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Applied Information Economics © 2010 HDR and Aliado Accesso LLC Measuring risk – What Works and What Doesn’t
Background ,[object Object],[object Object],[object Object],[object Object],© 2010 HDR and Aliado Accesso LLC
Challenges ,[object Object],[object Object],[object Object],© 2010 HDR and Aliado Accesso LLC
Key Lesson: Skepticism ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],© 2010 HDR and Aliado Accesso LLC
Analysis Placebos ,[object Object],[object Object],[object Object],Studies have shown that it is very easy for a decision-making process to increase confidence in forecasts and decisions even if measured outcomes (return on decisions, forecasts, etc.) are not improved – or even made worse © 2010 HDR and Aliado Accesso LLC
Errors in Expert Judgment ,[object Object],[object Object],[object Object],Human expertise is an important input in and it is hard to completely automate.  But there are certain types of errors in human judgment we know how to measure and control for: “ Experience is inevitable.  Learning is not.”  Paul Schoemaker © 2010 HDR and Aliado Accesso LLC
Real Reasons Decisions Change ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Our self-image of how tolerant or averse we are toward risk is much more fluid than we think.  We will imagine our risk appetite is a more permanent part of our character than it really is. Controlling for this means 1) being aware of the issue, 2) documenting risk aversion with “risk boundaries” 3) multiple estimates of risks © 2010 HDR and Aliado Accesso LLC
Scale Errors ,[object Object],[object Object],[object Object],Relative Impact of Sponsor Level on Project Failure Actual 3 4 1 Scale 2 C-Level SVP Manager VP Based on the relative value of the sponsor levels based on historical data of project failures (Note that the error was just enough that the order might even be wrong) Scales are simple.  But our response behaviors when we use them are not.  Typical scales combine several complex, subtle errors © 2010 HDR and Aliado Accesso LLC
What  Does  Work? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Each of these address known errors or been tested in multiple controlled experiments with measurable results –  not just case anecdotal case studies with reactions of users as an indicator of effectiveness . Don’t reinvent the wheel – scientifically proven, effective risk analysis methods have been applied to other equally difficult problems where there is limited historical data and lots of uncertainty.  Examples: nuclear power, insurance of rare and complex events, oil exploration © 2010 HDR and Aliado Accesso LLC
Calibrated Probabilities ,[object Object],[object Object],[object Object],Giga Analysts Giga Clients Statistical Error “ Ideal” Confidence 30% 40% 50% 60% 70% 80% 90% 100% 50% 60% 80% 90% 100% 70% Assessed Chance Of Being Correct Percent Correct 99 # of Responses © 2010 HDR and Aliado Accesso LLC 25 75 71 65 58 21 17 68 152 65 45 21
“ Smoothing” Inconsistencies ,[object Object],[object Object],Reduction in Forecasting Error Compared to Expert Judgment  R&D Portfolio Priorities Battlefield Fuel Forecasts IT Portfolio Priorities Cancer patient recovery Changes in stock prices Mental illness prognosis Psychology course grades Business failures 0% 10% 20% 30% My Studies Other Published Studies First Estimate Second Estimate Movie Box Office Forecasts © 2010 HDR and Aliado Accesso LLC
Quantitative Modeling: It Works ,[object Object],[object Object],[object Object],[object Object],© 2010 HDR and Aliado Accesso LLC Event A Event B Demand %Orders Lost OR Lost Revenue
Red Herrings of Modeling ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],© 2010 HDR and Aliado Accesso LLC
Applied Information Economics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],© 2010 HDR and Aliado Accesso LLC
Making the Best Bet © 2010 HDR and Aliado Accesso LLC Model The Current State of Uncertainty  – Initially use calibrated estimates and then actual measurements Optimize Decision  – Use the quantified Risk/Return boundary of the Decision makers to determine which decision is preferred. Define the Decision  and Identify Relevant Variables.  Set up the “Business Case” for the decision, using these variables – Calibration Training Compute the value of additional Information  – Determine what to measure and how much effort to spend on measuring it.  Measure where the information value is high  – Reduce uncertainty using any of the methods  No Yes Is there significant value to more information?
A Few Examples ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],AIE was applied initially to IT business cases.  But over the last 16 years it has also been applied to other decision analysis problems in all areas of Business Cases, Performance Metrics, Risk Analysis, and Portfolio Prioritization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],© 2010 HDR and Aliado Accesso LLC
