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Fuzzy arithmetic is not a conservative way to do risk analysis Scott Ferson and Lev Ginzburg Applied Biomathematics

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Presentation on theme: "Fuzzy arithmetic is not a conservative way to do risk analysis Scott Ferson and Lev Ginzburg Applied Biomathematics"— Presentation transcript:

1 Fuzzy arithmetic is not a conservative way to do risk analysis Scott Ferson and Lev Ginzburg Applied Biomathematics,

2 Abstract The three chief disadvantages of Monte Carlo methods are computational burden, sensitivity to uncertainty about input distribution shapes and the need to assume correlations among all inputs. Fuzzy arithmetic, which is computationally simple, robust to moderate changes in the shapes on input distributions and does not require the analyst to assume particular correlations among inputs, might therefore be considered a prime alternative calculus for propagating uncertainty in risk assessments. Theorists suggest that fuzzy measures are upper bounds on probability measures. By selecting fuzzy inputs that enclose the analogous probability distributions, one might therefore expect to be able to obtain a conservative (bounding) analysis more cheaply, conveniently and reliably than is possible with Monte Carlo methods. With a simple counterexample, however, we show that fuzzy arithmetic is not conservative to uncertainty about input shapes or correlations. While it may have uses in specialized analyses, fuzzy arithmetic may not be appropriate for routine use in risk assessments concerned primarily with variability and incertitude (measurement error).

3 Monte Carlo Used to propagate uncertainty and variability through risk assessments But you have to specify –precise input distributions –particular correlation and dependency assumptions If you’re not sure about these, the assessment could be wrong

4 Fuzzy arithmetic might be useful Distributions don’t have to be precise Requires no assumption about correlations Fuzzy measures are upper bounds on probability Fuzzy arithmetic might be a conservative way to do risk assessments that is more reliable and less demanding than Monte Carlo

5 Fuzzy numbers and their arithmetic Fuzzy numbers –Fuzzy sets of the real line –Unimodal –Reach possibility level one Fuzzy arithmetic –Interval arithmetic at each possibility level 2345678 0 1 Possibility

6 Level-wise interval arithmetic a 0123456 0 1 b a+b 0 1 46810122345678 0 1 Possibility +=

7 Features of fuzzy arithmetic Fully developed arithmetic and logic –Plus, minus, times, divide, min, max, –Log, exp, sqrt, abs, powers, and, or, not –Backcalculation, updating, mixtures, etc. Very fast calculation and convenient software Very easy to explain Distributional answers (not just worst case) Results robust to choice about shape

8 How does shape of X affect aX+b? a = (d * e) / (h + g) b =  f *e where d = [0.3, 1.7, 3] e = [ 0.4, 1, 1.5] f = [ 0.8, 6, 10] g = [ 0.2, 2, 5] h = [ 0.6, 3, 6] 0246 a Possibility 0 1 a -20-100 b Possibility 0 1 b

9 Robustness of the answer Different choices for the fuzzy number X all yield very similar distributions for aX + b -20-10010203040 0 0.5 1 aX + b Possibility

10 Fuzzy seems to bound probability A fuzzy number F is said to “enclose” a probability distribution P iff –the left side of F is larger than P(x) for each x, –the right side of F is larger than 1  P(x) for each x For every event X < x and x < X, possibility is larger than than the probability, so it is an upper bound

11 F encloses P 0 2468 10 1 Probability 0 Possibility Prob(X > x) Poss(X > x) 1 x Prob(X < x) Poss(X < x) 0 2468 10 1 Probability 0 1 Possibility x 0 2468 10 1 P Prob. density F 0 1 Possibility x

12 The lazy risk analyst conjecture If F and G enclose P and Q resp., F+G encloses P+Q, where F, G are fuzzy numbers, P, Q are probability distributions, F+G is obtained by fuzzy arithmetic, and P+Q is obtained by probabilistic convolution such as Monte Carlo simulation.

13 It’d be nice If the lazy risk analyst conjecture were true, we could do risk assessments by –getting fuzzy numbers that enclose each probability distribution –using fuzzy arithmetic to obtain results that bound the probabilistic answer Easy to get inputs, easy to get answers Results conservative (but not hyperconservative)

14 Counterexample 01020 0 0.5 1 CCDF, Possibility 2345678910 0 1 CCDF, Possibility A, B A+B Distributions (in red) for the sum A+B under different correlations and dependencies are not enclosed by the (blue) sum of fuzzy numbers The red parallelogram is the tightest region that encloses all of the possible distributions for A+B that could arise under different dependencies between A and B. A and B are identically distributed; their distribution is in red above (they are not independent)

15 Conclusion Like many ideas that would be really cool, the lazy risk analyst conjecture is false. Fuzzy arithmetic does not seem to allow us to conveniently and conservatively estimate risks from bounded probabilities

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