Behavioral Decision Research: A seminar

John W. Payne

BA 925

Fall, 2015

8/20/2015 1

Session # 1: Introduction

A theory of human rationality “must be as concerned with

procedural rationality – the ways in which decisions are

made – as with substantive rationality – the content of

those decisions” (Simon, 1981).

Agenda for Today

Overview of Course: This is a Ph.D. course designed to introduce you to the field of Behavioral Decision Research (BDR). It will cover concepts, findings, and methods of study.

Brief history of BDR including a simple model of the judgment, what

defines rationality, and the role of normative models in BDR. The concept of Bounded Rationality and cognitive limitations, mental

effort, and heuristics for judgment and choice (Simon, 1955 & Shah & Oppenheimer, 2008).

The Idea of Two-systems of thought – Kahneman (2011). System 1: Intuitive and Fast. System 2: Analytical and Slow. Using behavioral insights to improve decision making, e.g., the power of

Nudges and the debiasing of judgment or the raise of BDR 2.0.

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The Classic Components of Judgment and Decision

• J = f (truth, random error, bias or systematic error (?))

• Truth – how should it be defined? • Correspondence with some external fact or measure. – Coherence across decisions or agreement with a model. – Processes, e.g., considering all relevant information and making tradeoffs.

• Bias, not just mistakes or noise – “Predictably Irrational” (Ariely, 2008)

• Sources of bias and degree of “awareness”.

– Motivational • Strategic misrepresentation, not care, accountability to someone, you want something to

be true, etc. – Cognitive – heuristics or simplifications – Emotional – Fear vs. Anger vs. Sadness

– Do judges know when they are biased? Short answer = NO!

• General types of research questions:

• What types of biases (e.g., over (under) confidence), when, who, and why?

• How might such biases be overcome and decisions improved? • A question to think about: Is it true that the cost of suboptimal decision making is growing?

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History of Behavioral Decision Research

Rationality or being Truthful -1 • Outcomes: Standards for decision performance or Truth.

– Correspondence, e.g., predictions are accurate or afterwards people do not want to change the decision.

– Coherence or Consistency of decisions – not Substance, e.g., 1) transitivity where a ⋟ b and b ⋟ c implies a ⋟ c and 2) and dominance where multi-attribute option A dominates option B (and should be chosen) if ai > = bi for all attributes, and ai > bi for at least one attribute.

– Fit with “Normative” Models

• Expected Utility Theory – EU = Σ pi U(xi) for all i = 1 to n.

• Weighted Additive Value - WADD = Σ wiXi for i = 1 to n.

• Bayes’ theorem - P(Hi/D) = P(D/Hi) P(Hi) / P(D/Hi) P(Hi)

• Process Perspective, e.g., willingness to make trade-offs and consideration of all “relevant” information.

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History of Behavioral Decision Research

Rationality when? Transparent versus nontransparent problems and when dominance is satisfied. (Tversky & Kahneman, 1986)

• Consider the following two lotteries, described by the percentage of marbles of different colors in each box and the amount of money you win or lose depending on the color of a randomly drawn marble. Which lottery do you prefer?

• Option C

– 90% white - $0

– 6% red – win $45

– 1% green - win $30

– 3% yellow – lose $15

• Option D

– 90% white - $0

– 7% red – win $45

– 1% green – lose $10

– 2% yellow – lose $15

• Consider the following two lotteries, described by the percentage of marbles of different colors in each box and the amount of money you win or lose depending on the color of a randomly drawn marble. Which lottery do you prefer?

• Option A

– 90 % white - $0

– 6% red - win $45

– 1% green – win $30

– 1% blue – lose $15

– 2% yellow – lose $15

• Option B

– 90 % white - $0

– 6% red – win $45

– 1 % green – win $45

– 1% blue – lose $10

– 2% yellow – lose $15

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History of BDR

2 wins?

History of Behavioral Decision Research -1

• Decision “theory” has been traced back hundred, if not thousands, of years. However, the field is relatively young as an area of scientific study, typically defined as post WWII.

• Three types of research: Normative analysis (How people should behave),

• Descriptive Studies (How people do behave),

• Prescriptive Interventions (increasingly “Behavioral Solutions’ or Debiasing efforts to close the gap between should and how)

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Early History: 1940s - 1960s of BDR

– Ideal of rational decision making - von Neumann and Morgenstern (1947). If errors – why?