Uncertainty, Risk & Measurement ,[object Object],[object Object],[object Object],[object Object],Measuring Uncertainty, Risk and the Value of Information are closely related concepts, important measurements themselves, and precursors to most other measurements   © 2010 HDR and Aliado Accesso LLC
The Impact of Computing Information Value ,[object Object],[object Object],[object Object],[object Object],Traditional Measurement Priorities Value of Information © 2010 HDR and Aliado Accesso LLC
Five Useful Assumptions ,[object Object],[object Object],[object Object],[object Object],[object Object],“ It’s amazing what you can see when you look” Yogi Berra  © 2010 HDR and Aliado Accesso LLC
Measuring the “Impossible” ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],WWII German Tank Production Estimates © 2010 HDR and Aliado Accesso LLC
Quantifying Risk Aversion Acceptable Risk/Return Boundary Investment Region ,[object Object],[object Object],[object Object],Investment © 2010 HDR and Aliado Accesso LLC
Cost vs. Value of AIE ,[object Object],[object Object],[object Object],[object Object],© 2010 HDR and Aliado Accesso LLC
Final Tips ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],© 2010 HDR and Aliado Accesso LLC
Questions? ,[object Object],[object Object],[object Object],[object Object],© 2010 HDR and Aliado Accesso LLC
Supplementary Material © 2010 HDR and Aliado Accesso LLC
“ Proper” Incentives ,[object Object],[object Object],[object Object],[object Object],[object Object],i Results of Brier Score applied to weather forecasts © 2010 HDR and Aliado Accesso LLC
Prediction Markets ,[object Object],[object Object],Source: Servan-Schreiber, et. al.  Electronic Markets, v  14-3, September 2004 © 2010 HDR and Aliado Accesso LLC
Increasing Value & Cost of Info. ,[object Object],[object Object],[object Object],Dollar Value/Cost $0 $$$ Low certainty High certainty EVPI Aim for this range Perfect Information © 2010 HDR and Aliado Accesso LLC EVI ECI
The Value of the “First Few” ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],-100% 0 100% 0 5 10 15 20 25 30 35 40 90% CI Number of samples Mean of the sample vs. actual ,[object Object],[object Object],[object Object],© 2010 HDR and Aliado Accesso LLC
Bayesian Sampling ,[object Object],[object Object],3 Launches,  0 Failures 5 Launches,  1 Failure Baseline Failure Rate of a Rocket After 5 Launches 1 Launch,  0 Failures 0 Launches,  0 Failures © 2010 HDR and Aliado Accesso LLC
Issues with a “Risk Map” ,[object Object],[object Object],[object Object],[object Object],[object Object],Likelihood Impact © 2010 HDR and Aliado Accesso LLC
The Illusion of Cognition ,[object Object],[object Object],[object Object],[object Object],The “Illusion of Cognition” is a phrase in the decision psychology literature that refers to the misconception that our choices are based on rational thinking.  Risk assessment methods employ structures that can introduce these problems. © 2010 HDR and Aliado Accesso LLC
First, Do No Harm ,[object Object],[object Object],[object Object],© 2010 HDR and Aliado Accesso LLC Method Gut Feel Weighted Score Traditional Financial Quantitative Models Measured Improvement to Judgment? Baseline No: Remove no errors and add new errors Maybe: Decomposition helps; false precision Yes: Proven w/controlled tests Does it quantify risk? Only intuitively No, it attempts to  describe  risk; No, but may attempt to adjust for it Yes Determines High-Payoff Measures? No No:  Turns some good measures into scores No Yes (w/AIE) Net Benefit? Baseline No: Probably Worse Maybe Slightly Best

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Measuring Risk - What Doesn’t Work and What Does

  • 1. Applied Information Economics © 2010 HDR and Aliado Accesso LLC Measuring risk – What Works and What Doesn’t
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  • 15. Making the Best Bet © 2010 HDR and Aliado Accesso LLC Model The Current State of Uncertainty – Initially use calibrated estimates and then actual measurements Optimize Decision – Use the quantified Risk/Return boundary of the Decision makers to determine which decision is preferred. Define the Decision and Identify Relevant Variables. Set up the “Business Case” for the decision, using these variables – Calibration Training Compute the value of additional Information – Determine what to measure and how much effort to spend on measuring it. Measure where the information value is high – Reduce uncertainty using any of the methods No Yes Is there significant value to more information?
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Hinweis der Redaktion

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