BFF5902 S1-2019 Lecture Week 2.pptx - BFF5902 Introduction to Risk Principles Lecture 2 \u2013 Philosophy of Risk Learning objectives \u25aa Explain the

BFF5902 Introduction to Risk Principles Lecture 2 – Philosophy of Risk

2 Learning objectives ▪ Explain the difference between data, information and knowledge, and different approaches to gaining knowledge ▪ Define and explain the concepts and factors affecting risk perception and risk tolerance ▪ Identify and explain sources of perception and heuristic biases ▪ Explain the use of probability measures to describe likelihood of outcomes in a decision tree ▪ Describe risk aversion and how it may be included in decisionmaking under conditions of uncertainty

Foundations of Risk

5 Uncertainty and knowledge are related ▪ Recall that uncertainty is caused by a lack of knowledge and is a necessary precondition for risk to be present ▪ For example, if we know for certain that there will be an explosion in a factory, then there is no reason for us to talk about that explosion as a risk. Similarly, if we know that no explosion will take place, then there is no reason either to talk about risk. What we refer to as a “risk of an explosion” is a situation in which it is not known whether or not an explosion will take place. ▪ Does a certain event contain risk? If not, why not? ▪ Does an impossible event contain risk? If not, why not? ▪ What about highly likely or improbable ?

6 How do we obtain knowledge? ,

7 Pros and Cons of empiricism versus rationalism for risk management Empiricism Rationalism Pros • Verifiable • Easy to communicate and understand • Can be subject to statistical testing for confidence • Precise answers • Conclusive logic • Powerful insights into the nature of a risk • May be only choice for very rare events Cons • Data errors • Sample unrepresentative (too small, too short, irrelevant to the problem) • Past may not reflect the future (or risk is new) • Events may be too rare to generate reliable data • Never 100% conclusive • Theory and its assumptions may be too simple or wrong • Precision may mislead users suggesting greater truth than warranted