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Chapter 19 decision-making under risk

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Chapter 19 decision-making under risk

  1. 1. Slide Sets to © 2005 by McGraw-Hill,19-1 Developed By: Dr. Don Smith, P.E. Department of Industrial Engineering Texas A&M University College Station, Texas Executive Summary Version Chapter 19 More on Variation and Decision Making Under Risk
  2. 2. Slide Sets to © 2005 by McGraw-Hill,19-2 LEARNING OBJECTIVESLEARNING OBJECTIVES 1. Certainty and risk 2. Variables and distributions 3. Random samples 4. Average and dispersion 5. Monte Carlo simulation
  3. 3. Slide Sets to © 2005 by McGraw-Hill,19-3 Sct 19.1 Interpretation of Certainty, Risk,Sct 19.1 Interpretation of Certainty, Risk, and Uncertaintyand Uncertainty  Certainty – Everything know for sure; not present in the real world of estimation, but can be ‘assumed’  Risk – a decision making situation where all of the outcomes are know and the associated probabilities are defined  Uncertainty – One has two or more observable values but the probabilities associated with the values are unknown  Observable values – states of nature  See Example 19.1 – about risk
  4. 4. Slide Sets to © 2005 by McGraw-Hill,19-4 Types of Decision MakingTypes of Decision Making  Decision Making under Certainty  Process of making a decision where all of the input parameters are known or assumed to be known  Outcomes – known  Termed a deterministic analysis  Parameters are estimated with certainty  Decision Making under Risk  Inputs are viewed as uncertain, and element of chance is considered  Variation is present and must be accounted for  Probabilities are assigned or estimated  Involves the notion of random variables
  5. 5. Slide Sets to © 2005 by McGraw-Hill,19-5 Two Ways to Consider Risk inTwo Ways to Consider Risk in Decision MakingDecision Making Expected Value (EV) analysis Applies the notion of expected value (Chapter 18) Calculation of EV of a given outcome Selection of the outcome with the most advantageous outcome Simulation Analysis Form of generating artificial data from assumed probability distributions Relies on the use of random variables and the laws associated with the algebra of random variables
  6. 6. Slide Sets to © 2005 by McGraw-Hill,19-6 Sct 19.2 Elements Important to DecisionSct 19.2 Elements Important to Decision Making Under RiskMaking Under Risk  The concept of a random variable A decision rule that assigns an outcome to a sample space Discrete variable or Continuous variable  Discrete variable – finite number of outcomes possible  Continuous variable – infinite number of outcomes  Probability Number between 0 and 1 Expresses the “chance” in decimal form that a random variable will take on any specific value
  7. 7. Slide Sets to © 2005 by McGraw-Hill,19-7 Types of Random VariablesTypes of Random Variables  Continuous  Discrete
  8. 8. Slide Sets to © 2005 by McGraw-Hill,19-8 Distributions - Continuous VariablesDistributions - Continuous Variables  Probability Distribution (pdf)  A function that describes how probability is distributed over the different values of a variable P(Xi) = probability that X = Xi  Cumulative Distribution (cdf)  Accumulation of probability over all values of a variable up to and including a specified value F(Xi) = sum of all probabilities through the value Xi = P(X ≤ Xi)
  9. 9. Slide Sets to © 2005 by McGraw-Hill,19-9 Three Common Random VariablesThree Common Random Variables  Uniform – equally likely outcomes  Triangular  Normal Study Example 19.3
  10. 10. Slide Sets to © 2005 by McGraw-Hill,19-10 Discrete Density and Cumulative ExampleDiscrete Density and Cumulative Example pdf cdf
  11. 11. Slide Sets to © 2005 by McGraw-Hill,19-11 Sct 19.3 Random SamplesSct 19.3 Random Samples  Random Sample A random sample of size n is the selection in a random fashion with an assumed or known probability distribution such that the values of the variable have the same chance of occurring in the sample as they appear in the population Basis for Monte Carlo Simulation  Can sample from: Discrete distributions … or Continuous distributions
  12. 12. Slide Sets to © 2005 by McGraw-Hill,19-12 Sampling from a Continuous DistributionSampling from a Continuous Distribution  Form the cumulative distribution in closed form from the pdf  Generate a uniform random number on the interval {0 – 1}, called U(0,1)  Locate U(0,1) point on y-axis  Map across to intersect the cdf function  Map down to read the outcome (variable value) on x-axis
  13. 13. Slide Sets to © 2005 by McGraw-Hill,19-13 Sct 19.4 Expected Value and StandardSct 19.4 Expected Value and Standard DeviationDeviation  Two important parameters of a given random variable: Mean - µ  Measure of central tendency Standard Deviation - σ  Measure of variability or spread  Two Concepts to work within Population Sample from a population
  14. 14. Slide Sets to © 2005 by McGraw-Hill,19-14 Sample Population vs SamplePopulation vs Sample Population  µ - population mean  σ2 - population variance  σ - population standard deviation  Often sample from a population in order to make estimates X 2 s s Sample mean Sample variance Sample standard deviation These values, properly sampled, attempt to estimate their population counterparts
  15. 15. Slide Sets to © 2005 by McGraw-Hill,19-15 Important RelationshipsImportant Relationships Population Mean µ  Distribution  E(x) =  Sample  Measure of the central tendency of the population  If one samples from a population the hope is that sample mean is an unbiased estimator of the true, but unknown, population mean ( )i iX P X∑ i i iX f X n n = ∑ ∑
