Assessing Systemic Risk

Assessing Systemic Risk

Ian I. Mitroff (University of California-Berkeley, Berkeley, CA, USA) and Abraham Silvers (University of California, San Francisco, CA, USA)
Copyright: © 2016 |Pages: 10
DOI: 10.4018/IJRCM.2016040104


Far too many applications of Risk Analysis (RA) and Risk Management (RM) treat risks as though they are distinct and independent. Thus, risks are largely treated as though they can be evaluated and mitigated independently of one another. This paper takes a fundamentally different approach. The basic argument is that there are no such things as independent and separate risks. All risks are part of a larger system of interrelated issues, problems, and risks. Each risk is not only connected to all of the other risks that are parts of the system, but if only in part, each risk is responsible, in causing and triggering of all of the other risks. Using recent findings in the probability of implication (Mitroff and Silvers, 2013), a simple mathematical treatment of the interconnectedness of risks is developed. The treatment is capable of being expanded indefinitely to include more complex situations. Finally, the mathematical treatment shows how risks and crises are interrelated.
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The Problems With Traditional Risk Analysis (Ra) And Risk Management (Rm)

The theory and methods of RA and RM are of course well known. Risk R equals the Probability of the occurrence of a potentially damaging event times the Consequences of the event should it occur. Thus, R = P(Event) x C(Event). Probabilities P are involved because damaging events only occur with a certain probability. They do not occur automatically, and they are not certain to occur all the time.

The problems with traditional RA and RM are many. For one, the probabilities and the consequences of events are not always well understood or well known. Even for those events that have occurred repeatedly, one has to make the critical assumption that the future will be like the past so that one can use past probabilities and consequences in computing future risks. Often, this assumption is not warranted.

For another, computing or estimating the probabilities of rare events or those that have never occurred before is highly problematic. For still another, low probability high consequence events like 9/11 are typically excluded from consideration, often with disastrous results. That is, problematic situations with R values below a certain cutoff point are often excluded from consideration.

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