The Role of Cognitive Biases_ScottLeek.pdf

8/14/2019 The Role of Cognitive Biases_ScottLeek.pdf

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2012 Sigma Consulting Resources, LLC 1

Why People Make Bad DecisionsThe Role of Cognitive Biases

Scott LeekSigma Consulting Resources, LLC

American Society for Quality Denver Section

October 16, 2012


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Cognitive Biases & Decision Making

Topics

Cognitive Biases and decision making Review of Common Biases and Mitigation Strategies

! Hindsight Bias! Confirmation Bias! Anchoring and Adjustment Heuristic! Availability Heuristic! Representativeness Heuristic

o Insensitivity to Sample Sizeo

Insensitivity to Prior Probabilityo Conjunction Fallacy

Decision Quality Control Checklist


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Exercise

Decision Making

Identify at least 3 decisions you have made, or beeninvolved in making, that turned out to be wrong or not so

good. The decisions can be recent or in the past, large or

small.


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Cognitive Bias

Definition

a replicable pattern in perceptual distortion,inaccurate judgment, illogical interpretation, or what is

broadly called irrationality

Arise from multiple confounded sources! Information-processing shortcuts (e.g., availability heuristic)! Mental noise (wrong way on a one-way street)! Limited information processing capacity (e.g., Bayesian

probabilities)

! Emotional or moral motivations (e.g., just-world hypothesis)! Social influence (e.g., groupthink)


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Decision Making

Exercise Test

By a show of hands how people

Identified threeor more examples of not so good decisions?

Identified twoor more examples of not so good decisions?

Identified one or more examples of not so good decisions?

If you were unable to identify an example you may besuffering from the


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Hindsight Bias

Cognitive Biases


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Definition

Hindsight Bias

In hindsight

the consistent exaggeration of what could have beenanticipated in foresight (I knew it all along or creepingdeterminism)

the inclination to see events that have alreadyoccurred as being more predictable than they were

before they took place


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Problems (So What)

Hindsight Bias

Failure to learn from the outcome of events, not being surprised byanomalous outcomes (if we are unable to acknowledge when our

predictions are wrong, they will never be right)

Influences attributions of blame after unforeseen catastrophicevents

People tend to misremember (memory distortion) their predictionsin order to exaggerate in hindsight what they knew in foresight

Causes people to rely too heavily on knowledge of the outcomes ofhistorical events, leading to accepting sufficient, though not

necessary explanations too easily (tried it, didnt work turns out

there was an interaction)


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Preventing or Mitigating

Hindsight Bias

Awareness is not enough to mitigate Use of the scientific method or derivative like Plan-Do-Study-Act

(PDSA)

Recording predictions prior to events (a priori) like processchanges, experiments, et cetera and reviewing those predictions

after the events (a posteriori), formally updating current knowledge

Focus on why outcomes occur, not just if the predictions arecorrect, try to explain alternative or anomalous outcomes

Reward people based on logic of judgment, not just outcomes(Hogarth, e.g., control charts and Type I errors, testing with true/

false give a reason)


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Confirmation Bias

Cognitive Biases


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Case Study

Confirmation Bias

A sales manager believes that a new marketing methodwill increase the sales call success rate. An experiment

was designed to test the effectiveness of the new method.

The experiment was run for one week when 480 sales

calls were made. The new method was randomly assignedto sales calls and the number of sales made was recorded.The brochure resulted in 270 sales.

Treatment # Sale Made # No Sale MadeNew Method 270 90

Old Method 90 30

Conclusions?


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Definition

Confirmation Bias

A tendency for people to favor information that confirms existingbeliefs or theories (paradigm, Kuhn)

Ambiguous evidence is interpreted as supporting existing beliefs ortheories

Fail to search for disconfirming evidence Typically falls into three categories of bias:

! Search for information! Interpretation! Memory (hindsight bias)

In light of the confirmation bias the oft quoted Ill believe it when I see itmight better be stated Ill see it when I believe it. (see Thomas Kuhn, TheStructure of Scientific Revolutions


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Problems (So What)

Confirmation Bias

Overconfidence in decision-making based on ignoring or notseeking all relevant data

Leads to flawed causal models which in turn influences what weobserve, leading to flawed causal models in what can be a self-

reinforcing loop (Senges Reflexive Loop)

