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Pandemic politics highlight how predictions need to be transparent and humble to invite insight, not blame.

Cartoon of scientists and policymakers inspecting the inside of a black box that is outputting a policy document — Illustration by David Parkins

The COVID-19 pandemic illustrates perfectly how the operation of science changes when questions of urgency, stakes, values and uncertainty collide — in the ‘post-normal’ regime.

Well before the coronavirus pandemic, statisticians were debating how to prevent malpractice such as p-hacking, particularly when it could influence policy¹. Now, computer modelling is in the limelight, with politicians presenting their policies as dictated by ‘science’². Yet there is no substantial aspect of this pandemic for which any researcher can currently provide precise, reliable numbers. Known unknowns include the prevalence and fatality and reproduction rates of the virus in populations. There are few estimates of the number of asymptomatic infections, and they are highly variable. We know even less about the seasonality of infections and how immunity works, not to mention the impact of social-distancing interventions in diverse, complex societies.

Mathematical models produce highly uncertain numbers that predict future infections, hospitalizations and deaths under various scenarios. Rather than using models to inform their understanding, political rivals often brandish them to support predetermined agendas. To make sure predictions do not become adjuncts to a political cause, modellers, decision makers and citizens need to establish new social norms. Modellers must not be permitted to project more certainty than their models deserve; and politicians must not be allowed to offload accountability to models of their choosing²^,³.

This is important because, when used appropriately, models serve society extremely well: perhaps the best known are those used in weather forecasting. These models have been honed by testing millions of forecasts against reality.

…click on the above link to read the rest of the article…

Can we Predict Collapses Before they Happen? What we Learned from the Pandemic

October 15, 2020

Can we Predict Collapses Before they Happen? What we Learned from the Pandemic

My 2019 book “Before the Collapse.” In it, I examined several scenarios of the future of humankind. Was I able to predict the current pandemic? Of course not in the details, but I think that I did note an important facet of the story: epidemics are never very deadly when they come alone. They become true killers only when they are associated famines. In the case of the current coronavirus pandemic, the human population is not so badly debilitated by famines that we should have expected disasters comparable to those caused by ancient epidemics. So, we could have been better prepared if we had paid more attention to history. But the main thing we learn from history is that people never learn from history. And so it goes. This post includes a review of the book written by Daniel Ruiz.

After nearly one year from the start of the COVID-19 pandemic in China, we can say that, at the very least, we learned a lot from it. One lesson was that we should be much more careful about “model hubris”, to think that because a model is complex and detailed, it can predict the future. This problem is well described in a recent paper by Saltelli et al. in a recent paper in “Nature.”

But perhaps the most important lesson we learned was how easy the future can surprise us and how our perception of it can be remote from reality. We tend to judge on the basis of our past experience, but our mental models are often poorly calibrated. When the COVID-19 started diffusing in the West, many people panicked, some seemed to think that it really was the end of the world. Maybe they had in mind as a model the great plague of the Middle Ages, an image that has been with us for centuries.

…click on the above link to read the rest of the article…

A question you always wanted to ask but you never had the time to: Is the “EROI” of energy studies the same as the R factor in epidemiology?

June 4, 2020

A question you always wanted to ask but you never had the time to: Is the “EROI” of energy studies the same as the R factor in epidemiology?

There is a certain logic in the way the universe works and so it is not surprising that the same models can describe phenomena that seem to be completely different. Here, I’ll show you how the same equations describe chain reactions that govern such different phenomena as the spread of an epidemic, the cycle of extraction of crude oil, and even the nuclear reaction that creates atomic explosions. All these phenomena depend on the efficiency of energy transfer, the parameter that’s known in energy studies as EROI (energy return on energy invested), related to the “transmission factor” (R) of epidemiological models. Above, a classic clip from Walt Disney’s 1957 movie, “Our friend, the atom.”

You may be surprised to discover that epidemiological models share the same basic core of peak oil models. And it is not just about peak oil, the same models are used to describe chemical reactions, resource depletion, the fishing industry, the diffusion of memes on the Web, and even the nuclear chain reaction that leads to nuclear explosions. It is always the same idea: reinforcing feedbacks lead the system to grow in a frenzy of exploitation of an available resource: oil, fish, atomic nuclei, or people to be infected. In the end, it is perhaps the most typical way the universe uses dissipate potentials. As always, entropy rules everything!

Modeling these phenomena has a story that starts with the model developed in the 1920s by Vito Volterra and Alfred Lotka. They go under the name of “Lotka-Volterra” models or, sometimes, “Prey-Predator” models. This heritage is not normally recognized by people in the field of epidemiology, but the model is the same: the virus is a predator and we are the prey.

