According to Collins & Michalski, "something is plausible if it 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 risks that can't be known. Plausibility
is an ineluctable phenomenon of everyday life and ubiquitous. However, 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 which was
used by scientists to explain and predict decision-making, was Bayesian statistics.
Named for Thomas Bayes, an 18th-century English minister. Bayes 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 decisions,
and managers 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 it is negative,
it should be avoided.
Limitations of Bayesian statistics
At first sight, this may seem like an orderly working method. However unfortunately,
the Bayesian way of explaining decisions faces at least two phenomena that
are difficult to explain:
- The appreciation of downside risk. People normally take a gamble at
a 50% chance to earn $10 when they have to pay $5 if they have bad luck.
But why generally they refuse to take the same gamble at a 50% chance if
they can win $1.000.000 versus a potential loss of $500.000?
- How to deal with risks that can't be known. 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
Both of these phenomena can be dealt with if the Bayesian calculation of
"Expected Value" 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 than 2% risk of making
a (major) loss. Clearly, plausibility can resolve both weaknesses of Bayesian
thinking: the tendency of managers to avoid unacceptable downside risks, and
the tendency of managers to avoid taking risks that can't be known.
Typical examples of the application of plausibility theory are the new
Basel II rules for capital allocation in the financial services industry.
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