Cognitive Psychology Winter 2004 -Discussion Section-

1. Ψ Cognitive Psychology Winter 2004 -Discussion Section- Pascal Wallisch University of Chicago

2. Problem solving Decision making

3. Categorization Reasoning problem-solving Decision-making Cognitive functions Perception Attention Emotion Motivation Action Memory Imagery Language Reasoning, problem-solving Decision-making

4. Categorization Cognitive functions Perception Perception Attention Emotion Motivation Action Memory Memory Imagery Imagery Language Reasoning, problem-solving Decision-making Decision making

5. Overview Announcements (2 minutes) Decision-making (25 minutes) Problem solving (15 minutes) Primer on Nobel Prizes (5 minutes) Kahneman & Tversky Paper (8 minutes)

6. Announcements Lecture on Monday: 03/08/2004: Cognitive Neuroscience (by me) Review session on Friday: 03/12/2004: In class, just us Final exam on Wednesday: 03/17/2004 Anything else?

7. Decision making • Old economical theories • New psychological theories  Bounded rationality

8. Decision making • Economy: Expected utility theory • Normative theory: Yields optimal payoff • Based on expected value • EV = ∑ ( pi x vi ); Sum of prob times value • Applicable to many situations (Renting an apartment, chosing a major, deciding where to go for holidays, etc.)

9. Example: Expected utility theory

10. Problems: • This obviously works only for small, well defined problems. • Most real life problems have many options • Are hard to quantify both in value and probability • Need quick decisions. • Even if people were able to collect all that information, they were probably unable to combine it appropriately to act optimally.

11. Conclusions • People do generally not use expected utility theory to make their decisions. • The model helps economists to make predictions. • The predictions are usually not very accurate • The model has to be enriched with psychological mechanisms.

12. Alternative view: • People use heuristics in decision making (not algorithms!) • They suffer from psychological biases

13. Algorithms and Heuristics • Algorithms yield optimal solutions (if they exist). They provide certainty. But are often practically impossible to apply. Example: Solving chess. • Heuristics are rules of thumb. They yield a solution that is good enough. But no certainty that it will work. Quick and dirty. Example: Doing what everyone else does.

14. Example: Constrast of Algorithms and Heuristics • Outfielder in Baseball. Objective: Catch ball. • Can either estimate ball speed, distance, wind speed, throwing angle, own speed, ball aerodynamics, etc. (in a short time, then start running). • Or: One can keep the angle of the ball in the viewing field constant by running.  Will catch ball AND will already be running.

15. Availability heuristic • People are not good at estimating probabilities • They use shortcuts • Basically: Instances that are overrepresented in the mental representation and hence easier to call to mind are deemed to be more probable. • This may reveal something about the structure of our mental representations! • A priori, it is unclear why we should have a harder time thinking of words ending in p than words starting with p. But people think the former case is much more frequent than the latter. • This heuristic can be both very efficient or misleading. Depending on the correlation with the criterion.

16. Representativeness heuristic • If something is easier to represent in a certain way, people will think that it is more likely that the things are physically like the mental representation. • Example: Repetition avoidance in generating random numbers. • Example: Illusory effectiveness of rain dances, many medications and therapies.

17. Knowledge heuristic (availability) • The less is more effect: • More citizens in Baltimore or Seattle? • More citizens in Minneapolis or Sacramento? • More citizens in Berlin or Leipzig? • More citizens in Duisburg or Munich? • If the probability of you knowing about it is correlated with the criterion asked, you will be better off, if you just chose the one you know.

18. Biases • Cognitive illusions • Like perceptual illusions, it is hoped that we can understand decision making by investigating it’s illusions. Like in Perception. • Unlike Heuristics, they generally diminish the quality of the decision.

19. Framing bias • The way a certain problem is phrased influences peoples behavior in response to it. • Examples 90% Chance of survival vs. 10% Chance of Death influences surgeons decision to operate. • Example: Losing vs. Gaining not the same.

20. Anchoring bias • The initial starting point of our considerations will have a big effect on the final estimate/decision. • Example: 1x2x3x4x5x6x7x8x9 vs 9x8x7x6x5x4x3x2x1

21. Sunk Cost Bias • Once an investment has been made, the probability to continue the undertaking is higher, proportional to the magnitude of the investment. • Rationally, it should NOT influence our decision to continue. • In the real world, investment often increases probability of success (it’s an estimate of that probability). • Also, this is structurally similar to cognitive dissonance. • Example: Continue to fight wars that one can’t win.

22. Illusory correlation bias • People see structure wherever possible. • Even in random noise (some people see faces) • Probably an offshot of the representativeness heuristic: What fits the mental model is seem to be more likely. These features then get more attention and are overepresented in the mental representation.

23. Hindsight bias • Hindsight is always 20/20 • Prime example: 9/11. Wasn’t it obvious that something like that would happen soon, given that they already tried? Why did two presidents do nothing about it? • People think their original expectations of what will happen were closer to what actually happened. • Evidence for constructiveness of mental rep.

