Popularizing Systems Thinking

Models of complex behavior increasingly sit at the background of vital political discussions like global warming and pandemics. It is time to make them a more integral part of our political discussion. Until voters can understand and participate developing models to predict the behavior of systems, we will have unstable politics, particularly in a country like ours that puts so much stock in the opinion of everyone. This country was defined by a way of thinking. It’s time to expand that.

Our founding fathers did not pioneer Enlightenment thinking but they were the first to create a community organized around it. The Enlightenment shifted people from a reliance on authority and tradition (church and king) to reason and debate (science and democracy). Our founding fathers popularized education – most notably, Thomas Jefferson founded the University of Virginia – with an emphasis on rhetoric and analysis as essential to creating smart voters. They generally believed that education was necessary to freedom and democracy. But as it turns out, rhetoric is a poor way to understand or communicate complexity. We need to update what constitutes a good education.

Today we have expert systems thinkers but we haven’t popularized systems thinking, made it a part of the way we organize and act or even a part of what we include in education. Analysis focuses on parts at one point in time; systems thinking focuses on interactions over time, like how viruses spread at different rates depending on how we behave or how CO₂ builds in the atmosphere depending on our technology. Systems thinking is as important to an effective democracy in this 21^st century as Enlightenment philosophy was to an effective democracy in the 19^th and 20^th centuries. We can’t coherently debate systems as varied and crucial as our financial, environmental, education, and healthcare systems with fluency in systems thinking.

Hearing Bill Gates talk about a pandemic in 2015 and how serious it will be, he mentions what "our models told us." Listening to California governor Gavin Newsom in press conferences, he, too, references "our models." Models have the potential to explain futures we haven't yet experienced. Models will never be perfect; they can, however, be sufficient to inform good policy.  Once you understand compound interest, you may not be able to predict how much wealth you’ll have in 30 years but you know what to do: invest early and often to maximize that wealth. Once you understand how rapidly the coronavirus can spread, it informs policies like shelter-in-place. Even though models are sensitive to changes in assumptions and inputs, they can still point us in the right direction. The better people understand them, both their limits and the insights they provide, the more credible and helpful these models.

I work with really bright scientists and engineers to plan – or model – their projects to develop new products like drugs, medical devices and computer chips. Two benefits inevitably follow. One, each person gets insights into what others are doing and how that impacts them. Good models are key to coordination. Two, they learn more of what is possible as they play with the model, play a game of “what if” to see how they might accelerate launch. “What if we hired one more circuit engineer?” “What if we doubled the number of clinical trial sites so that we could enroll patients more quickly?” The models let them answer what-if questions and become tools for making really smart people even smarter, in the same way that a spreadsheet can help a financial planner to get and communicate insights. Models that a group jointly creates and maintains could be used to inform an entire populace about their policy options on issues like economic stimulus, global warming, or the spread of a pandemic. Even very simple models can help to illustrate important dynamics more clearly than rhetoric.

Democracy depends on education. Change is accelerating. We’re increasingly dependent on systems. Education needs to include system thinking. In a crisis like a pandemic, we have to react to what the models predict about consequences because if we wait to react to actual consequences or rely on our intuition (intuition informed by completely different circumstances) our actions will be tragically late. Models let us learn from the past and from possible futures. The AI that recently beat the world champion Go player Ke Jie was able to make a move no one had ever before seen, a move learned from millions of game simulations it had simulated play even before playing its “first” game with Ke Jie. When a community encounters something like the coronavirus, it would be nice to be at least as prepared as one might be for a game of Go.

There are a variety of ways to popularize systems thinking. One way might look like video games. Imagine kids learning about global warming or economic development by getting exposed to simple models that play out over time. They first learn to turn the knob on this variable and then that variable. They see which variables are akin to the butterfly's wings in Brazil that causes a snowstorm in Minneapolis and which are akin to a hundred moths beating their wings uselessly against a light bulb. Over time they begin to introduce their own data, their own variables, or even change the structure of the model. The class as a whole could build a model that represents their collective insights and predicts outcomes few – if any – minds are sophisticated enough to foresee.

