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Posts Tagged ‘probability’

An Example Scenario Using Program Evaluation And Review Technique (PERT)

April 2nd, 2013 2 comments

This post demonstrates how to use PERT (Program Evaluation and Review Technique) to compute the weighted average duration, standard deviation, and the variance of each activity in a given project. It then calculates the average or expected project duration, which is the sum of the average activity times on the critical path. Finally knowing the average project duration and the variances of all activities, the probability (Z) of completing the project by a certain time is then calculated using standard statistical tables.

Categories: Time Management

The Impact Of Risk: Consequences Or Likelihood?

June 10th, 2012 No comments

It has been asserted through research that risk assessment is a function of two variables, the likelihood or probability of the event, and the severity of the consequences of the event. Literatures have been divided between those that consider the vital role that likelihood information might play when quantitatively and analytically evaluating the magnitude of risk versus those that intuitively overestimate risks when formulating their judgments due to the emotional effects of severity without considering much the likelihood information. It has been concluded that adopting a neutral approach between these two perspectives, with careful consideration of their biases, would optimally help managers in identifying and mitigating their risks.

Categories: Risk Management

Risk In The Spectrum Of Uncertainty And Knowledge

May 20th, 2012 2 comments

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).

Categories: Risk Management