I just read the longer blog post laying out the thesis of the separate book which I may buy. Very thought provoking. As a fellow engineer with a minor in economics, I can vouch for the feeling referenced in the “blurb” for the book… “Not another engineer…”
When the average engineer takes an economics class in Money and Banking and is exposed to the fundamentals of fractional reserve lending and fiat currency, there’s a weird feeling that overtakes you about four weeks into the semester. THIS is what our modern economy is based upon? This cannot possibly end well…
I’ve told the story in this forum previously about the professor I had for that Money and Banking class, Hyman Minksy. He was famous in economic circles for his theories of cyclical fraud and collapse in banking systems due to Ponzi-like schemes being adopted by the entire system, virtually gauranteeing its failure given the mechanics of banking systems.
I’ve also written on my blog and this forum about the hidden complexity of interest rates and how a simple percentage actually conveys risks to an investor or borrower from multiple dimensions:
- inflation due to monetary policy
- time-value-of-money over a period of time
- individual moral risk (will you pay me back or not?)
- individual business risk (is your individual business operation sound?)
- industry business risk (is your business field going to survive?)
- country-specific economic or political risks (buying a McDonald’s franchise in Ukraine during a war)
Two other factors that would have to merit inclusion in an all-encompassing “engineer explains economics and society with math” theory would be:
The L factor reflects some upper bound of human brain biochemical functionality that limits how much an entire population can “learn” per year. Individual humans obviously vary greatly based on their formal education and the circumstances in which they live. Certainly, different types of learning for different skills have different VALUE in different contexts. A computer engineer has the ability to learn a new programming language faster than a tribal leader living on a savannah in Africa. On the other hand, that tribal leader can adapt faster to a change in hunting prospects the computer engineer can’t even comprehend.
The L factor I envision reflects a wider “average” ability for mankind to learn something that advances society forward. It’s more like a “Moore’s Law” of the aggregate human mind. If there is a upper bound to this learning parameter, there is also an upper bound in improvements in “productivity” as economists would attempt to measure it. That means there is an upper bound in a modern industrial society’s ability to LEGITIMATELY “grow the pie” over any narrow range of time.
This concept has crucial implications for each of us purely as a member of society or as a wise investor and for governments. If you assume there is such a thing as a natural L factor of humanity that is related to a natural upper limit in productivity gains P, it acts as a useful sanity check when evaluating claims by companies about their growth prospects and share price or evaluating promises by politicians to put a goose in every pot and a $45,000 battery electric vehicle in every drive in seven years. Nothing grows to the sky without bounds.
(Anecdotally, I remember around 1997 pondering an investment in SUN after seeing how many little SUN pizza box servers my company was buying at the time. I looked at SUN’s revenue growth estimates, divided those by the cost per server of their most popular model and concluded that within about five years, there would be nearly 20,000,000 of these running. Okay, CLEARLY tweny million Americans are not all going to want or need or have their own personal web server in five years so what’s up with this stock price?)
The takeaway from that abstraction is that a wise investor should expect a LOWER growth rate from a company as its capitalization goes up. It’s easy to grow 1000% from a base of $100. Harder to hit 500% with $10,000. Harder still to hit 100% with $1,000,000. Much harder to hit 20% with $1 billion. Impossible to achieve 7% over 10 years with $10 billion. Anyone telling you otherwise is merely attempting to separate you from your money.
The greed factor G is even less mathematically computable but it ties directly into your theory of the relationship between energy consumption and economic growth. In theory, one of the things that the L (learning) factor SHOULD accomplish is allow a society of fixed size to get more VALUE (food, shelter, entertainment, etc.) with LESS work. If you aren’t getting smarter each day through learning, you cannot become more productive at finding your next meal, maintaining shelter over your head, etc. Over many eras of history, some of our leaps in productivity were achieved by leveraging energy and machinery to replace manual labor. Cotton gins, looms, steam engines, automobiles, etc. reduced or eliminated human labor in SOME forms but created demand for additional human labor to produce, refine and distribute the energy needed to drive the machines. To some extent that looks like extra economic activity and value but it really amounts to just a quantification of energy exerted in a different form in a prior means of production.
The key point is that if new technology is capable of reducing the demand for human labor, one would expect to see net labor hours worked per week DECLINE for the same amount of generic “consumption.” If a community of 100 people could maintain 25 homes with 700 square feet per house, tend 25 acres of corn, a herd of cattle, 200 chickens and be self sustaining, once someone introduces a tractor to the ecosystem, in theory the average work load of that community should go down a bit. In reality, the community in aggregate finds something else to want to begin occupying the time freed up by the new tractor. Maybe the first owner of a tractor can harvest his corn in a third of the time and spends the other two thirds making nick nacks to sell to the adjacent town, bringing in more income and now he decides he wants a 1000 square foot house rather than an extra 20 hours of sleep during planting and harvesting season.
It’s this G factor – perjoratively termed greed here – that contributes to the linkage to energy consumption. Somewhere in our 23 pairs of chromosomes is a gene that drives this G factor, either just as pure greed or just a curiosity factor unique to humans that always has us looking for something new to stimulate the brain and keep it active that puts us in this feedback loop of constantly wanting more. It may not be the exact same value across all individuals but the average across the species seems well above 1.0 which feeds into these other mechanisms to create the behavior and consequences we see every day.
Seriously… A very interesting set of concepts and a topic worthy of much serious discussion. Congratulations on the book.