Complementarity (Mental Models XVII)

Niels Bohr proposed that one fundamental insight of quantum physics was that some phenomena or systems could be described in two or more mutually exclusive ways and that it would be a mistake to pick one description as the “right one” – both could be accurate.

This violates the logical dictum of the excluded middle, in a sense, since it suggests that when we ask if a system is A or B, the answer is “both”. It is a radical view, and just how radical is illustrated by physicist Frank Wilczek’s use of another example: legal liability.

Wilczek suggests that humans can be described as physical systems wholly determined by physical laws, or acting intentional agents with motives and responsibility – and that both descriptions are accurate representations. It would be a mistake, then, to argue that we have no legal responsibility because we can be described as physical systems, because we can equally well be described as acting, intentional agents.

Bohr’s coat of arms — opposites are complementary…

This feels like cheating – and at least my inner high-school philosopher wants to know “but which is it?” — the challenge here is if we accept the lack of a singular ontological ground truth we suddenly seem to be on a slipper slope to Feyerabend-land, where everything is permissable.

This is an interesting problem – it echoes of Dostoyevsky’s observation that if God is dead, then all is permitted — the lack of a single foundational layer of reality, the lack of a hierarchy of reality, seems to unmoor us from truth. But maybe the answer to that is in the “mutually exclusive”? If two descriptions overlap in any way, then they do not count — what we need to have developed a complementarity is to have a description that is excludes the others.

We then end up with a new meta-layer of ontology: all the mutually exclusive descriptions of a phenomenon that we can devise. This is intriguing, because it suggests that the idea of the model has a much more foundational role in describing reality than we may have guessed; rather than just abstract things away, complementarity suggests that the world is made of mutually exclusive models.

One could also imagine a pragmatic version of a complementarity metaphysics: the time spent reducing sufficiently mutually exclusive descriptions to a single one is not well-invested; it is much better to seek ways of using these mutually exclusive descriptions and judge them on their usefulness.

Capability forecasting

A lot of work has gone into what is sometimes called “technology forecasting” – attempting to understand how semiconductors, artificial intelligence, quantum computing, proteomics etc will evolve over the coming years. Such research is valuable and interesting – and can be interestingly contrasted with something we could call “capability forecasting”.

Capability forecasting is focusing not on the technology as much on what we will be able to do. Now, you may argue that capability forecasting is dependent on technology – and you would be partly right – but the point of forecasting capabilities is to try to approach the problem of forecasting generally from a new angle.

The question that interests us is this: what will we be able to do routinely and well in the coming ten years?

As we start generating capabilities we can figure out what they would require, and doing so allows us to see how technology can be used with organizational innovations — allowing us to forecast technology in context rather than as an isolated roadmap.

Here are a few future capabilities that could be interesting to explore – and think about. When do we think we will be able to do the following ?

  • Cure the top 10 killer cancers at 80% efficiency
  • Mine asteroids cost effectively
  • Generate 80% of our energy without any environmental impact
  • Educate 80% of the world’s kids
  • Extend our life spans to an average 120 years in the top 10 life span economies today
  • Reduce sum total prison time overall by 50% in the US

For some of these the answer maybe “never”, and that is not just a viable answer – it is a very interesting answer! It suggests that that path in the capability tree is closed off to us, and then we can ask why that is — eliminating possible future capabilities is something that allows us to back out scenarios more credibly; whenever a scenario depends on a capability that is ruled out that scenario falls.

If we, for example, say that humanity will never be able to terraform planets to become more hospitable to life, well, then interstellar expansion of humanity will depend on finding already inhabitable planets or some other means (large ships, space stations etc etc). Or to take a less sensational example: if we believe we will never be able to reduce overall time spent in prison, how do we then approach prison design to ensure that it prison time can be used productively, say?

Lists of things we will never be able to do tend to age badly, though, so one way to explore capability forecasting is to survey people on exactly this – what do you think we will absolutely not be able to do in the coming years? And then forecast a world where we do at least 50% of those things…

Thinking in indices (Mental Models XVI)

The idea of an index in economy is simple: find a way to measure a change in an ensemble of values in a single value, and then track that single value over time.

The challenges are many: how do you pick the values in your basket, and do you weight them differently? Do you update them, and if so with what periodicity? And then – of course – how do you interpret the index? What does it mean? And how do you make sure that it is not interpreted in a way that overloads it with meaning?

Indices can have an emotional, almost visceral, effect on us. Days the stock index is up some of us feel more elated and happy – and when it is down we feel less successful and confident, and that is not just us amateurs. Even someone like Warren Buffett admits to buying different breakfasts depending on how bouyant the market makes him feel!

The challenge of building an index is that you need to find good data sets – or produce them – and then refine the index over time. This takes hard, honest econometric work, so when new indices are presented is almost always worthwhile to examine them more clearly.

