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![Page 1: Rational is Practical: SH · PDF filePalantir Economic Solutions Ltd John Schuyler Denver, Colorado, USA PetroSkills and Decision Precision Copyright © 2006-2008 by John R. Schuyler.](https://reader038.fdocuments.in/reader038/viewer/2022110221/5a75dae57f8b9a9c548cdc5f/html5/thumbnails/1.jpg)
OPTIMIZER’S CURSENov. 4, 2008
(revised 7-Nov-08)
Seminar Hosted by
Palantir Economic Solutions Ltd
John SchuylerDenver, Colorado, USA
PetroSkillsand
Decision Precision
Copyright © 2006-2008 by John R. Schuyler. All rights reserved.
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Optimizer’s CurseOptimizer’s Curse
Background
Optimizer’s Curse phenomenon
Implications for Portfolio Forecasts
An Approach for Correcting this Bias
– Portfolio forecasts
– Individual project value estimates
Discussion and Wrap-Up
![Page 3: Rational is Practical: SH · PDF filePalantir Economic Solutions Ltd John Schuyler Denver, Colorado, USA PetroSkills and Decision Precision Copyright © 2006-2008 by John R. Schuyler.](https://reader038.fdocuments.in/reader038/viewer/2022110221/5a75dae57f8b9a9c548cdc5f/html5/thumbnails/3.jpg)
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Optimizer’s CurseOptimizer’s Curse
Background
Optimizer’s Curse phenomenon
Implications for Portfolio Forecasts
An Approach for Correcting this Bias
– Portfolio forecasts
– Individual project value estimates
Discussion and Wrap-Up
![Page 4: Rational is Practical: SH · PDF filePalantir Economic Solutions Ltd John Schuyler Denver, Colorado, USA PetroSkills and Decision Precision Copyright © 2006-2008 by John R. Schuyler.](https://reader038.fdocuments.in/reader038/viewer/2022110221/5a75dae57f8b9a9c548cdc5f/html5/thumbnails/4.jpg)
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BackgroundBackground
Risk, Uncertainty and Investment Decision-Making in the Upstream Oil and Gas Industry– PhD Dissertation by Fiona Macmillan– University of Aberdeen, Oct. 2000
“Best Practices in Project Evaluation and Influence on Company Performance”– John Schuyler– Surveyed some former class participants– Journal of Petroleum Technology, Aug. 1997
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InterestsInterests
Shareholder value creation:measurement and forecasting
Capital investment decisionsWhat criteria to use?– PV discount rate– Adjusting for market risk or correlation– Risk tolerance
Modeling the project, portfolio, and enterprise
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Present thinking –rocket science?Present thinking –rocket science?
Most companies are using PV discount rates that are too high.Most companies are too conservative, when the diversified investor wishes them to be essentially risk-neutral.– Scaling up the typical investor’s decision
policy to the corporate level
– Market Value Discount adjustment
Consider the company’s portfolio in establishing decision policyOptimizer’s curse: Yet one more detail
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Interesting PhenomenaInteresting Phenomena
Lemon Market
Survivorship Bias
Brown’s Note
Winner’s Curse
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Lemon MarketLemon Market
Used cars in the market are either ‘good’ or ‘bad’ (lemons).
The owner of a car knows the condition of his car.
<asymmetric information>
The buyer of a car doesn’t know the condition and must therefore assume that a car for sale is of ‘average’ quality.
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Lemon MarketLemon Market
There are few good cars on the market– The owner of a good used car cannot get a high
enough price to induce him to sell.
Buyers are disappointed, on average, because the cars available and purchased are mostly lemons.
Described by George Akerlof (1940-), who shared the 2001 Nobel Prize in Economics
Excluding candidates causes the bias.
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Survivorship BiasSurvivorship Bias
Failed companies are excluded from studies because they no longer exist.
With mutual funds, 90% accurately claim to have performed in the top quartile of their peers. The other 3/4 of funds have closed.
Publication bias: scientific journals tend to publish studies where something was found out.– Studies where “nothing happened” tend to be rejected
or never submitted for publication
– “File drawer problem”Ignoring all data causes the bias.
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Brown’s NoteBrown’s Note
Brown, K. C. (1974)“A note on the apparent bias of net revenue estimates for capital investment projects”
“Projects approved tend to be those where
revenue is overstated and costs and/or
investment are understated.”
J. Finance 29 1215–1216; cited by Smith & Winkler
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Winner’s CurseWinner’s Curse
At an auction, the winner will tend to overpay (unless he recognizes the winner’s curse when bidding)
Auction analyses usually assume
– Common-value auction, where value similar for all bidders but uncertain at the time of the auction
– Similar bidding methods
– Similar bidding strategy
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Winner’s CurseWinner’s Curse
The winning bidder is usually the one with the highest value estimate.
Even if value estimates are unbiased, on average
– The person who is randomly most-optimistic this time will estimate highest.
– The person with the highest estimate will likely bid highest and win the auction.
Ranking bids causes the bias.
