Chernobyl’s Lessons for Data Scientists
By: Alvin T. Tan
Tags: Data Science, Decision Making, Chernobyl Tv Series, Statistics, Data

Wrangling through Dataland

Chernobyl’s Lessons for Data Scientists

Insights on decision-making amid uncertainty from the HBO-Sky TV series

Image by Денис Резник from Pixabay

Chernobyl (2019) is a mesmerizing drama of human incompetence, ingenuity and courage in the face of disaster. The show’s analytical examination of the swirling confusion and haphazard reponse in the aftermath of an unprecedented catastrophe also offers valuable lessons for data scientists facing a dynamic situation amid great uncertainty. Moreover, the show cast a scientist (a physicist no less!) as the main hero, and put the science and scientific reasoning up front and center.

I am not going to venture into the debate over the accuracy of the HBO-Sky production, but focus on the content of the show rather than the actual Chernobyl incident. Dramatic license was undoubtedly taken, but the heightened drama and suspense serve I think to encapsulate how people make decisions in the presence of high anxiety, incomplete information and changeable circumstances.

A Bayesian perspective is useful

After the explosion, the nuclear power plant’s senior managers quickly assumed that a water service tank had exploded because it was utterly inconceivable that an RBMK-class reactor could explode. When challenged on this by their underlings, the managers countered by asking them to explain how the reactor could explode, and no one could answer. Even Chernobyl’s hero physicist, Valery Legasov, could not at the outset explain how the reactor could possibly explode. Not only was it supposedly impossible for an RBMK reactor to explode, but there was of course no historical example of a reactor explosion to serve as a guide.

As Sherlock Holme’s famous quip goes, “Once you eliminate the impossible, whatever remains…must be the truth.” From a statistical perspective, it was as if the managers were frequentist straw men thrown into a scenario without any historical samples to guide them. The probability of a reactor explosion was judged to be zero, and so the managers ruled it out, facilitated by a big serving of self-interest. Using a bit of Bayesian terminology, the managers had a very strong prior belief, hardened further into concrete by their self-interest in downplaying the accident’s severity.

Legasov however raised the possibility of a reactor explosion after reading a report of graphite pieces littering the ground outside the plant. Graphite, he explained, is only found in the reactor core, so if it is observed on the outside then it must imply that there had been a reactor explosion. Let’s bring back Holme’s quip in full: “Once you eliminate the impossible, whatever remains, no matter how improbable, must be the truth.” Legasov allowed for the second impossible scenario (graphite on the ground) to condition the initial impossible scenario of a reactor explosion to at least prompt an urgent investigation of the situation.

Let us say that there is a 1 in a ten million chance of a nuclear reactor explosion on any given day, or 0.00001%. It may not be zero, but it is infinitesimal to such a degree that one could not be faulted for assuming it is zero. Then let’s say that the probability of finding graphite on the ground outside the reactor if there were no reactor explosion is 0.5%. Maybe it sprouted out of the ground spontaneously or someone dumped it there. So the probability of graphite given a reactor explosion is 99.5%.

Please note that I am not a nuclear engineer, and I have generated these numbers out of thin air purely for illustrative purposes. Given these probabilities, what is the conditional probability that there had been a reactor explosion given the presence of graphite on the ground?

We need to use Bayes’ Theorem to figure this out. Let’s use “ex” as short for reactor explosion, “no ex” for no reactor explosion, and “g” for graphite on the ground:

When you plug in the various probabilities it goes:

The computed conditional probability of a reactor explosion is 0.00199% or approximately 0.002%. So we have gone from a 1 in ten million chance to a 1 in fifty thousand. The probability remains low, but it’s now firmly within the realm of possibility. If you assumed that the probability of finding graphite on the ground given no reactor explosion is 0.1%, then the conditional probability of a reactor explosion rises sharply to 1 in ten thousand.