– Early focus on a comparison against “normative” models such as Expected Utility, Bayesian Probability, Additive Value (Linear) Models, and their variants. Edwards (1955), Meehl (1954), Simon (1955).

– Extension of BDR to “Experts” For example, do doctors make biased judgments?

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History of Behavioral Decision Research #2

History of Behavioral Decision Research -3

• Growth of the field: 1970s - 1980s

– New models and approaches: The cognitive revolution

• Heuristics and biases in judgments

• Non-compensatory evaluation strategies in choice

• Prospect theory instead of EU for risky decisions

• Task and Context contingent decisions and processing

– New research methods, including “process tracing” that reflected a greater concern with how judgments and choices were being made.

– Expansion to applied domains including business, medicine, and law.

• For most of human history, doctors have done more harm than good. (Dr. Brent James, quoted in NY Times magazine article on making health care better, 11/8/2009.

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History of Behavioral Decision Research -4

• Maturing of the field: 1990s – 2010s – Revisions of older models, e.g, nonlinear expected utility models

such as Cumulative Prospect Theory.

– Controversies dealing with models and methods of BDR.

– New topics, e.g., emotions and the social aspects of decisions.

– Alternative modes of thought, e.g., conscious versus unconscious thought and dual-process models.

– Attempts to integrate individual level BDR with Markets

– New methods, e.g., neuroscience and BDR

– Renewed focus on improving decisions including the role of public policies on such issues as health and retirement. BDR 2.0.

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Source: Johnson, et. al (2003)

Opt In

Opt Out

Participation Rates

in Organ Donation Programs

Behavioral Inertia and the Power of Defaults: Organ Donation

Example of a highly successful Nudge:

The Design of Retirement Plans – The role of Defaults

• Should retirement plans be opt-in or opt-out?

• Idea: Make a behavioral bias, i.e., decision inertia work for you, not against you.

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Objections to BDR evidence of non-rational behaviors

– Unreliable, irrelevant, and unsystematic results.

– Lack of incentives – “there is no replicated study in which a theory of rational choice was rejected at low stakes …and accepted at high stakes” (Camerer & Hogarth, 1999)

• The environments in which tests of phenomenon like various context effects in choice are artificial, and don’t reflect the “real” world.

– Misinterpretations by subjects, e.g., if an experimenter provides an item of information it must be relevant.

• Only applies to college sophomores?

– Inappropriate or incomplete tests of rationality, e.g., cost of thinking and speed of response not considered. An example is the classic speed – accuracy tradeoff in performance.

• Cost of an otherwise beneficial style of thinking, i.e., works well most of the time.

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History of Behavioral Decision Research

Two BIG Psychological Frameworks for BDR

• Bounded Rationality

– Key idea is that limits on how we can think cause us to deviate from pure economic rationality. In particular, limits on computational capacity may be an important constraint.

• Two Systems of Thought

– Key idea is that we can (and do) think in two distinct ways – 1) a fast and intuitive process, and 2) a slow and more analytical process.

– Note the System 2 form of thinking is assumed to exhibit cognitive bounds, and therefore will involve the use of heuristics.

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Bounded Rationality: Simon 1955 -1 • What are the Economic Man assumptions?

– Complete Knowledge: Will people with sufficient information always make

good decisions? – Computational Capacity: Assumed unlimited. – Existing Preferences: Too what extent to people have existing values?

• A common assumption made by economists is that “each individual have stable and coherent preferences” (Rabin, 1998). Lucas (1986) suggests that this maybe due to the situations economists tend to focus on, situations in which a person is expected to “know” or to have “learned” stable preferences.

• While the assumption of well-defined preferences is common, David Kreps (2004), sees this as the “errant” assumption about behavior made by almost all economists.

– Maximization (search for the “best”) as the Goal

• Information processing limits and attention as the scarce resource for decision making.

– Memory Limitations. • Working Memory (STM) vs. Long-term Memory, Associative Search, Inhibition, etc. Recent work argues for 4 +

or – 1.

– Perceptual Effects. • Detection of change more than state • Context Sensitivity

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Bounded Rationality: Simon 1955 -2

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• The term “bounded rationality” was introduced to focus attention upon the discrepancy between the perfect rationality assumed in classical economic theory and the reality of human behavior (Simon, 1992, p. 3).

• In terms of models of procedural rationality, “the task is to replace the global rationality of economic

man with a kind of rational behavior that is compatible with the access to information and the computational capacities that are actually possessed” by humans (Simon, 1955).

