Consumer Behavior Summary
Lecture 1
A preference is the degree of liking for something. It
motivates choices.
Rational choice theory: people have well-defined
preferences and make decisions to satisfy these
preferences.
In making decisions we can not integrate all attributes into our decision so
we focus on the things that are most important to us.
Preference construction; preferences are labile, inconsistent, subject to
factors we are unaware of and not always in our own best interests. In
some situations our true preference might be rejected.
A theory is a system of ideas intended to explain something, like a model.
But all theories have blind spots. A theory is useful when it
- Is internally consistent (=doesn’t contradict itself)
- Has testable predictions.
- Is empirically supported.
Generality-specifity tradeoff
- General: covers many phenomena & behaviors
- Specific: able to predict behavior with high precision
Gambler’s fallacy: treat independent events as non-
independent, assuming it will tend toward evening out.
A cautionary note on highly specific explanations
- Sometimes they are tautological.
- This can be masked by “dressing things up” in different names
- Not really a why explanation
o Proximate mechanism: how behavior is generated
o Ultimate mechanism: why behavior is favored
Lecture 2 Heuristics, biases and nudging
The traditional view is the rational choice theory in which people have
well-defined preferences, process all information, weigh the relevant
information, and consistently choose what satisfies their preferences.
However, people aren’t rational and often deviate from these principles.
People sometimes construct their preferences in the moment, ignore or
avoid relevant information, rely on irrelevant information, and make
mistakes.
Preferences are influenced by context:
,- Compromise effect: an option is chosen more often when it’s attributes
are not at the extremes. People more often choose the middle size
coffee.
- Attraction/decoy effect: option A is more strongly preferred over option
B when A obviously dominates another option.
Kahneman & Tversky studied how people actually make decisions with
integrating psychological principles
Biases
- Endowment effect: people value something more when they feel a
sense of ownership. For example; test driving, free returns trials etc.
- Framing effect: preferences can shift depending on how information is
presented.
- Sunk-cost fallacy: people will keep investing when they’ve invested in
something because they otherwise will feel as if they wasted money.
Biases are labels for behaviors, not theories for explaining them.
Heuristics
- Availability heuristics: people judge the likelihood of events by the ease
with which they can generate an instance of that event. From memory,
imagine, generate examples, ability to visualize it. Cognitive ease:
people rely on things that come to mind easily.
- Anchoring and adjustment: people start form an initial value then
adjust up- or downwards, but the adjustment is often insufficient. By
manipulating the anchor, final judgments can be manipulated.
- Mental accounting: put money into different accounts.
Prospect theory
The prospect theory is about how people value losses and gains and think
about probabilities.
Value function:
1. Reference point = everything is valued
in comparison to some reference point.
There is no absolute value.
2. Diminishing sensitivity = diminishing
marginal return
3. Steeper loss function
a. Loss function is steeper than gains
function, losses loom larger than
gains.
b. People are more risk-seeking in
the loss domain
c. People avoid marking something
down as a loss, sometimes at high
cost.
,- Disposition effect: traders tend to sell winning stocks too early, hold
onto losing stocks too long. Due to reference points and tendency to
seek risk under loss.
- Status quo bias: people tend to stick with default/status quo (even
random).
- End-of-the-day effect: as gamblers lose more money, they are more
likely to bet on long-shots. After realized losses more risk averse,
however, after paper losses more risk seeking.
Probability weighting function is how people weight probability. We
overweight low probabilities and underweight high probabilities.
For example, people play the lottery, but also take out insurance for rare
events.
Certainty effect: the (sometimes small) step from a high probability
to certainty has a large psychological effect.
Nudging = changing the choice architecture that alters people’s behavior
in a predictable way without forbidding options or significantly changing
their economic incentives.
This is a good way to get people to change their ways but it has some
problems, and some solutions:
- Limited access to relevant information
o Increase visibility
o Make easier to interpret
o Provide reference points
Decision information, example = nutri-score instead of just macro’s
- Limited capacity to evaluate and compare options
o Use defaults
o Remove effort
o Structure and group to facilitate comparisons
Decision structure, example = “Independer” to compare retirement
plans
- Limited attention & self-control
o Provide reminders
o Facilitate commitment
Decision assistance, example = running club instead of going alone
or planning a gym session.
There is a lot of research done on nudges. They work, have a large effect,
are cheap, easy and preserve decision autonomy. But does this always
apply?
A meta-analysis by Mertens et al., (2022) showed that they do work, but
not always. Johnson & Goldstein (2003) showed that they can have a large
effect, but that effects are much smaller in the real world and there are
individual differences in how nudgeable people are.
Nudges are sometimes cheap and easy, but may require extensive testing,
which makes it more expensive. It can also backfire if this research is not
done.
, There is also debate on if they preserve decision autonomy. Who decides
what to push for that should make people better off and which goals are
reasonable or not to push for.
Lecture 3 Intuition, Reflection & Self-control
Herbert Simon came up with the concepts of bounded rationality &
satisficing. Decision making problems are complex and because of limited
cognitive resources and time we have a limit on how rational we can be in
this process, even if we do have the motivation.
Decision makers can satisfice by either finding an optimal solution in a
simple world or satisfaction in the real world. People don’t optimize but
satisfice.
The idea behind the dual process model is understanding this limit. It
combines decision making and cognitive psychology but is not an exact
representation.
It consists of 2 systems:
System 1 is automatic and fast and builds
on what is experienced. It is impulsive;
what is needed now, not in the long term.
System 2 is slow and controlled and is
limited by working memory. It can only
think of so many things at once. It is
reflective: not just reacting but thinking
about what might be best given different
goals. It is analytic; uses the information
in front of you, not experiential, but using
probability given and deciding what could happen.
It is inhibitory, not impulsive. It takes time to figure out course of action.
Some things seem to come to mind automatically (intuitions) and these
can be hard to override. For example, compromise, attraction, framing all
have intuitive responses. You only realize this when you put the
outcomes/options next to each other.
Two different ways of interaction in the dual process theory
- Default-interventionist models = start out with a intuitive
response tendency (system 1) that can be suppressed or over-ridden
by a later response (system 2). System 2 checks system 1
before exhibiting behavior.
- Parallel-competitive models = system 1 and 2 operate in a
parallel tug of war. Doesn’t require system 1 to be first.
But both models assume only one of the two systems is used.
Application of the dual-process theories
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