Modelling the Psychology of
Decision Making
Palisade EMEA
2012 Risk Conference
London
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© 2012 Captum Capital Limited
The Psychology of
Decision Making
The world is complex
How do people cope
with it?
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Heuristics and Biases
Simple rules of thumb
Debatable about how accurate they are
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Linda
 31 years old
 Studied
philosophy
Very concerned
about social
discrimination
 Took part in
anti-war
demonstrations
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What did she become?
A bank teller
A bank teller who is active in the feminist
movement
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The Representativeness Heuristic
All
female
bank
tellers
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All female
bank
tellers
who are
also
active
feminists
The Anchoring Effect
The average
raccoon weighs 2
kilograms
How much does
the average giraffe
weigh, in kg?
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Anchoring
Judgements are often made by fixing on
something and then adjusting for other
evidence
The “anchor” can even be unrelated, but
non-arbitrary anchors have a larger effect
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Sunk Costs
You go to a play and the first act is
terrible. You paid $40 each for a pair of
tickets and waited on line for 2 hours to
get them. Do you leave at intermission?
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Sunk Costs
Imagine you go to a play and the first act
is terrible. Your friend gave you the tickets
after discovering that she and her partner
couldn’t make it to the show that night. Do
you leave at intermission?
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Sunk cost and loss aversion
Losses are seen as more
significant than gains
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Modelling Thought and
Behaviour
We build models to aid our decisions
Similar models can explain how we make
those decisions
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The Probabilistic Turn
Cognitive science (and related fields!)
increasingly using probabilistic models
But aren’t people bad at judging
probability?
Probabilities are difficult to reason with,
but frequencies produce accurate
judgements
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Flip a coin…
Is the coin fair?
Depends on how it
lands
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Degrees of belief
HHHHH will lead to a strong belief that the
coin is biased
But if HHHHH occurs after 1000 previous
trials that were 50% tails, belief will not
change
 This is captured by Bayesian models
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Moral Judgement and
Decision Making
Rapidly expanding field
How does it differ from
other JDM?
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The Trolley Problems
Pull the lever?
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Push the man?
The Trolley Problems
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Pull the lever?
Push the man?
~80% say yes!
~20% say yes!
Why?
Utility vs Rights?
Personal vs Impersonal?
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Why?
“In real life it would hardly ever be certain
that the man on the narrow track would be
killed. Perhaps he might find a foothold on
the side of the tunnel and cling on as the
vehicle hurtled by. The driver of the tram
does not then leap off and brain him with a
crowbar.”
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Causal Influence
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@Risk implementation
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And if we run them…
Probability of Action Acceptable
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0.9
0.8
0.7
0.6
Model (5000 iterations)
0.5
Observed
0.4
0.3
0.2
0.1
0
Trolley
Footbridge
Problem
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Criticism
Not a great fit for the data
But not bad for a simple model!
What about a more complex model?
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The Side-Effect Effect
"We're thinking of starting a new program. It will help us
increase profits, but it will also harm the environment.”
"I don't care at all about harming the environment. I just
want to make as much profit as I can. Let's start the
program.”
They started the program. Sure enough, the environment
was harmed.
Did the chairman intentionally harm the environment?
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The Side-Effect Effect
~80% say yes, environment was
intentionally harmed
What if “help” replaces “harm”?
Only ~20% say help was intentional
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Sloman et al. Model of
Intentionality Judgements
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Or, simplified…
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Implementation
0.9
0.8
0.7
0.6
0.5
Model
0.4
Observed
0.3
0.2
0.1
0
Harm
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Help
To summarise…
Decision making processes are
predictably irrational
DecisionTools software can be used to
model these processes
DecisionTools also makes an intuitive
teaching tool
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About Captum
Innovation in Life Sciences
Licensing
Technology Valuation
Modelling behaviour, innovation, value
See us at
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Risk Analysis using
Contact
Captum Capital Limited
Chris Brand
e: cmb@captum.com
t: +44 (0) 115 988 6154
m: +44 (0) 7800 829 012
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