Archive for October, 2020
Risk Neutrality and Corporate Risk Frameworks
Posted by Bill Storage in Uncategorized on October 27, 2020
Wikipedia describes risk-neutrality in these terms: “A risk neutral party’s decisions are not affected by the degree of uncertainty in a set of outcomes, so a risk-neutral party is indifferent between choices with equal expected payoffs even if one choice is riskier”
While a useful definition, it doesn’t really help us get to the bottom of things since we don’t all remotely agree on what “riskier” means. Sometimes, by “risk,” we mean an unwanted event: “falling asleep at the wheel is one of the biggest risks of nighttime driving.” Sometimes we equate “risk” with the probability of the unwanted event: “the risk of losing in roulette is 35 out of 36. Sometimes we mean the statistical expectation. And so on.
When the term “risk” is used in technical discussions, most people understand it to involve some combination of the likelihood (probability) and cost (loss value) of an unwanted event.
We can compare both the likelihoods and the costs of different risks, but deciding which is “riskier” using a one-dimensional range (i.e., higher vs. lower) requires a scalar calculus of risk. If risk is a combination of probability and severity of an unwanted outcome, riskier might equate to a larger value of the arithmetic product of the relevant probability (a dimensionless number between zero and one) and severity, measured in dollars.
But defining risk as such a scalar (area under the curve, therefore one dimensional) value is a big step, one that most analyses of human behavior suggests is not an accurate representation of how we perceive risk. It implies risk-neutrality.
Most people agree, as Wikipedia states, that a risk-neutral party’s decisions are not affected by the degree of uncertainty in a set of outcomes. On that view, a risk-neutral party is indifferent between all choices having equal expected payoffs.
Under this definition, if risk-neutral, you would have no basis for preferring any of the following four choices over another:
1) a 50% chance of winning $100.00
2) An unconditional award of $50.
3) A 0.01% chance of winning $500,000.00
4) A 90% chance of winning $55.56.
If risk-averse, you’d prefer choices 2 or 4. If risk-seeking, you’d prefer 1 or 3.
Now let’s imagine, instead of potential winnings, an assortment of possible unwanted events, termed hazards in engineering, for which we know, or believe we know, the probability numbers. One example would be to simply turn the above gains into losses:
1) a 50% chance of losing $100.00
2) An unconditional payment of $50.
3) A 0.01% chance of losing $500,000.00
4) A 90% chance of losing $55.56.
In this example, there are four different hazards. Many argue that rational analysis of risk entails quantification of hazard severities, independent of whether their probabilities are quantified. Above we have four risks, all having the same $50 expected value (cost), labeled 1 through 4. Whether those four risks can be considered equal depends on whether you are risk-neutral.
If forced to accept one of the four risks, a risk-neutral person would be indifferent to the choice; a risk seeker might choose risk 3, etc. Banks are often found to be risk-averse. That is, they will pay more to prevent risk 3 than to prevent risk 4, even though they have the same expected value. Viewed differently, banks often pay much more to prevent one occurrence of hazard 3 (cost = $500,000) than to prevent 9000 occurrences of hazard 4 (cost = $500,000).
Businesses compare risks to decide whether to reduce their likelihood, to buy insurance, or to take other actions. They often use a heat-map approach (sometimes called risk registers) to visualize risks. Heat maps plot probability vs severity and view any particular risk’s riskiness as the area of the rectangle formed by the axes and the point on the map representing that risk. Lines of constant risk therefore look like y = 1 / x. To be precise, they take the form of y = a/x where a represents a constant number of dollars called the expected value (or mathematical expectation or first moment) depending on area of study.
By plotting the four probability-cost vector values (coordinates) of the above four risks, we see that they all fall on the same line of constant risk. A sample curve of this form, representing a line of constant risk appears below on the left.
In my example above, the four points (50% chance of losing $100, etc.) have a large range of probabilities. Plotting these actual values on a simple grid isn’t very informative because the data points are far from the part of the plotted curve where the bend is visible (plot below on the right).
Students of high-school algebra know the fix for the problem of graphing data of this sort (monomials) is to use log paper. By plotting equations of the form described above using logarithmic scales for both axes, we get a straight line, having data points that are visually compressed, thereby taming the large range of the data, as below.
The risk frameworks used in business take a different approach. Instead of plotting actual probability values and actual costs, they plot scores, say from one ten. Their reason for doing this is more likely to convert an opinion into a numerical value than to cluster data for easy visualization. Nevertheless, plotting scores – on linear, not logarithmic, scales – inadvertently clusters data, though the data might have lost something in the translation to scores in the range of 1 to 10. In heat maps, this compression of data has the undesirable psychological effect of implying much small ranges for the relevant probability values and costs of the risks under study.
