Briefly a history of Decision Psychology then, Introduction to the Lens Model and Linear Models of Human Judgment




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(Briefly) A History of Decision Psychology then, Introduction to the Lens Model and Linear Models of Human Judgment

Psychology 466: Judgment & Decision Making Instructor: John Miyamoto 9/27/2012

Outline

  • Background to Cognitive Psychology and Judgment and Decision Making

  • Decision Trees and the Analysis of the “Sunk Costs” Fallacy

  • The lens model of judgment

Introduction to linear models of human judgment
    • What are they?
    • Why are they useful when people need to make decisions?
    • What do they show about human judgment?


Normative Decision Analysis

  • Enumerate outcomes

  • Enumerate options

  • Construct a decision analysis for the decision

  • Evaluate the probabilities of different possible outcomes

  • Determine which option has the greatest "expected utility."



What Are Typical Characteristics of Human Decision Making?

  • Usually not analytic.

  • Difficulty integrating feelings and thoughts (affect and cognition).

  • Fast. Reasonably accurate.

  • Difficulties with complex information.

  • Cognitive factors

This course will help you be a better decision maker, especially for careful, reflective decisions.

History of the Psychology of Decision Making

  • Victorian rationality, Freudian irrationality, behaviorist arationality.

  • Expected utility theory (Von Neumann & Morgenstern, 1944)

  • Rational agent model of economic behavior

  • Heuristics and biases movement, 1970 – 1990 (approx.)

  • Reactions to heuristics and biases movement Evolutionary psychology, ecological psychology, naturalistic decision making, Bayesian models of psychological processes

  • Psychology of happiness

  • Separate development – neuroscience of decision making (current hot topic!)



The Cognitive Approach to Judgment & Decision Making

  • Cognitive limitations – limitations on human cognitive capacity affect judgment and decision making

  • Heuristics and biases movement: 1970 – 1990 (approx.)

  • Reactions to heuristics and biases movement

    • Evolutionary psychology
    • Ecological psychology
    • Naturalistic decision making
    • Bayesian models of psychological processes
    • Emotion in decision processes


The Standard Cognitive Model of Human Memory



Sensory registers retain the sensory information for very brief periods of time.

  • Sensory registers retain the sensory information for very brief periods of time.



Working memory (WM) holds a limited amount of information for 10 – 20 seconds. Thoughts are actively manipulated in WM.

  • Working memory (WM) holds a limited amount of information for 10 – 20 seconds. Thoughts are actively manipulated in WM.



Long-term memory (LTM) retains information over longer periods of time. LTM interacts with WM.

  • Long-term memory (LTM) retains information over longer periods of time. LTM interacts with WM.



General Hypothesis of Cognitive Research

General Hypothesis of Cognitive Research
    • Limitations in working memory impose limitations on human ability to engage in complex reasoning.
    • Decision making requires complex reasoning.


Basic Message from Many Cognitive Studies

  • When people are confronted with complex information, limitation in working memory force people to simplify the information. Simplifications can lead to distortions.

  • Exception: Experience can teach one to integrate specific types of complex information but only in some cases.

    • Example: Expert pig buyers are very good at predicting future value of immature pigs.
    • Example: Sports bookies are very good at judging the odds of winning and point spread.
    • Example: Expert chess players can reason about complicated chess problems.
  • Next a complex representation of decisions under risk (decision trees).



Decision Trees & Decision Analysis

  • The decision is a gamble:

Invest: Win large $ with probability p; Lose initial $ with probability 1 – p

Don't Invest: Win moderate $ with probability 1.0 (or something very high)

Decision Trees & Decision Analysis (cont.)

represents a choice node – decision maker gets to make a choice.

represents a chance node – what happens is not under the control of the decision maker.

Figure 2.2 in H&D

  • Hypothetical medical decision whether to operate on a knee.



Summary re Decision Trees

  • Represents choice options as gambles.

  • Can be oversimplified or mistakenly constructed.

  • Can be helpful in understanding a decision.

  • Many cognitive experiments have studied people’s preferences among risky options.

