Decision making

1. Decision making • Decision making is an important area within cognitive psychology, because of the applied interest: everyone would like to make better decisions, including armies, corporations, and individuals. • So, how is the science doing? Do we have a good understanding of human decision making? Not really…

2. 2 approaches to study of decision making • Behavioural decision making • Basic idea: people make deliberate choices • requires assessment of self-interest, computed from attributes of alternatives • Loewenstein’s embryonic view • People do not compute value, but construe situations and select appropriate rules

3. Behavioural decision making • Decision making is defined as choice among alternatives. • Traditional view: people analyze alternatives for their attributes. They then evaluate these attributes comparatively, and choose the one that maximizes value. • Dominant view has been Expected Value theory.

4. Expected value models • Originally advanced by economists. • Basic idea: value of an outcome is product of $ value (if it occurs) and probability. • E.g., a one-tenth chance of winning $200 is worth (1/10 * $200) = $20 • Problem: humans do not make their choices on the basis of expected value.

5. Problems with Expected Value models • Three reasons humans do not use expected value approach to decision making: • Specification of relevant attributes for any particular alternative is difficult. • B. Evaluation of alternatives is difficult. • C. For many problems, processing all attributes of all possible alternatives would require more capacity than humans have. 

6. Problems with Expected Value models Specification of attributes is hard. Evaluation of alternatives is hard Processing capacity is limited

7. Specification of alternatives • Klein (1989) • 150 interviews with 5 populations of decision makers (e.g., managers) • Conclusion: few decisions involve generating a variety of options and comparing their strengths & weaknesses. • Most alternative we choose between can’t be broken down into clear attributes

8. Problems with Expected Value models Specification of attributes is hard. Evaluation of alternatives is hard Processing capacity is limited

9. Evaluation of alternatives (1) • Which would you prefer - a 1/10th chance of winning $100, or a 9/10th chance of winning $8? • 1/10th X $100 = $10 • B. 9/10th X $8 = $7.20 • But suppose you are hungry - really hungry. Then, higher probability more attractive, so you choose B. How do we quantify the extra value associated with certainty?

10. Evaluation of alternatives (2) • There are important differences between objective and subjective value of money:  • Subjective value is not a linear function of objective value. • $1 million = 1000 X $1000 • $1 billion = 1000 X $1 million. • Is $1 billion worth 1000 times as much as $1 million to you?

11. Evaluation of alternatives (2) • 2. Subjective value is not symmetric for gains and losses (losses are more important). Kahneman & Tversky (1984): • A. You flip a coin. Heads – you win $20. Tails – you pay $20. • B. You do not flip a coin. You win nothing. • Most people chose B.

12. Problems with Expected Value models Specification of attributes is hard. Evaluation of alternatives is hard Processing capacity is limited

13. Processing capacity is limited • Humans often do not consider all possible outcomes because doing so would take too long. Example: • An HR person must hire a new employee. • She has 500 applications. Each takes 10 minutes to review -> 80 hours 20 minutes • She has 6 hours for the task. What should she do?

14. Processing capacity is limited • In 6 hours, she can review 36 applications. • She reads 36 at random, hires the best of those 36. • This is called satisficing(doing well enough). • Instead of analyzing problems the way a computer would, humans use heuristics such as satisficing.

15. Heuristics • A heuristic is a 'rule of thumb,' a procedure which is easy to use, though it may not work. • In contrast, an algorithm is a step-by-step procedure guaranteed to produce the correct result. • We do not always have an algorithm, and when we do have one, it may take too long (as in the case of the HR person). Then, we use heuristics

16. Processing capacity is limited Because processing capacity is limited, humans often use heuristics. We’ll talk about three important heuristics that have been studied by psychologists: Availability Representativeness Simulation

17. Three important heuristics • Availability • judgment that the more easily an event comes to mind, the more likely it is to occur. • Often works - e.g., when was the last time you met a professor who liked to be yelled at? • Sometimes doesn’t work – as in cases of illusory correlation.

18. Illusory Correlation • IC is found in cases of rare events that happen to co-occur. • E.g., sports announcer says "X hasn't dropped the ball for 100 plays," and X drops the ball on the next play. • You only notice this when the rare, newsworthy event happens - not when X doesn't then drop the ball. • IC also may have a role in racism.

19. Three important heuristics • 2. Representativeness • An event considered typical of a large class of events will be considered more probable than an atypical event. • So, if you are judging the likelihood of something, you are influenced by how typical it is. • Judging likelihood part of choice.