A rich example of this effect is seen in the 2002 PmBok (Project Management Body of Knowledge) published by the Project Management Institute. It assigns a score (which it curiously calls a rank) of 10 for probability values in the range of 0.5, a score of 9 for p=0.3, and a score of 8 for p=0.15. It should be obvious to most having a background in quantified risk that differentiating failure probabilities of .5, .3, and .15 is pointless and indicative of bogus precision, whether the probability is drawn from observed frequencies or from subjectivist/Bayesian-belief methods.
The methodological problem described above exists in frameworks that are implicitly risk-neutral. The real problem with the implicit risk-neutrality of risk frameworks is that very few of us – individuals or corporations – are risk-neutral. And no framework is right to tell us that we should be. Saying that it is somehow rational to be risk-neutral pushes the definition of rationality too far.
As proud king of a small distant planet of 10 million souls, you face an approaching comet that, on impact, will kill one million (10%) in your otherwise peaceful world. Your scientists and engineers rush to build a comet-killer nuclear rocket. The untested device has a 90% chance of destroying the comet but a 10% chance of exploding on launch thereby killing everyone on your planet. Do you launch the comet-killer, knowing that a possible outcome is total extinction? Or do you sit by and watch one million die from a preventable disaster? Your risk managers see two choices of equal riskiness: 100% chance of losing one million and a 10% chance of losing 10 million. The expected value is one million lives in both cases. But in that 10% chance of losing 10 million, there is no second chance. It’s an existential risk.
If these two choices seem somehow different, you are not risk-neutral. If you’re tempted to leave problems like this in the capable hands of ethicists, good for you. But unaware boards of directors have left analogous dilemmas in the incapable hands of simplistic and simple-minded risk frameworks.
The risk-neutrality embedded in risk frameworks is a subtle and pernicious case of Hume’s Guillotine – an inference from “is” to “ought” concealed within a fact-heavy argument. No amount of data, whether measured frequencies or subjective probability estimates, whether historical expenses or projected costs, even if recorded as PmBok’s scores and ranks, can justify risk-neutrality to parties who are not risk-neutral. So why is it embed it in the frameworks our leading companies pay good money for?
The Dose Makes the Poison
Posted by Bill Storage in Uncategorized on October 19, 2020
Toxicity is binary in California. Or so says its governor and most of its residents.
Governor Newsom, who believes in science, recently signed legislation making California the first state to ban 24 toxic chemicals in cosmetics.
The governor’s office states “AB 2762 bans 24 toxic chemicals in cosmetics, which are linked to negative long-term health impacts especially for women and children.”
The “which” in that statement is a nonrestrictive pronoun, and the comma preceding it makes the meaning clear. The sentence says that all toxic chemicals are linked to health impacts and that AB 2762 bans 24 of them – as opposed to saying 24 chemicals that are linked to health effects are banned. One need not be a grammarian or George Orwell to get the drift.
California continues down the chemophobic path, established in the 1970s, of viewing all toxicity through the beloved linear no-threshold lens. That lens has served gullible Californians well since the 1974, when the Sierra Club, which had until then supported nuclear power as “one of the chief long-term hopes for conservation,” teamed up with the likes of Gov. Jerry Brown (1975-83, 2011-19) and William Newsom – Gavin’s dad, investment manager for Getty Oil – to scare the crap out of science-illiterate Californians about nuclear power.
That fear-mongering enlisted Ralph Nadar, Paul Ehrlich and other leading Malthusians, rock stars, oil millionaires and overnight-converted environmentalists. It taught that nuclear plants could explode like atom bombs, and that anything connected to nuclear power was toxic – in any dose. At the same time Governor Brown, whose father had deep oil ties, found that new fossil fuel plants could be built “without causing environmental damage.” The Sierra Club agreed, and secretly took barrels of cash from fossil fuel companies for the next four decades – $25M in 2007 from subsidiaries of, and people connected to, Chesapeake Energy.
What worked for nuclear also works for chemicals. “Toxic chemicals have no place in products that are marketed for our faces and our bodies,” said First Partner Jennifer Siebel Newsom in response to the recent cosmetics ruling. Jennifer may be unaware that the total amount of phthalates in the banned zipper tabs would yield very low exposure indeed.
Chemicals cause cancer, especially in California, where you cannot enter a parking garage, nursery, or Starbucks without reading a notice that the place can “expose you to chemicals known to the State of California to cause birth defects.” California’s litigator-lobbied legislators authored Proposition 65 in a way that encourages citizens to rat on violators, the “citizen enforcers” receiving 25% of any penalties assessed by the court. The proposition lead chemophobes to understand that anything “linked to cancer” causes cancer. It exaggerates theoretical cancer risks stymying the ability of the science-ignorant educated class to make reasonable choices about actual risks like measles and fungus.
