encounters with risk ppp (perception,policy and practice) during a career in operations research

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

4

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

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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


“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


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


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


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


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


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


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


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


“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


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


“CHANCE CONSTRAINED PROGRAMMING”

min f(x) s.t. Prob. { g(x, Z) ≥ 0 } ≥ p

WHERE Z IS NOW A RANDOM VARIABLE

Oct 3, 2007

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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


•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


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


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


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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OVERALL SALES

Oct 3, 2007

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NORMAL SEASON, CONTENDER PROBABILITIESOR CONDITIONAL DEMAND DISTRIBUTIONS?

WHICH PROBS ARE ALEATORYWHICH ARE EPISTEMIC?

Oct 3, 2007

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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


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


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


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


“TAGUCHI” LOSS FUNCTION C(d - x)2

x

d

Oct 3, 2007

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d

POLICY

f(x|d)

UNCERTAINTY

X

CONSEQUENCE


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


MORE REALISTIC MANUFACTURING LOSS FUNCTION

x

d

Oct 3, 2007

56

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

61

d

POLICY

f(x|d)

UNCERTAINTY

X

CONSEQUENCE

ADD UNCERTAINTY TO DVH

Oct 3, 2007

62

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

63

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

70

d

POLICY

f(x|d)

UNCERTAINTY

X

CONSEQUENCE


“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


“MORAL HAZARD”

•DECISION MAKER DOES NOT SUFFER CONSEQUENCES•HURRICANE INSURANCE•SUB-PRIME LENDING INSTITUTIONS•“DONORCYCLES”

encounters with risk ppp (perception,policy and practice) during a career in operations research

Documents

risk aspects codification

risk policy

riskeveryday conception

risk pppperception

risk of loss

codification of risklevitt

mixture of conception

sees options codification