    • Decision trees can represent the choices in these experiments.
    • Decision trees are not usually used as stimuli. Stimuli in human experiments are often represented as gambles.
    • This field is called the study of “risky decision making” or “decision under risk” or “preference under risk.” .


Honoring Sunk Costs – A Decision Fallacy

Next:
  • Explain what it means to “honor sunk costs”

  • Use decision trees to explain why decision theorists think it is a mistake to honor sunk costs



“Sunk Costs” Problem

Choice A:
    • You and a friend have purchased discount ski tickets for $70.
    • The tickets are non-refundable and are good for only one particular day.
    • When you arrive at the ski resort, the conditions are mediocre, plus you and your friend both feel a little sick.
    • Question: Should you ski anyway, or should you go home?

Choice B:
    • A telemarketer gave you and a friend some free ski tickets that are worth $70.
    • The tickets are non-refundable and are good for only one particular day.
    • When you arrive at the ski resort, the conditions are mediocre, plus you and your friend both feel a little sick.
    • Question: Should you ski anyway, or should you go home?
  • People who would keep skiing in Choice A, but would go home in Choice B are said to commit the fallacy of honoring sunk costs.



Decision Trees for the Sunk Costs Problem

  • Would you make the same choice in Choice A and Choice B? Decision theory says that you should.

  • If you prefer “stay and ski” in Choice A, and “go home” in Choice B, then you are honoring sunk costs (bad idea according to decision theory)



Definition: Sunk Cost Fallacy

Sunk Cost Fallacy: To choose a course of action that builds on past investments that one would not choose if one were in exactly the same position but with a different history of investments.

Your choice:
    • Maintain course: Keep investing your time, money and effort on a project in which you have already invested some time, money or effort.
    • Change course: Pursue a new project.

Advice – Ignore the time, money and effort in the past when deciding what to do next.
    • Ask yourself, “What would I do given my present situation if I had not already sunk money or time into a particular project or course of action.”


Sunk Cost Fallacy – Other Examples

  • "Completing Tennessee-Tombigbee is not a waste of taxpayers' dollars. Terminating the project at this late stage of development would, however, represent a serious waste of funds already invested." (Senator James Sasser, Nov. 1981, quoted in Hastie & Dawes).

  • "I have already invested so much in the Concorde airliner ... that I cannot afford to scrap it now." (Business man quoted in Hastie & Dawes).



Sunk Cost Fallacy – When Does It NOT Apply

A choice of a course of action is NOT a sunk cost fallacy if:

... the payoffs are different when you maintain or switch course of actions.
  • E.g.,

    • Maintain course, possibly achieve rewards, don't admit you are wrong, don't look like such a fool.
    • Change course, have somewhat greater chance at rewards, admit you were wrong initially, possibly be criticized for a previous bad decision.


Sunk Cost Fallacy – When Does It NOT Apply

A choice of a course of action is NOT a sunk cost fallacy if:

... the payoffs are different when you maintain or switch course of actions.

... there are start up costs in the new course of action that have already been covered in the current course of action,
  • E.g., you have 20 credits towards an economics major but now you are considering a switch to a history major. Switching to a history major would require taking 20 credits of courses that can be avoided by staying as an economics major.



Summary re Sunk Costs

  • It is a decision-making mistake to honor sunk costs.

  • Why is it a fallacy to honor sunk costs? The decision should be based on what might happen in the future, not on the “loss” of past investments.

  • Decision trees illustrate a method for taking decision complexity into account.



Where We Are in the Lecture

  • Normative and prescriptive decision models require complex representations and processing

  • Cognitive limitations cause us to simplify decisions, and this can produce errors

NEXT:
  • Intuitive clinical judgment versus statistical models

  • Brunswik’s Lens Model of Human Judgment

  • Linear models applied to making better choices

  • Applications to clinical judgment



Intuitive Judgment versus Acturial Judgment

  • Intuitive judgment

    • Combine complex information in your head
    • Make decision based on gut feeling
  • Actuarial judgment (a.k.a. statistical model or linear model)

    • Base decisions on a statistical decision rule.


