PSYCHOLOGY OF DECISION MAKING

Classical theories, Nov 5, 2011

Bernoulli and expected utility In the presence of risky outcomes, a decision maker

could use the expected value criterion as a rule of choice: higher expected value investments are simply the preferred ones.

For example, suppose there is a gamble in which the probability of getting a $100 payment is 1 in 80 and the alternative, and far more likely, outcome, is getting nothing. Then the expected value of this gamble is $1.25. Given the choice between this gamble and a guaranteed payment of $1, by this simple expected value theory people would choose the $100-or-nothing gamble.

However, under expected utility theory, some people would be risk averse enough to prefer the sure thing, even though it has a lower expected value, while other less risk averse people would still choose the riskier, higher-mean gamble.

Indifference curves

Indifference curves for marginal rate of substitution for two products X and Y

Goods X and Y are perfect substitutes

Indifference curves for perfect comple-ments X and Y

Why is Bernoulli’s concept wrong? Consider two people got info from

brokers today: A is told her first bonus will be 40.000 CZK

out of maximum 60.000 CZK B is told her first bonus will be 30.000 CZK,

which is 10.000 CZK more than was expected

Who is happier today? Who is better off overall? Bernoulli’s concept focuses on long-term

outcomes, but we work based on changes

About authors

Daniel Kahneman With Amos Tversky and others,

Kahneman established a cognitive basis for common human errors using heuristics and biases (Kahneman & Tversky, 1973; Kahneman, Slovic & Tversky, 1982; Tversky & Kahneman, 1974), and developed Prospect theory (Kahneman & Tversky, 1979).

He was awarded the 2002 Nobel Memorial Prize in Economics for his work in Prospect theory. Currently, he is professor emeritus of psychology and public affairs at Princeton University's Woodrow Wilson School. Kahneman is a founding partner of The Greatest Good, a business and philanthropy consulting company.

About authors

Amos Tversky cognitive and mathematical psychologist, a

pioneer of cognitive science, a longtime collaborator of Daniel Kahneman, and a key figure in the discovery of systematic human cognitive bias and handling of risk. Much of his early work concerned the foundations of measurement.

He was co-author of a three-volume treatise, Foundations of Measurement (recently reprinted). His early work with Kahneman focused on the psychology of prediction and probability judgment. Amos Tversky and Daniel Kahneman worked together to develop prospect theory, which aims to explain irrational human economic choices and is considered one of the seminal works of behavioral economics.

Where prospect theory stems from attempting to account for systematic

features of human decision-making neither explained nor predicted by the Expected utility theory

focused mainly on adapting the main parts of the previous model and expanding the model to accompany the systematic deviances of human decision-making observed in research

Inadequacy of the classical model

What is prospect theory

Analogy with perception – similarly to perception, some information must be computed and cannot be directly derived from perceptions

This computation influences our judgments Changes, not final states drive our judgments

Our judgments depend on differences and changes to current state

Average, not sums that drive our judgments Our judgments depend on the most representative

state and not the sum of all states

Changes NOT states

Judgments depends on changes and states

Gamble 1: 50% chance to win 300 CZK 50% chance to loose 200 CZK Gamble 2: 50% chance to own WEALTH + 300 CZK 50% chance to own WEALTH - 200 CZK

How much do you need to accept the gamble

Value function

Not expected utility but value of the gamble Loss aversion in domain of gains vs. domain of losses (2:1) Results in status quo bias

Averages NOT sums

Longer or wider?

Heuristics and biases

Intuition, just like perception, deceives us in a systematic and consistent way Status quo bias Anchoring bias Representativeness heuristic Availability heuristic Isolation effect Recognition heuristic Sunk cost fallacy Etc.

Design of choice – status quo

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Design of choice – anchoring

Zdroj: Dan Ariely: Jak drahé je zdarma, s.16-19 (Predictably irrational)

16%

0%

84%

68%

32%

Design of choice - representativeness There are 2 packs each containing 100

balls. One has 60 red and 40 white balls The other 40 red and 60 white balls What is the probability you took from the

urn with more red balls if you: pulled 3 red and 0 white balls? pulled 7 red and 3 white balls?