Using Bayesian terminology again, Legasov updated his prior belief as new information became available, which was crucial in cutting through the confusing narratives in an evolving situation that was unprecedented in human history. The lesson here is to always keep an open mind, and be ever ready to update our priors in the wake of new information.

Be guided by expected value considerations

One of the memorable subplots in Chernobyl involved a group coal miners recruited to dig under the reactor. There was a risk that the melted reactor core (corium) could breach through the nuclear plant’s foundation and contaminate the Ukraine’s groundwater. The miners were tasked to excavate an area under the reactor so that a refrigeration source could be installed to cool the corium. Needless to say, the operation would expose the miners to potentially dangerous doses of radiation, but this terrible cost was judged necessary to eliminate the risk.

The probability of the contamination outcome was difficult to quantify, given the unprecedented circumstances, and in actual fact the whole operation was judged to have been unnecessary in hindsight. Nonetheless, the operation was prompted by the potentially catastrophic outcome in the event that the corium were to melt through.

Let us examine the expected value of a variable:

A very low probability outcome can heavily affect the entire variable’s expected value if it has a sufficiently sizeable payoff. For example, let us say an oncoming event has two outcomes, one with 99.9% probability (scenario A) and another with 0.1% probability (scenario B). So the chance of scenario B happening is very low. If the payoff from scenario A is $100 and the payoff from scenario B is -$1,000,000, however, the event’s expected value is 99.9%*($100) + 0.1%*(-1,000,000), or -$900.01. Thus, scenario B dominates the expected value despite its rather low probability.

In such cases, the highly consequential outcome overrides the low or uncertain probability associated with it, and we need to consider preventing the risk scenario from occuring at all. On the flipside, it also means that we could ignore risk scenarios that have limited outcomes. By so doing, we can evaluate what risk scenarios need urgent attention, and what may be safely ignored.

This concept was first explored by Blaise Pascal in his famous argument that one should believe in God despite the unknown probabilities (“Pascal’s Wager”). I won’t go into the game theoretic and theological arguments inherent in the wager. It is sufficient to say that in an uncertain world we should evaluate potential outcomes not just by their probabilities, but also by their payoffs. When making decisions amid uncertainty and incomplete information, we should be guided by the expected value.

Influence requires communication skills

Legasov’s coutroom testimony in the series’ final episode is a masterclass in elucidating complex scientific concepts to a lay audience. But before we got there, Legasov had already demonstrated his communication skills in the high-level policy meeting covened to discuss the accident. At the meeting he explained why he thought the reactor might have exploded, and persuaded General-Secretary Gorbachev to order an urgent investigation of the situation.

He effectively countered an official’s earlier casual dismissal of the recorded levels of radiation by stating that it was in fact equivalent to “400 chest x-rays” in one case, and possibly “4 million chest x-rays” in another”. He described neutron radiation as “billions, trillions of bullets” whizzing out of the open reactor, and imperiling the entire country. By using understandable language and metaphors, he succeeded in conveying the gravity of the situation.

The point is that scientists, including data scientists, need to be able to explain their scientific viewpoints to non-specialist audiences if they want to influence policy and business decision-making. This is crucial in the case of data scientists, unlike say theoretical physicists or mathematicians, because data science is primarily concerned with solving real-world problems.

When dealing with an uncertain and fast-evolving situation, there is frequently competing perspectives and explanations in the air, compounded by often unspoken human emotions and biases. Clear-headed thinking is not enough. Influence requires persuasion, and communication skills are critical for this purpose.

Conclusion

The TV production undoubtedly took dramatic license, and the actual Legasov may or may not have done the things depicted in Chernobyl. Nonetheless, there are valuable insights to be gained from the show’s analytical portrayal of the human reaction to the unfolding nuclear disaster. There was incompetence, blame-shifting and lies, but also perceptive reasoning, clear-eyed risk assessment and persuasive communication. From these, I draw three key lessons for data scientists facing an uncertain and dynamic situation: