Decision Making with Unknowable Risks 
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Summary/Abstract
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 18thcentury 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.
👀  TIP: On this website you can find much more about dealing with unknown risks and Plausibility Theory! 
Compare with Plausibility Theory: Real Options  RAROC  Scenario Planning  Root Cause Analysis  CAPM  Dialectical Inquiry  Theory of Constraints
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