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

BFF5902 S1-2019 Lecture Week 2.pptx - BFF5902 Introduction...

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BFF5902 Introduction to Risk Principles Lecture 2 – Philosophy of Risk
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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
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Foundations of Risk
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4
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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 ?
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6 How do we obtain knowledge? ,
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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
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8 Example – 2008 US Sub-Prime crises A cause of the 2008 US sub-prime mortgage crisis was the
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