Leads to the We have made the decision now find the data tosupport it scenario

Plays a role in Groupthink


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Observable data

and experiences

I select datafromwhat I observe

I addmeanings

I makeassumptions

based on themeanings I added

I draw

conclusions

I adopt beliefsabout the world

I take actions

based on mybeliefs

The Reflexive Loop(our beliefs affect what

data we select next time)

From the Fifth Discipline Field Book by Peter Senge

Confirmation Bias

Problems (So What)


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Confirmation Bias

Beware of asking (or being asked) to prove something. When theobjective is to prove, that will be the bias

Build into questions, data collection and analysis a search fordisconfirming information (use all quadrants of the 2X2 table)

Adopt the opposing or contrary point of view or position, in a groupallow someone to play devils advocate

Use of the scientific method or derivative like Plan-Do-Study-Act(PDSA)


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Anchoring and Adjustment

Cognitive Biases


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Case Study


An engineer was asked to prepare a budget for completingengineering projects over the next year. The engineer

obtained the budget for the previous year and after a brief

analysis prepared a budget similar to the previous years

budget but 5% higher.

What was the basis for the budget (goal)?


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Definition


In the process of making estimationspeople start with animplicitly suggested reference point (anchor) and make adjustments

to it to reach their estimate, even if the anchor is irrelevant

In an early study (Tversky and Kahneman) spun a roulette wheel infront of a group of experimental subjects. The result was 65.

Subjects were asked to record this result. They were then asked to

estimate the percentage of African nations that were members of

the United Nations. The process was repeated with a second group

of subjects, but the result from the roulette wheel was 10

The median estimates for the two groups were significantly differentwith the group shown the 65 having a median estimate of 45% and

the group shown the 10 having a median estimate of 25%


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Problems (So What)


May lead to frustration and failure to accomplish goals andobjectives because the goal was not realistic or attainable

May lead to the waste of underachievement, much more could havebeen accomplished if the goal was set higher

Application and implications for process improvement teams orfunctional teams with measureable goals


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Anchors analogous to last years budget will always influenceestimates but can be balanced by an exploration of the causal

factors influencing the estimate (outcomes)

Use of models like the SMART (Specific, Measureable, Attainable,Relevant, Time-bound) criteria when creating goals

Can have profound implications when negotiating


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Availability Heuristic

Cognitive Biases


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Case Study


Structure A

XXXXXXXX

XXXXXXXX

XXXXXXXX

A path in a structure is a line that

connects an element in the top

row to an element in the bottom

row and passes through one andonly one element in each row.

In which structure (A or B) are

there more paths? How many?

Structure B

XX

XX

XXXXXX

XX

XX

XX

XX

The number of paths in each

structure is the same 83= 29= 512


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Case Study


In a study (Tversky and Kahneman) 85% of respondents foundmore paths in Structure A than in Structure B

The bias towards Structure A is explained by the eight columnswhich make the paths more distinctive and available


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Definition


[A] mental shortcut that uses the ease with which examples cometo mind to make judgments about the probability of events. The

availability heuristic operates on the notion that if you can think of

it, it must be important

How many words start with the letter k? How many words havethe third letter of k?

The heuristic can be beneficial, but the frequency that events cometo mind are usually not accurate reflections of their actual

probability in reality


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Problems (So What)


If the available instances or associations reasonably represent thecircumstances there is not a problem, otherwise correct conclusions

and decisions are more a matter of good fortune

Customers and stakeholders are often surveyed about theirexperience's and perceptions regarding a product or service, theresponses can often be biased by the availability heuristic


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Prior to decisions check the data used to make the decision, wasthe most available data used? If so, is there bias?

Is the data used to make the decision representative? Is base rate data available?


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Representativeness Heuristic

Cognitive Biases


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Definition


Used when making judgments about the probability of events oftenbecause of its ease of computation

Representativeness is "the degree to which [an event] (i) is similarin essential characteristics to its parent population, and (ii) reflects

the salient features of the process by which it is generated


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Problems (So What)


Just because something is more representative doesnot mean it is more likely (base rate vs. case rate data)

People overestimate their ability to predict the likelihoodof an event

Rooted in three types of biases! Insensitivity to sample size! Insensitivity to prior probabilities! Conjunction fallacy


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Case Study

Insensitivity to Sample Size

A certain town is served by two hospitals. In the larger hospital about

45 babies are born each day, and in the smaller hospital about 15

babies are born each day. As you know, about 50% of all babies are

girls. However, the exact percentage varies from day to day.

