Second-Order Thinking

E.g. 30 years ago I had a lenghty discussion with my fellow electronics engineering classmates. I predicted then that in the future computers will consist of a single chip that comes with everything necessary and is going to be individually configurable for each customer. The others could not even imagine something like this happening. If we look at smartphones (ARM) and modern computers (Apple Silicon) this is exactly ehat is happening. Apart from power supply, interfaces and storage, everything including GPU and Ram is on one chip.

"The term cobra effect was coined by economist Horst Siebert based on an anecdote of an occurrence in India during British rule. The British government, concerned about the number of venomous cobras in Delhi, offered a bounty for every dead cobra. Initially, this was a successful strategy; large numbers of snakes were killed for the reward. Eventually, however, enterprising people began to breed cobras for the income. When the government became aware of this, the reward program was scrapped. When cobra breeders set their now-worthless snakes free, the wild cobra population further increased"

Long-term just means doing the same analysis far out into the future: "If I spend less on maintaining software product $LEGACY, I will save money. If I continue to do this, I will save more and more money by each passing month."

Second-order means discovering neighbouring causal links. "If I spend less on maintaining software product $LEGACY, I will save money. I will however also have to prioritise which bugs I fix. This will lead to some bugs that would be fixed today not getting fixed. That might mean that some users of $LEGACY move to a competitor. In turn, it costs more to acquire new customers than to retain old ones, so just maintaining the same customer count while dissatisfying customers until they quit will cost more money than by trying to make existing customers happy.

"Quitting $LEGACY customers will however lead to fewer bugs being discovered in $LEGACY, meaning the reduced maintenance budget might be sufficient again. However, quitting customers doesn't only force me to spend more to re-acquire similar number of customers – it might also be negative for marketing, so it becomes harder to acquire new customers, thus having a non-linear effect on the customer replacement costs.

"Decreased maintenance of $LEGACY might make some developers happier, but signal to others that we are not serious about the lifecycle of our product. This might result in them not putting in as much effort to make maintenance easier in future products, which will increase the maintenance demand in future products. Thus, insufficient maintenance efforts on $LEGACY may lead to increasing maintenance demand, which leads to insufficient maintenance efforts, which leads to increasing maintenance demand, and so on."

As the last parts of that hint at, what's really important is not all the neighbouring causal links, but those that lead to nonlinearities and feedback cycles. By mucking about with things in a stable system, in particular when there are nonlinearities and feedback cycles involved, you can accidentally create an unstable system that wants to drive itself into the ground.