One such new index recently presented is the North American Container index. The authors state that:

this index can be incorporated into a structural vector autoregressive model of the US economy that includes, in addition, a measure of real personal consumption and US manufacturing output. The model facilitates the identification of shocks to domestic US demand as well as foreign demand for US manufactured goods, while accounting for unexpected frictions in North American container trade associated with shipping delays, port congestion, labour strife, and foreign supply chain bottlenecks. The model shows that, on average, shocks related to frictions in the container shipping market have a nontrivial effect on the US economy. They account for 29% of the variation in US manufacturing output relative to trend and 38% of the variation in detrended real personal consumption.

And here is a chart:

Notes: Based on the number of twenty-foot equivalent (TEU) containers processed in the major North American ports. Seasonally adjusted and log-linearly detrended.

Certainly an index worth thinking about, and one peaking these days!

But more interestingly – what are some indices that you would like to be able to construct? Which data sets would you need? Imagining indices a fine summer day may sound like the nerdiest thing you could imagine, but it is a good way to think through problems.

Indices are surprisingly powerful mental models.

Geopolitical races in technology 2.0

Looking back, it is fairly easy to see that the race to the moon was a geopolitical competition, an attempt to use a technological task as a proxy for answering the question of which political system was the most robust, innovative and effective. But was we enter an age of new geopolitical races, it seems much less clear what this would look like.

The first question is what the new geopolitical race between the US and China will be a race to. There are plenty of candidates: AI, quantum computing, central bank digital currency…and there is no clear leader. Even if we knew that say AI was the field of competition, it seems unclear what would be the equivalent of landing on the moon.

Producing General Artificial Intelligence is unlikely to be something that is achieved in a single push from a single country or actor, even if we imagine that the effort was organized in a Manhattan-project like way. What would the test be? That a piece of software passes the Turing test?

The Turing test sounds like a conclusive test that could rival the landing on the moon, but it has far too many subjective components to be a really interesting test of technological capability – it is, in a sense, much more a test of shallow intelligence design; the design of an intelligence that can mimic human intelligence in limited experimential contexts. Some would argue that the Turing test already has been passed in numerous cases depending on your contextual requirements, and so using this to determine the outcome of a geopolitical race seems less helpful.

Even in a highly technical area like quantum computing we find no clear test of the much discussed “quantum supremacy” (horrible term). The current holder of this title seems to be China, after a recent experiment with a problem set that was solved successfully in an hour, whereas a traditional computer would have required 8 years.

But here, as well, we are demonstrating a narrow capability that is hardly generalizable into geopolitical power. The point, or one of the points, of the space race was that it was a proxy test for organizational, technical and innovative capabilities of a nation — AI, quantum computing and similar initiatives do not qualify as such. Better technology in any of these fields are incremental geopolitical advantages, not decisive superior strategic positions.

The country that dominated space could literally also use that advantage to control what happened on earth.

Interestingly we lived through a mini-geopolitical race during the pandemic – the race to a vaccine – and here we see something else: when it comes to medicine and to vaccines Russia and China were first, China in June, and Russia in August – but the test was determined not by a documented technological feat, as much as government approval of the new vaccine.

The mRNA-vaccines that the US managed to produced were instead hailed as the greatest medical innovation and accomplishment that we have seen in a long time (and arguably rightly so, although it did not happen in a year, the science had been around and the companies working on mRNA had looked at the vaccine problem for a lot longer) – and so the US seems to have, in the eyes of the world, won that race. It was not just being first that counted, but also the world admitting that you were the first.

The geopolitical nature of the race were clear – and if anyone missed it, Russia called their vaccine Sputnik – but the outcomes were not as obvious. Who won?

So, what does this imply for the future? It could evolve in a number of ways – clear tests for any of the existing technologies could emerge, or the field of competition might shift into areas where such tests exist – such as fighting cancer. The ability to cure the more common forms of cancer would be a feat close to the moon landing. Another area that could be interesting is energy technology – an order of magnitude more efficient solar power technology could be another field that would give a lasting geopolitical advantage to the country able to produce it.

But maybe the geopolitical competition we are now seeing will not converge on the mental model of a “race” where you need to get there first, but instead be organized more like frontier where the winner is the one who advances the entire field of science the most; it is the sum total state of technological capability that counts.

One thing that would support this hypothesis is that the technologies we are looking at here support each-other. Quantum computing could change AI and revolutionize medical science. Instead of a race, what we see looks more like the strengthening of a network of technical capacity – a general technological capability.

Winning that is much more complex than winning a race, and requires a much more thoughtful approach to R&D. The good thing is that it can be a distributed effort between different countries. The idea that R&D strategies and innovation programmes should be national may have served out its usefulness.

One possible scenario would be a NATO for technological innovation and science emerging to balance the Chinese efforts – sharing innovation across geopolitical lines rather than national ones.