1. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Psychological Aspects of Risk Management and Technology – Overview

2. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Risk perception  Identify relevant events –Restricted cognitive capacity, selective attention –Impact of experience, expectations, needs  Assess probabilities –Handling conditional probabilities –Cognitive heuristics (e.g., availability, anchoring) –Control expectations (e.g., unrealistic optimism) –Impact of assumed benefit –Certainty versus risk as benefit in itself

3. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Risk perception  Identify relevant events –Restricted cognitive capacity, selective attention –Impact of experience, expectations, needs  Assess probabilities –Handling conditional probabilities –Cognitive heuristics (e.g., availability, anchoring) –Control expectations (e.g., unrealistic optimism) –Impact of assumed benefit –Certainty versus risk as benefit in itself

4. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Different views on probability  Frequentists: probability = the long-run expected frequency of occurrence  Bayesians: probability = plausibility of an event given incomplete knowledge

5. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Handling conditional probabilities: The Monty Hall problem  The situation: 3 closed doors - behind one is a car, behind the other two are goats; player aims to get the car.  Player gets to pick one door  Game host opens one of the other doors choosing always one that hides a goat  Game host offers the player to switch his/her choice to the second unopened door  Should the player switch?

6. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Handling conditional probabilities: The Monty Hall problem - simplified solution Player's pick has a 1/3 chance while the other two doors have a 2/3 chance. Player's pick has a 1/3 chance, other two doors a 2/3 chance split 2/3 for the still unopened one and 0 for the one the host opened  Switching increases probability of selecting the car.

7. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Handling conditional probabilities: The taxi problem  The situation: There are two taxi companies in a town (green: 85% of all taxis; blue: 15% of all taxis). A taxi is involved in a hit-and-run accident. A witness reports that it was a blue taxi. Based on a vision test, the witness is right 80% of the time.  What is the probability that the taxi was indeed blue?

8. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Handling conditional probabilities: The taxi problem Probability that taxi was indeed blue 120 : (170 + 120) =.41 or with Bayes-theorem: p (blue I witness says blue)p (blue) p (witness says blue I blue) __________________________________ = ____________ x ___________________________________ p (green I witness says blue)p (green) p (witness says blue I green)

9. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Risk perception  Identify relevant events –Restricted cognitive capacity, selective attention –Impact of experience, expectations, needs  Assess probabilities –Inadequate handling of conditional probabilities –Cognitive heuristics (e.g., availability, anchoring) –Control expectations (e.g., unrealistic optimism) –Impact of assumed benefit –Certainty versus risk as benefit in itself

10. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Examples for anchoring  Subjects are asked whether the number of African nations in the United Nations is greater than or less than the number obtained on a wheel of fortune with numbers ranging from 0 to 100. Then they are instructed to estimate the actual figure. Estimates were significantly related to the number spun on the wheel (the “anchor”). (Tversky & Kahneman, 1974)  An audience is first asked to write the last 2 digits of their social security number, and, second, to submit mock bids on items such as wine and chocolate; the half of the audience with higher two-digit numbers submit bids that are up to 107% higher than those of the other half. (Ariely et al., 2003)

11. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Risk perception  Identify relevant events –Restricted cognitive capacity, selective attention –Impact of experience, expectations, needs  Assess probabilities –Inadequate handling of conditional probabilities –Cognitive heuristics (e.g., availability, anchoring) –Control expectations (e.g., unrealistic optimism) –Impact of assumed benefit –Certainty versus risk as benefit in itself

12. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Certainty as benefit (Kahneman & Tversky, 1979)  Certain lower gains are preferred over uncertain higher gains. –A: 50% chance to win a three-week tour of England, France, and Italy –B: one-week tour of England with certainty  Less probable higher gains are preferred over more probable lower gains. –C: 5% chance to win a three-week tour of England, France, and Italy –D: 10% chance to win a one-week tour of England  The opposite is true for losses ("reflection effect") –Uncertain higher losses are preferred over certain lower losses. –More probable lower losses are preferred over less probable higher losses.

13. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Risk as benefit: Risk seeking behavior  Risk seeking versus risk avoidance –Personality characteristic –Cultural norm –But also affected by situational characteristics (e.g., gain versus loss; personal control)

14. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Risk perception: “Experts” versus “lay people”  Experts are trained in making judgments based on the "probability x event" model  Lay people use mainly qualitative information –Magnitude of events more important than probability –Personal control over risk –Familiarity with risk –Uneven distribution of costs and benefits for different stakeholders

15. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Images of risk (Renn, 2008)  Fatal threat –artificial source, catastrophic potential, uneven cost-benefit distribution  Stroke of fate –natural source, cyclical nature, control belief  Personal thrill –Personal control and skills, voluntary  Gamble –monetary gains/losses, probabilistic thinking  Slow killer –delayed effects, little personal experience

16. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Decision-making: Normative models  maximizing subjective expected utility –choose the alternative with the highest expected payoff –requires knowledge of probabilities and utilities –certain prerequisites must be met, e.g. no framing effects, no a priori preference for (un)certainty  maximizing multi-attribute utility based on knowledge of all relevant dimensions/ alternatives and of their relative importance for the decision

17. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Decision-making: Bounded rationality  Heuristiscs for assessing probabililities, e.g. –Availability: probability estimates based on examples ("I was knew a person that...") –Anchoring: using easily available information as anchor  Heuristics for combining decision dimensions, e.g. –conjunctive rule: minimum thresholds for all dimensions must be met –disjunctive rule: satisfying threshold for at least one dimension must be met ("satisficing") –lexicographic rule: prioritize decision dimensions (e.g., "take the best")

18. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Example for "Take the best" (Gigerenzer & Goldstein, 1999)  Two cues for predicting the outcome of sports games –number of games won by a team in the whole season –scores at half-time  Analysis of students' responses –If a team was consistently better during the whole season, they assumed this team to have won. –If the two teams' performance had been similar for the whole season, they assumed that the team with higher scores at half-time to have won.  The students were right for 78% of the games and thereby outperformed multiple regression which can combine a multitude of dimensions.  Expert intuition is about knowing which information is important and ignoring the rest.

19. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Further perspectives on errors in decision-making  Errors at different levels of information processing –Skill level/Habitual actions: errors due to lack of attention –Rule level/Practiced choices of actions: errors due to oversimplification –Knowledge level/Problem-solving: errors due to inadequate understanding  Group decision-making –preference for shared information –groupthink

20. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Rationality in decision-making  Normative models of decision-making imply a narrow understanding of rationality (= con- sistent and maximum use of information)  Evidence against rational decision-making in this narrow sense holds up in ecologically valid settings/tasks.  Open questions: –How much rationality in the narrow sense is needed to succeed in real world tasks? –Should rationality be understood more broadly as adaptive human functioning?

21. Psychological Aspects of Risk Management and Technology – G. Grote ETHZ, Fall09 Current thinking about decision-making: Dual-process models  Naturalistic decision making (Klein, 2008) –Recognition-primed decisions by matching given situation with known patterns (combinations of cues, expectancies, goals, actions) and simulating effects of possible actions  System 1 and System 2 (Evans, 2008) –System 1: intuition (implicit, automatic, low effort, holistic, fast, emotional) –System 2: reasoning (explicit, controlled, high effort, analytic, slow, cognitive)  But many questions remain, e.g., –when to trust which process –when to switch between systems