RISK AND RELIABILITY IN OPTIMIZATION UNDER UNCERTAINTY

Transcription

1 RISK AND RELIABILITY IN OPTIMIZATION UNDER UNCERTAINTY Terry Rockafellar University of Washington, Seattle AMSI Optimise Melbourne, Australia 18 Jun 2018

2 Decisions in the Face of Uncertain Outcomes = especially serious modeling challenges in optimization appropriate formulation of constraints and objectives? Asset management in finance: a portfolio must be chosen with intelligent safeguarding performance known at best from history and economic factors the threat of losses cannot be eliminated Logistics in operations research: supplies must be stockpiled to meet demands distribution and replacement expense should be kept at bay demands and costs are random variables, but partly guesswork Design problems in engineering: a structure must be engineered to withstand various impacts possible impacts can only be estimated potential for failure must be kept low

3 Reliability of Structures in Engineering Engineering areas that are affected: civil, mechanical, electrical, manufacturing, mining, Examples of structures: buildings, bridges, tunnels, reservoirs/dams, vehicle frames, ship hulls, airplane wings, offshore platforms... Tasks that are influenced: design, maintenance, retrofit Sources of uncertainty loads that a structure may experience in the future earthquakes, floods, wind storms, impacts in crashes variability in materials utilized or encountered unknown aspects of composition, imprecision in manufacture environmental and economic circumstances climate effects, evolving demands in usage

4 Objectives and Constraints in Stochastic Optimization Typical considerations: minimizing costs, maximizing performance keeping various good properties at adequate levels preventing bad properties from reaching dangerous levels Helpful resources: statistical methodology in assessing uncertainty choosing good values of design variables organizing good routines of maintenance and inspection Technical challenges: avoiding formulations that suffer from DISCONTINUITY promoting formulations that take advantage of CONVEXITY all of this must be integrated with good modeling! guidelines for that must be understood and incorporated!

5 A Stochastic Framework of Uncertainty General cost expression in decision-making: c(x, v) with x = decision vector, v = data vector x = (x 1,..., x n ), v = (v 1,..., v m ) Stochastic uncertainty: v is replaced by a random variable vector V = (V 1,..., V m ) then the cost becomes a random variable: c (x) = c(x, V ) Key consequence: the distribution of c (x) can only be shaped by the choice of x but how then can constraints or minimization be understood?

6 Failure From a Standard Engineering Perspective X = c(x 1,..., x n ; V 1,, V r ) = a cost that signals danger Probability of failure: p f = prob { X > 0 } How to compute or at least estimate? How to cope with dependence on x 1,..., x n in optimization? both p f and the threshold shift with changes in x 1,..., x n! Troubles with this concept: poor mathematical behavior is a serious handicap failure probability ignores the degree of failure

7 Trade-offs and Preferences Source of difficulties in modeling objectives and constraints nothing can be perfect, risks will almost always remain trade-offs must be contemplated between different risks preferences and intentions in handling risk must be articulated computational viability must be taken into account Crucial issue for effectiveness: How does the risk relate to the decision (x 1,..., x n )? What are the mathematical properties of this dependence? Coherent approaches to quantifying risk must play a role

8 A Broad Pattern for Quantifying Risk Risk measures: functionals R that quantify the risk in a random variable X by a value R(X ) ( risk uncertainty ) Systematic prescription Faced with an uncertain cost c (x) = c(x, V ) articulate it numerically as c(x) = R(c (x)) for a choice of risk measure R Constraints: keeping c (x) sufficiently b modeled as: constraint c(x) = R(c (x)) b Objectives: keeping c (x) sufficiently low modeled as: minimizing c(x) = R(c (x)) = finding lowest b such that x with c (x) R-sufficiently b Supporting theory: exploration of examples and guidelines but note: choosing R expresses a decision-maker s preferences

9 Some Familiar Approaches Subject to Pros and Cons [ random cost c (x) = c(x, V ) reduced to a numerical cost c(x) ] Focusing on worst cases: c(x) = sup[c (x)] [ taking R(X ) = sup X (ess. sup) ] then c(x) b c (x) b almost surely Passing to expectations: c(x) = µ[c (x)] = E[c (x)] [ taking R(X ) = µx = EX ] then c(x) b c (x) b on average Adopting a safety margin: c(x) = µ[c (x)] + λσ[c (x)] [ taking R(X ) = µx + λσ(x ) ] then c(x) b unless in tail beyond λ standard deviations Looking at quantiles: c(x) = q p [c (x)] [ taking R(X ) = quantile at level p (0, 1) ] then c(x) b prob{c (x) b} p

10 Quantiles and Superquantiles : VaR and CVaR F X = cumulative distribution function for random variable X Quantile: value-at-risk in finance q p (X ) = VaR p (X ) = F 1 X (p) Superquantile: conditional value-at-risk in finance Q p (X ) = CVaR p (X ) = E[ X X q p (X ) ] = p p q t(x )dt Replace quantiles by superquantiles in optimization? take c(x) = Q p (c (x)) in objective and constraint modeling? then c(x) b c (x) b on average even in p-quantile-tail

