Chapter 11

Judgment, Decision Making, and Reasoning

Judgment

• Step in the Decision making process.• involves estimates of probability.

Decision Making

Involves selecting from various options; full information is often not available so judgment is often involved.

Subjective Probability: describes an individual's personal judgment about how likely a particular event is to occur. It is not based on any precise computation but is often a reasonable assessment by a knowledgeable person.

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Judgment and decision making in situations of uncertainty

• The individual is not given all the information necessary to be certain of the answer and has to use previously acquired knowledge

• Primary problem: lack of knowledge and misinterpretation

Super-additivity Judgment Error Mandler (2005)

What is the risk of one terrorist attack over the following six months? 30%

What is the risk of an attack plotted by al-Qaida or not plotted by al-Qaida over the next 6 months. (30% + 18%) = 48%

Why is the combined probability of attacks estimated to be higher than the overall probability?

Support Theory

Tversky and Koehler (1994)Explicit descriptions draw attention to aspects of the event that are less obvious than in non-explicit descriptions.

Memory limitations mean that people don’t remember all of the relevant information if it isn’t supplied.

The likelihood of a hypothesis corresponds to the support for the hypothesis relative to the support for alternative hypotheses.

Do experts do this?

(Redelmeier, Koehler, Liberman, & Tversky, 1995)

Physicians were asked to provide probabilities for the following events, with respect to a particular hospitalized patient whose case had been summarized to them:a. Dies during the present hospital admission,b. Discharged alive but dies within 1 year,c. Lives more than 1 but less than 10 years, andd. Lives more than 10 years.

Yes, experts do it too.

Because the four events are exhaustive, additivity entails that their probabilities sum to 1. Each of 52 physicians assessed the probability of exactly one of the four outcomes (a-d), which was randomly assigned to him or her. Under these conditions, the mean judgments of the four component probabilities summed to 1.64, therefore many of the physicians' assessments were too high.

Example

Suppose that the rate of disease D is threetimes higher among Farmers than among non-farmers. Suppose, further, that Pat is diagnosed withthe disease, and this is all that you know about Pat. What is the likelihood that Pat is farmer?

This problem depends upon what percentage of the population is farmers (let's suppose that it is 10%) . Suppose that we have a population of 100 people, 10 of whom are farmers. Suppose, 3 of the farmers have disease D, which means that the rate of the disease among the farmers is 3 out of 10, or 30%. Since we are given that the rate of the disease among non-farmers is one-third of that among farmers, we must suppose that 10% of the non-farmers in the population have D, which means that 9 of the 90 non-farmers have D.

So, the total number of persons with the disease in our population is 12, three of whom are farmers. Thus, all that we know about Pat is that he or she has D, so Pat is one of the unlucky twelve. Therefore, the chance that Pat is a farmer is 3 in 12, or 25%.

Errors in Judgment

Base Rate NeglectPeople often fail to take base rate information (relative frequency with which an event occurs in the population) into account when making judgments.

The following statistics are known about age 60 women who participate in a routine mammogram screening.2% of women have breast cancer at the time of screening. Most of them will receive a positive result on a mammogram. There is a 6% chance that a woman without breast cancer will receive a positive result.

A woman gets a positive result, what are the chances she has breast cancer?

There is a 6% chance that a woman without breast cancer will have a harmless cyst that looks like a cancerous tumor and causes a positive result.

A woman gets a positive result, what are the chances she has breast cancer?

WHY? Krynski and Tenenbaum (2007)The alternative possibility of a benign cyst provides reasonably full causal knowledge allowing people to solve the problem.

Base-rate Neglect

We are able to use base-rate information and do so when the alternatives are more explicitlydefined. Sometimes people overemphasize base- rate.(see bus example, Teigen & Keren, 2007) .We use base-rate information when strongly motivated to do so. (see Ditto et al., 1998 in text).

AlgorithmsGenerate every possible solutionSystematically work through them

Advantage: If done correctly it guarantees a solution!

Disadvantages: Takes a great deal of time and cognitive effort.Requires full definition of the problem.

Heuristics – rules of thumb, mental short cuts, pre-stored strategies for solving problems.

Advantages: Less cognitive effort Allow you to proceed with incomplete understanding of the problem.

Disadvantages: Does not guarantee a solution

Representativeness Heuristic

If Eric lives in the United States, has several tattoos, and often wears dark sunglasses and a leather jacket, is it more likely that he owns a motorcycle or a car?

If people use the representativeness heuristic, they may judge that Eric is more likely to own a motorcycle. This happens because the description of Eric is more representative of motorcycle owners.

Stereotypes

Other Examples of the Representiveness Heuristic

Flip a coin four times.Which outcome is most likely?HHHH, TTTT HTTH

Multiple Choice Exam example.