24. Confirmation bias • We encountered this in the reasoning chapter. • Most people look for information that confirms original expectations instead of information that is more likely to falsify it.

25. Overconfidence bias • People generally have higher confidence in their judgement than is warranted by the data or by expectations. • The problem with this bias is that it immunizes people to seek a fix for their other biases. They don’t see the need.

26. A bad picture: • People suffer from all kinds of biases that prevent them from reaching the optimal decision. • The sunk cost effect keeps them to stick with their decision, the overconfidence effect keeps them from realizing that they are in need of a better solution. • Hindsight bias keeps them from learning in the long run. After all, our decisions weren’t so bad, right? • Are they just bad and can’t do any better?

27. Why? • Action control • Heuristics and biases enable us to reach a decision quickly, act on it and confidently STICK with it. • In the long run, this might be more rational than wavering and behaving erratically the first time new information comes around. • People need to act on information. These shortcuts often enough help them, particularly if they have to act on them (vs. just pondering them).

28. Example: Mate choice • Assumptions: • Can have everyone one wants • Only one shot. Decisions are final • Views candidates sequentially • Is able to assign a value to everyone • Doesn’t compromise standards. • From random distribution

29. Normally distributed pool

30. Equally distributed pool

31. Lessons: Specific • Make the cutoff at 20% of your expected pool • Actual size and distribution of pool doesn’t matter much. • Subjective expected pool-size does! • Breakup and divorce as means of “optimization” of utility in case of premature selection • Could explain increased divorce rate by that (more people around, longer integration time)

32. Lessons: General • Decision making and modeling of decisions do have very important real-life implications and applications!

33. 1 2 3 Problem solving • The three door problem: Behind one of the doors is 1 Mio $, nothing is behind the other 2. You choose a door. I open another door and show you that there is nothing Now you have the option to change. Do you?

34. Problems • Well defined problems: Clear start state, clear goal state, clear operations. • Example: Chess, Tower of Hanoi, Games in general. • Ill-defined, complex problems: Unclear start state, unclear goal state, unclear operations. • Example: Managing a company, waging a war, leading a good life.

35. Problem space • Every well defined problem can be represented by a problem space. • The space contains states and relations (steps) between them. • Even well-defined spaces get huge very quickly (e.g. chess). • Computers (AI) solve problems by exploring the problem space.

36. Strategies of problem solving • Generate and Test: Come up with a lot of solutions and then test them sequentially until success. • Means-End Analysis: Comparing the starting state with the goal state and generating intermediate steps that reduce the discrepancy. • Working backward: Determine the last step before the goal step and then generate steps from there on until one reaches the start position. Useful if there is only possible solution. • Backtracking: Making assumptions, deliberating as-if and undoing them if it turns out that it is a dead-end. • Using analogies: Realize the same underlying structure between a familiar situation and a problem situation and act as if the familiar situation would apply. Famous example: Tumor problem, the gamma knife.

37. Detriments to good solutions • Mental set • Constrained problem space

38. Mutilated checkerboard Detriments to good solutions • Functional fixedness • Inadequate mental representations Candle Box of nails How to tack the candle to the wall?

39. Detriments to good solutions • Lack of knowledge and expertise Experts pay attention to relevant information They have deep (vs. shallow) categories They can bring an enormous memory to bear, basically pattern matching (e.g. in chess). Experts are more likely to check for errors.

40. Creativity • Particularly useful to solve ill-defined problems. • Practice (“10 year” rule) • Productivity (the more, the merrier) • High IQ • Good mental representations • Willingness to take risks • Personality factors • Nothing mystical: Artificial creativity! Aaron. http://www.kurzweilcyberart.com/KCATaaron/STAFsample

41. Kahneman & Tversky Nobel prize in Economics, 2002 Daniel Kahneman Princeton University Amos Tversky  1996, Skin cancer

42. A primer on Nobel prizes Alfred Nobel: Bill Gates of the 19th century Born: 1833 in Stockholm, Died: 1896 Unmarried, no children Inventor and prime manufacturer of DYNAMITE

43. A primer on Nobel prizes 10 most prolific Countries (1900-2000) UofC: 75

44. A primer on Nobel prizes Decision: Nobel-committee, formed of members of Royal Academy Eligible to nominate: Members of the Royal Academy of Sciences of Sweden Former Nobel Prize Winners Professors at 6 Swedish Universities Professors at 6 selected international Universities Individuals asked by the academy to nominate someone  e.g. Dalen, 1912. Physics: Automatic lighthouse

45. A primer on Nobel prizes Prizes for psychological and psych-related topics

46. A primer on Nobel prizes Is the Buzz (still) adequate? Most fields have none Today, it´s a team effort – Big Science The lag between discovery and award is too long It´s a Swedish thing

47. Kahneman & Tversky: The paper • Q: • A: • L: • M: • R: • I: • P:

48. Kahneman & Tversky: The paper • Prospect theory What is prospect theory? Proposed by T & K to understand Framing Effects Function relating subjective value and losses is steeper for gains There is a reference point.

49. That’s it! • No more new content this quarter!