20. Representativeness • Tversky & Kahneman (1983): • Linda is 31 years old, single, outspoken and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in antinuclear demonstrations

21. Kahneman & Tversky (1983) • Which of these is more likely? • A. Linda is a bank teller. • B. Linda is a bank teller and is active in the feminist movement. • Most subjects picked B - but that is mathematically impossible. The set of people (bank tellers who are feminists) is smaller than the set of people (bank tellers).

22. Three important heuristics • 3. Simulation • Rushing to the airport, you miss your plane by 5 minutes – or by 2 hours. Which is more annoying? • It’s a lot easier to simulate (imagine) catching the plane if you only missed it by 5 minutes – so that is more annoying.

23. Simulation heuristic • The simulation heuristic lets us "see" the consequences of actions before we do them. • What if Frodo hadn’t volunteered to take the ring to Mordor? What if you took a year off from university and went travelling • Simulation shows both alternative futures and alternative pasts that you could learn a lesson from.

24. Expected Value models - Summary We considered a number of problems with EV: Specification of attributes is hard. Evaluation of alternatives is hard Evaluation is tricky Subjective & objective value of money differ Processing capacity is limited So we often use heuristics instead of computing which alternative is best.

25. Expected utility models • Because Expected Value models don’t capture human decision making, psychologists developed concept of Expected Utility: • essentially, “how much is it worth to you?” • Problem: there’s no way to specify this in advance. It’s subjective.

26. How can we have a science of value? • Expected value theory is meaningful but doesn't work. Expected utility theory works but is essentially empty: Subjective value is impossible to predict. • The first problem with a science of human decision making is that we can’t objectively measure value, so we can’t predict behaviour. (Perhaps this is why there are markets.)

27. How can we have a science of value? What should we do? Some psychologists and economists say, keep trying. We’ll eventually develop a science of decision making. Others say, don’t bother. We don’t need a science of decision making, because decision making doesn’t exist…

28. Some more recent alternative views • Some psychologists have argued that we don’t, in fact, make many decisions. Rather, they say, human behaviour is automatic (e.g., Bargh & Chartrand, 1999), or rule-guided (e.g., Anderson’s production rule system in his ACT model). • Loewenstein (2001) summarized arguments against the idea that humans make decisions.

29. Loewenstein’s (2001) arguments • Context effects • Intra-individual variation • Effects of careful consideration • Decision making anomalies • Do we know what we want?

30. Loewenstein’s arguments • Context matters. • E.g., choice between 2 gambles affected by whether decision was said to involve a gamble or insurance (Hershey et al. 1982). • Context effects influence construal of the situation, thus which set of rules of behaviour is applied (e.g., tough negotiator with a rival, soft negotiator with a child – but what if child becomes a rival?)

31. Loewenstein’s arguments • If decision-making is algorithmic, why is there so much intra-individual variation? • Fear is not based on objective risk: Many people are afraid of some risks (e.g., flying) but not of others (eating red meat). • Many people have long-term goals but also indulge in short-sighted behaviour. • So whether we are afraid, whether we are goal-directed, is subjectively determined.

32. Loewenstein’s arguments • If decision making theory is correct, making people consider their choices more carefully should lead to better choices. It doesn’t. In fact, it can make things worse. • Wilson et al. (1993): students selected favourite poster from a set. Those asked to provide reasons for choice less happy with choice, less likely to display poster. Wilson: deliberation interferes with “gut” reaction.

33. Loewenstein’s arguments • Decision making anomalies • People prefer sequences of outcomes that improve over time. But expected utility tells us that delayed rewards are discounted. • Diversification bias: if people choose several things from a set, they go for more diversity when they choose simultaneously than when they choose sequentially. Why?

34. Loewenstein’s arguments • Do we know what we want? • Ariely et al. (2001): some subjects asked, “Would you listen to 10 minutes of poetry if we gave you $10?” Others asked, “Would you pay $10 to hear 10 minutes of poetry?” • Later, first group had to be paid to listen to poetry; second group willing to pay. • Both groups named higher amounts for longer durations! They didn’t know whether listening to poetry was positive or negative, but knew it was more positive or more negative if longer.

35. Loewenstein’s alternative perspective • Consciousness mainly makes sense of behaviour afterwards. • People rely on highly-developed cognitive capacities such as perception and categorization, more than computations. • Two stage process: first, construe the situation. Then apply relevant rules.

36. Loewenstein’s alternative perspective • Intra-individual differences to be explained via situation construal. • 5. Identify choice heuristics people use and cues that trigger them. • 6. Don’t assume people evaluate outcomes consciously, or ‘know’ what they want.

37. Loewenstein’s alternative perspective • Overall, 2 themes: • Importance of subjective construals of situations, and • Importance of role of heuristics. • Question is, will we be able to have a science of such phenomena? We don’t yet know the answer.