11 Guidelines for Quantifying Risk in General Key axioms for risk measures: R : L 1 (Ω, A, P) (, ] (R1) R(C) = C for all constants C (R2) convexity, (R3) lower semicontinuity Supplementary properties of interest: (R4) positive homogeneity: R(λX ) = λr(x ) for λ > 0 (R5) monotonicity: R(X ) R(X ) when X X (R6) aversity: R(X ) > EX for nonconstant X Coherent measure of risk: R satisfying (R1), (R2), (R3), (R5) Averse measure of risk: R satisfying (R1), (R2), (R3), (R6) Artzner et al. (2000) introduced coherency without aversity Preservation of convexity under coherency of R c (x) = c(x, V ) convex in x = c(x) = R(c (x)) convex in x a further advantage of coherency: it promotes dualizations

12 Coherency or its Absence R(X ) = q p (X ) = VaR p (X ) fails (R2), (R3), and (R6)! R(X ) = Q p (X ) = CVaR p (X ) satisfies all axioms R(X ) = sup X satisfies all axioms R(X ) = µ(x ) = EX fails (R6) (coherency without aversity) R(X ) = µ(x ) + λσ(x ), λ > 0, fails (R5) (no monotonicity) = careful attention is needed in the choice of a risk measure!

13 Characterization of Coherent Risk Measures Dual formula for R { } R(X ) = sup E[XQ] J (Q) Q Q for some set Q of probability densities (i.e., Q 0, EQ = 1) and some function J from Q to IR such that inf J (Q) = 0 Q Q Interpretation: Q stands for a prob. measure P contemplated as an alternative to the nominal prob. measure P (having Q 1) J 0 on Q R(λX ) = λr(x ) for λ > 0 J (Q) = 0 for Q 1 = R(X ) EX

14 Buffered Failure A Better Approach to Safety Utilizing superquantiles in place of quantiles in reliability the danger cost : X = c(x 1,..., x n ; V 1,, V r ) Buffered probability of failure: P f = prob { X > q } with q determined so as to make E[ X X > q ] = 0! Proposal: adjust failure modeling to P f in place of p f safer by integrating tail information, and easier also to work with in computation!

15 Failure Concept Relationships q p (X ) = F 1 X (p), Q p(x ) = p p q s(x ) ds q p (X ) can depend poorly on p, but Q p (X ) depends nicely on p failure modeling: p f determined by q p (X ) = 0, p = 1 p f P f determined by Q p (X ) = 0, p = 1 P f

16 Some Practical Considerations Key formula { [ ]} Q p (X ) = min C + 1 C IR 1 p E max{0, X C} p (0, 1) q p (X ) = argmin (if unique, otherwise the lowest) Application to failure: X = c (x) = c(x, V ), want 0 ordinary probability of failure: constraint p f (c (x)) 1 p corresponds having x satisfy q p (c (x)) 0 buffered probability of failure: constraint P f (c (x)) 1 p corresponds to having Q p (c (x)) [ 0, hence equivalently ] to having x and C satisfy C p E max{0, c (x) C} 0 Monte Carlo sampling: the expectation becomes a finite sum research issue: what rules if the distribution of V depends on x?

17 Application to Objectives in Minimization Recall notation: c (x) = c(x, V ) = an uncertain cost = random variable depending on x and uncertain data V Typical goal: keep the cost c (x) = c(x, V ) as low as possible (subject to constraints on the decision x) Interpretation: minimize c(x) = R(c (x)) for a risk measure R i.e., minimize b such that x with c (x) b R-sufficiently 0 Risk-neutral approach: minimize c(x) = E[ c (x) ] for b = E[c (x)], costs c (x) > b and c (x) < b average out Risk-averse approaches: focus more on danger of cost overruns Quantile version: minimize q p (c (x)) for some p (0, 1) min b with failure probability p f ( c (x) b ) < 1 p Superquantile version: minimize Q p (c (x)) for some p (0, 1) min b with buffered failure probability P f ( c (x) b ) < 1 p

18 Some References [1] R. T. Rockafellar, J. O. Royset (2010), On buffered failure probability in design and optimization of structures, Journal of Reliability Engineering and System Safety [95], [2] R. T. Rockafellar, J. O. Royset (2015), Engineering decisions under risk-averseness, Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering [1]. [3] R. T. Rockafellar, J. O. Uryasev (2018), Minimizing buffered probability of exceedence by progressive hedging, Mathematical Programming B, submitted. [4] R.T. Rockafellar, S.P. Uryasev (2013), The fundamental risk quadrangle in risk management, optimization and statistical estimation, Surveys in Operations Research and Management Science [18], downloads: rtr/mypage.html