One hears repeatedly the question of how we in system dynamics might reach “decision makers.” With respect to the important questions, there are no decision makers. Those at the top of a hierarchy only appear to have influence. They can act on small questions and small deviations from current practice, but they are subservient to the constituencies that support them. This is true in both government and in corporations. The big issues cannot be dealt with in the realm of small decisions. If you want to nudge a small change in government, you can apply systems thinking logic, or draw a few causal loop diagrams, or hire a lobbyist, or bribe the right people. However, solutions to the most important sources of social discontent require reversing cherished policies that are causing the trouble. There are no decision makers with the power and courage to reverse ingrained policies that would be directly contrary to public expectations. Before one can hope to influence government, one must build the public constituency to support policy reversals.
Excel is rapidly becoming unusable as Microsoft tries to shift everyone into the OneDrive/Sharepoint cloud. Here’s a very simple equation from a population model:
='https://ventanasystems-my.sharepoint.com/personal/vrbo_onmicrosoft_com/Documents/_Mkt/lxpgi/Model/Model/[Cohort Model Natural Increase.xlsx]Boston'!S135+'https://ventanasystems-my.sharepoint.com/personal/vrbo_onmicrosoft_com/Documents/_Mkt/lxpgi/Model/Model/[Cohort Model Immigration.xlsx]Boston'!S119+('https://ventanasystems-my.sharepoint.com/personal/vrbo_onmicrosoft_com/Documents/_Mkt/lxpgi/Model/Model/[Cohort Model NPR.xlsx]Boston'!S119-'https://ventanasystems-my.sharepoint.com/personal/vrbo_onmicrosoft_com/Documents/_Mkt/lxpgi/Model/Model/[Cohort Model.xlsx]Boston'!R119
URLs as equation terms? What were they thinking? This is an interface choice that makes things easy for programmers, and impossible for users.
How many cases will there be on June 1? Beats me. But there’s one thing I’m sure of.
My confidence bounds on future behavior of the epidemic are still pretty wide. While there’s good reason to be optimistic about a lot of locations, there are also big uncertainties looming. No matter how things shake out, I’m confident in this:
The antiscience crowd will be out in force. They’ll cherry-pick the early model projections of an uncontrolled epidemic, and use that to claim that modelers predicted a catastrophe that didn’t happen, and conclude that there was never a problem. This is the Cassandra’s curse of all successful modeling interventions. (See Nobody Ever Gets Credit for Fixing Problems that Never Happened for a similar situation.)
But it won’t stop there. A lot of people don’t really care what the modelers actually said. They’ll just make stuff up. Just today I saw a comment at the Bozeman Chronicle to the effect of, “if this was as bad as they said, we’d all be dead.” Of course that was never in the cards, or the models, but that doesn’t matter in Dunning Krugerland.
Modelers, be prepared for a lot more of this. I think we need to be thinking more about defensive measures, like forecast archiving and presentation of results only with confidence bounds attached. However, it’s hard to do that and to produce model results at a pace that keeps up with the evolution of the epidemic. That’s something we need more infrastructure for.
Randomized experiments have enormous potential to improve human welfare in many domains, including healthcare, education, finance, and public policy. However, such “A/B tests” are often criticized on ethical grounds even as similar, untested interventions are implemented without objection. We find robust evidence across 16 studies of 5,873 participants from three diverse populations spanning nine domains—from healthcare to autonomous vehicle design to poverty reduction—that people frequently rate A/B tests designed to establish the comparative effectiveness of two policies or treatments as inappropriate even when universally implementing either A or B, untested, is seen as appropriate. This “A/B effect” is as strong among those with higher educational attainment and science literacy and among relevant professionals. It persists even when there is no reason to prefer A to B and even when recipients are treated unequally and randomly in all conditions (A, B, and A/B). Several remaining explanations for the effect—a belief that consent is required to impose a policy on half of a population but not on the entire population; an aversion to controlled but not to uncontrolled experiments; and a proxy form of the illusion of knowledge (according to which randomized evaluations are unnecessary because experts already do or should know “what works”)—appear to contribute to the effect, but none dominates or fully accounts for it. We conclude that rigorously evaluating policies or treatments via pragmatic randomized trials may provoke greater objection than simply implementing those same policies or treatments untested.
I like e day better, but I think I’m in the minority. Pi still makes many appearances in the analysis of dynamic systems. Anyway, here’s a cool video that links the two, avoiding the usual infinite series of Euler’s formula.
Notice that the definition of e^x as a function satisfying f(x+y)=f(x)f(y) is much like reasoning from Reality Checks. The same logic gives rise to the Boltzmann distribution in the concept of temperature in partitions of thermodynamic systems.