Examples of Judgment Problems

  • We will only consider decisions to which intuitive judgment and actuarial judgment (statistical methods) both apply. --------------------------------------------------------------------------

E.g., Clinicians attempt to identify patients with progressive brain dysfunction.
    • Data = intellectual test results
    • Experienced clinicians achieved 58% correct detection of new cases.
    • Statistical model achieved 83% correct detection of new cases.
  • E.g., Bank loan officer must decide which loan applications are “good risks” and which are “bad risks.”

  • E.g., Professors must decide which applicants will do well in grad school and which will not do well.



Critique of Clinical Judgment

  • Clinical insight – does it exist?

  • Clinical judgment – what is it good for?

  • Clinical judgment – what are its weaknesses?

  • Accusation: Belief in the efficacy of intuitive clinical judgment is a cognitive conceit.



General Finding: Stat Models Outperform Human Judges

  • Statistical models almost always outperform the human judges on clearly defined decision tasks.

  • Human cognitive processes are good at noticing particular pieces of information.

    • Does my friend look happy? Sad? Stressed? Irritated?
    • Is the patient nervous? Defensive? Exhibitionistic?
  • Human cognitive processes are not good at integrating multiple pieces of information.

    • Can I predict how my friend will feel about a surprise party?
    • Can the clinician predict how the patient will progress after 4 months of therapy?


Implications of this Topic

  • We can improve human decisions by stressing what humans are good at:

... noticing what are important issues that are relevant to a decision;

... evaluating how good or bad is an outcome on a specific dimension;

while avoiding what we are not good at: ... combining complex information in our heads.
  • Know thyself → Make better decisions



Comments re Assignment 1

  • Assignment 1 requires students to compare intuitive judgment with linear judgment models.

  • Decision Task: Choose 1 UW course.

    • You have freedom to define the decision in a way that is interesting for you, as long as it is a real decision.
    • Minimize issues of major or minor requirements.


Introduction to the Lens Model

  • The next 4 slides explain the Lens Model of Egon Brunswik.

  • The Lens Model is a description of the judgment problem for a person who can observe cues that give indications of an unobserved true state of the world.

    • Example: The cues are a patient’s symptoms. The true state is the disease that is really causing the patient’s symptoms.
    • Example: The cues are a person’s appearance and performance at a job interview, e.g., does this person seem confident, smart, hard-working, etc., and does she have good recommendations. The true state is the reality of whether this person turns out to be a good, average or bad worker after she is hired.


The Lens Model of Egon Brunswik

To-be-judged criterion = the thing you are trying to predict.

The Lens Model of Egon Brunswik

Human judgments regarding the criterion are mediated by the cues.

Judgments are accurate to the extent that judgments are consistent with the criterion.

Examples of Judgment Problems

Note: The criterion (true state) is not known by the judge at the time of the judgment, although we may find out the true state later.

Example of Judgment Problems

Basic Idea: The true state of the criterion produces observable cues. The judge makes a guess about the true state based on these cues.
  • What items in the table correspond to the parts of Figure 3.3?

  • What’s a good way to make judgments (guesses)?



Preview of Next Lecture: Discussion of Linear Judgment Models

  • Concrete example: Predicting college performance (GPA)

  • Predictions based on a linear model – how to produce them

  • Predictions based on a model of the judge – how to produce them

  • Predictions based on SMART method

  • Predictions based on unit weights – how to produce them

Assignment 1: Pick one of these four methods. Describe this method and then discuss its strengths and weaknesses relative to other methods (including intuitive judgment.

Broad Outline of Psych 466

  • Weeks 1 & 2:

    • Complex information overwhelms human cognitive capacity
    • Linear models of judgment
  • Weeks 3, 4 & 5:

    • Introduction to the heuristics & biases program
    • Heuristics of probability judgment / human response to uncertainty
    • How likely or unlikely are different possibilities?
  • Weeks 6, 7, 8, & 9

    • Preference under certainty; preference under risk
    • Heuristic strategies in evaluations and choices
    • What do I want? What should I want?
  • Week 10

    • What have we learned about how to make a decision?


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