In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

The Social Psychology of Organizations.

The " sharing economy " has attracted a great deal of attention in recent months. Platforms such as Airbnb and Uber are experiencing explosive growth, which, in turn, has led to regulatory and political battles. Boosters claim the new technologies will yield utopian outcomes—empowerment of ordinary people, efficiency, and even lower carbon footprints. Critics denounce them for being about economic self-interest rather than sharing, and for being predatory and exploitative. Not surprisingly, the reality is more complex. This essay, based on more than three years of study of both non-profit and for-profit initiatives in the " sharing economy, " discusses what's new and not so new about the sector and how the claims of proponents and critics stack up. While the for-profit companies may be " acting badly, " these new technologies of peer-to-peer economic activity are potentially powerful tools for building a social movement centered on genuine practices of sharing and cooperation in the production and consumption of goods and services. But achieving that potential will require democratizing the ownership and governance of the platforms.

Debating the sharing economy

Research on crisis management and resilience has sought to explain how individuals and organizations anticipate and respond to adversity, yet—surprisingly—there has been little integration across t...

Organizational response to adversity: Fusing crisis management and resilience research streams

Preface. Acknowledgments. PART ONE AN INTRODUCTION TO THE CRISIS. CHAPTER 1 Healthy Skepticism for Risk Management. Common Mode Failure. What Counts as Risk Management. Anecdote: The Risk of Outsourcing Drug Manufacturing. What Failure Means. Scope and Objectives of This Book. CHAPTER 2 Risk Management: A Very Short Introduction to Where We've Been and Where (We Think) We Are. The Entire History of Risk Management (in 800 Words or Less). Methods of Assessing Risks. Risk Mitigation. The State of Risk Management According to Surveys. CHAPTER 3 How Do We Know What Works? An Assessment of Self-Assessments. Potential Objective Evaluations of Risk Management. What We May Find. PART TWO WHY IT'S BROKEN. CHAPTER 4 The "Four Horsemen" of Risk Management: Some (Mostly) Sincere Attempts to Prevent an Apocalypse. Actuaries. War Quants: How World War II Changed Risk Analysis Forever. Economists. Management Consulting: How a Power Tie and a Good Pitch Changed Risk Management. Comparing the Horsemen. Major Risk Management Problems to Be Addressed. CHAPTER 5 An Ivory Tower of Babel: Fixing the Confusion about Risk. The Frank Knight Definition. Risk as Volatility. A Construction Engineering Definition. Risk as Expected Loss. Risk as a Good Thing. Risk Analysis and Risk Management versus Decision Analysis. Enriching the Lexicon. CHAPTER 6 The Limits of Expert Knowledge: Why We Don't Know What We Think We Know about Uncertainty. The Right Stuff: How a Group of Psychologists Saved Risk Analysis. Mental Math: Why We Shouldn't Trust the Numbers in Our Heads. "Catastrophic" Overconfidence. The Mind of "Aces": Possible Causes and Consequences of Overconfidence. Inconsistencies and Artifacts: What Shouldn't Matter Does. Answers to Calibration Tests. CHAPTER 7 Worse Than Useless: The Most Popular Risk Assessment Method and Why It Doesn't Work. A Basic Course in Scoring Methods (Actually, It's the Advanced Course, Too-There's Not Much to Know). Does That Come in "Medium"?: Why Ambiguity Does Not Offset Uncertainty. Unintended Effects of Scales: What You Don't Know Can Hurt You. Clarification of Scores and Preferences: Different but Similar-Sounding Methods and Similar but Different-Sounding Methods. CHAPTER 8 Black Swans, Red Herrings, and Invisible Dragons: Overcoming Conceptual Obstacles to Improved Risk Management. Risk and Righteous Indignation: The Belief that Quantitative Risk Analysis Is Impossible. A Note about Black Swans. Frequentist versus Subjectivist. We're Special: The Belief that Risk Analysis Might Work, But Not Here. CHAPTER 9 Where Even the Quants Go Wrong: Common and Fundamental Errors in Quantitative Models. Introduction to Monte Carlo Concepts. Survey of Monte Carlo Users. The Risk Paradox. The Measurement Inversion. Where's the Science? The Lack of Empiricism in Risk Models. Financial Models and the Shape of Disaster: Why Normal Isn't so Normal. Following Your Inner Cow: The Problem with Correlations. "That's Too Uncertain": How Modelers Justify Excluding the Biggest Risks. Is Monte Carlo Too Complicated? PART THREE HOW TO FIX IT. CHAPTER 10 The Language of Uncertain Systems: The First Step Toward Improved Risk Management. Getting Your Probabilities Calibrated. The Model of Uncertainty: Decomposing Risk with Monte Carlos. Decomposing Probabilities: Thinking about Chance the Way You Think about a Budget. A Few Modeling Principles. Modeling the Mechanism. CHAPTER 11 The Outward-Looking Modeler: Adding Empirical Science to Risk. Why Your Model Won't Behave. Empirical Inputs. Introduction to Bayes: One Way to Get around that "Limited Data for Disasters" Problem. Self-Examinations for Modelers Who Care about Quality. CHAPTER 12 The Risk Community: Intra- and Extraorganizational Issues of Risk Management. Getting Organized. Managing the Global Probability Model. Incentives for a Calibrated Culture. Extraorganizational Issues: Solutions beyond Your Office Building. Miscellaneous Topics. Final Thoughts on Quantitative Models and Better Decisions. Appendix Calibration Tests and Answers. Index.

