encounters with risk ppp (perception,policy and practice) during a career in operations research
DESCRIPTION
Encounters With Risk PPP (Perception,Policy and Practice) During a Career in Operations Research. Stephen Pollock University of Michigan. A PERSONAL VIEW OF RISK PPP. MY EXPERIENCES WITH RISK PPP WHILE DOING O.R. CLEARLY A BIASED PERSPECTIVE - PowerPoint PPT PresentationTRANSCRIPT
Oct 3, 2007
1
Encounters With Risk PPP(Perception,Policy and Practice)
During a Career in Operations Research
Stephen PollockUniversity of Michigan
Oct 3, 2007
2
A PERSONAL VIEW OF RISK PPP
• MY EXPERIENCES WITH RISK PPP WHILE DOING O.R.
• CLEARLY A BIASED PERSPECTIVE
• A NUMBER OF ANECDOTAL EXAMPLES THAT MIGHT SERVE TO FORESHADOW OR TIE TOGETHER THE DIVERSE ISSUES OF THIS WORKSHOP
Oct 3, 2007
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O.R./I.E/ ENGINEERING?
• OPERATIONAL PROBLEM SOLVING• MOSTLY MATHEMATICAL MODELS• ALSO A WAY OF THINKING:
– WATER GLASS– GUILLOTINE– FORK
• UNCERTAINTY ALWAYS PRESENT
Oct 3, 2007
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TYPICAL DECISION ANALYSTS VIEW
d4
d3
d1
d2
DECISION
CONSEQUENCE x2
CONSEQUENCE x1
CONSEQUENCE x3
CONSEQUENCE x4
p1
1-p1
1- p2
p2
CHANCE
Oct 3, 2007
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MORE GENERIC VIEW
CONSEQUENCE x
d
DECISION
f(x|d)
CHANCE
Oct 3, 2007
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MORE GENERAL VIEW
*”UNCERTAINTY”
f(x|d)d
DECISION CHANCE*
f(y|d’,x)d’(d,x)
DECISION CHANCE
CONSEQUENCE (X,Y)
Oct 3, 2007
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WHERE DOES RISK POLICY AND PERCEPTION FIT THIS SCHEMA?
d
DECISION
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
Oct 3, 2007
8
WHERE DOES RISK POLICY AND PERCEPTION FIT THIS SCHEMA?
d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
RISK
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
RISK HAS TWO NECESSARY ASPECTS:• UNCERTAINTY -- WHAT WILL HAPPEN?• CONSEQUENCES -- WHY DOES ONE CARE?
CONFOUNDING THESE ARE
• CONCEPTION (WHAT ONE THINKS THE “RISK” ASPECTS ARE)• PERCEPTION (HOW ONE “SEES” THE RISK ASPECTS)• CODIFICATION (HOW ONE “TALKS” ABOUT -- OR SHOULD TALK ABOUT -- RISK ASPECTS)
RISK
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
THE POLICY COMPONENT (DECISION/MITIGATION) -- WHAT SHOULD ONE DO?
THIS ALSO INVOLVES A MIXTURE OF• CONCEPTION (WHERE DO POSSIBLE DECISIONS/OPTIONS/POLICIES COME FROM?)• PERCEPTION (HOW ONE “SEES” OPTIONS)• CODIFICATION (HOW TO DESCRIBE OPTIONS)
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
FINALLY (AND PERHAPS MOST IMPORTANT):
PRACTICE (WHAT ACTUALLY GETS DONE)
??
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
RISK
TO THE “PERSON IN THE STREET”, ARE THE FOLLOWING “RISKY”?
•BUNGEE JUMPING•NOT BUCKLING UP•BUCKLING UP•PICKING UP A $20 BILL FROM THE STREET•INOCULATING A CHILD AGAINST MEASLES•NOT INOCULATING A CHILD AGAINST MEASLES•LIVING
“EVERYDAY” ANSWERS SHOW ALL SORTS OF COGNITIVE BIASES, BUT NEGATIVE FRAMING SEEMS TO BE PERVASIVE
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
SOME “EVERYDAY”* CONCEPTION, PERCEPTION, AND CODIFICATION OF RISK:
*NY TIMES, NEW YORKER, NPR, ETC.