In English are there more words that begin with the letter “R” or that have “R” as the third letter?

Conjunction FallacyExamine the following personality sketch:Bill is 34 years old. He is intelligent, but unimaginative, compulsive and generally lifeless. In school, he was strong in mathematics but weak in social studies and humanities.

Which statement is more probable:A. Bill is an accountant that plays jazz for a hobby, orB. Bill plays jazz for a hobby?

From elementary probability theory, the probability of a conjunction cannot exceed the probability of either of its constituents, This is the conjunction rule. In the example, A. was erroneously selected by 92% of subjects including those who were informed in matters of statistics.

Availability Heuristic

When people use this heuristic, they estimate probability based on how readily they can remember relevant instances of an event. If people can quickly remember instances of some event, then they will estimate that event as being quite likely.

Summer of the Shark

Overestimating the Improbable and Underestimating the Probable

Using the availability heuristic can cause people to overestimate improbable events. This happens because rare but memorable events come to mind easily. Example: Recalling a few dramatic TV reports of plane crashes could make people overestimate the likelihood of a plane crash.

9/11 Anniversary Media Coverage: Anxiety and Expectations of Future

Terrorist Attacks

Corinne S. Enright

University of Wisconsin-

Platteville

                                           

              

Abstract Posttraumatic stress symptoms and

expectations of future terrorist attacks occurring in the United States were higher for respondents who viewed media coverage of the fifth anniversary of the 9/11 terrorist attacks than for those who did not. Results are discussed in terms of the availability heuristic

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4thAnniversary

5thAnniversary

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Figure 1. Impact of Event Scale scores following the Fourth and Fifth Anniversary of 9/11.

Heuristics are Adaptive (Gigerenzer)

Much of the research on Heuristics and Decision making have focused on the errors people make using these short-cuts. Gigerenzer stresses the accuracy.

People use Heuristics because they work!They are fast and fugal!

“Take the Best” heuristic.“Adaptive Toolbox of heurisitcs!

E.g., Recognition Heuristic

Which City is the Capital of Canada?

Toronto or Ottawa

Which has a larger population?

E.g., Recognition Heuristic

Which City is the Capital of Canada?

Toronto or Ottawa

Which has a larger population?

Do either Toronto or Ottawa have any sports teams that I have heard of?

Familiarity information is retrieved very quickly!!

Natural FrequenciesPeople are more able to solve problems when stated in frequencies than in probabilities or percentatages. (Fiedler, 1988).

i.e., the explanation of the disease D problem is Much easier to understand when worked throughIn frequencies rather than in probabilities.

But this does not explain all probability errors.

Figure 11.2 Percentage correct inferences by advanced medical students given four realistic diagnostic tasks expressed in probabilities or frequencies. From Hoffrage et al. (2000). Reprinted with permission from AAAS.

When do we use Heuristics for judging Probability and when do we use more formal and effortful methods?

Dual-Process Model (Kahneman & Fredrick, 2005)

System 1 System 2-intuitive, fast, automatic, Analytical, slower, controlled, rule- effortless and often emotionally governed and consciously monitored. charged.

- Used when the decision is not - Used when the task is deemed to deemed to be important, time is be important, time is available to not available for more analytic for more analytic processes, and processes and full information is full information is available. not easily available.

Both processes may be active at the same time.

De Neys, 2006 – Conjunction –fallacy problem.

• System 1 is faster. People who got the problem correct (i.e., used system 2) took 40% longer than those who got it incorrect (system 1).

• System 2 requires cognitive resources. When give as a dual task procedure, participants performed worse.

De Neys and Glumicic (2008)Base-Rate problems

• System 1 more likely to produce stereotypic answers.

Used congruent , incongruent and neutral problemsCongruent – both system 1 and 2 produce same answer.Incongruent – system 1 and system 2 produce conflicting answers.Neutral – system 2 processing needed to solve.

Figure 11.3 Mean times to read and make correct decisions with incongruent, congruent, and neutral problems and incorrect decisions with incongruent problems. Reprinted from De Neys and Glumicic (2008). Copyright © 2008, with permission from Elsevier.

Took longer for incongruent. Why?Talk aloud protocols showed no use of base- rate information – yet they were effected by the fact that there was a conflict between answers.Despite sensing at some level the conflict they fail to inhibit the incorrect heuristic-based answer.

Much worse at incongruent than congruent problems (22% vs 97% correct).

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Omission Bias

The omission bias is manifested when a more harmful act of omission is preferred to a less harmful act of commission.