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Winner’s CurseWinner’s Curse
Experienced, successful buyers at auctions have learned to shade their bids
– bid lower than what they think the item is worth
Longstanding experiences teaches:
– Bid lower when the value of the item is more uncertain;
– Bid lower as the number the number of bid competitors increases.
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Winner’s CurseWinner’s Curse
“Winner’s curse” term coined in the 1950s in the context of bidding for an oil field.
Capen, E. C., R. V. Clapp, and W. M. Campbell, 1971, "Competitive Bidding in High-Risk Situations," Journal of Petroleum Technology (now JPT), June, p. 641-53.
Richard Thaler, 1992, The Winner's Curse: Paradoxes and Anomalies of Economic Life
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Optimizer’s CurseOptimizer’s Curse
Background
Optimizer’s Curse phenomenon
Implications for Portfolio Forecasts
An Approach for Correcting this Bias
– Portfolio forecasts
– Individual project value estimates
Discussion and Wrap-Up
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Smith and Winkler’s PaperSmith and Winkler’s Paper
The Optimizer’s Curse: Skepticism andPostdecision Surprise in Decision Analysis
– James Smith and Robert WinklerFuqua School, Duke University
– Management Science
– March 2006
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Their ConclusionsTheir Conclusions
Portfolio results tend to be well below their forecast mean
“A decision maker who consistently chooses alternatives based on her estimated values should expect to be disappointed on average, even if the individual value estimates are conditionally unbiased.”
Caused by screening and ranking
Same phenomenon as winner’s curse?
Selecting best candidates causes the bias.
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Why DA Values are OverstatedWhy DA Values are Overstated
Estimates may be unbiased– No biases in input judgments
– No biases introduced by calculations
Unbiased: The hallmark of a good evaluation
– If the decision-maker or company wants to be conservative, then that is best addressed by a risk policy (using a utility function).
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Why DA Values are OverstatedWhy DA Values are Overstated
The individual project value estimates may be unbiased, on average,
Yet …
Any screening or optimizing effort will preferentially retain cases that tend to be optimistic, e.g. selecting projects with:
– EMV > 0
– DROI > cutoff hurdle
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Optimizer’s CurseOptimizer’s Curse
Background
Optimizer’s Curse phenomenon
Implications for Portfolio Forecasts
An Approach for Correcting this Bias
– Portfolio forecasts
– Individual project value estimates
Discussion and Wrap-Up
![Page 22: Rational is Practical: SH · PDF filePalantir Economic Solutions Ltd John Schuyler Denver, Colorado, USA PetroSkills and Decision Precision Copyright © 2006-2008 by John R. Schuyler.](https://reader038.fdocuments.in/reader038/viewer/2022110221/5a75dae57f8b9a9c548cdc5f/html5/thumbnails/22.jpg)
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Portfolio Forecast BiasPortfolio Forecast Bias
Brown (1974) observed:
A project is more likely to be accepted if its revenues have been overestimated and its costs underestimated.
Journal of Finance, vol. 29, 1215-1216
Corollary:
If a project has been accepted, then it’s value tends to have been overestimated.
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Our paperOur paper
OPTIMIZER'S CURSE:REMOVING THE EFFECT OF THIS BIAS IN PORTFOLIO PLANNING
by John Schuyler and Timothy Nieman– Society of Petroleum Engineers paper no. 107852,
Proceedings, 2007 SPE Hydrocarbon Economics and Evaluation Symposium held in Dallas, Texas, U.S.A., 1–3 April 2007
– Reviewed, edited and republished in SPE Projects, Facilities & Construction, March 2008
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Example in the paper and articleExample in the paper and article
Revenue=Lognormal(μ = 100, σ = 50) $million
CapInv= Lognormal(μ = 40, σ = 20) $million
OpExp= Lognormal(μ = 40, σ = 20) $million
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EMV average is $20 million
CapInv and OpExp distributions are each correlated to Revenue with a 0.5 Spearman rank correlation coefficient (ρs).
The E/A are beta distributions and independent.
Example in the paper
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-35.2848.6583.93Those with DROI > 1
-17.5935.7853.37Those with EMV > 0
0.0419.9919.95All Candidates
AverageError
AverageActual
AverageEstimate
Project Values$millions
Value overstated by 49%
Value overstated by 72%
Example in the paper
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Optimizer’s CurseOptimizer’s Curse
Background
Optimizer’s Curse phenomenon
Implications for Portfolio Forecasts
An Approach for Correcting this Bias
– Portfolio forecasts
– Individual project value estimates
Discussion and Wrap-Up
![Page 28: Rational is Practical: SH · PDF filePalantir Economic Solutions Ltd John Schuyler Denver, Colorado, USA PetroSkills and Decision Precision Copyright © 2006-2008 by John R. Schuyler.](https://reader038.fdocuments.in/reader038/viewer/2022110221/5a75dae57f8b9a9c548cdc5f/html5/thumbnails/28.jpg)
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A calculation approach – Minimize biasA calculation approach – Minimize bias
Actuals vs. Estimates
0.1
1.0
10
100
0.1 1.0 10 100
Estimate EUR
Act
ual E
UR
Unbiased estimateswill appear balancedabout the 45° line.
