elicited 0.0.2

Helpers for expert elicitation into distributions.

Elicit Distributions

Assists with the construction of probability distributions built from expert elicited data for use in monte carlo simulations.

Usage

Until this is packaged for pip, copy elicit_distibutions.py in your code. Then:

import elicit_distributions
import numpy as np

elicit_distributions is just a helper tool for using numpy.

Lognormal

See Occurance and Applications for examples of lognormal distributions in nature.

Expert: I have assets at risk that would generate a wide range of losses.

Elicitor: What is the most common value of these assets?

Expert: About $ 20K (val_mod)

Elicitor: What's the largest asset value you can imagine?

Expert: I suppose it could go as high as $2.5M (val_max)

Lognormal requires mean and standard deviation.

logN_mean, logN_stdv = elicitLogNormal(val_mod, val_max)

Pareto

The 80/20 rule. See Occurance and Applications

Expert: The legal costs of an incident could be devastating.

Elicitor: How devastating are we talking?

Expert: Well, typically costs are zero (val_min), but a black swan could be $100M (val_max).

Elicitor: So we can assume yoru minimum legal costs for an incident are zero, and your maximum costs are $100M?

Expert: Sure.

b = elicitPareto(val_min, val_max)
p = pareto(b, loc=val_min-1., scale=1.))

PERT

See PERT Distribution

Expert: We have accounts that could be lost and result in losses.

Elicitor: What is the dollar value of these accounts?

Expert: About $500-$6000 (val_min / val_max).

Elicitor: What's the most common account? (val_mod)

Expert: Probably around $4500.

PERT_a, PERT_b = elicitPERT(val_min, val_mod, val_max)
pert = beta(PERT_a, PERT_b, loc=val_min, scale=val_max-val_min)

Poisson

See Occurance and Application

This is done in numpy without assistance from elicitor. As a courtesy for those looking to use it, here's an example.

Example Code

Zipf's

See Applications

Expert: We are concerned about lawsuits relatd to a breach.

Elicitor: Assuming a breach happens, how many litigants will there be?

Expert: One or a few. We could also see an Equifax-like situation. (nMin)

Elicitor: So most likely only a handful of litigants. What's a nightmare situation?

Expert: I'd guess maybe 30 or more litigants? (nMax)

Elicitor: How likely would it be to have more than 30 litigants?

Expert: Very unlikely, most cases would only have a few, as I said.

Elicitor: Let's give it a number. Is it one-in-a-thousand, or million cases?

Expert: I'd say one in a million cases. (pMax)

Zs = elicitZipf(nMin, nMax, pMax, report=True)

pd = zipf(Zs, nMin-1)