Ordinary risk management typically applies utilitarian decision criteria to simple models (reductionist, often linear) of the system. It is best adapted to cases where the outcome set is well defined, probabilities can be estimated accurately, and the law of large numbers is in effect (as when the decision maker is assured of many draws from a stable distribution, or can transfer the risk to an insurer who has such assurance). In this chapter, we look a little deeper into the nature of the hard cases, considering some limitations of utilitarian decision theory (issues of irreversibility, and unlikely but catastrophic events), and challenges to ORM models of the way the world works that are raised by the emerging understanding of complex systems. Complexity theory warns us that uncertainty, gross ignorance, and unknown unknowns are greater problems, and surprises are more likely, than we might have thought. That is, we are more likely than we might have thought to encounter the kinds of cases that stretch ORM beyond its limits.

Challenges to utilitarian decision theory

Utilitarian decision theory is committed to a weighing of the prospective benefits and costs of the various risky alternatives. There is no question that modern extended benefit–cost analysis has facilitated a more sophisticated weighing. The idea of extended BCA is to obtain a more complete accounting of benefits and costs, especially in the cases involving rare and perhaps fragile environmental resources, by systematically estimating total economic value, including options values, and passive use values where they are relevant.