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Checking ObjectivityChecking Objectivity
Random Errors in Estimation
.4 .6 .8 1.0 1.2 1.4 1.6 1.8 2.0 2.2
Ratio Estimate/Actual
Freq
uenc
yMean= 1.0 if unbiased
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A calculation approachA calculation approach
Error functions
– Strive to ensure unbiased judgments and ‘normal’ unbiased calculations.
– Also want to improve precision
Estimate/Actual
μ = 1
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A calculation approachA calculation approach
As project values can be negative, we decomposed value into three PV components:– Capital Expenditure– Revenue– Operating Maintenance Cost
Brute force Bayesian calculations– Generate a “cloud” synthetic dataset of actual
values and estimates– Get conditional values by partitioning the cloud
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A calculation approachA calculation approach
A population of similar project is judged, also along the dimensions of (all PVs):– Capital Expenditure (CapEx)– Revenue (Revenue)– Operating Maintenance Cost (O&Mcost)
EMV = Revenue – CapEx – O&Mcostexpected monetary value
DROI = EMV / PV CapExdiscounted return on investment
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Optimizer’s CurseOptimizer’s Curse
Background
Optimizer’s Curse phenomenon
Implications for Portfolio Forecasts
An Approach for Correcting this Bias
– Portfolio forecasts
– Individual project value estimates
Discussion and Wrap-Up
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Example Spreadsheet Model - PortfolioExample Spreadsheet Model - Portfolio
Zoom-in shots follow
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Example Spreadsheet Model - PortfolioExample Spreadsheet Model - Portfolio
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Example Spreadsheet Model - PortfolioExample Spreadsheet Model - Portfolio
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Example Spreadsheet Model - PortfolioExample Spreadsheet Model - Portfolio
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Reserves Analogy with Economic CutoffReserves Analogy with Economic Cutoff
0 80 160
240
320
400
0 80 160
240
320
400
.55
___
___
.45
___
.65
.35
.10
μ=137
μ =160
No minimum economic field size
Minimum economic field size = 83Bcf
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Optimizer’s CurseOptimizer’s Curse
Background
Optimizer’s Curse phenomenon
Implications for Portfolio Forecasts
An Approach for Correcting this Bias
– Portfolio forecasts
– Individual project value estimates
Discussion and Wrap-Up
![Page 40: Rational is Practical: SH · PDF filePalantir Economic Solutions Ltd John Schuyler Denver, Colorado, USA PetroSkills and Decision Precision Copyright © 2006-2008 by John R. Schuyler.](https://reader038.fdocuments.in/reader038/viewer/2022110221/5a75dae57f8b9a9c548cdc5f/html5/thumbnails/40.jpg)
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Example Spreadsheet Model - ProjectExample Spreadsheet Model - Project
Zoom-in shots follow
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Example Spreadsheet Model - ProjectExample Spreadsheet Model - Project
This portion of the worksheet is the same
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Example Spreadsheet Model - ProjectExample Spreadsheet Model - Project
EMV
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Mean-Reversion BiasMean-Reversion Bias
Most of the effect, seen here, we’re calling “mean-reversion bias.”
As filtering becomes more exclusive, the OC-bias begins to dominate.
MR-bias Corrected ValuesOriginal Estimates
means
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Projects near the population meanProjects near the population mean
0
10
20
30
40
50
20 25 30 35 40 45 50
Unadjusted Project Estimate
Adj
uste
d P
roje
ct E
stim
ate
Project revisions ⎯ reversing themean-reversion bias effect ⎯are adjusted toward this point
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The Most ImportantDA Publication in Decades?The Most ImportantDA Publication in Decades?
Optimizer’s curse seems analogous to– Winner’s Curse– Survivorship Bias
How did we miss this??No easy solution --- yet
If your job requires you to objectively forecast either projects or portfolios, here’s one more detail to deal with.
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Open IssuesOpen Issues
Might people already be self-correcting for the OC effect?– Data for E/A comparisons are usually from funded
projects
Is it really necessary to describe the parent population?– If so, assessments for these should be reasonably
consistent:
Project type population
E/A distribution
Project’s NPV distribution
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Open IssuesOpen Issues
How does a real options analysis affect the optimizer’s curse?
– Real options in a project almost always increase project value and will never decrease project value:
There is a higher value estimate
Unsure whether ROA will increase or decrease the OC-correction
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Open IssuesOpen Issues
How does a real options analysis affect the optimizer’s curse?
– Most projects are not evaluated with options, so the typical project estimate can be increased by real options analysis.
E E’increases with ROA added
– Yet value engineering through project execution increases actual value regardless of whether these options were recognized in project evaluation
A A’increases in implementation as decision opportunities are recognized
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Wrap-UpWrap-Up
Comments?
Questions
☺
Follow-up questions?
Contact info. for John in U.S.: 303-693-0067 (GMT-7)
Links for copies of the slides are posted temporarily at www.maxvalue.com/palantir.htm