…click on the above link to read the rest of the article…

The problem with models…is getting stuck on just one

September 24, 2018

The problem with models…is getting stuck on just one

Alfred Korzybski, the father of general semantics, first uttered what must now seem like a well-worn phrase: “The map is not the territory.” And yet, I don’t think this view has yet been well-incorporated into human culture.

In a time when social media outlets are trying to sort what is “fake” from what is “genuine” or “true,” very little thought is being put into what we even mean by “fake,” “genuine” or “true.” Facebook, for example, has resorted to third-party fact-checkers, a mix of news organizations and fact-checking nonprofits. It is also hiring thousands of new employees to check what it calls “non-news” information posted on Facebook pages.

A lot of checking revolves around whether someone said or did what is claimed. That’s not too hard. The next level involves the effect of a policy or position. That’s more difficult since some of the policies in question aren’t in effect and even for those that are, it is always hard to trace cause and effect from a policy to a specific result.

Then, there are what I’ll call “model” questions. Some claims fit into one model, but not into another. So, it’s very important to know what model one is using. In physics what’s true in our everyday experience isn’t true in the world of quantum mechanics, the domain of the very small. For example, it takes time for information, say, in the form of electrical signals through a cellphone network, to travel from where I am in Washington, DC to a client in Eastern Europe. We notice ourselves overlapping in our conversation sometimes because of the delay.

In an actual experiment of quantum effects, electrons that were a mile apart influenced each other with no time delay, suggesting that information at this level somehow travels instantaneously from one place to another. It’s a weird result, but consistent with the theory.

…click on the above link to read the rest of the article…

Time For Some Climate Honesty

August 4, 2018

kienyke.com

Time For Some Climate Honesty

Half-truths are doing no good

Let’s assume that you have doubts about ‘global warming’.

Some people do and, truthfully, we utterly lack the ability to accurately model how much warming will happen, where and by when (emphasis on accurately).

The reason is not for lack of trying or continual learning and model refinement, but centers on the complexity of the task.

Even seemingly simple systems that are actually complex are impossible to predictively model. An example is a pile of sand growing grain by grain that will finally slump at some unpredictable time and in an unpredictable way. You would think that such a simple system could be accurately modeled, but that’s not the case. Exactly when the pile will finally slump is unpredictable. Exactly how large the resulting slump will be is also unpredictable. The “when” and the “how much” are unknowable (using current modeling techniques).

All that can be calculated for certain is that a higher pile with steeper sides/areas (a.k.a. “fingers of instability”) is more likely to slump sooner and more catastrophically.

Now consider the tasks laid out before the global climate modelers where feedback loops abound, unknown variables still lurk, and the final result is the summation of multiple interacting complex systems. It’s not a pile of sand granules, it’s a gigantic interconnected system composed of heat flows, cloud formation, wind and oceanic patters and currents, variable solar and cosmic radiation, volcanoes and dust and jet trails and a thousand other inputs all interreacting with and influencing each other…in unpredictable ways.

It’s chaos theory (i.e. the butterfly effect) which means it’s way beyond anything we can currently model with any accuracy or confidence.

…click on the above link to read the rest of the article…

Closing the Gap between the Science and Politics of Progress

February 13, 2018

Closing the Gap between the Science and Politics of Progress

Ed.note: This article draws on a paper published this month in the international journal, Social Indicators Research. The paper is available on Richard’s website, www.richardeckersley.com.au

Global politics is based on an outmoded and increasingly destructive model of human progress and development. Can science change a dire situation?

‘My view of human progress has stayed surprisingly constant throughout my presidency. The world today, with all its pain and all its sorrow, is more just, more democratic, more free, more tolerant, healthier, wealthier, better educated, more connected, more empathetic than ever before. If you didn’t know ahead of time what your social status would be, what your race was, what your gender was, or your sexual orientation was, what country you were living in, and you asked what moment in human history you would like to be born, you’d choose right now.’ Barack Obama, President of the United States 2009-2017

It is unusual for a national leader to articulate his worldview in this way. Nonetheless, Obama’s view of progress is one that is, broadly speaking, shared by politicians and governments throughout the developed world and beyond (partly framed here by the ‘identity politics’ that characterises political debate today). The view reflects the dominant or orthodox model of development.

However, this model is increasingly at odds with what science tells us about the world. It is not that the specific achievements are wrong, but that they are incomplete, and so present a false picture of progress. The growing gap between the conventional view and the realities of people’s lives helps to explain the widespread public disquiet in many countries and its political consequences, evident in growing political volatility and extremism.