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Normative Theory – how people should make decisions (attempt to maximize utility)

Expected Utility = (prob of X) x (Utility of X)

Decision Making is all about Taking Risks

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Prospect Theory (Kahneman & Tversky)• People identify a reference point• People are much more sensitive to potential losses than to potential gains

(Loss aversion)

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Loss Aversion losing $100

produces more pain than gaining

$100 produces pleasure.

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Prospect Theory

Kahneman & Tversky (1981)

The resulting value function is steeper for losses than for gains

"We have an irrational tendency to be less willing to gamble with profits than with losses."

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Deal or No Deal StudyBrooks et al. (2009)

• Most contestants were risk adverse.

• More risk adverse with higher stakes.

• Males were less risk adverse than females.

Individual Differences (Josephs et al., 1992)High Self-Esteem associated with more risky gambles!

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Two programs to combat disease:

• If program A is adopted, 200 people will be saved.

• If program B is adopted, there is a 1/3 probability that 600 people will be saved, and 2/3 probability that no one will be saved.

The prospect of saving 200 lives with certainty was more promising than the probability of a one-in-three chance of saving 600 lives. This risky prospect B was of equal expected value as the first prospect A.

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A second group of respondents were provided with different program options.

Two programs to combat disease:• If program C is adopted, 400 people will be

die.

• If program D is adopted, there is a 1/3 probability that nobody will die, and 2/3 probability that 600 people will die.

Risk taking: the certain death of 400 people is less acceptable than the two-in-three chance that 600 people will die.

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Framing EffectsYou start with $0.Would you rather:a) have a 80% chance of winning $45 and a 20%

chance of winning nothingb) with certainty win $30

I give you $45Would you rathera) have a 80% chance of losing nothing and a 20%

chance of losing it allb) with certainty lose $15

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Why does Framing work?Loss Aversion – when framed as a loss (deaths) people choice the less risky option.

Only find Framing effects when there is real life value for the decision maker (Humans vs. Aliens)

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Social and Moral Factors

Deterministic – Definite survival of 2/3rds.Probabilistic – 1/3 chance all survive and 2/3rds change

no one survives.When asked about strangers, picked Deterministic option.When asked about loved ones, picked probabilistic. Why? Probabilistic has lower expected value, but appears fairer.

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Framing – Cont.

When subjects are asked to provide a rationale for their choice, the framing effect is either reduced or eliminated(Larrick et al., 1992; Miller & Fagley, 1991: Almashat et al., 2008).

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Sunk Cost Effects – tendency to continue an endeavor once an investment in money, effort, or time has been made”.

President Bush has offered several reasons for staying the course in Iraq. One of them is the almost 2,000 Americans who have already died in the war. "We owe them something," the president said on Aug. 22, 2005 "We will finish the task that they gave their lives for."

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As casualties mounted in Vietnam in the 1960s, it became more and more difficult to withdraw, because war supporters insisted that withdrawal would cheapen the lives of those who had already sacrificed. We "owed" it to the dead and wounded to "stay the course." We could not let them "die in vain." What staying the course produced was perhaps 250,000 more dead and wounded.

Barry Schwartz is a professor of psychology at Swarthmore College and the author of The Paradox of Choice: Why More Is Less.

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Emotional Factors

Anxiety Makes Us More Loss Averse.Shiv et al. (2005) Compared patients with brain damage to emotion regions showed less loss aversion than controls.

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Social and Cultural Context

NY Cab Driver Study (Camerer et al., 1997)According to standard economic theory, the “Rational Man” would work more on the days when business was good and less on the days when business is slow.Cab Drivers do the opposite. They can work for four hours on a rainy day and make about the same as they make working for eight hours on a beautiful day. In fact, that is precisely what most taxi drivers do.

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Accountability Pressure(Need to Justify our Decisions to Other)

Increases Sunk Cost and loss Aversion

Simonson & Straw (1992)Schwartz et al. (2004) – medical Doctors

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Complex Decision Making

Multiple criteria need to be taken into consideration. Might not be able to fully meet all.

- identify criteria

- weight criteria

- assign subjective value (utility) to each

- select option with highest utility.

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Bounded Rationality

People are as rational as their processing limitations permit!(Simon, 1957)

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Complex Decision Making Strategies (e.g., choosing an apartment)Start by identifying our options then: Elimination by aspects

- eliminate those too far away- eliminate those that are too

expensive- eliminate those that don’t allow cats.etc. etc.

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Satisfactory Sufficing

Optimal DecisionsSatisficing

Satisficers are happier, more optimistic and more satisfied with their lives than optimizers (perfectionists). They do less second guessing than optimizers.

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On Line Dating Study

Women were asked to select the one man they would most like to contact

from a dating Web site containing 4, 24, or 64 profiles: As the number of options increased, participants self-reported lesser use of a comprehensive choice strategy and greater use of heuristic choice strategies (Lenton & Stewart, 2008).