Where would you put the average consumer or investor? Where would you put the average corporate manager? Where would you put the “average” doctor or other expert? Where would you put yourself?

A continuum of cognitive capabilities

“What a piece of work is man!

how noble in reason!

how infinite in faculties!”

(W. Shakespeare, Hamlet).

“Lord, what fools

these mortals be.”

(W. Shakespeare,

Midsummer Nights Dream).

Bounded Rationality: Simon 1955 -3 • What are some methods for “Satisficing”?

– Simple payoff schemes relative to an aspiration

level, examples? – Satisficing search, implications for constraints on

the order of search? – Selective Processing and Heuristics

• When will selective processing lead to good (bad) decisions?

• Processing limits as a cost and/or constraint?

• Implications of bounded rationality for the level of consistency in judgment and choice behavior?

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Heuristics and Effort Reduction -1 (Shah & Oppenheimer, 2008)

• What is a heuristic?

– A simple process (method involving modest

amounts of computation) for arriving at satisfactory solutions.

– Reflecting a goal to reduce the effort associated with decision processes. Kahneman (2011) and the principle of least effort.

• Note, this is more Simon (1955).

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Heuristics and Effort Reduction -2

• What is the complex algorithm that Shah and Oppenheimer (2008) view as the best example of optimal (rational) decision making?

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Heuristics and Effort Reduction -3: A Few Methods for effort-reduction

• Examine fewer cues compared to, for example, the weighted additive model. (Integrate less information).

• Reduce the difficulty of retrieving and storing cue values, e.g., use simple payoff schemes or just binary coding.

• Simplify the weighting principle for cues, e.g., equal weighting.

• Examine fewer alternatives – satisfice?

• Attribute substitution – answer a hard to answer question by substituting an answer to an easier question, e.g., how representative is Linda of a feminists bank teller (which ignores base-rate information) instead of how likely is it that Linda is a bank teller (which should include base-rate information). See next material on System 1 and System 2 thinking.

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What Questions do you have about the concept of Bounded Rationality

and the concept of a heuristic?

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Thinking, Fast and Slow

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More than one Mode of Thought: Different systems evoked by different tasks

• What is

• 17 x 24?

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What are the Properties of System 1 and System 2 Thinking?

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Cognitive / Deliberative

Relatively slow

Demanding of cognitive capacity

Controlled

Analytic

Rule-based

Doubt

Intuitive / Affective

Relatively fast

Undemanding of cognitive capacity

Automatic

Holistic

Associative

Certainty

Aka – Your gut reactions versus your reflective self. Note, System 1 provides intuitive opinions about almost anything – quickly and often

without you asking a question.

One versus Multiple Options

System 1 is much better at integrating

information about one thing.

System 2 is the one only that can “compare

objects on several attributes, and make

deliberate choices between options (p. 36,

Kahneman, 2011).

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Important: System 1 operates on the WYSIATI principle

• What You See Is All There Is (WYSIATI) (Kahneman, 2011).

• Focuses on existing evidence (top-of-the-mind) and ignores absent evidence (WYSIATI).

• Aka “The spotlight effect” (Heath & Heath, 2013).

• This spotlight or WYSIATI property of System 1 contributes to more certainty when we reason about probabilistic events.

– Neither the quantity nor the quality of the evidence counts for much in subjective confidence (Kahneman, 2011, p. 87).

• A Key point: Missing can mean known information that is simply not being recognized. That is, it is not in the spotlight.

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Other Features of System 1 Thinking Builds on a deep-seated desire and capability identify

patterns in data. Intuition is highly contextual. This can be good and bad.

Can execute skilled responses (skilled intuitions) after

adequate training. This is a key point – System 1 thinking can be both fast and accurate.

Is biased to believe and confirm Exhibits an Optimism Bias Relatively invulnerable to aging Frames decision problems narrowly, in isolation from one

another. 26

The Linda Problem: Classic Example of Type 1 Thinking

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• 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 anti-nuclear demonstrations.

• Below are eight statements about Linda. Rank all eight statements in terms of their probability of being true. Use 1 for the most probable and 8 for the least probable.

• A) Linda is a teacher in elementary school. • B) Linda works in a bookstore and takes Yoga classes. • C) Linda is active in the feminist movement. • D) Linda is a psychiatric social worker. • E) Linda is a member of the League of Women Voters. • F) Linda is a bank teller. • G) Linda is an insurance salesperson. • H) Linda is a bank teller and is active in the feminist movement.