Good education changes life outside the classroom. Eventually democracy might mean that we have collective, online models that represent our best knowledge and are as widely understood as an op-ed or debate. Policy could come out of millions of simulations that are largely transparent and contributed to and understood by millions of citizens. Perhaps working on models will become as much a part of citizenship as working on campaigns or reading and arguing about op-eds. In the same way that a car lets us travel further than we could on foot, good models can let us create better policy than we can with debates.

Ron Davison lives in San Diego County, wrote The Fourth Economy: Inventing Western Civilization and works with teams in Fortune 500 firms and startups to accelerate product launch. @iamrondavison

Acting on Forecasts Rather than Proof

The forecasts for COVID-19 deaths are falling. That's wonderful news. It turns out that measuring the cost for exponential growth of a virus has something in common with measuring the value of startups. It is subject to error but can still inform you how to act.

One key lesson is to move first and move fast. By the time you have data proving the value of a startup, you pay far more for it. Similarly, by the time you have data proving the severity of a virus, you pay far more for it.

Number of deaths thru 11-Apr:
San Francisco: 14
New York: 6,898

"[San Francisco mayor London] Breed ordered businesses closed and issued a citywide shelter-in-place policy effective on March 17, at a point when San Francisco had fewer than 50 confirmed coronavirus cases. (California Governor Gavin Newsom followed with a similar statewide order 19 March.) On that date, New York City already had more than 2,000 positive cases. But New York Governor Andrew Cuomo and New York City Mayor Bill de Blasio, reluctant either to shutter schools or issue a stay-at-home directive for the nation’s largest city, didn’t take similar action for several days. By the time New York City fully shut down on March 22, more than 10,000 cases were reported across its five boroughs." [From the Atlantic, "The City That Has Flattened the Coronavirus Curve"]

5 days can make a big difference when facing a virus that spreads or contracts exponentially.

I see people wondering how stock prices can go up when we're still in a pandemic. Stock prices represent an attempt to price an endless stream of future profits. It is true that the Dow is up 30% in the last couple of weeks. It is also true that it is still down 25% from its peak a couple of months ago. Stock prices fluctuate because people are trying to estimate something in the future that is continually changing as events and best estimate methodologies change. Investors still agree that the coronavirus and measures to protect against it have lowered the value of future profits; their margin of error in estimating that means that stock prices are going to fluctuate. A lot.

I see people dismissing the models forecasting coronavirus deaths as being wrong. Models are always wrong but that doesn't mean that they aren't helpful. One catch-22 with models and policy is that the group taken least seriously could be proven most accurate. What do I mean? Let's say that we had ignored the coronavirus warnings from experts and continued as normal - never socially distancing and not changing anything. In that scenario, New York would be a best case for cities and fatalities would be multiples of what they are now. We could easily have 1 million deaths rather than 100,000. The best-case forecasts would now seem tragically naive. On the flip side, if we listened to those who warned of the worst and took serious measures to protect against that, moving fast and dramatically, the worst-case forecasts would now seem morbidly pessimistic. We would have far less than 100,000 dead and would never come close to a million. Even without behavior change, forecasts of anything that grows or contracts exponentially are likely to be off. Hugely. The outcome could easily be 10X or 1,000X better or worse given just small changes in the rate of contagion or mortality.

Early investors in Apple likely never once stopped to think that it could be worth a trillion early the next century. But they didn't have to know it would be worth that much to know it was a good investment. Given the Bay Area is the epicenter for trying to value the future, it is unsurprising that it would become a model for how to minimize the harm of a virus. Among the many things the folks in the Bay Area have learned is that it is better to move first to pursue a possibility - whether that possibility is avoiding fatalities from a pandemic or owning shares of a startup that later make you rich - and then learn from and adapt to reality than it is to wait for the data to come in and by then to have missed your opportunity.

As the future comes at us with increasing speed, the ability to quickly assess what models suggest rather than what data confirms could make all the difference.

What To Do About the Immaturity of Systems Modeling

Sam Harris recently had a conversation with Scott Adams (Dilbert creator and author of How to Fail at Everything and Still Win Big) about Trump. Adams predicted Trump's victory because he sees Trump as a master persuader. There's a lot to say about that but Adams made a really useful distinction about what he saw as the three stages of climate change policy. He distinguishes between:
1) The reality of climate change as an ongoing phenomenon that seems to be man made;
2) The ability to simulate future climate change with good models; and,
3) An appreciation of the economic policy implications of the above.

