Nature knows how to self-regulate. The cycles of feast or famine are simple examples of such systems. Too much browse for caribou produces too many caribou calves, which then feeds too many wolves which then produce too many pups which then grow up and eat too many caribou… Leading to too few caribou, starving too many wolves, which end up producing too few new pups, which then let too many new caribou to prosper. Yeah, The Lion King was right, it’s a circle, sometimes it’s a big circle and sometimes it’s a little one. But around and around it goes.
Other self regulating system examples are the human body: when we get hot, we sweat, which cools us down. If we get too cool then we shiver which produces excess heat which warms us up.
The climate is generally a homeostatic system – a system that reaches an equilibrium (or oscillates between extremes, the average of which is steady over time). Ignoring humans impact for now, too much CO2, produces too much plant growth, which then extracts much more CO2 (a greenhouse gas) which then allows the planet to cool, which kills or retards plant growth allowing the decay of plant material to return the CO2 to the atmosphere which then heats up and allows plants to thrive again.
Over the years I’ve tried to figure out how to apply such self-regulating behaviors to social systems. For instance, I dreamed up a number of Constitutional Amendments, one of which addressed campaign contribution limits. I figured that if we used median wage as the basis for contributions — this would self regulate: every citizen could contribute one days gross wage, per candidate, per year. If politicians wanted greater contributions — they should work to elevate wages.
Another one is: what should be the minimum wage for any one location? I figured that the cost of living should determine the minimum wage; it would cost more to live in New York, NY than in Lincoln, NB. To create an algorithm for this: if we use the median monthly cost of an apartment as the basis for minimum wage: $1000 / mo. rent multiplied by 2 and divided by 100 would give us $20/hr. At 20 dollars an hour, a $1000 a month rent seems reasonable. If you want to raise rent, you have to raise wages too. If you want to lower wages, you have to lower the cost of rent.
All sort of systems can be redesigned with self-regulation in mind. Taxes for instance. Or how to handle income inequality. I’ve posted on these topics here if you’re curious. But what about other applications? Healthcare? Are there self-regulatory aspects we could apply there? I’ve posted my thoughts on the “win/win” vs the “win/lose” aspect of capitalism. That seems like a candidate for determine when free-markets should be used. In fact when the “win/win” impact of capitalism is applied, supply and demand also finds its own equilibrium.
Tragedy of the commons algorithms? Water rights algorithms? Gun laws? Energy production and consumption? Land use? The elimination of biases in the hiring process for employers? I’m sure there are many facets of life that would benefit from an intelligent analysis and design of algorithms which would produce a self-regulating system.
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“This kind of bottom-up, self-organised business network is what Trebor Scholz, a professor at the New School in New York, calls ‘platform cooperativism’. Take Uber, currently valued in the tens of billions of dollars. There’s no reason why a group of drivers couldn’t build a similar platform for themselves; the software tools to develop it are open-source and readily available, and development and design talent can be given incentives to participate.
As it happens, such an initiative is already out there, in the form of a ride-sharing, blockchain-based platform called La’Zooz. Currently anyone can join and begin to earn Zooz tokens – the currency you use to buy a ride, and also a store of value that represents your share of ownership of the business. You earn tokens each time you drive more than 20km with the app, or by contributing code to the design, or by getting others to join.
The dual purpose of Zooz tokens at once democratises the ownership of a platform and allows users to benefit from the value their presence is creating. Recall that in closed platforms, as network effects increase, rents are subtracted and channelled to the shareholders or capital owners. But the users are not partaking in the spoils of their own participation. By contrast, in a tokenised platform, the value created by users’ transactions boosts the value of the tokens they own. Users can extract the maximum gain from their participation via token appreciation, rather than having the economic value of network effects skimmed off by remote shareholders.”