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Understanding Risk & Uncertainty

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Risk and uncertainty are related, but different concepts that many people struggle to understand. This presentation defines and explains the difference between risk and uncertainty and how they are measured, so that they can be properly managed in a business context.

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Feel free to contact me via LinkedIn if you have any questions:

http://www.linkedin.com/in/kelvinstott

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Published in: Business, Technology
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  • Kelvin - thanks for sharing. You have done a stellar job in setting forth key points in regard to in-depth understanding of risk and uncertainty, around which considerable discussion takes place among academics, consultants and practitioners. A real challenge is making use of appropriate analytics, appropriate depth and appropriate communication processes to engage decision makers.
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Understanding Risk & Uncertainty

  1. 1. Kelvin Stott PhDPharma R&D Portfolio Strategy, Risk & Decision ConsultantMarch 2012 ©KelvinStott2012
  2. 2. Risk & uncertainty are closely related, but slightlydifferent conceptsBoth risk and uncertainty are: Based on current lack of certainty in a potential fact, event, outcome, or scenario, etc. Defined by probabilities or probability distributions Include both upside and downside potential Subjective: they both depend on who knows whatDifferences Unlike uncertainty, risk involves exposure to impact: potential consequences that matter to a subject Hence, risk is even more subjective: depends on how much the potential consequences matter, to whomDefinitions will follow, after more background… ©KelvinStott2012
  3. 3. Known knowns (no risk/uncertainty) Facts, outcomes or scenarios that we know with absolute certainty, based on deterministic processesUnknown knowns Certain facts that others know but we don’t Based on information asymmetry or poor communicationKnown unknowns Potential facts, outcomes, scenarios that we are aware of, but don’t yet know with any certainty Based on stochastic processes and known probability lawsUnknown unknowns Potential facts, outcomes or scenarios that we are not yet aware of, have not even considered Often rare and extreme events or outliers (“black swans”), not considered due to lack of experience/imagination ©KelvinStott2012
  4. 4. Discrete Based on uncertainty in discrete variables No intermediate outcomes or scenarios E.g., succeed/fail, true/false, event/no event, etc. Defined by discrete probabilitiesContinuous Based on uncertainty in continuous variables Intermediate scenarios/outcomes are possible E.g., sales, costs, time, market share, etc. Defined by continuous probability distributionsComplex Combination of discrete & continuous uncertainty Most real-life cases fall into this category ©KelvinStott2012
  5. 5. Discrete ComplexContinuous ©KelvinStott2012
  6. 6. PDF: Probability Density Function Probability density vs value Area under curve = CDF (see below)CDF: Cumulative Distribution Function Cumulative probability vs value Gradient = PDF (see above) Area = probability x difference in valueInverted PDF Value vs probability densityInverted CDF Value vs cumulative probability ©KelvinStott2012
  7. 7. PDF Inverted PDF densityProbability Value Invert Probability density Value Integrate / Differentiate Value Inverted CDF Cumulative probability CDF Invert Cumulative probability Value ©KelvinStott2012
  8. 8. Mean Dispersion Skewness KurtosisDescribes the Describes the Describes the Describes thelocation of the spread of the asymmetry of shape of thedistribution distribution distribution distribution ©KelvinStott2012
  9. 9. Expected Value (EV) is the probability-weighted averagevalue of a given variable across all potential scenariosUncertainty is the mean absolute deviation (MAD) fromthe Expected Value Includes upside and downside uncertainty Upside = downside: they always balance!Risk is the mean absolute deviation (MAD) from a giventarget, objective, or threshold Includes upside and downside risk Upside risk ≠ downside risk: depends on EV vs targetRisk and uncertainty correspond to areas under CDF (orinverted CDF) value-probability curves Areas correspond to Probability x Impact Impact is a deviation (difference) in value ©KelvinStott2012
  10. 10. Value Upside uncertainty Expected value Downside uncertainty Cumulative probability → ©KelvinStott2012
  11. 11. Value Upside uncertainty Expected value Downside uncertainty Cumulative probability → ©KelvinStott2012
  12. 12. Value No upside uncertainty No d’nside Expected uncertainty value Cumulative probability → ©KelvinStott2012
  13. 13. Value Upside risk Target or threshold Expected value Downside risk Cumulative probability → ©KelvinStott2012
  14. 14. Value Upside Target or risk threshold Expected value Downside risk Cumulative probability → ©KelvinStott2012
  15. 15. Value Target or No upside threshold risk No down- Expected side risk value Cumulative probability → ©KelvinStott2012
