Summary BDM lectures with the
research papers incorporated
Behavioural Decision Making - EBB104A05 - RUG
Inhoudsopgave
Lecture 1 Designing Experiments & Understanding Bounded Rationality.......................2
Lecture 2 – Pricing, Loyalty, Goal Pursuit & Decision Under Risk (Cognitive Biases).......3
Lecture 3 – Procrastination, Savings, Weight-Loss & Self-Regulation (Intertemporal
Choice)........................................................................................................................... 5
Lecture 4 – Saving, Spending, Tax Refunds & Safety Capital (Mental Accounting)..........7
Lecture 5 – Reward/Loyalty Programs, Progress Visualization, Delivery Tracking (Goal-
Directed Behavior).......................................................................................................... 9
Lecture 6 – Advice Taking, Recruitment, Healthcare, Product Preference (AI & Decision
Making)......................................................................................................................... 11
Lecture 7 – Nudging: Defaults, Reminders, Commitment Devices, Social Accountability
..................................................................................................................................... 13
All frameworks, processes, mechanisms, biases and heuristics....................................16
,Lecture 1 Designing Experiments & Understanding
Bounded Rationality
Traditional economics assumes the “rational economic agent” —
someone who logically maximizes utility based on stable preferences and
perfect information. But behavioral decision-making research showed that
real humans are predictably irrational, influenced by emotions,
context, and cognitive limitations.
This shift was made possible through controlled experiments, which
isolate causal effects by manipulating variables in controlled settings —
allowing researchers to observe how people behave rather than how they
should behave in theory.
Prospect Theory (Kahneman & Tversky, 1979) emerged as the
landmark alternative to Expected Utility Theory. It introduced three major
insights:
Loss aversion — losses feel psychologically stronger than
equivalent gains.
Reference dependence — outcomes are evaluated relative to a
mental reference point, not final wealth.
Framing effects — logically identical options can produce different
choices depending on wording.
Barberis (2013) shows how Prospect Theory isn’t just experimental — it
fundamentally reshaped how economists explain real-world behaviors. In
the finance domain, loss aversion explains stock market overreaction,
equity premium puzzles, and people holding losing stocks too long
(“disposition effect”). This confirms that the irrationalities shown in
experiments actually scale up to real economic behavior.
The core takeaway is that humans are not irrational in random ways —
but in systematically predictable ways, which is why experimental
behavioral research became so powerful. It doesn’t just explain — it allows
us to design smarter interventions.
Key Ideas
Rational vs. Irrational Behavior:
Traditional economics assumes people are rational (maximize utility,
driven by incentives). Behavioral science shows decisions are
shaped by biases, emotions, and context.
Expected Utility Theory (EUT): Normative model assumes
rational choice based on maximizing expected outcomes.
Limitations: Real decisions deviate due to risk aversion, framing, and
emotions.
Prospect Theory (Kahneman & Tversky): Descriptive model of
actual behavior.
o Loss Aversion: Losses hurt more than equivalent gains
please.
o Reference Dependence: People evaluate outcomes relative
to a reference point.
, o Framing Effect: Gain frames = risk aversion; loss frames =
risk seeking.
o Diminishing Sensitivity: Small differences matter more
when near zero.
Behavioral Concepts: Framing, endowment effect, status quo bias,
loss aversion, mental accounting.
Experiments in BDM: Used to identify causal effects on decision-
making by manipulating variables under control.
Lecture 2 – Pricing, Loyalty, Goal Pursuit & Decision
Under Risk (Cognitive Biases)
Building on Lecture 1, this lecture shifts from why humans are not fully
rational to how exactly they make biased decisions — through fast,
automatic System 1 heuristics rather than effortful System 2 reasoning.
Tversky & Kahneman (1974) introduced three foundational heuristics:
Representativeness — judging likelihood based on similarity to
stereotypes (e.g., packaging that “looks premium” boosts perceived
quality even if objectively equal).
Availability — events that are vivid or recent feel more likely (e.g.,
fear of shark attacks vs diabetes).
Anchoring & adjustment — people irrationally cling to whatever
number they see first — even if arbitrary.
These heuristics directly explain marketing strategy effectiveness,
especially in pricing and product positioning.
For example, Allard et al. (2019) show that simply reframing a premium
product as “€50 more” (differential price framing) rather than “€250
total” significantly increases premium choice share. This works because
people anchor on the smaller difference, not the full price — a pure
exploitation of anchoring and effort-minimizing mental shortcuts.
Simonson (1989) adds another layer: when people are uncertain, they
choose whichever option is easiest to rationalize — not necessarily the
objectively best. This explains:
Attraction (decoy) effect — adding an intentionally inferior third
option makes the target option look more reasonable.
Compromise effect — people avoid extremes and choose the
“middle” option to appear rational.
This means preferences are not fixed — they are constructed by the
context and architecture of the options.
Implication:
Marketers don’t just “influence choices”—they design the perception of
rationality by making the desired option easiest for System 1 to justify.
Key take aways:
Dual-System Thinking (Kahneman):
System 1: Fast, automatic, emotional — prone to bias.
