Why hands-on experience, from political activism to crypto mining, can reveal economic insights no textbook alone can teach.
There is an adage, sometimes (probably wrongly) attributed to Confucius*, that I cherish a lot and it goes like this:
„If you tell me I will forget,
if you show me I will remember,
if you let me play a part I will understand.“
Heterodox Economics Newsletter
Der Heterodox Economics Newsletter wird herausgegeben von Jakob Kapeller und erscheint im dreiwöchentlichen Rhythmus mit Neuigkeiten aus der wissenschaftlichen Community multiparadigmatischer ökonomischer Ansätze. Der Newsletter richtet sich an einen Kreis von mehr als 7.000 Empfänger*innen und zählt schon weit mehr als 250 Ausgaben.
This nice saying not only carries a helpful and compact pedagogical lesson (which I too often violate by holding long lectures ;-)), but also points us to an alternative way of learning: as researchers our primary instinct is to understand things by doing exactly that: research, that is, reading the literature, comparing past results and findings, collecting some data and doing some, more or less disciplined, data analysis. Doing research is a great way to enhance our knowledge, but it is not the only route to gain a better understanding of some subject. And indeed, often simply doing something – instead of researching it – can offer you new perspectives on some subject or context. Especially in economics, with its complex, manifold, contingent and dynamically changing problem setups, doing something can be a valuable shortcut for quickly gaining a better understanding of a novel subject or phenomenon.
Two key examples of such „learning-by-doing“ that I believe to profit a lot from as a researcher, teacher and person are my experiences as a political activist and my passion for construction works of all sorts. The former gave me a better understanding and appreciation of the inner workings of political institutions and, relatedly, how the fads, randomness and networks impact on the transmission pipeline of ideas between academia and politics. The latter helped me to gain a clearer understanding of how crucial technical complementarities are to eventually achieve efficient setups; moreover, by having worked with a great diversity of tools and materials first-hand, I can occasionally also make much better sense of some economic data descriptions, e.g., details on input-output tables, dependencies in global value chains or how work-tasks differ across economic sectors.
This tendency – simply do something instead of researching it – has thereby also led to slightly more spectacular setups. One such example dates back a few years ago, where my two older kids developed some fascination for cryptos. Evil parents as we are, we decided to leverage this curiosity to do some education exercise and, thereby, demystify crypto: we put some old computer up and then started crypto-mining as a sort of family project, carefully recording energy use, mined money and related time-use. My kids quickly understood that crypto-mining is actually not akin to mining, but rather akin to speculating on the relative development of two time-series – energy prices and crypto prices – where the price of the former can be influenced by additional investments (e.g. in heavy GPUs). So my kids learned an important economic lesson – and I still have 0.1 units of Monero 🤑.**
But fun aside, I too learned something novel and intriguing while setting this up: as a miner you try to solve some puzzle and, if you do, you join a lottery, and if you win the lottery you are awarded a „block“, i.e., a pre-designated number of coins. However, as many miners will join this lottery the final probability for you to receive the block is truly minimal. To confront this challenge of uncertainty posed by a low-probability, high-payoff setup, miners resort to something of a heterodox economic practice: they create institutions that pool risks, so-called mining pools, quite akin to say, social insurance systems, which are similarly based on the key rationale of sharing low probability risks associated with high pay-offs.
In a nutshell, cooperation is employed to confront structural risks and ensure continued participation of all members thereby pushing the aggregate growth rate of the network. Interestingly, this pattern aligns well with what ergodicity economists call the „Farmer’s Fable“, that is, the finding that a communal sharing of profits will in general increase aggregate growth rates in contexts, where growth proceeds multiplicatively and has a relevant stochastic element.
Now, as you see, while the pecuniary and moral merit of me engaging in some crypto-mining stands in doubt, the pedagogical lessons derived were manifold. While I found it intriguing to observe how cooperative settings confronting uncertainty can emerge even in such supposedly hostile contexts, we should bear in mind that this is actually unsurprising: by simply „doing something“ I directly engaged with real-world economic processes, so it should, at the end of the day, not come as a surprise to have discovered something „heterodox“ while doing so ;-)
All the best
Jakob«
* A related author, Xunzi, offers these lines, which could be a possible source: „Not hearing is not as good as hearing, hearing is not as good as seeing, seeing is not as good as knowing, knowing is not as good as acting; true learning continues until it is put into action.“
** For a more serious academic treatment of digital currencies and crypto-currences please see the recent Special Issue of the International Journal for Political Economy, featured below.
