risk and decision analysis course overviewm.vidyasagar/fall-2015/...probabilistic risk analysis:...

Self-Introductions and ExpectationsCourse Philosophy and Objectives

Course Outline

Risk and Decision AnalysisCourse Overview

M. Vidyasagar

Cecil & Ida Green ChairThe University of Texas at DallasEmail: [email protected]

August 22, 2015

M. Vidyasagar Course Overview


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1 Self-Introductions and Expectations

2 Course Philosophy and Objectives

Learning Outcomes

Governing Philosophy

3 Course Outline

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Methodology

Marking Scheme



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My Contact Information

Office: Room ECSN 3.926Phone: x4679Emai: [email protected] Hours: By appointment; prefer Thursdays and Fridays

Departmental Office:Kathryn Owens, x4479



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Course Materials

Lectures notes and other supporting material will be available at:http://www.utdallas.edu/∼m.vidyasagar/Fall-2015/6304Software: Matlab (Student Edition), available at Bookstore.Matlab can also be executed from any campus IP address.

Suggested References (Not Mandatory to Buy):

Probabilistic Risk Analysis: Foundations and Methods, by TimBedford and Roger Cooke, Cambridge University Press, 2001.(Very mathematical treatment; good for later reference.)

Risk Assessment and Decision Making in Business andIndustry, Glenn Koller, Taylor & Francis, 2005. (Very chattyand informal; good for getting an initial idea.)



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Self-Introductions and Expectations from the Course

Please introduce yourself briefly, your role within your organization,and your expectations from the course.

If possible please explain the current role of risk modeling and/oranalysis within your organization (and your specific work unit), andhow this course may serve to strengthen and/or modify this aspect.

If you already know what project you would like to do (unlikely),please share that too!



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Learning OutcomesGoverning Philosophy

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Learning Outcomes


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Learning Outcomes

At the end of the course, the student should be able to:

Understand the main tools from probability and statistics thatare used in modeling and analyzing risk in a business context

Become familiar with the relevant tests and tools for carryingout statistical computations.

Become familiar with the basic concepts and methods of riskanalysis

Become acquainted with the applications or risk analysis toseveral domains such as valuation with risk, financial riskminimization, supply chain management, and programmanagement

Be able to carry out an independent project



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Risk is pervasive – all decision-making must be made underuncertainty and ignorance.

Worst-case analysis usually leads to paralysis.

Modern systems (organizations, transactions, ...) are far toocomplex to permit ‘gut feel’ analysis of risk.

This is the main reason for using mathematical methods.



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Rationale for Using Probability as the Formalism

The formalism for modeling and analyzing uncertainty and risk isprobability theory and statistics.

This is especially important if we wish to quantify risk in ouranalysis and decision-making.

Quantification is essential if we wish to combine models ofsubsystems into an overall risk model, or to make decisions abouttrade-offs.



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Typical Risk Analysis Problem

Given data, and an eventuality, estimate the probability of theeventuality happening.

Example: You have to undertake a project similar to others thatyour company or others have done hundreds of times.

Data: Number of days it took to do similar projects in the past.Eventuality: That there will be a huge time (or cost) over-run.

You need to estimate (i) the expected time required to completethe project, and (ii) the likelihood of a catastrophic over-run thatmight incur a penalty.



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Typical Risk Analysis Problem (Cont’d)

To do this, you would need to

Choose a class of models for the data, and then

Determine the best fit for the data within that model class(Maximum Likelihood Estimation or MLE), and then

Assess whether the best fit is good enough (goodness of fittest), and if not, choose a different model class.

Remember: Any data can be fitted with any model class – butthat doesn’t mean that you will get a good fit!

Many times the data is not readily available, and has to begenerated via simulation.



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Typical Risk Analysis Problem (Cont’d)

We need to distinguish between analyzing routine events andextreme events.

Example: If you have done a project 500 times, then you canreliably estimate the average time needed to do the next project.Similarly if you get paid in a foreign currency but have to do thework in the USA, you can reasonably estimate currencyfluctuations.

But if you have had a catastrophic over-run in time only twice outof 500, you cannot reliably estimate the likelihood of over-run.

Much or risk analysis involves estimating the likelihood of extremeevents, such as margin calls for investors, penalty clauses beinginvoked for project over-runs, flood walls collapsing due to excessrain or snow melt-off, etc., for which there is inadequate data.



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What is Excluded from the Course

We will not be covering risks that cannot be estimated on thebasis of historical data, for example geo-political risk of a foreigncurrency suddenly getting devalued.

Those kinds or risks are usually “guessed,” not “analyzed.”



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Programming Environment: Matlab

Matlab is a very versatile and powerful programming environment.

Standard commands are available for implementing mostprobabilistic modeling and computational techniques.

Thus we will not be covering “writing code” to any extent, otherthan some Matlab coding.

But you still need to understand the nature of the computations!



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Review of basic probability

Fitting distributions to data, goodness of fit

Methods of risk analysis

Monte Carlo simulation

Project and supply chain management

Forecasting extreme events

Portfolio optimization

Student project presentations



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Methodology

Exercises will be assigned and can be done using Matlab.

Matlab diaries can be submitted as homework. Make surethat the diaries have lots of comments to enable the instructorto follow what you have done.

A small number of ‘drill problems’ to allow concepts to sink in

Projects will be proposed by each student and approved bythe instructor; don’t wait too long to choose a project topic!

Project presentation during last session of course

Classroom participation, discussion, and peer evaluation willalso contribute to final grade



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Methodology (Cont’d)

Please

Ask lots of questions

Identify data sets that interest you

Suggest topics that interest you



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Marking Scheme

Class participation: 10%

Peer evaluation: 10%

Assignments: 40%

Final project: 40%


risk and decision analysis course overviewm.vidyasagar/fall-2015/...probabilistic risk analysis:...

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