System 2: Slow, deliberate, logical — effortful and reliable.
research papers incorporated
Behavioural Decision Making - EBB104A05 - RUG
Inhoudsopgave
Lecture 1 Designing Experiments & Understanding Bounded Rationality.......................2
Lecture 2 – Pricing, Loyalty, Goal Pursuit & Decision Under Risk (Cognitive Biases).......3
Lecture 3 – Procrastination, Savings, Weight-Loss & Self-Regulation (Intertemporal
Choice)........................................................................................................................... 5
Lecture 4 – Saving, Spending, Tax Refunds & Safety Capital (Mental Accounting)..........7
Lecture 5 – Reward/Loyalty Programs, Progress Visualization, Delivery Tracking (Goal-
Directed Behavior).......................................................................................................... 9
Lecture 6 – Advice Taking, Recruitment, Healthcare, Product Preference (AI & Decision
Making)......................................................................................................................... 11
Lecture 7 – Nudging: Defaults, Reminders, Commitment Devices, Social Accountability
..................................................................................................................................... 13
All frameworks, processes, mechanisms, biases and heuristics....................................16
,Lecture 1 Designing Experiments & Understanding
Bounded Rationality
Traditional economics assumes the “rational economic agent” —
someone who logically maximizes utility based on stable preferences and
perfect information. But behavioral decision-making research showed that
real humans are predictably irrational, influenced by emotions,
context, and cognitive limitations.
This shift was made possible through controlled experiments, which
isolate causal effects by manipulating variables in controlled settings —
allowing researchers to observe how people behave rather than how they
should behave in theory.
Prospect Theory (Kahneman & Tversky, 1979) emerged as the
landmark alternative to Expected Utility Theory. It introduced three major
insights:
Loss aversion — losses feel psychologically stronger than
equivalent gains.
Reference dependence — outcomes are evaluated relative to a
mental reference point, not final wealth.
Framing effects — logically identical options can produce different
choices depending on wording.
Barberis (2013) shows how Prospect Theory isn’t just experimental — it
fundamentally reshaped how economists explain real-world behaviors. In
the finance domain, loss aversion explains stock market overreaction,
equity premium puzzles, and people holding losing stocks too long
(“disposition effect”). This confirms that the irrationalities shown in
experiments actually scale up to real economic behavior.
The core takeaway is that humans are not irrational in random ways —
but in systematically predictable ways, which is why experimental
behavioral research became so powerful. It doesn’t just explain — it allows
us to design smarter interventions.
Key Ideas
Rational vs. Irrational Behavior:
Traditional economics assumes people are rational (maximize utility,
driven by incentives). Behavioral science shows decisions are
shaped by biases, emotions, and context.
Expected Utility Theory (EUT): Normative model assumes
rational choice based on maximizing expected outcomes.
Limitations: Real decisions deviate due to risk aversion, framing, and
emotions.
Prospect Theory (Kahneman & Tversky): Descriptive model of
actual behavior.
o Loss Aversion: Losses hurt more than equivalent gains
please.
o Reference Dependence: People evaluate outcomes relative
to a reference point.
, o Framing Effect: Gain frames = risk aversion; loss frames =
risk seeking.
o Diminishing Sensitivity: Small differences matter more
when near zero.
Behavioral Concepts: Framing, endowment effect, status quo bias,
loss aversion, mental accounting.
Experiments in BDM: Used to identify causal effects on decision-
making by manipulating variables under control.
Lecture 2 – Pricing, Loyalty, Goal Pursuit & Decision
Under Risk (Cognitive Biases)
Building on Lecture 1, this lecture shifts from why humans are not fully
rational to how exactly they make biased decisions — through fast,
automatic System 1 heuristics rather than effortful System 2 reasoning.
Tversky & Kahneman (1974) introduced three foundational heuristics:
Representativeness — judging likelihood based on similarity to
stereotypes (e.g., packaging that “looks premium” boosts perceived
quality even if objectively equal).
Availability — events that are vivid or recent feel more likely (e.g.,
fear of shark attacks vs diabetes).
Anchoring & adjustment — people irrationally cling to whatever
number they see first — even if arbitrary.
These heuristics directly explain marketing strategy effectiveness,
especially in pricing and product positioning.
For example, Allard et al. (2019) show that simply reframing a premium
product as “€50 more” (differential price framing) rather than “€250
total” significantly increases premium choice share. This works because
people anchor on the smaller difference, not the full price — a pure
exploitation of anchoring and effort-minimizing mental shortcuts.
Simonson (1989) adds another layer: when people are uncertain, they
choose whichever option is easiest to rationalize — not necessarily the
objectively best. This explains:
Attraction (decoy) effect — adding an intentionally inferior third
option makes the target option look more reasonable.
Compromise effect — people avoid extremes and choose the
“middle” option to appear rational.
This means preferences are not fixed — they are constructed by the
context and architecture of the options.
Implication:
Marketers don’t just “influence choices”—they design the perception of
rationality by making the desired option easiest for System 1 to justify.
Key take aways:
Dual-System Thinking (Kahneman):
System 1: Fast, automatic, emotional — prone to bias.
System 2: Slow, deliberate, logical — effortful and reliable.