Design of choice - availability Are there more words starting with R or

with R on the third position in a word?

Availability depends on how easy it is to recall an instance of certain kind Take air transport safety Take probability of getting fined for fast

driving

Design of choice – isolation effect Redelmeister and Shafir (1995)

Doctors were given a case, result was to send the patient to hip replacement

Then half: reviewed the case and found you did not try Ibuprofen yet

Other half: reviewed the case and found you did not try Ibuprofen and Piroxicam

Ariely (2009) Lindt for 15 cents or Hersheys for 1 cent: 73% to

27% Lindt for 14 cents or Hersheys for FREE: 31% to

69%

Design of choice – sunk cost fallacy Scenario 1: You bought a $10 non-

refundable ticket to a show. (And note that you definitely would not have done so if the show cost $20.) As you get to the theater you realize you lost your ticket. Luckily, they have more available, still at $10. Do you buy another ticket?

Scenario 2: You didn't buy a ticket ahead of time. As you get to the theater you realize that $10 has fallen out of your pocket and is lost. Luckily, you still have enough to buy a ticket. Do you do so?

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Sunk cost fallacy with time?

TimeMonetary

Source: Nina Bakošová: Diplomová práce – Making Exit Decisions Alex Boese: Elephants on acid and other bizarre experiments

Design of choice – recognition heuristic Which city has a larger population?

(a) Detroit (b) Milwaukee Gigerenzer and Goldstein (1996)

90% German but only 60% American students got it right

There is limited knowledge at all times (we cannot know all)

Therefore we leverage algorithms that can help, such as Take the best - assumes a subjective rank order of cues according to their validities

Gerd Gigerenzer

German psychologist who has studied the use of bounded rationality and heuristics in decision making, especially in medicine. A critic of the work of Daniel Kahneman and Amos Tversky, he argues that heuristics should not lead us to conceive of human thinking as riddled with irrational cognitive biases, but rather to conceive rationality as an adaptive tool that is not identical to the rules of formal logic or the probability calculus.[1]

Gigerenzer is currently director at Max Planck Institute for Human Development and former Professor of Psychology at the University of Chicago and John M. Olin Distinguished Visiting Professor, School of Law at the University of Virginia.

Fast and frugal heuristics

The algorithm is hardly a standard statistical tool for inductive inference: It does not use all available information, it is non-compensatory and nonlinear, and variants of it can violate transitivity.

Herbert Simon proposed looking for models of bounded rationality instead of classical rationality.

Simon (1956, 1982) argued that information-processing systems typically need to Satisfice rather than optimize. Satisficing, a blend of sufficing and satisfying, is a word of Scottish origin, which Simon uses to characterize algorithms that successfully deal with conditions of limited time, knowledge, or computational capacities.

Gigerenzer proposes that such algorithms have ECOLOGICAL VALIDITY - it works fast and reliably enough given resources!

Convex or concave?

1.) light comes from above (in relation to retinal coordinates), and2.) there is only one source of light.

I think, therefore I err

Why Can’t Players Predict Where a Fly Ball Lands?

How does a baseball player catch a fly ball? It seems that the brain, at an unconscious level, somehow computes the trajectory of the ball. In The Selfish Gene, biologist Richard Dawkins writes: When a man throws a ball high in the air and catches it

again, he behaves as if he had solved a set of differential equations in predicting the trajectory of the ball. He may neither know nor care what a differential equation is, but this does not affect his skill with the ball. At some subconscious level, something functionally equivalent to themathematical calculation is going on (1989: 96).gaze heuristic, which works in situations where a

ball is already high up in the air: Fixate on the ball, start running, and adjust your running speed so that the angle of gaze remains constant.

References

Baron J. Thinking and deciding. 2nd ed. Cambridge, United Kingdom: Cambridge University Press, 1994.

Gladwell, M. Blink: the power of thinking without thinking. London: Allan Lane, 2005. 230 p.

Gigerenzer, G.: I think, therefore I err. Social Research, 2005.

Kahneman, D. Nobelprize.org [online]. 2002 [cit. 2010-05-24]. Prize Lecture.

Ariely, D. Are we in control of our own decisions? [online]. 2008 [cit. 2009-05-20].