This is a great talk by Nate Osgood on the intersection of systems and complexity with data science and machine learning:
Overconsumption isn’t green.
Tesla’s strategy of building electric cars that are simply better than conventional cars has worked brilliantly. They harnessed lust for raw power in service of greener tech (with the help of public subsidies – the other kind of green involved).
That was great, but now it’s time to grow up. Not directly emitting CO2 just isn’t good enough. If personal vehicle transport continues to grow exponentially, it will just run into other limits, especially because renewable electricity is not entirely benign.
The trucks on the horizon are perfect examples. The Cybertruck consumes nearly twice the energy per mile of a Model 3 (and presumably still more if heavily loaded, which is kind of the point of a truck). That power is cheap, so anyone who can afford the capital cost can afford the juice, but if it’s to be renewable, it’s consuming scarce power that could be put to greener purposes than stroking drivers’ egos. It’s also consuming more parking and road space and putting more rubber into waters.
The EV transition isn’t all bad – it’s a big climate mitigation enabler. But I think we could find wiser ways to apply technology and public money that don’t simply move the externalities to other areas.
More from Galbraith on the crash of ’29:
Some of those in positions of authority wanted the boom to continue. They were making money out of it, and they may have had an intimation of the personal disaster which awaited them when the boom came to an end. But there were also some who saw, however dimly, that a wild speculation was in progress, and that something should be done. For these people, however, every proposal to act raised the same intractable problem. The consequences of successful action seemed almost as terrible as the consequences of inaction, and they could be more horrible for those who took the action.
A bubble can easily be punctured. But to incise it with a needle so that it subsides gradually is a task of no small delicacy. Among those who sensed what was happening in early 1929, there was some hope but no confidence that the boom could be made to subside. The real choice was between an immediate and deliberately engineered collapse and a more serious disaster later on. Someone would certainly be blamed for the ultimate collapse when it came. There was no question whatever who would be blamed should the boom be deliberately deflated.
This presents an evolutionary problem, preventing emergence of wise regulators, even absent “power corrupts” dynamics. The solution may be to incise the bubble in a distributed fashion, by inoculating the individuals who create the bubble with more wisdom and memory of past boom-bust cycles.
I picked up John Kenneth Galbraith’s account of The Great Crash at a used bookstore. I’m not far into it, but there’s a nice assertion of the importance of a systemic view over event-based descriptions right at the start:
… implicit in this hue and cry was the notion that somewhere on Wall Street … there was a deus ex machina who somehow engineered the boom and bust. This notion that great misadventures are the work of great and devious adventurers, and that the latter can and must be found if we are to be safe, is a popular one in our time. … While this may be a harmless avocation, it does not suggest and especially good view of historical processes. No one was responsible for the great Wall Street crash. No one engineered the speculation that preceded it. Both were the product of the free choice of hundreds of thousands of individuals. The latter were not led to the slaughter. They were impelled to it by the seminal lunacy which has always seized people who are seized in turn with the notion that they can become very rich. …
Galbraith’s purpose in writing the book is itself systemic, to weaken the erosion of memory that permits episodic boom bust cycles:
Someday, no one can tell when, there will be another speculative climax and crash. There is no chance that, as the market moves to the brink, those involved will see the nature of their illusion and so protect themselves and the system. … There is some protection so long as there are people who know, when they hear it said that history is being made in this market or that a new era has been opened, that the same history has been made and the same eras have been opened many, many times before. This acts to arrest the spread of illusion. …
With time, the number who are restrained by memory must decline. The historian, in a volume such as this, can hope that he provides a substitute for memory that slightly stays that decline.
Jim Hines gets to the heart of the matter in under 4 minutes:
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.
In my last post, stress takes center stage as both a driver and an outcome of the cortisol-cytokine-serotonin system. But stress can arise endogenously in another way as well, from the interplay of personal goals and work performance. Jack Homer’s burnout model is a system dynamics classic that everyone should explore:
Jack B. Homer
This paper explores the dynamics of worker burnout, a process in which a hard‐working individual becomes increasingly exhausted, frustrated, and unproductive. The author’s own two‐year experience with repeated cycles of burnout is qualitatively reproduced by a small system dynamics model that portrays the underlying psychology of workaholism. Model tests demonstrate that the limit cycle seen in the base run can be stabilized through techniques that diminish work‐related stress or enhance relaxation. These stabilizing techniques also serve to raise overall productivity, since they support a higher level of energy and more working hours on the average. One important policy lever is the maximum workweek or work limit; an optimal work limit at which overall productivity is at its peak is shown to exist within a region of stability where burnout is avoided. The paper concludes with a strategy for preventing burnout, which emphasizes the individual’s responsibility for understanding the self‐inflicted nature of this problem and pursuing an effective course of stability.
You can find a copy of the model in the help system that comes with Vensim.