  16. 16. Slide Sets to © 2005 by McGraw-Hill,19-16 Variance and Standard DeviationVariance and Standard Deviation Notes relating to variance and standard deviation properties Illustration of variances for discrete and continuous distributions
  17. 17. Slide Sets to © 2005 by McGraw-Hill,19-17 Population vs. SamplePopulation vs. Sample  Variance of a population  Standard deviation of a population: N 2 i i=1 (x ) N σ µ− = ∑ 2 Var(X)X Xσ σ= = N 2 i 2 i=1 (x ) N σ µ− = ∑  Variance of a sample  Standard deviation of a sample 2 2 2 1 1 iX n s X n n = − − − ∑ S is termed an unbiased estimator of the population standard deviation
  18. 18. Slide Sets to © 2005 by McGraw-Hill,19-18 Combining the Average and StandardCombining the Average and Standard DeviationDeviation  Determine the percentage or fraction of the sample that is within ±1, ±2, ±3 standard deviations of the sample mean . . .  In terms of probability…  Virtually all of the sample values will fall within the ±3s range of the sample mean  See example 19.6 , t = 1,2,3X ts± P( )X ts X X ts− ≤ ≤ +
  19. 19. Slide Sets to © 2005 by McGraw-Hill,19-19 Continuous Random VariablesContinuous Random Variables  Expected value: Variance:  For uniform pdf in Example 19.3: ( )( ) R Xf x dxE X = ∫ 2 2 ( ) ( ) [ ( )] R Var x X f X dX E X= −∫ R represents the defined range of the variable in question. 10 2 15 R 2 2 3 15 2 10 1 0.2 $10 X $15 5 E(X)= (0.2)Xdx=0.1X 0.1(225 100) $12.50 0.2 ( ) (0.2) (12.5) (12.5) 3 =0.06667(3375-1000)-156.25=2.08 2.08 $1.44 ( ) R X Var X X dX X f x σ = ≤ ≤ = − = = − = − = = = ∫ ∫
  20. 20. Slide Sets to © 2005 by McGraw-Hill,19-20 Sct 19.5 Monte Carlo Sampling andSct 19.5 Monte Carlo Sampling and Simulation AnalysisSimulation Analysis  Simulation involves the generation of artificial data from a modeled system  Monte Carlo Sampling The generation of samples of size n for selected parameters of formulated alternatives The sampled parameters are expected to vary according to a stated probability distribution (assumed)
  21. 21. Slide Sets to © 2005 by McGraw-Hill,19-21 Key Assumption - IndependenceKey Assumption - Independence  For a given problem: All parameters are assumed to be independent One variable’s distribution in no way impacts any other variable’s distribution Termed: Property of independent random variables  The modeling approach follows a 7-step process (page 678)
  22. 22. Slide Sets to © 2005 by McGraw-Hill,19-22 SimulationSimulation Monte Carlo Sampling is a traditional approach (method) for generating pseudo- random numbers (RN) to sample from a prescribed probability distribution.  Pseudo-random refers to the fact that a digital computer can generate approximately random numbers due to fixed word size and round off problems.
  23. 23. Slide Sets to © 2005 by McGraw-Hill,19-23 Sampling ProcessSampling Process  Requires:  The cdf of the assumed pdf;  A uniform random number generator;  Application of the inverse transform approach.  Why require the cdf?  The y-axis of a cdf is scaled from 0 to 1.  That is the same as the range of U(0,1).  Facilitates mapping a RN to achieve the outcome value on the x-axis.  The U(0,1) selects a X-value from the cdf.
  24. 24. Slide Sets to © 2005 by McGraw-Hill,19-24 The Need for the cdfThe Need for the cdf x-axis: Outcome values Cumulative probability on the y-axis Generate a U(0,1) random number: Locate that value on the y-axis: Map across to the cdf then map down to the x-axis to obtain the outcome
  25. 25. Slide Sets to © 2005 by McGraw-Hill,19-25 Summary of the Modeling StepsSummary of the Modeling Steps  Formulate the economic analysis: The alternatives – if more than one; Define which parameters are “constants” and which are to be viewed as random variables. For the random variables, assign the appropriate pdf: Discrete and or continuous. Apply Monte Carlo sampling – a sample size of “n” where it is suggested that n = 30. Compute the measure of worth (PW, AW, . . ) Evaluate and draw conclusions.
  26. 26. Slide Sets to © 2005 by McGraw-Hill,19-26 Examples to StudyExamples to Study  Examples 19.7 and 19.8 offer detailed analyses of a simulated economic analysis  Also, refer to the Additional Examples at the end of the chapter Example 19.10 – applying the normal distribution
  27. 27. Slide Sets to © 2005 by McGraw-Hill,19-27 Chapter SummaryChapter Summary  To perform decision making under risk implies that some parameters of an engineering alternative are treated as random variables.  Assumptions about the shape of the variable's probability distribution are used to explain how the estimates of parameter values may vary.  Additionally, measures such as the expected value and standard deviation describe the characteristic shape of the distribution.
  28. 28. Slide Sets to © 2005 by McGraw-Hill,19-28 Summary - continuedSummary - continued  In this chapter, we learned several of the simple, but useful, discrete and continuous population distributions used in engineering economy -uniform and triangular - as well as specifying our own distribution or assuming the normal distribution.  It is important to note that a sound background in applied statistics is vital to the complete understanding of the simulation process
  29. 29. Slide Sets to © 2005 by McGraw-Hill,19-29 Chapter 19Chapter 19 End of SetEnd of Set