…click on the above link to read the rest of the article…

Why Models Fail

January 2, 2018

Why Models Fail

QUESTION: Mr. Armstrong; Did AIG use the Black-Scholes Model and that is what created the crisis again in 2007?

ANSWER: No. It’s my understanding that AIG developed different models, they called a “Value-at-Risk Model,” (VaR) which used a binomial-expansion-technique to start valuing their positions. I believe the original model was developed at Moody’s. However, like the Black-Scholes Model, it too lacked depth. In model development, it is extremely complex.

Virtually every model created tends to be predominately flat with a minimum of dynamic variables lacking understanding of TIME. Then the testing period lacks the database reflecting all conditions. In the case of Black-Scholes, they back-tested only with data to 1971. If I created a model with only data from 2009 forward, then it would be biased to presume a bull market is normal in the stock market.

The Value at risk (VaR) model is a measure of the risk of investments. It estimates how much a set of investments might lose, given normal market conditions, in a set time period such as a day. VaR is typically used by firms and regulators in the financial industry to gauge the number of assets needed to cover possible losses. It obviously failed in 2007-2009 because once again it was not a “normal market condition” for it fails utterly to understand CONTAGION when sound assets are sold to raise cash for other assets that collapse. The assumption of the model is its own nemesis.

For example, if a portfolio of stocks has a one-day 5% VaR of $10 million, this actually means that there is a 5% probability that the portfolio will fall in value by more than $1o million over a one-day period if there is no trading.

…click on the above link to read the rest of the article…

What Should Be the Criteria for Model Selection?

October 20, 2017

WHAT SHOULD BE THE CRITERIA FOR MODEL SELECTION?

In order to make the data “talk,” economists utilize a range of statistical methods that vary from highly complex models to a simple display of historical data. It is generally held that by means of statistical correlations one can organize historical data into a useful body of information, which in turn can serve as the basis for assessments of the state of the economy. It is held that through the application of statistical methods on historical data, one can extract the facts of reality regarding the state of the economy.

Unfortunately, things are not as straightforward as they seem to be. For instance, it has been observed that declines in the unemployment rate are associated with a general rise in the prices of goods and services. Should we then conclude that declines in unemployment are a major trigger of price inflation? To confuse the issue further, it has also been observed that price inflation is well correlated with changes in money supply. Also, it has been established that changes in wages display a very high correlation with price inflation.

So what are we to make out of all this? We are confronted here not with one, but with three competing “theories” of inflation. How are we to decide which is the right theory? According to the popular way of thinking, the criterion for the selection of a theory should be its predictive power.

On this Milton Friedman wrote,

The ultimate goal of a positive science is the development of a theory or hypothesis that yields valid and meaningful (i.e., not truistic) predictions about phenomena not yet observed.^[1]

So long as the model (theory) “works,” it is regarded as a valid framework as far as the assessment of an economy is concerned. Once the model (theory) breaks down, we look for a new model (theory).

…click on the above link to read the rest of the article…

How Bad Can This Get, And How Fast?

October 3, 2015

How Bad Can This Get, And How Fast?

There’s so much negative real bad economic and financial news out there that it’s hard to choose a ‘favorite’, but I guess I’m going to have to go with what underlies and ‘structures’ it all, the IIF stating that for the first time since 1988 and the Reagan presidency, there’s more money flowing out of emerging markets than there’s flowing in. That is for sure a watershed moment.

And no, that trend is not going to be reversed either anytime soon. Emerging economies, even if they wouldn’t include China -but they do-, have relied exclusively on selling ‘stuff’ to the rich world which combined cheap commodities with cheap labor, and now they see their customer base shrink rapidly just as they were preparing to harvest the big loot.

Now, I hope I can be forgiven for thinking from the get-go that this was always a really dumb model. That emerging nations would provide the cheap labor, and the west would kill of its manufacturing base and turn into a service economy.

This goes very predictably wrong if and when we figure out that A) economies that don’t manufacture anything can’t buy much of anything, and B) that we can sell those services our economies are ‘producing’ only to ourselves, as long as the emerging nations maintain a low enough pay model to make their products worth our while to import.

It makes one wonder how many 6 year-olds would NOT be able to figure this out. In the same vein, how many of them would be hard put to understand that our economies, overwhelmed by, and drowning in, debt, cannot be rescued by more debt? Here’s thinking the sole reason so many of us don’t get it is that we’ve been told it’s terribly hard to grasp, and you need a 10-year university course to ‘get it’.

…click on the above link to read the rest of the article…

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