A decade or three ago, it was fashionable to dismiss climate change. This has become problematic for at least two reasons. One, the science is not that sophisticated. Certain industrial activity releases greenhouse gases. These gases - as the name suggests - work like a greenhouse and trap heat. That science is not exactly quantum entanglement and the data for greenhouse gas emissions and resultant warming seems to track pretty well to the theory. So Adams cedes this point and allows that climate change is probably real and ongoing.

Adams worked as a financial analyst at a bank, though, and challenges the second point: the ability to simulate future climate change. He said that it reminds him of the financial models he ran as an analyst that - should they reveal something his boss didn't like - could readily be changed with just a tweak of a few variables. Given we can't really forecast accurately what might happen, it is good to be skeptical, he says.

In this he has sort of put his finger on something really important and is sort of missing the point (probably intentionally).

What is really important is that systems define so much about what does or does not go well in our world - systems as varied as the economy and financial markets, energy systems, ecosystems and school systems - and yet we really don't understand system dynamics that well. Systems are tough to model and our models are not great. This is reason to be skeptical about any predictions but it also suggests that systems modeling deserves a massive infusion of research money. A crowd was gathered to watch a hot air balloon ascend and some woman said, "What is the use of all this new technology." Benjamin Franklin answered, "Madam, what is the use of a new born infant?" Systems simulation matters a great deal and is not that mature. Better to invest more heavily in it than to walk away from it. (And I think that computers' ability to simulate systems is maturing just when that capability is most needed for shaping policy dependent on such systems.)

And even with admitted limitations of models for any systems, it is worth asking whether even the models Adams was tweaking for his boss were all that bad. Once you understand a model for an economy or business, you articulate risks, a range of outcomes, and important variables. With good models you learn what factors they are most sensitive to (housing mortgages are sensitive to widespread economic downturns or refinancing from a drop in interest rates, for instance) and even spotty historical data can give you some sense of the probability of those events. (Yes. Nassim Taleb has rightfully pointed out that markets can be rocked by unpredictable events but risk mitigation can protect you from some of these rare events. A person who has saved three years of salary is better prepared for an event "they never could have predicted" than is someone with only three months of salary.)  Financial models are a little sketchy in prediction but there are ways to gauge their efficacy in spite of a large margin of error. (For instance, only about 20% of businesses succeed past 5 years. If your bank lending model assumes that is going to raise to 50%, it will probably be wrong; if it assumes that it will raise to 25% or drop to 15%, it could be right but done properly even that should require a coherent explanation that tracks to the numbers rather than arbitrary tweaks.) Further, to the extent that Adam's boss was unique in cheating the models so that they showed what he wanted, his bank would suffer. There is a drive to make models more accurate and - within the financial world at least - big rewards for such accuracy.

Models force questions and conversations about what variables matter and they bound reasonable outcomes. It is true that climate change models will be wrong but the simplest truth is pretty easy to predict: we will emit more greenhouse gases and temperatures will be higher than they would have been without these emissions. There are a host of unknowns that come with that (will particular regions benefit or lose, will changes in wind or sea currents result in unexpected cooling in certain regions, might unforeseen natural phenomenon or new technology absorb these gases, etc.) but the general story is known. If you invest in stocks over a 25 year period you can't be sure of when your portfolio will drop by half or raise 20% a year for successive years but you can reasonably guess that over your lifetime you'll be a better shape for having saved 10% of your income than not. Same with greenhouse gases; reducing emissions will drive less uncertainty, disruption and climate change.

Denying climate change is in a long tradition of denying scientific results like the health hazards of tobacco or the notion that we orbit the sun. Climate change deniers are traditionalists who conflate market economies with oil and gas and see an admission of climate change as a threat to those forces. (It does seem like climate change will threaten oil and gas. The possibility of oil and gas being displaced by alternative energy is not a refutation of markets that periodically unleash gales of creative destruction, though, but is instead an affirmation. Markets are no more dependent on oil than horses and markets don't treat fossil fuel industries as sacred.)

As to Adams' third point about evaluating the economics of climate change policy, I'll just say this. If it is inevitable that we'll adapt new energy technologies, there is less likely to be a penalty for rushing into creating and then converting to alternative energies than there is to be a penalty for delaying that change.