  16. 16. Value Target or Upside threshold risk Expected value Downside risk Cumulative probability → ©KelvinStott2012
  17. 17. Value Target or threshold Upside risk Expected value Downside risk Cumulative probability → ©KelvinStott2012
  18. 18. Value Target or No upside threshold risk Downside Expected risk value Cumulative probability → ©KelvinStott2012
  19. 19. Value Upside risk Expected value Downside Target or risk threshold Cumulative probability → ©KelvinStott2012
  20. 20. Value Upside Expected risk value Downside risk Target or threshold Cumulative probability → ©KelvinStott2012
  21. 21. Value Expected value Upside risk No down- Target or side risk threshold Cumulative probability → ©KelvinStott2012
  22. 22. Risk = Uncertainty when EV = target/thresholdUnlike uncertainty, risk cannot exist without atarget, objective, or thresholdRisk can exist without uncertainty (but we don’tcall it risk), when EV ≠ target/threshold Downside risk always exists when EV < target Upside risk always exists when EV > targetWithout uncertainty, risk = expected loss/gain If EV = target: upside risk = downside risk = 0 If EV < target: upside risk = 0; downside = target - EV If EV > target: upside risk = EV - target; downside = 0 ©KelvinStott2012
  23. 23. Standard deviation (SD) Root mean square deviation from Expected Value Measures overall (upside + downside) uncertainty vs EV Non-linear, places more weight on outliers (tails)Variance Mean square deviation from Expected Value Non-linear measure of uncertainty, equal to SD squaredExpected downside uncertainty Probability-weighted average negative deviation from EV Linear measure of downside uncertainty only Equal to 0.5 x mean absolute deviation (MAD) vs EVExpected upside uncertainty Probability-weighted average positive deviation from EV Linear measure of upside uncertainty only Equal to 0.5 x mean absolute deviation (MAD) vs EV ©KelvinStott2012
  24. 24. MAD vs EV / EV Mean absolute deviation from EV, as % of EV Linear measure of overall (upside + downside) uncertainty vs EVSD / EV Non-linear measure of overall uncertainty, as % of EV Also called the Coefficient of Variation (CV)Variance / EV Non-linear measure of overall uncertainty vs EV; not a % ratio Also called Dispersion Index or Variance-to-Mean Ratio (VMR)Expected downside uncertainty / EV Probability-weighted negative deviation from EV, as % of EV Linear measure of downside UC, equal to 0.5 x MAD vs EV / EVExpected upside uncertainty / EV Probability-weighted positive deviation from EV, as % of EV Linear measure of upside UC, equal to 0.5 x MAD vs EV / EV ©KelvinStott2012
  25. 25. Value at Risk (VaR) Maximum negative deviation from target/threshold at X% probability Does not consider upside, or potential impact of worst case scenariosExpected Shortfall (ES) Probability-weighted average deviation from target in X% worst cases Measures downside risk across worst case scenarios only Also called Expected Tail Loss (ETL) or Conditional Value at Risk (CVaR)Probability of success or failure to reach target/threshold Commonly used, but does not measure actual risk! Does not consider potential impact of success or failureExpected downside risk Probability-weighted average negative deviation from target/threshold Linear measure of downside risk (probability x negative impact)Expected upside risk Probability-weighted average positive deviation from target/threshold Linear measure of upside risk (probability x positive impact) ©KelvinStott2012
  26. 26. Value Target or Upside threshold risk Probability Downside of success (or failure) Value at risk Risk (VaR) at X% Expected Shortfall below X% Cumulative probability → ©KelvinStott2012
  27. 27. MAD vs target / target Mean absolute deviation from target, as % of target Linear measure of overall risk vs target/thresholdVaR / target Value at Risk at X% probability, as % of target Like VaR, does not consider worst case scenariosES / target Expected Shortfall in X% worst cases, as % of target Linear measure of extreme downside risk vs targetExpected downside risk / target Probability-weighted negative deviation, as % of targetExpected upside risk / target Probability-weighted positive deviation, as % of target ©KelvinStott2012
  28. 28. Risk and uncertainty are based on lack of certaintyin a potential fact, event, outcome, or scenarioThey include both upside & downside componentsand are described by probability distributionsUncertainty is measured relative to expected valueRisk is measured relative to a settarget/threshold, with potential consequences thatmatter (impact)They can be measured in many ways, but the bestmeasures are based on probability-weightedaverage deviation in value (probability ximpact), corresponding to areas under a CDF curve ©KelvinStott2012
  29. 29. Think and reflectClick a link to share this presentation Linkedin Google+ Facebook Twitter E-mailVisit or join our Linkedin discussion group Big Ideas in Pharma R&D Productivity & Project / Portfolio MgtContact or connect with me kelvin.stott@gmail.com Kelvin Stott on Linkedin ©KelvinStott2012
  30. 30. kelvin.stott@gmail.comwww.linkedin.com/in/kelvinstott ©KelvinStott2012

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