There exist several mitigation strategies that help managers in understanding risks and their impact on the business and on the organizational objectives. These treatment strategies are mainly branched into four common categories; risk avoidance or elimination, risk transference or share, risk mitigation, and risk acceptance. The choice of which of these methods to implement depends on the kind of the adopted decision-making model and on certain weighted, influential, organizational factors that should be taken into consideration in prioritizing, evaluating, and resolving risks. Since elimination of risk is unrealizable or almost impossible (Cervone 2006), managers should utilize the least cost methodology and the most appropriate control to lessen risks to a level corresponding to minimal impact. Besides, priority should be given to mitigating severe or high-impact risks since it would be impractical to address all recognized risks (Stoneburner, Goguen et al. 2002).
Several great thinkers defined risk in different ways and laid the foundation of our understanding of the meaning of risk and its corresponding relationship with uncertainty and knowledge. The most influential in this domain, Frank Knight, had devised an analytical framework to clarify an important distinction among risk, uncertainty, and full knowledge (Langlois and Cosgel 1993). He based his categorization on the fact that it is ultimately based on whether the classification of states (or instances) of a particular uncertain event is exhaustive, and not on the assigned subjective probabilities of these states (Langlois and Cosgel 1993). This classification scheme is based on three types of probabilities, a priori probability (the universe of outcomes is known and thus can be mathematically determined), statistical probability (lack of homogeneity, empirical determination of the universe of outcomes), and estimated probability (universe of outcomes can not be defined) (Jarvis 2011). Based on these types of probabilities, Knight associated risk with a lack-of-knowledge situation or state where outcomes are either known (a priori probability) or probable (statistical probability), meaning that the list of outcomes with their frequencies of occurrence and their impacts can be assessed objectively. On the other hand, he associated uncertainty with a black vacuum (knowledge vaccum) characterized by the inability to exhaustively classify all of its ‘unique’ outcomes, yet the latter can be judged through qualitative and estimated probabilities (Jarvis 2011). Recent interpretations have associated uncertainty with the inability to make predictions due to discontinuities, complexities, and heterogeneity of environments (Richard and Susan 2010).