California’s linear no-threshold conception of chemical carcinogens actually started in 1962 with Rachel Carson’s Silent Spring, the book that stopped DDT use, saving all the birds, with the minor side effect of letting millions of Africans die of malaria who would have survived (1, 2, 3) had DDT use continued.
But ending DDT didn’t save the birds, because DDT wasn’t the cause of US bird death as Carson reported, because the bird death at the center of her impassioned plea never happened. This has been shown by many subsequent studies; and Carson, in her work at Fish and Wildlife Service and through her participation in Audubon bird counts, certainly had access to data showing that the eagle population doubled, and robin, catbird, and dove counts had increased by 500% between the time DDT was introduced and her eloquence, passionate telling of the demise of the days that once “throbbed with the dawn chorus of robins, catbirds, and doves.”
Carson also said that increasing numbers of children were suffering from leukemia, birth defects and cancer, and of “unexplained deaths,” and that “women were increasingly unfertile.” Carson was wrong about increasing rates of these human maladies, and she lied about the bird populations. Light on science, Carson was heavy on influence: “Many real communities have already suffered.”
In 1969 the Environmental Defense Fund demanded a hearing on DDT. Lasting eight months, the examiner’s verdict concluded DDT was not mutagenic or teratogenic. No cancer, no birth defects. In found no “deleterious effect on freshwater fish, estuarine organisms, wild birds or other wildlife.”
William Ruckleshaus, first director of the EPA didn’t attend the hearings or read the transcript. Pandering to the mob, he chose to ban DDT in the US anyway. It was replaced by more harmful pesticides in the US and the rest of the world. In praising Ruckleshaus, who died last year, NPR, the NY Times and the Puget Sound Institute described his having a “preponderance of evidence” of DDT’s damage, never mentioning the verdict of that hearing.
When Al Gore took up the cause of climate, he heaped praise on Carson, calling her book “thoroughly researched.” Al’s research on Carson seems of equal depth to Carson’s research on birds and cancer. But his passion and unintended harm have certainly exceeded hers. A civilization relying on the low-energy-density renewables Gore advocates will consume somewhere between 100 and 1000 times more space for food and energy than we consume at present.
California’s fallacious appeal to naturalism regarding chemicals also echoes Carson’s, and that of her mentor, Wilhelm Hueper, who dedicated himself to the idea that cancer stemmed from synthetic chemicals. This is still overwhelmingly the sentiment of Californians, despite the fact that the smoking-tar-cancer link now seems a bit of a fluke. That is, we expected the link between other “carcinogens” and cancer to be as clear as the link between smoking and cancer. It is not remotely. As George Johnson, author of The Cancer Chronicles, wrote, “as epidemiology marches on, the link between cancer and carcinogen seems ever fuzzier” (re Tomasetti on somatic mutations). Carson’s mentor Hueper, incidentally, always denied that smoking caused cancer, insisting toxic chemicals released by industry caused lung cancer.
This brings us back to the linear no-threshold concept. If a thing kills mice in high doses, then any dose to humans is harmful – in California. And that’s accepting that what happens in mice happens in humans, but mice lie and monkeys exaggerate. Outside California, most people are at least aware of certain hormetic effects (U-shaped dose-response curve). Small amounts of Vitamin C prevent scurvy; large amounts cause nephrolithiasis. Small amounts of penicillin promote bacteria growth; large amount kill them. There is even evidence of biopositive effects from low-dose radiation, suggesting that 6000 millirems a year might be best for your health. The current lower-than-baseline levels of cancers in 10,000 residents of Taiwan accidentally exposed to radiation-contaminated steel, in doses ranging from 13 to 160 mSv/yr for ten years starting in 1982 is a fascinating case.
Radiation aside, perpetuating a linear no-threshold conception of toxicity in the science-illiterate electorate for political reasons is deplorable, as is the educational system that produces degreed adults who are utterly science-illiterate – but “believe in science” and expect their government to dispense it responsibly. The Renaissance physician Paracelsus knew better half a millennium ago when he suggested that that substances poisonous in large doses may be curative in small ones, writing that “the dose makes the poison.”
To demonstrate chemophobia in 2003, Penn Jillette and assistant effortlessly convinced people in a beach community, one after another, to sign a petition to ban dihydrogen monoxide (H2O). Water is of course toxic in high doses, causing hyponatremia, seizures and brain damage. But I don’t think Paracelsus would have signed the petition.
The Multidisciplinarian 