The lecture discusses judgment and decision-making from various psychological perspectives, highlighting heuristics, biases, and the differences between clinical and actuarial predictions. It emphasizes the challenges of dynamic decision-making and group processes, noting the influence of factors such as framing effects and sunk costs on decision quality. The document concludes that individuals often make inconsistent decisions and may perform suboptimally, particularly in group settings.

1. Judgment and Decision Making:
Psychological Perspectives
Dr David Hardman
London Metropolitan University

2. Lecture overview
• General approach taken to judgment &
decision making by psychologists
• Heuristics & biases
• Dynamic decision making
• Decisions in groups and teams
• Taking advice

3. General psychological approach
• Limited capacity information processors
• Bounded rationality (Simon, 1955; 1956)
• Use of heuristics (Kahneman, Slovic, & Tversky,
1982), often associated with biases
• Ecological vs. normative rationality
(Gigerenzer, Czerlinski, & Martignon, 2002)
• Intuitive vs. Reflective thinking

4. Heuristic processes in judgment
• Anchoring and adjustment
• When was George Washington elected
president of the USA?
(Epley & Gilovich, 2001)

5. Clinical versus actuarial prediction
• Linear models are better predictors than
human judges
• E.g. Einhorn (1972), predicting survival time
following a diagnosis of Hodgkin’s Disease
• Interviews are poor predictors of future
performance (e.g. DeVaul et al, 1987)

6. Clinical versus actuarial prediction
Why are statistical models better?
• Always applied consistently (e.g. people experience fatigue or boredom)
• People sometimes focus on information that has little or no relevance
• People may select appropriate information but weight it inappropriately
• When given additional information people often identify individual cases
as exceptions to the rule
• People may be exposed to skewed samples
• People are subject to the fundamental attribution error in interview
situations
• Prompt accurate feedback is not always available to people
• People may be unduly influenced by recent experience or irrelevant
variations in task description

7. Biases in decision making
• Framing effects (e.g. Tversky & Kahneman,
1981)
• Sunk costs (e.g. Arkes & Blumer, 1985)
(There are many other biases!)

8. Framing effects
Imagine that the US is preparing for the outbreak of an
unusual Asian disease that is expected to kill 600 people. Two
alternative programs to combat the disease have been
proposed. Assume that the exact scientific consequences of
the programs are as follows.
Program A. If Program A is adopted 200 people will be saved.
[72%]
Program B. If Program B is adopted there is a 1/3 probability
that 600 people will be saved and a 2/3 probability that no
people will be saved. [28%]
Which of the two programs would you favour?

9. Framing effects
Imagine that the US is preparing for the outbreak of an
unusual Asian disease that is expected to kill 600 people. Two
alternative programs to combat the disease have been
proposed. Assume that the exact scientific consequences of
the programs are as follows.
Program C. If Program C is adopted 400 people will die. [22%]
Program D. If Program D is adopted there is a 1/3 probability
that nobody will die and a 2/3 probability that 600 people will
die. [78%]
Which of the two programs would you favour?

10. Sunk cost effects
• Ohio University Theatre study
(Arkes & Blumer (1985)
• Season tickets randomly sold
at $15, $13, and $8

11. Dynamic Decisions
The Beer Distribution Game
(Sterman, 1989)
• Four players: manufacturer,
distributor, wholesaler,
retailer. Each begins the
game with 12 cases of beer.
1. Retailer turns over
“demand card”, places
order with wholesaler.
2. Wholesaler orders from
distributor.
3. Distributor orders from
manufacturer.

12. Complicating factors in the Beer Distribution
Game:
• Time delay between ordering and receiving
beer.
• There is a charge of $0.50 for each case of
beer held in inventory.
• If a player runs out of beer there is a $1 fine
for each case currently being demanded.

13. Pattern of ordering in the Beer Distribution
Game:
• The first four weeks (trials) are practice only.
• In the first three weeks everyone is directed to
order four cases. From week four they can
order what they like.
• In week 5 the retailer’s demand card jumps to
eight cases and remains there for the rest of
the game.

