I’m not really a member of the neoclassical economics fan club, but I think this is on point:
“Subsidies pose a more general problem in this context. They attempt to discourage carbon-intensive activities by making other activities more attractive. One difficulty with subsidies is identifying the eligible low-carbon activities. Why subsidize hybrid cars (which we do) and not biking (which we do not)? Is the answer to subsidize all low carbon activities? Of course, that is impossible because there are just too many low-carbon activities, and it would prove astronomically expensive. Another problem is that subsidies are so uneven in their impact. A recent study by the National Academy of Sciences looked at the impact of several subsidies on GHG emissions. It found a vast difference in their effectiveness in terms of CO2removed per dollar of subsidy. None of the subsidies were efficient; some were horribly inefficient; and others such as the ethanol subsidy were perverse and actually increased GHG emissions. The net effect of all the subsidies taken together was effectively zero!” So in the end, it is much more effective to penalize carbon emissions than to subsidize everything else.” (Nordhaus, 2013, p. 266)
I think there’s a lot in Marsalis’ commentary that will make sense to systems thinkers. Listen to the person next to you. Stay optimistic, but mindful of the problem. Confronted by crisis, transform your paradigm. And of course it’s a beautiful tune.
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.
When suppliers are unable to fill orders, delivery delays increase and customers receive less than they desire. Customers often respond by seeking larger safety stocks (hoarding) and by ordering more than they need to meet demand (phantom ordering). Such actions cause still longer delivery times, creating positive feedbacks that intensify scarcity and destabilize supply chains. Hoarding and phantom ordering can be rational when customers compete for limited supply in the presence of uncertainty or capacity constraints. But they may also be behavioral and emotional responses to scarcity. To address this question we extend Croson et al.’s (2014) experimental study with the Beer Distribution Game. Hoarding and phantom ordering are never rational in the experiment because there is no horizontal competition, randomness, or capacity constraint; further, customer demand is constant and participants have common knowledge of that fact. Nevertheless 22% of participants place orders more than 25 times greater than the known, constant demand. We generalize the ordering heuristic used in prior research to include the possibility of endogenous hoarding and phantom ordering. Estimation results strongly support the hypothesis, with hoarding and phantom ordering particularly strong for the outliers who placed extremely large orders. We discuss psychiatric and neuroanatomical evidence showing that environmental stressors can trigger the impulse to hoard, overwhelming rational decision‐making. We speculate that stressors such as large orders, backlogs or late deliveries trigger hoarding and phantom ordering for some participants even though these behaviors are irrational. We discuss implications for supply chain design and behavioral operations research.