RISK
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
“EVERYDAY” CONCEPTION, PERCEPTION AND CODIFICATION OF RISK
Souffle (NPR 7/23)
David Denby’s review of “A Fine Romance”: (referring to romantic film comedies)
“ …with a married couple, romance is like “…a duel with slingshots at close quarters –- exciting but a little risky” (New Yorker 7/23)
RISK
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
“EVERYDAY” CONCEPTION, PERCEPTION AND CODIFICATION OF RISK
“The Black Swan” popular book dealing with “The role of the unexpected” in financial trading (NYT B.R. 7/29)
”What is “unexpected”? {13 craps in a row}{this particular person dies within the next five years}{this levee fails}{a levee fails}
RISK
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
“EVERYDAY” CONCEPTION, PERCEPTION AND CODIFICATION OF RISK
Louis Menan’s review of Caplan’s “The Myth of the Rational Voter: Why Democracies Choose Bad Politics” (New Yorker 7/19)
“You can’t use futures markets for assessing probabilities like you can for guessing the number of jellybeans in a bowl, or odds in sports gambling.”
RISK
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
“EVERYDAY” CONCEPTION, PERCEPTION AND CODIFICATION OF RISK
Louis Menan’s (continued): “People exaggerate the risk of loss; they like the status quo and tend to regard it as a norm; they overreact to sensational but unrepresentative information (shark attack phenomenon) … Most people, even if you explained …the economically rational choice … would be reluctant to make it, because … in particular, they want to protect themselves from the downside of change. They would rather feel good about themselves than to maximize ... profit.”
RISK
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
“EVERYDAY” CONCEPTION, PERCEPTION AND CODIFICATION OF RISK
Levitt and Dubner’s article “The Jane Fonda Effect”
“... in 1916 … the legendary economist Frank Knight made a distinction between two key factors in decision making: risk and uncertainty. The cardinal difference, he declared, is that risk — however great — can be measured, whereas uncertainty cannot.” (NYT Magazine 9/16)
RISK
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
“EVERYDAY” CONCEPTION, PERCEPTION AND CODIFICATION OF RISK
Levitt and Dubner’s* article “The Jane Fonda Effect”
“… Has fear of a [nuclear] meltdown subsided, or has it merely been replaced by the fear of global warming? …. in 1916 … the legendary economist Frank Knight made a distinction between two key factors in decision making: risk and uncertainty. The cardinal difference, he declared, is that risk — however great — can be measured, whereas uncertainty cannot.” (NYT Magazine 9/16)
* Authors of “Freakonomics”
RISK
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
“EVERYDAY” CONCEPTION, PERCEPTION AND CODIFICATION OF RISK AND POLICY
HELMET WEARING BY NHL PLAYERS (1979-80 SEASON REQUIREMENT)
RISK
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF UNCERTAINTY
HOW MUCH TIME TO CARRY A GARBAGE CAN FROM A BACK YARD TO THE CURB?
WHICH CUTTER HEADS WERE THE DEFECTIVE ONES?
WILL A SUB PASS BY DURING AN ASWEX?
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
CONCEPTION, PERCEPTION AND CODIFICATION OF UNCERTAINTY
ELLSBERG PARADOX
BAG A HAS 500 RED BALLS AND 500 GREEN BALLSBAG B HAS 1000 RED AND GREEN BALLSYOU CHOSE A BAG, DRAW A BALL AND WIN $100 IF IT IS RED.WHICH BAG DO YOU PREFER?
MOST PEOPLE PREFER A
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
CONCEPTION, PERCEPTION AND CODIFICATION OF UNCERTAINTY
ELLSBERG PARADOX DEMONSTRATES MANY PEOPLE’S PREFERENCE FOR “CRISP” PROBABILITIES VS. “AMBIGUOUS” ONES
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
CONCEPTION, PERCEPTION AND CODIFICATION OF UNCERTAINTY
ELLSBERG PARADOX -- PREFERENCE FOR “CRISP” PROBABILITIES VS. “AMBIGUOUS” ONES
BAG A HAS 450 RED BALLS AND 550 GREEN BALLSBAG B HAS 1000 RED AND GREEN BALLSYOU CHOSE A BAG, DRAW A BALL AND WIN $100 IF
IT IS RED.NOW WHICH BAG DO YOU PREFER?
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
CONCEPTION, PERCEPTION AND CODIFICATION OF UNCERTAINTY
ELLSBERG PARADOX -- PREFERENCE (?) FOR “CRISP” PROBABILITIES VS. “AMBIGUOUS” ONES
BAG A HAS 200 RED BALLS AND 800 GREEN BALLSBAG B HAS 1000 RED AND GREEN BALLSYOU CHOSE A BAG, DRAW A BALL AND WIN $100 IF
IT IS RED.NOW WHICH BAG DO YOU PREFER?