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The Failure of Risk Management: Why It's Broken and How to Fix It

Although organizations appear to learn from obvious failures, we argue that it is harder for them to learn from “near-misses”---events in which chance played a role in averting failure. In this paper, we formalize the concept of near-misses and hypothesize that organizations and managers fail to learn from near-misses because they evaluate such events as successes and thus feel safer about the situation. We distinguish perceived (“felt”) risk from calculated statistical risk and propose that lower levels of perceived risk encourage people with near-miss information to make riskier subsequent decisions compared to people without near-miss information. In our first study, we confirm the tendency to evaluate near-misses as successes by having participants rate a project manager whose decisions result in either (a) mission success, (b) near-miss, or (c) failure. Participants (both students and NASA employees and contractors) give similar ratings to managers whose decisions produced near-misses and to managers whose decisions resulted in successes, and both ratings are significantly different from ratings of managers who experienced failures. We suggest that the failure to hold managers accountable for near-misses is a foregone learning opportunity for both the manager and the organization. In our second set of studies, we confirm that near-miss information leads people to choose a riskier alternative because of a lower perceived risk following near-miss events. We explore several alternative explanations for these findings, including the role of Bayesian updating in processing near-miss data. Ultimately, the analysis suggests that managers and organizations are reducing their perception of the risk, although not necessarily updating (lowering) the statistical probability of the failure event. We speculate that this divergence arises because perceived risk is the product of associative processing, whereas statistical risk arises from rule-based processing.

How Near-Misses Influence Decision Making Under Risk: A Missed Opportunity for Learning

In the aftermath of many natural and man-made disasters, people often wonder why those affected were underprepared, especially when the disaster was the result of known or regularly occurring hazards (e.g., hurricanes). We study one contributing factor: prior near-miss experiences. Near misses are events that have some nontrivial expectation of ending in disaster but, by chance, do not. We demonstrate that when near misses are interpreted as disasters that did not occur, people illegitimately underestimate the danger of subsequent hazardous situations and make riskier decisions (e.g., choosing not to engage in mitigation activities for the potential hazard). On the other hand, if near misses can be recognized and interpreted as disasters that almost happened, this will counter the basic “near-miss” effect and encourage more mitigation. We illustrate the robustness of this pattern across populations with varying levels of real expertise with hazards and different hazard contexts (household evacuation for a hurricane, Caribbean cruises during hurricane season, and deep-water oil drilling). We conclude with ideas to help people manage and communicate about risk.

This paper was accepted by Teck Ho, decision analysis.

https://www.libertyunderattack.com/wp-content/uploads/2015/06/120418-How-Near-Miss-Events-Amplify-or-Attenuate-Risky-Decision-Making-Management-Science.pdf

How Near-Miss Events Amplify or Attenuate Risky Decision Making

Prior research shows that when people perceive the risk of some hazardous event to be low, they are unlikely to engage in mitigation activities for the potential hazard. We believe one factor that can lower inappropriately (from a normative perspective) people's perception of the risk of a hazard is information about prior near-miss events. A near-miss occurs when an event (such as a hurricane), which had some nontrivial probability of ending in disaster (loss of life, property damage), does not because good fortune intervenes. People appear to mistake such good fortune as an indicator of resiliency. In our first study, people with near-miss information were less likely to purchase flood insurance, and this was shown for both participants from the general population and individuals with specific interests in risk and natural disasters. In our second study, we consider a different mitigation decision, that is, to evacuate from a hurricane, and vary the level of statistical probability of hurricane damage. We still found a strong effect for near-miss information. Our research thus shows how people who have experienced a similar situation but escape damage because of chance will make decisions consistent with a perception that the situation is less risky than those without the past experience. We end by discussing the implications for risk communication.

Why Near-Miss Events Can Decrease an Individual's Protective Response to Hurricanes

Disasters garner attention when they occur, and organizations commonly extract valuable lessons from visible failures, adopting new behaviors in response. For example, the United States saw numerous security policy changes following the September 11 terrorist attacks and emergency management and shelter policy changes following Hurricane Katrina. But what about those events that occur that fall short of disaster? Research that examines prior hazard experience shows that this experience can be a mixed blessing. Prior experience can stimulate protective measures, but sometimes prior experience can deceive people into feeling an unwarranted sense of safety. This research focuses on how people interpret near-miss experiences. We demonstrate that when near-misses are interpreted as disasters that did not occur and thus provide the perception that the system is resilient to the hazard, people illegitimately underestimate the danger of subsequent hazardous situations and make riskier decisions. On the other hand, if near-misses can be recognized and interpreted as disasters that almost happened and thus provide the perception that the system is vulnerable to the hazard, this will counter the basic "near-miss" effect and encourage mitigation. In this article, we use these distinctions between resilient and vulnerable near-misses to examine how people come to define an event as either a resilient or vulnerable near-miss, as well as how this interpretation influences their perceptions of risk and their future preparedness behavior. Our contribution is in highlighting the critical role that people's interpretation of the prior experience has on their subsequent behavior and in measuring what shapes this interpretation.

Robin L. Dillon

Papers

How Near-Misses Influence Decision Making Under Risk: A Missed Opportunity for Learning

How Near-Miss Events Amplify or Attenuate Risky Decision Making

Why Near-Miss Events Can Decrease an Individual's Protective Response to Hurricanes

Near‐Misses and Future Disaster Preparedness

Probabilistic risk analysis for the NASA space shuttle: a brief history and current work