Sometimes it may be higher than 50%, sometimes lower.

For a period of 1 year, each hospital recorded the days on which more

than 60% of the babies born were girls. Which hospital do you think

recorded more such days?

A. The larger hospitalB. The smaller hospital

C. About the same (that is, within 5% of each other)


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Case Study


In a study (Tversky and Kahneman) 56% of respondents selectoption C, and 22% selected options A and B respectively

According to sampling theory the larger hospital is much more likelyto report a ratio close to 50% on a given day compared to the

smaller hospital


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Definition


Tendency to expect different sized groups of samples to be equallyrepresentative of a process or population

Insensitivity to, or lack of knowledge of the role sampling error playsin sample statistics


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Case Study

Insensitivity to Prior Probability

A study (Tversky and Kahneman) involved telling one group ofparticipants that a provided description of a person came from a

group of 70 engineers and 30 lawyers and then asking them to

assess the probability that the described person was an engineer

(or lawyer).

A second group was told that the description came from a group of30 engineers and 70 lawyers and asked to assess the same

probability.

The experiment was repeated with variations in descriptions.


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Case Study


Tversky and Kahneman found a strong tendency for participants todisregard the base rate (frequency of occurrence) information

preferring to rely on the descriptive information

When considered the base rate probabilities were not adjustedappropriately (Bayesian probabilities) given the additionalinformation


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Definition


Tendency to ignore or improperly weight base rate probabilities Improperly weighting additional information when discounting base

rate probabilities

A related bias is the Conjunction Fallacy which states that theconjunction of two events cannot be more likely than the

occurrence of either event alone (Linda study)


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Now What?

Cognitive Biases


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Now What?

Cognitive Biases

Kahneman, Lovallo, and Sibony have proposed a Decision Quality

Control Checklist involving three phases of assessment

Preliminary Challenge Evaluation


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Preliminary


1. Check for Self-interested Biases (overoptimistic)2. Check for the Affect Heuristic (in love with the solution)3. Check for Groupthink (dissenting opinions explored)


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Challenge


4. Check for Representativeness Bias*5. Check for the Confirmation Bias (credible alternatives)6. Check for Availability Bias (imagine perfect information)7. Check for Anchoring Bias (where did the numbers come from)8. Check for Halo Effect (assumption success will be transferable)9. Check for Sunk-Cost Fallacy (overly attached to history)

*Kahneman et al refer to this as the Saliency Bias


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Evaluation


10. Check for Optimistic Biases (game it)11. Check for Disaster Neglect (worst case bad enough)12. Check for Loss Aversion (overly cautious)


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Summary

Cognitive Biases

Cognitive Biases and decision making Review of Common Biases and Mitigation Strategies

! Hindsight Bias! Confirmation Bias! Anchoring and Adjustment Heuristic! Availability Heuristic! Representativeness Heuristic

o Insensitivity to Sample Sizeo Insensitivity to Prior Probabilityo Conjunction Fallacy



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Questions

Cognitive Biases


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Design of Experiments (DOE)

Kahneman, D., Lovallo, D., Sibony, O., The Big Idea: Before You Make That BigDecision, Harvard Business Review June 2011, Harvard Business Publishing.

Lovitt, M. R., Pragmatic Knowledge and Its Application to Quality, 1992 ASQC Quality

Congress Transactions,ASQ (formerly ASQC), Milwaukee, WI 1992.

Lovitt, M. R., Using Quality Tools and Methods to Reduce Bias in Judgment, Quality

Engineering 8(1), 93-116 (1995-96), Marcel Dekker, Inc. 1995.

Moen, Ronald D., Nolan, Thomas W., Provost, Lloyd P., (1991): Improving Quality Through

Planned Experimentation, McGraw-Hill, New York.

Other References

Bazerman, M. A., Judgment in Managerial Decision Making, John Wiley and Sons, NewYork, 1990.

Hograth, R., Judgment and Choice, John Wiley and Sons, New York, 1987.

Kahneman, D., Slovic, P., and Tversky, A., Judgment Under Uncertainty: Heuristics and

Biases, Cambridge University Press, Cambridge, 1982.

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