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According to Collins & Michalski,
"something is plausible if is conceptually supported by prior
knowledge". The Plausibility Theory of Wolfgang Spohn (1985-), Collins & Michalski
(reasoning, 1989), Lemaire & Fayol (arithmetic problem solving, 1995),
Connell & Keane (cognitive model of plausibility, 2002) provides new
insights into decision making with unknowable risks. Although
plausibility is an ineluctable phenomenon of everyday life and
ubiquitous, it was ignored in cognitive science for a long time and
treated only as an operational variable rather than being explained or
studied in itself.
Until the arrival of the plausibility
theory, the common theory used by scientists to explain and
predict decision making was Bayesian statistics, named for Thomas Bayes, an 18th-century English minister who developed rules for weighing
the likelihood of different events and their expected outcomes. Bayesian
statistics were popularized in the 1960s by Howard Raiffa
for usage in business environments. According to Bayesian theory,
managers make and should make decisions based on a calculation of the
probabilities of all the possible outcomes of a situation. By weighing
the value of each outcome by the probability and summing the totals,
Bayesian decision makers calculate "expected values" for a
decision that must be taken. If the expected value is positive, then the
decision should be accepted; if negative, avoided.
This may seem an orderly way to
proceed. However unfortunately, the Bayesian way of explaining decisions
faces at least two phenomena's that are difficult to explain:
1. Downsize risk appreciation
(why do people take a gamble at a 50% chance to make 10$ when they have
to pay 5$ if they loose, but generally refuse to take the same gamble at
a 50% chance if they can win $1.000.000 versus a potential loss of
$500.000?)
2. Dealing with unknowable risks
(These kind of risks, that do not involve predictable odds, are typical
for business situations! Why do managers prefer risks that are
known over risks that can not be known?)
Both of these phenomena can be dealt
with if the Bayesian Expected Value calculation is replaced by
the Risk Threshold of the Plausibility Theory. Like its
predecessor, the Plausibility Theory assesses the range of possible
outcomes, but focuses on the probability of hitting a threshold point -
such as a net loss - relative to an acceptable risk. For example: a
normally profitable decision is rejected if
there is a higher then 2% risk of making a (major) loss. Clearly,
plausibility can resolve the weaknesses of Bayesian thinking: both the tendencies of managers to avoid unacceptable
downsize risks and taking unknowable risks can be explained.
A typical example of the application of
plausibility theory are the new Basel II
rules for capital allocation in the financial services industry.
Compare with Plausibility Theory:
Real Options |
RAROC |
Scenario Planning |
Root Cause Analysis |
CAPM |
Dialectical Inquiry |
Theory of
Constraints
More management models
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