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. The basic problem in decision making • The basic problem is that DM involves choice among alternatives. This is only possible if • You know what the alternatives are • You know what value each has • Those values are expressed in a form which allows comparison

3. The problems for science • The problems for scientific study of decision making primarily have to do with value. As in, how can we measure the value of any outcome (any choice)? • Two basic approaches: • Quantitative (expected value models) • Qualitative (expected utility models)

4. Expected value models • These models originally advanced by economists. • Basic idea: value of an outcome is product of $ value and probability. • E.g., a one-tenth chance of winning $200 is worth .10 * $200 = $20

5. Expected utility models • Problem: humans do not make their choices on the basis of expected value. • Solution: Expected utility – essentially, “how much is it worth to you?” • Problem: there’s no way to specify this in advance. It’s subjective.

6. Some more recent alternative views • Some psychologists have argued that we don’t, in fact, make many decisions. Rather, they say, humans 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.

7. Problems with decision making theory • Loewenstein (2001) • Decision making would require too much capacity. Few decisions result from analysis and comparison of options. • Few alternatives in everyday life can be analyzed in terms of “attributes” useful in decision making. • Computers are good at computing. Humans are good at pattern-matching, categorizing.

8. Problems with decision making theory • Loewenstein (2001) • Why is context so important? E.g., choice between 2 gambles affected by whether decision was said to involve a gamble or insurance (note: buying insurance is a gamble) (Hershey et al. 1982). • Why is there so much intra-individual variation (if decision-making is algorithmic)?

9. Problems with decision making theory • Loewenstein (2001) • Decision making anomalies – people prefer sequences of outcomes that improve over time. But expected utility tells us that delayed rewards are discounted. • Do we know what we want? Ariely et al. (2001)

10. Decision making - definition Decision making occurs when you have several alternatives and you choose among them. There are two characteristics of good decision making: 1. You make the best (most valuable) choice. 2. You are consistent in the choice you make in a given situation.

11. Decision making – how well do we do it? The best choice: There are two reasons humans do not always make the best choice: A. We don't always pick the outcome with the highest value. B. We don't always evaluate all possible outcomes before choosing. 

12. Picking the outcome with the highest value Economists offer several schemes for measuring value. The most famous is the theory of expected value. E.V. = outcome measured in dollars X probability Thus a 1/10th chance of winning $200 is worth 1/10 X $200 = $20

13. The problem with expected value theory Which would you prefer - a 1/10th chance of winning $100, or a 9/10th chance of winning $8? A. 1/10th X $100 = $10 B. 9/10th X $8 = $7.20 But suppose you are hungry - really hungry. Then, higher probability may be more attractive, so you choose B.

14. Expected utility theory Expected utility theory argues that we choose utility. Utility = what it is worth to you right now.  2 differences between objective and subjective value of money:  1. Subjective value is not a linear function of objective value. ($1 million = 1000 X $1000 $1 billion = 1000 X $1 million.)

15. 2 differences between objective & subjective value 2. Subjective value is not symmetric for gains and losses (losses are more important). Kahneman & Tversky (1984) A. You flip a coin. Heads - win $20. Tails - pay $20. B. You do not flip a coin. You win nothing. Most people chose B.

16. How can we have a science of value? Expected value theory is informative but doesn't work. Expected utility theory works but is not informative. Subjective value is impossible to predict. So the first problem with making the best choice is that we can’t objectively measure value. At least, not as individuals – but there are markets…

17. The second problem with making the best choice • Humans often do not consider all possible outcomes because doing so would take too long. • An HR person at a big company hires a new employee. She has 500 applications. Each takes 10 minutes to review. 500 X 10 = 5000 minutes = 80 hours and 20 minutes of work. She has 6 hours for the task. What should she do?

18. Heuristics In 6 hours, she can review 36 applications. She reads 36 at random and hires the best person among those 36 candidates. This is called satisficing (doing well enough). Sometimes we don't choose the most valuable outcome because discovering it takes too much time. In such cases, we use sensible strategies called heuristics. Heuristics let us satisfice.

19. 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 sometimes when we do have one, it cannot be used (as in the case of the HR person). Then, we use heuristics.

20. Important heuristics in decision making 1. Availability – judgment that the more easily an event comes to mind, the more likely it is. 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.

21. Illusory correlations • 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.

22. Representativeness heuristic • 2. Representativeness - an event considered typical of a large class of events will be considered more probable than an atypical event. • 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

23. Representativeness heuristic 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).

24. Simulation heuristic 3. Simulation Rushing to the airport, you miss your plane by 5 minutes or by 2 hours. Which is more annoying? You can imagine a few things changing on your trip to the airport, so that you make up the 5 minutes and catch the plane. But you can't imagine a few things changing to make up 2 hours.

25. Simulation heuristic • The simulation heuristic lets us "see" the consequences of actions before we do them. • What if Ben Stiller's character in Meet the Parents hadn't gone out on the roof for a cigarette? 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.

26. Review There are three things we lack as decision makers: An objective way of evaluating outcomes Unlimited cognitive processing capacity Unlimited time to make our decisions That’s alright. What we have to do to survive as a species is make good decisions more often than not. Heuristics help us to do that.