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Lectures and wrap up JDM
This document includes all the theory from the lectures, inclusive examples and at the end of the document a wrap up in which everything is explained in short!
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Lecture 1 - Judgment and Decision Making - 02-09-2024When people are not doing what they should do.
Biases in business decisions
Human beings are rational, thoughtful beings. A system therefore should be rational. It is an implicit
assumption. The norm that people should be rational. But people are not rational.
-> Rational models?
● How people are not making decisions how you would think they would.
●
Bad decisions
-
- People will make a lot of bad decisions.
- Bad decisions are not made by bad leaders. These are good, even great, leaders who make
predictable bad decisions.
Humans are not perfect decision makers. Not only are we not perfect, but we depart from perfection
or rationality in systematic and predictable ways. The understanding of these systematic and
predictable departures is core to the field of judgment and decision making. By understanding these
limitations, we can also identify strategies for making better and more effective decisions.
Human decision makers are rational decision makers, which is what we expect from our leaders. We
focus on the bad decisions. Deviation to what you expect to be a decision and what the decision
really is.
,Governments: Many governmental organizations actively leverage behavioral science to assist policy
makers in various areas.
- More value of behavioral insights.
- Create interventions to help people make better decisions.
- Show them what bad decisions are.
Decision making task during the lecture
,Lecture 2 - Judgment and Decision Making - 04-09-2024
Reading: Tversky, A., and Kahneman, D. (1974): This article shows that people rely on a limited
number of heuristic principles which reduce the complex tasks of as-sessing probabilities and
predicting values to simpler judgmental operations.
- Three heuristics are discussed in the article:
- Representativeness: The representativeness heuristic, in which probabilities are
evaluated by the degree to which A is representative of B, that is, by the degree to
which A resembles B.
- Problem: Judgment of probability.
- Similarity, or representativeness, is not influenced by several factors
that should affect judgments of probability.
- Availability: Availability is a useful clue for assessing frequency or probability,
because instances of large classes are usually recalled better and faster than
instances of less frequent classes.
- Problems: Familiarity, salience, effectiveness of a search set, imaginability,
- Adjustment and Anchoring: Different starting points yield different estimates, which
are biased toward the initial values. = Anchoring. & Adjustment = To rapidly answer
such questions people may perform a few steps of computation and estimate the
product by extrapolation or adjustment.
Types of decisions:
● Major reflective decisions:
○ Buy a house
○
● Low level decisions
○ What to wear
○ Which road to take
○
What factors should affect a decision:
- Three concrete decision examples:
- Whether or not to take a particular course.
- Whether or not to by a new, better computer.
- Whether or not to end a relationship.
, -
- Rational model: about what you should do, what you should take into account.
How should decisions be made?
● Rational Decision Making Process:
○ Define the problem
○ Identify the decision criteria: how important?
○ Weight the identified decision making criteria
○ Generate possible alternatives
○ Rate each alternative against the decision maker’s criteria
○ Compute the optimal decision
● Assumptions:
○ Assumes the decision maker is rational.
○ Assumes the problem is clear and unambiguous
○ Assumes the decision maker has complete information.
○ No time or costs constraints.
○ Choice will be one with the maximum payoff.
○ A lot of assumptions, maybe a person will make a completely different decision
based on the assumptions.
● About what we should do, for important decisions such as buying a house, to come to an
outcome where they can compute the optimal decision.
Normative decision analysis:
- Enumerate options
- Enumerate outcomes
- Construct a decision analysis for the decision
- Evaluate the probabilities of different possible outcomes
- Determine which option has the greatest 'expected utility’.
- Decision making research before 1970s: Normative theories that prescribe how people
“ought” to make decisions in a perfectly rational way, and many implicitly assumed that most
people, in daily lives, followed these normative rules.
- Computing the most optimal. The best possible outcome.
- Normative: Prescribing people should make decisions, not how people are doing it, it is
about how they should do it.
Sometimes we are not rational at all.
Normative theories are needed to know what people should do. And to see when it is not normative.
People act in a way that is in alignment with rationality.
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