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF UNCERTAINTY
“CRISP” VS. “AMBIGUOUS” PROBABILITIES
•TEST OF CONCEPT OF TOTAL RE-DESIGN OF A GLOBAL LOGISTIC CHAIN VIA M.C. SIMULATION
•USED PREVIOUS YEAR’S DEMAND DISTRIBUTION FOR TO PROVE OUT RE-DESIGN CONCEPT
•MASSIVE CORPORATE PRESSURE AGAINST USING DISTRIBUTION OVER SIMULATION MODEL’S PARAMETERS -- SINCE “WE WON’T KNOW WHAT THEY MIGHT BE”
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF UNCERTAINTY“CRISP” VS. “AMBIGUOUS” PROBABILITIES
•ORDER SIZE FOR CRITICAL MATERIAL BASED ON PROJECTED PRODUCT SALES AND MATERIAL PRICE•SALES BASED UPON “TARGETS”•MATERIAL PRICE BASED ON SPECIALIST’S FORECASTS•MASSIVE CORPORATE PRESSURE AGAINST USING DISTRIBUTION OVER EITHER SALES OR PRICES “THESE ARE EXPERTS, THEY SHOULD KNOW THE
ANSWER”
Oct 3, 2007
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v ($)
Probability {loss > v}
“mean” prob.
5%
95%
uncertainty in
Probabilityuncertainty in Loss
d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
CONCEPTION, PERCEPTION AND CODIFICATION OF UNCERTAINTYAMBIGUITY EXPRESSED AS PROBABILITY DISTRIBUTIONS OVER PROBABILITIES (REF: H. KUNREUTHER)
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF UNCERTAINTY
WHAT IF ADVERSARIES CHOOSE THE PROBABILITIES?
Oct 3, 2007
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ADVERSARIAL RISK ANALYSISFOOTBALL ANALOGY [OFFENSE’S VIEW]
d
OFFENSE PLAYER PERSONNEL
f(x|d)
DEFENSIVE ALIGNMENT
d’(x)
AUDIBLE PLAY CALL
f(y|x,d’(x))
PLAY OUTCOME (y)
d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF POLICY
WHAT IF THE DECISION MAKER CHOOSES THE PROBABILITIES?
CONSIDER THE MATHEMATICAL PROGRAMMING PROBLEM:
min f(x) s.t. g(x, z) ≥ 0
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF POLICY
“CHANCE CONSTRAINED PROGRAMMING”
min f(x) s.t. Prob. { g(x, Z) ≥ 0 } ≥ p
WHERE Z IS NOW A RANDOM VARIABLE
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
EXAMPLE: DETERMINE d = BANK’S CASH RESERVESBANK WANTS TO MINIMIZE dREGULATORS REQUIRE SMALL PROBABILITY OF RUNNING OUT OF CASH; Z = DEMAND FOR CASH (A R.V.)
min d s.t. Prob. { Z ≥ d } ≥ .90
x = 2M
.10
z
p(z)
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
HOW ABOUT A RANDOMIZED POLICY?
min x s.t. Prob. {x ≥ Z} ≥ .90
z
Prob. = .2 .8
x = 2.1Mx = .6M
Prob. { x > Z} = .2(.55) +.8(.99) = .901 (OK)E(X) = .2(.6M) + .8(2.1M) = 1.93 ( better than 2)BUT -- WOULD THE REGULATORS APPROVE??
.01.55
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF POLICY
•CAMSHAFT HARDENING•SEWAGE TREATMENT IN ***•1979 NHL HELMET REGULATION (REVISITED)
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF UNCERTAINTY
A US FEDERAL DEPARTMENT REPORT USES, WITH LITTLE DIFFERENTIATION:
PROBABILITYLIKELIHOODCHANCEFREQUENCYRELATIVE PROBABILITYSTOCHASTIC PROBABILITY
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF UNCERTAINTY
A US FEDERAL DEPARTMENT, IN A PRA REPORT, USES, AS SYNONYMS:
MEANAVERAGE
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF UNCERTAINTY
ANOTHER US FEDERAL DEPARTMENT USES, AS SYNONYMS:
DISTRIBUTION FUNCTIONDENSITY FUNCTIONPROBABILITY FUNCTIONPROBABILITY DISTRIBUTIONDISTRIBUTION
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF RISK (computation)
FEDERAL DEPARTMENT REPORT:
RISK = "the probability or frequency of an event multiplied by the consequences of the event”
RISK
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
CONCEPTION, PERCEPTION AND CODIFICATION OF RISKSociety for Risk Analysis (SRA) Glossary (http://sra.org/resources_glossary.php)
RISK = “The potential for unwanted, adverse
consequences to human life, health, property, or the
environment;
BUT THEN SRA GOES ON TO SAY:
“estimation of risk is usually based on the expected value of the conditional probability of the event occurring times the consequence of the event given that it has occurred."
