitive model that emphasizes budget constraints. Verbal reports reveal a desire to balance two conflicting goals in deductible decisions: keeping premiums affordable’ and keeping deductible level affordable.’ Thus, wealth does not distinguish people by risk aversion, but by ability to pay. In other words, the behavior of less wealthy people is not driven by greater risk aversion, but by their lesser ability to pay, both now and later. We find that a simple heuristic using only vehicle value accounts for most decisions of whether to purchase optional collision coverage: out of 45 respondents who did not have loans on their vehicles, 90% of those with vehicles worth more than 1000 carried collision coverage, while less than 30% of those with lower-valued vehicles did.
Link(s) to publication:
Honours & Awards
- Outstanding New Case Writer Award, international competition organized by the Case Centre (2015). For teaching case UCSD: A cancer cluster in the literature building? Copenhagen: Copenhagen Business School
- Editor’s Choice Article. Austin, Laurel C. and Baruch Fischhoff. (2012). Injury Prevention Risk Communication: A Mental Models Approach. Injury Prevention. 18(2), 124-129.