14. Behaviour in the Beer Distribution Game
• Oscillatory patterns between over-ordering
and under-ordering. Costly cycles of boom
and bust.

15. “Many participants are quite
shocked when the actual
pattern of customer orders is
revealed; some voice strong
disbelief. Few ever suggest
that their own decisions were
the cause of the behaviour
they experienced. Fewer still
explain the pattern of
oscillation in terms of the
feedback structure, time
delays, or stock and flow
structure of the game”
(Sterman, 1989, p.336)

16. Dynamic Decisions
• On other tasks, • Longer feedback delays
performance is typically are associated with
sub-optimal (though worse performance
not necessarily a cyclical (Diehl & Sterman, 1995)
pattern)
• Learning tends to be
implicit (Berry &
Broadbent, 1984)
• Learning tends to be
local

17. Individual differences in dynamic
decision making
Intelligence Decision styles
• In DDM higher cognitive • Evidence is rather weak
ability is associated with (a) • Different studies claim to
less use of heuristics and (b) identify different styles
better performance (e.g. • Though intuition vs
Gonzalez, 2004) reflection are often
• IQ is a better predictor of identified
job performance than any • Can “styles” be
other known factor distinguished from
• Though see also Stanovich “ability”?
& West (2008) • Few studies investigate real-
world performance (but see
Scott & Bruce, 1995)

18. Groups, Teams, and Leadership
Socially cohesive teams Diverse teams
• More willing to share • More unique knowledge
unique information held by members
• But have less unique • But may be less willing to
information to share share it (problem of hidden
profiles)

19. Decisions in Groups and Teams
Some problems of group decision making:
• Conformity to majority opinion (a problem if
the majority is wrong)
• Obedience to authority
• Group polarisation
• Groupthink

20. Possible techniques for improving
group processes
• Brainstorming? Ineffective.
• Electronic brainstorming. Effective.
• Decision rules:
- averaging (in the absence of discussion)
works well for numerical estimates
- majority rule for decisions appears superior
(Hastie & Kameda, 2005)
• Systematic procedures: Delphi technique;
Decision conferencing.

21. Taking advice
• One of the most robust findings is egocentric advice
discounting (Bonaccio & Dalal, 2006)
• People weight advice more heavily if they’ve paid for it
• Some advisers have more influence than others
• What should you do if two advisers provide conflicting
forecasts?
• People often rely on a confidence heuristic (Price &
Stone, 2004)
• People experiencing feelings of power discount advice
from both novices and experts (Tost et al, 2012) and
are more overconfident in their decisions (Fast et al,
2012)

22. Summary
• People are imperfect, inconsistent decision
makers
• Susceptible to various influences, e.g. framing,
sunk costs
• Suboptimal performance on dynamic
decisions; implicit learning
• Group decision making isn’t a cure
• Egocentric advice discounting

23. References
Key reading:
Hardman, D. (2009). Judgment and decision making psychological perspectives.
Chichester, UK: BPS-Blackwell. [see especially chapters 11 and 13]
Selected papers:
Bonaccio, S., & Dalal, R.S. (2006). Advice taking and decision-making:An integrative
literature review, and implications for the organizational sciences. Organizational
Behavior and Human Decision Processes, 101, 127-151.
Diehl, E., & Sterman, J.D. (1995). Effects of feedback complexity on dynamic decision
making. Organizational Behavior and Human Decision Processes, 62 (2), 198-215.
Gonzalez, C. (2004). Learning to make decisions in dynamic environments: Effects of
time constraints and cognitive abilities. Human Factors, 46 (3), 449-460.
Hastie, R., & Kameda, T. (2005). The robust beauty of majority rules in group decisions.
Psychological Review, 112 (2), 494-508.
Sterman, J.D. (1989). Modeling managerial behavior: Misperceptions of feedback in a
dynamic decision making experiment. Management Science, 35 (3), 321-339.