RISK
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
CODIFICATION OF RISK IN A FORTHCOMING PROPOSED LEXICON
RISK = The potential for unwanted, adverse consequences. It is important to distinguish between the term "risk,” which involves uncertainties, consequences and conditioning statements, and "expected risk" [q.v.] which combines these factors using a linear additive operation.
PROBABILITY = One of a set of numerical values between 0 and 1 assigned to a collection of random events (which are subsets of a sample space) in such a way that the assigned numbers obey axioms [ …]
RISK
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
MORE CODIFICATION FROM THE PROPOSED LEXICON
CONSEQUENCE (OUTCOME) = A description of a scenario, in terms of measurable factors, that a decision-maker may consider when assessing preferences over different scenarios; these factors are often random variables.
EXPECTED RISK = A summary measure of risk for an event, scenario, etc., as expressed by the expected value of any one of the measurable consequences associated with the risk.
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
CODIFICATION OF UNCERTAINTY IN A FORTHCOMING PROPOSED LEXICON
•ALEATORY PROBABILITY = A measure of the uncertainty of an unknown event whose occurrence is governed by some random physical phenomena that are either: a) predictable, in principle, with sufficient information (e.g., tossing a die); or b) phenomena which are essentially unpredictable (radioactive decay).•EPISTEMIC PROBABILITY = A representation of uncertainty about propositions due to incomplete knowledge. Such propositions may be about either past or future events.
RISK
Oct 3, 2007
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(ANECDOTE) “NEWSVENDOR” STADIUM HOTDOG DEMAND
NORMAL SEASON
CONTENDER
Oct 3, 2007
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(ANECDOTE) “NEWSVENDOR” STADIUM HOTDOGS
NORMAL SEASON PROBABILITY = ?
CONTENDER PROB = 1-?
Oct 3, 2007
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(ANECDOTE) “NEWSVENDOR” STADIUM HOTDOGS
OVERALL SALES
Oct 3, 2007
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(ANECDOTE) “NEWSVENDOR” STADIUM HOTDOGS
NORMAL SEASON, CONTENDER PROBABILITIESOR CONDITIONAL DEMAND DISTRIBUTIONS?
WHICH PROBS ARE ALEATORYWHICH ARE EPISTEMIC?
Oct 3, 2007
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(ANECDOTE) “NEWSVENDOR” STADIUM HOTDOGS
DIFFERENCE BETWEEN IGNORANCE AND APATHY?
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF CONSEQUENCES
WE’RE REALLY TALKING ABOUT APPROPRIATE PERFORMANCE MEASURES
THIS IS DIFFICULT ENOUGH TO DO IN DETERMINISTIC O.R. -- THE UNCERTAINTY ASPECT ONLY MAKES THINGS MORE “INTERESTING”
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF CONSEQUENCES
SUBMARINE SEARCH:MINIMIZE EXPECTED TIME TO DETECT ?MAXIMIZE PROB. {DETECT TIME ≤
Tcritical} ?
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF CONSEQUENCES
ALLOCATION OF POLICE PATROLS:MINIMIZE EXPECTED TIME TO RESPOND ?MINIMIZE VARIANCE OF RESPONSE TIME ?
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF CONSEQUENCES
QUALITY CONTROL AND 6-SIGMA“TAGUCHI” LOSS FUNCTIONESSENTIALLY QUADRATIC:DECISION IS d, RANDOM VARIABLE IS X
LOSS = CONST. (d - x)2
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF CONSEQUENCES
“TAGUCHI” LOSS FUNCTION C(d - x)2
x
d
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF CONSEQUENCES
WITH “TAGUCHI” LOSS FUNCTIONBEST DECISION IS d* = E(X)WHICH RESULTS INMINIMUM EXPECTED LOSS = CONST•VAR(X)MINIMUM EXPECTED RISK = CONST•S.D.(X) = CONST•SIGMA
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF CONSEQUENCES
MORE REALISTIC MANUFACTURING LOSS FUNCTION
x
d
Oct 3, 2007
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INTENSITY MODULATED RADIATION TREATMENT (IMRT)
critical (healthy) tissue
tumorradiation beam
Oct 3, 2007
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PAROTID
GLAND
TUMOR
SPINAL CORD
typical 2-D slice of 3-D imagetypical 2-D slice of 3-D image
Oct 3, 2007
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THE FUNDAMENTAL PROBLEM IN IMRT
DETERMINE THE NUMBER, ANGLES AND INTENSITIES OF THE BEAMLETS SO THAT
a) THE TUMOR RECEIVES A SUFFICIENT DOSE, BUT b) CRITICAL TISSUES (E.G. NORMAL ORGANS) ARE
SPARED HIGH DOSES
d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
Oct 3, 2007
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CONFLICTING CONSEQUENCES• want to have:
– at least a “critical dose” of radiation absorbed by the tumor “target”
– as little radiation as possible absorbed by healthy tissue
• this is a mathematical programming problem, right?
