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Decision Making Under Risk and Uncertainty

Decision Making Under Risk and Uncertainty. DMD #7 David Kopcso and Richard Cleary Babson College F. W. Olin Graduate School of Business. Objectives. To learn how to use decision trees to model a decision. In particular, how to account for uncertainty in decisions.

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Decision Making Under Risk and Uncertainty

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  1. Decision Making Under Risk and Uncertainty DMD #7 David Kopcso and Richard Cleary BabsonCollege F. W. Olin Graduate School of Business

  2. Objectives • To learn how to use decision trees to model a decision. In particular, how to account for uncertainty in decisions. • To learn how to measure and interpret the risk that we face due to uncertainty in the decision.

  3. Elements of Decisions • Values and objectives • Why we care • Decisions to make • Choices we face now and later • Uncertain events • What may happen in the future • Consequences • What happens to us over planning horizon

  4. Decision models • By creating a decision model, we are forced to think through the various aspects of the decision. We start by breaking the problem down into its elements, and think carefully about each piece. As we model each element, the decision tree reconstitutes the pieces into a whole.

  5. Decision Tree Basics • A decision tree represents all of the possible paths that might be followed. • Time flows from left to right. • Squares represent decisions, circles representuncertainties. Large return on investment Venture succeeds Invest Funds lost Venture fails “Typical” return earned on less risky investment Do not invest

  6. Summer Decision 1 Questions that arise while building the decision tree: What options do we have? Often new alternatives occur to us as we build the decision model. For example, can we hedge? What are the uncertainties? What the possible outcomes and how likely is each? How do you want to measure success? Failure? What factors are important?

  7. Decision Trees: The Rules • Chance nodes: Branches represent outcomes. Mutually exclusive and collectively exhaustive. One path will be followed, but not more than one. • Decision nodes: The branches represent alternatives. You can only go down one path. • E.g. Two alternative products ( ProStat and Graph Easy) ==> four branches: Introduce ProStat Introduce Graph Easy Introduce both Introduce neither

  8. Decision Trees: Chronology Requirements • Decisions ordered chronologically with respect to other nodes. • Chance nodes before or after decisions depending on whether outcome is known. • When the chance node follows the decision, then we do not know which outcome will occur when we make the decision. This is how we model uncertainty. • When the chance node precedes the decision, then we know which outcome has occurred when we decide.

  9. Publishing Decision

  10. Uncertainty –What to do? • Clearly, uncertainties make decisions hard. We will model every uncertainty as a probability distribution. Knowing the chance of a successful entrepreneurial launch before making the decision would greatly simplify deciding which course to pursue. • We could simple ignore the uncertainties we face. ProsCons Simplifies analysis Misses the range of possible outcomes. Probabilistic thinking is hard Ignores risk, overstates confidence No opportunity to ponder risk reduction actions (hedging) or ways to leverage the uncertainty (options).

  11. Uncertainty –Modeling • Probabilistic thinking requires us to carefully think through the possible range of outcomes and likelihood that these outcomes will occur. • We model uncertainties using chance nodes as a discrete set of outcomes, usually 2 – 5. Profit $2100 $1500 $1700 $1900

  12. Publishing Decision But, how do we decide which alternative is the best one?

  13. Calculating the expected value (the red numbers) where P(x) is the probability of xoccurring. The expected value is simply the weighted average of the outcomes using the probabilities as the weights.

  14. Risk –Communicating A simple, but effective, way to communicate risk is to use descriptive language. For example, if we publish Book B, then we have a 10% chance of profit being $1500 or a 90% chance of profit being greater than $1500.

  15. Risk –Numerical Measures • There are a number of numerical measures of risk, such as the range, the standard deviation, the coefficient of variation, risk profiles, etc. • The standard deviation, which is the square root of the variance, measures the average degree of uncertainty in the outcomes. • The greater the standard deviation, the greater the amount of uncertainty in the outcomes, i.e., the greater the deviation from the expected (on average).

  16. Coefficient of Variation Criterion CoV =  / EV * 100% Where:  = standard deviation EV = expected value CoV shows “$ at risk” per “$ of return” and is one measure of the relative riskiness of a particular decision choice

  17. Summer Decision 2 How are the black, red, green, and blue numbers obtained?

  18. Walkaway Points • The decision making model is only as good as the information that supports it. • The final choice of the decision-maker depends upon the particular preferences in balancing risk against return.

  19. Assignment Presentation Draw a decision tree for Margolin’s initial decision to pursue the steroid vs. the non-steroid as described in the top of page 3 of the Idiopathic Pulmonary Fibrosis case. What are the expected values of each alternative and what are the risks? Since Margolin decided to pursue the non-steroid pirfenidone, how was risk treated?

  20. Hands-on Active Learning • Volunteer draws on chalk board • Then we examine Precision Tree version. Marnac Simple Decision.xls • Viewing the tree how many decisions for Marnac are represented? • What are the risks Marnac faces?

  21. Hands-on Active Learning Draw a decision tree representing Margolis utilizing the FDA average costs and probabilities by phase from Table I and preceding paragraph (pages 3 and 4 of the ipf case) as well as his original estimated revenue ($1762.5M) and total cost ($247.5M). Note: By so doing he replaced his estimate of 14.85% as the probability of FDA approval for pirfenidone.

  22. Assignment Presentation • Volunteer draws on chalk board • Then we examine Precision Tree version. Marnac_Table1_IND_NDA_NO_hedging.xlsx • Viewing the tree how many decisions for Marnac are represented? • What are the risks Marnac faces?

  23. Risk Management Are there any ways in which Marnac can reduce its risk exposure? What insights can you glen from the potential losses from Phase II or Phase III? Could there be any additional decisions added to the tree? What can be done to reduce or manage the risk surrounding the FDA approval process?

  24. For DMD 09 not DMD 08 Draw a decision tree for InterMune’s decision to license pirfenidone as described on the bottom of page 4 and the top of page 5 of the Idiopathic Pulmonary Fibrosis case. What are the expected values of each alternative and what are the risks?

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