Impact of Task and Statistical Sophistication on the Linda Problem Solving

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• Linda is a bank teller

• Make the relationship less transparent, e.g., Linda is an insurance salesperson- such filler statements serve to make the relationship less transparent. Original form.

• Linda is a bank teller and is active in the feminist movement. F&H together.

% of

violations

8 items 2 items

Naïve

undergrad 89 85

Sophistica

ted Ph.D.

students

85 36

Yeah, but

• Stephen Jay Gould (1991) is reported to have said – “I know that [the right answer], yet a little homunculus in my head continues to jump up and down, shouting at me – ‘but she can’t just be a bank teller; read the description!’”

• As noted by Thaler and Sunstein (2008), Gould’s homunculus is System 1 thinking in action.

• Note, also Stephen Gould is often considered to be a genus.

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Key Characteristic Weaknesses of Each System of Thought

• The bias of System 1 is that of attribute substitution: it tries (and almost always succeeds) to emit an answer from within its capabilities when it is asked a question that it cannot strictly answer.

• System 2 is mobilized when a question arises for which System 1 does not offer an answer, e.g., 17 x 24 = ?

• The bias of System 2 is that it is lazy. (Bounded Rationality applies.)

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We are all prone to the biases inherent in the way System 1 operates!!!

• “My intuitive thinking is just as prone to overconfidence, extreme predictions, and the planning fallacy as it was before I made a study of those issues (Kahneman, 2011, p. 417).”

• Kahneman does believe that he has improved his ability to recognize a situation in which errors are likely. That is, he may be better at picking the right tool from his toolbox.

– Note, how often do we “stop and think”?

– Do people differ in their tendency to “stop and think”? 31

Issues to think about - 2

• Are some individuals (extreme cognitive misers or less prone to “cognitive reflection”) much less well suited for some decision environments?

• For a given individual are there changes in cognitive capabilities over time?

• If the above is true, what should the government do about it?

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How do Systems 1 and 2 interact?

• A key function of System 2 is to monitor System 1, identifying potentially incorrect judgments, and taking corrective action when needed.

• Unfortunately, this monitoring function does NOT work as often or as well as we might hope.

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How might System 1 and System 2 Thinking Interact?

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Is an initial judgment

or preference readily

accessible thru system1?

Is the initial

response likely

to be satisfactory?

Map initial judgment

or decision

onto required response

Correct initial judgment

or decision using

system 2 thinking

Engage in system 2

thinking reflecting

both effort and accuracy

considerations.

Yes No

Yes No

Occurs much less than we think

The main characteristic of system 2 is that it is lazy (Kahneman, 2011).

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System 2: How much effort?

When to Trust System 1: Two Environments

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• Benign (Friendly) – Contains useful cues (it is

predictable). Examples?

– There have been opportunities for System 1 to have learned the cues (frequent and good feedback).

– There are not others who will exploit those relying only on System 1 heuristics.

• Hostile

– There are no (few) good cues that are usable by System 1 (causing the substitution of an attribute only weakly correlated with the true target).

– Other agents are able to discern the simple cues that are triggering the cognitive miser’s System 1; and the other agents start to arrange the cues for their own advantage.”

Also, how good are people at judging the friendliness of decision environments?

• The intuitions of professionals are more worthy of trust when situations have a certain level of predictability. Examples? However, do people overestimate predictability? See article by Highhouse (2008) and the prediction of job success, e.g., faculty members.

• More worthy of trust when decision makers get frequent and clear feedback. Examples? Becoming an expert often (always) requires years of deliberate practice. However, do people overestimate the degree to which performance is improved through experience? See article by Highhouse (2008).

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People make major mistakes in their judgments and choices not because

they are dumb or bad but simply because they are human. Further, we are

often faced with difficult (perhaps increasingly difficult or hostile) decision

environments that make such mistakes both more likely and more consequential.

Key Point

“Because we believe that we are

smarter than the average bear,

we begin to believe that we are not bears.”

Move from BDR 1.0 to BDR 2.0

• Awareness of biases

• Understanding of Limits and Reasons

– Behavioral Decision Research 1.0

•Solutions for Real Problems –Behavioral Decision Research 2.0

BDR 2.0

Use our BDR 1.0 knowledge to design effective (and low cost) Behavioral Solutions to Decision Problems (BDR 2.0). Several examples were offered today from health to energy.