min (radiation to healthy tissue)s.t. (radiation to tumor) critical dose
ormax (radiation to tumor)s.t. (radiation to healthy tissue) damaging dose
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
GRAPHICAL REPRESENTATION OF CONSEQUENCES: DOSE-VOLUME HISTOGRAM (“DVH”)
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
ADD UNCERTAINTY TO DVH
Oct 3, 2007
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v ($)
Probability {loss > v}
“mean” prob.
5%
95%
uncertainty in
Probabilityuncertainty in Loss
d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
NOTE SIMILARITY TO KUNREUTHER’S REPRESENTATION
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
REMEMBER PRACTICE? (WHAT ACTUALLY GETS DONE)
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
PRACTICE
REMOVING SKUS FROM EOQ POLICIESFACULTY RETIREMENT OPTIONSCLASS ACTION SINKING FUNDSIMULATED ANNEALING AND IMRT“X BAR” CONTROL CHART DESIGN
PRACTICE
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
HERE IS A FINAL CHALLENGE:
DURING THIS WORKSHOP, TRY TO DISCOVER IMPLIED RESOLUTIONS (USUALLY VIA ASSUMPTIONS) TO THE INHERENTLY PROBLEMATIC NATURE OF RISK Conception, Perception, Policy, and Practce
PRACTICE
Oct 3, 2007
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CLICK TO HEAR NEXT SPEAKER
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
PRACTICE (UNLIKELY DERIVATIVES)From the New York Times, 2/23/89 -- p. 1(!):“The underlying rate of inflation is accelerating”If X(t) = PRICE INDEX, then
INFLATION = X(t)RATE of INFLATION = X(t)“ ... is accelerating” ==> [X(t) > 0 ==> X(t) >0
From the NYT 2/4/02“The increase in Chip Speed is Accelerating …”[work it out …]
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(EVERYDAY) CONCEPTION, PERCEPTION AND CODIFICATION OF UNCERTAINTY“CRISP” VS. “AMBIGUOUS” PROBABILITIES
Building a prison in Berlin NH would produce a revenue increase of $264,000/year (NYT 9/2)
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
INTERESTING CONTRAST IN USAGE INTELLIGENCE COMMUNITY
“ANALYSIS”: GATHER INFORMATION ABOUT OPPONENT’S INTENTIONS AND CAPABILITIES, “ASSESSMENT”: USE THIS INFORMATION TO PRESENT A STATEMENT OF THE CURRENT SITUATION
RISK AND DECISION COMMUNITY
“ASSESSMENT”: OBTAIN INFORMATION ABOUT UNCERTAINTY OF EVENTS (AND ALSO CONSEQUENCE)“ANALYSIS”: USE THIS INFORMATION AND COMBINE IT IN SUCH A WAY THAT ONE CAN MAKE BETTER DECISIONS
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF POLICY
“MORAL HAZARD”?: SEEDING HURRICANES
•CONSEQUENCES = WIND SPEED•SPEED NOW IS 70 MPH•WITH NO SEEDING, FORECAST: 140 MPH AT LANDFALL•WITH SEEDING, FORECAST: 100 MPH AT LANDFALL•DECISION IS TO SEED ==> WIND IS 90 MPH AT LANDFALL•SEEDING “CLEARLY” INCREASED THE SPEED, SO SUE THE GOVERNMENT
Oct 3, 2007
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d
POLICY
f(x|d)
UNCERTAINTY
X
CONSEQUENCE
(ANECDOTAL) CONCEPTION, PERCEPTION AND CODIFICATION OF POLICY
“MORAL HAZARD”
•DECISION MAKER DOES NOT SUFFER CONSEQUENCES•HURRICANE INSURANCE•SUB-PRIME LENDING INSTITUTIONS•“DONORCYCLES”