That is, knowing why, and when, humans make the decision errors that they do can help us to design interventions that might actually work to improve decisions.

• Nudges – Choice and Information Architectures

• Simple Thinking Tools

– BDR 2.0 seems to be working.

• The retirement story successes.***

• But, it can also hurt, again a retirement story with Social Security Claiming.

• The Behavioral Insights (Nudge) Team in the U.K.

– General Principles are being developed:

• Make doing the “right” thing easy.

• Do lots of small experiments.

– Ethics of BDR 2.0? That is, when are Nudges ethical?

Often people's preferences are unclear and ill-formed, and their

choices will inevitably be influenced by Choice Architectures. Thaler and Sunstein - Libertarian Paternalism is Not an Oxymoron.

• A key concept underlying much of the Nudge approach is to go from Mindless to Mindlessly Better. That is, to take advantage of System 1 thinking to overcome the limitations of System 1 thinking by the design of information and choice architectures.

• Note, however, that key properties of System 1 thinking are that it is automatic and often associated with little or no awareness.

• Note also, that there is an assumption that the designer of the choice and information architectures know what is better for the individual or for society.

• When are we shoving not nudging?

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Information Architecture, e.g.,

Make Important Information = Most Salient

Ensure Affective Ease

Use Linear (positive) Relationships

Choice Architecture, e.g.,

Use Smarter Defaults

Require Active Choice

Use Two plus 1,2, or 3 options

Thinking Architecture, e.g.,

Do more Value Focused Initially

Use Simplified Bayesian Reasoning (Base-rates)

Use Checklists for Thoroughness and Consistency

Decisions about End-of-Life Care

• I want my health care providers and agent to treat me by helping me to live as long as possible, even if that means that I may have more pain or suffering.

• I want my health care providers and agent to treat me by helping relieve my pain and suffering, even if that means that I may not live as long.

• I do not want to specify one of the above goals. My health care providers and agent may direct

• I want my health care providers and agent to treat me by helping relieve my pain and suffering, even if that means that I may not live as long.

• I want my health care providers and agent to treat me by helping me to live as long as possible, even if that means that I may have more pain or suffering.

• I do not want to specify one of the above goals. My health care providers and agent may direct

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Order varied as well as checkmarks

Default Default

Questions

• If decisions about life and death are, to some extent, constructed, what does that say about most consumer decisions? What might be the differences in the nature of the problems?

• If important decisions can be “nudged” in a very transparent fashion, what are the implications for nudging less important consumer (managerial) decisions in less transparent fashion?

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Summary • We are all prone to the biases inherent in the way System 1

operates. We may be smarter than the average bear but we are still bears.

• The biases of System 1 matter little in benign decision environments.

• The biases of System 1 can lead to seriously suboptimal behavior in hostile environments.

• Environments matter but so does the monitoring done by System 2, which means there will be variation in the degree of rationality displayed in a particularly situation.

• Overriding of System 1 biases by “better” System 2 thinking will require Awareness X Ability X Motivation.

• Instead of more System 2 thinking we may do better with better designed choice and information architectures.

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Conclusions • BDR (BDT) is a growing field that has had a major impact on a

number of areas of study and practice. While it has a strong psychological foundation, it is an intensely interdisciplinary, and increasingly solutions focused. Some now referred to this as “behavioral economics.”

• Schedule of Topics to be covered: – Values and Beliefs combined: The classic problem of Risky

Choice (3-4 weeks) – Beliefs and Thinking about Uncertainties or Probabilistic

Reasoning (3-4 weeks) – Values, Preferences and Dealing with Conflicting Values (3-4

weeks) – Improving Individual Judgment and Choice (1 week) – Are Group Decisions better? (1 week)

• QUESTIONS?

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Acknowledgements

The slides above, and in future slide decks, borrow heavily from materials provided by many colleagues over the years. Below is a partial list of the

friends who have shared materials with me. I offer thanks.

• Doug Breeden (Duke)

• Shlomo Benartzi (UCLA)

• Craig Fox (UCLA)

• Eric Johnson (Columbia)

• Yuval Rottenstreich (NYU)

• Jack Soll (Duke)

• Rick Larrick (Duke)

• J. Edward Russo (Cornell)

• *Other contributions are acknowledge on

individual slides. If I have missed someone, my apologies. Please let me know.

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