By Rick Korzekwa, 22 April 2020
Throughout our investigation into discontinuous progress, we had some discussions about what precisely it’s that we’re making an attempt to do once we analyze discontinuities in technological progress. There was some disagreement and (not less than on my half) some confusion about what we had been making an attempt to study from all of this. I believe this comes from two separate, extra normal questions that may come up when forecasting future progress. These questions are carefully associated and each might be answered partly by analyzing discontinuities. First I’ll describe these questions generically, then I’ll clarify how they’ll change into confused within the context of analyzing discontinuous progress.
Query 1: How did tech progress occur up to now?
Realizing one thing about how historic progress occurred is essential to creating good forecasts now, each from the standpoint of understanding the underlying mechanisms and establishing base charges. Merely having the ability to describe what occurred and why might help us make sense of what’s occurring now and will allow us to make higher predictions. Such descriptions range in scope and element. For instance:
- The variety of transistors per chip doubled roughly each two years from 1970 to 2020
- Wartime funding throughout WWII led to the speedy growth and scaling of penicillin manufacturing, which contributed to a 90% lower in US syphilis mortality from 1945 to 1967
- Massive jumps in metrics for technological progress weren’t at all times accompanied by elementary scientific breakthroughs
This kind of evaluation could also be used for different work, along with forecasting charges of progress. Within the context of our work on discontinuities, answering this query principally consists of describing quantitatively how metrics for progress evolve over time.
Query 2: How would we now have fared making predictions up to now?
That is really a household of questions geared toward growing and calibrating prediction strategies primarily based on historic information. These are questions like:
- If, up to now, we’d predicted that the present pattern would maintain, how typically and by how a lot would we now have been incorrect?
- Are there domains through which we’d have fared higher than others?
- Are there heuristics we are able to use to make higher predictions?
- Which strategies for characterizing tendencies in progress would have carried out one of the best?
- How typically would we now have seen hints {that a} discontinuity or change in charge of progress was about to occur?
These questions typically, however not at all times, require the identical method
Within the context of our work on discontinuous progress, these questions converge on the identical strategies more often than not. For a lot of of our metrics, there was a transparent pattern main as much as a discontinuity, and describing what occurred is basically the identical as trying to (naively) predict what would have occurred if the discontinuity had not occurred. However there are occasions once they differ. Particularly, this could occur when we now have completely different data now than we’d have had on the time the discontinuity occurred, or when the naive method is clearly lacking one thing vital. Three instances of this that come to thoughts are:
The pattern main as much as the discontinuity was ambiguous, however later information made it much less ambiguous. For instance, advances in steamships improved instances for crossing the Atlantic, but it surely was not clear whether or not this progress was exponential or linear on the time that flight or telecommunications had been invented. But when we take a look at progress that occurred for transatlantic ship voyages after flight, we are able to see that the general pattern was linear. If we wish to reply the query “What occurred?”, we’d say that progress in steamships was linear, in order that it will have taken 500 years on the charge of development for steamships to convey crossing time all the way down to that of the primary transatlantic flight. If we wish to reply the query “How a lot would this discontinuity have affected our forecasts on the time?”, we’d say that it regarded exponential, in order that our forecasts would have been incorrect by a considerably shorter period of time.
We now have entry to data from earlier than the discontinuity that no one (or nobody particular person) had entry to on the time. Up to now, the world was a lot much less linked, and it’s not clear who knew about what on the time. For instance, constructing heights, altitude information, bridge spans, and navy capabilities all confirmed progress throughout completely different components of the world, and it appears possible that no one had entry to all the data that we now have now, in order that forecasting might have been a lot more durable or yielded completely different outcomes. Info that’s actively stored secret might have made this drawback worse. It appears believable that no one knew the state of each the Manhattan Challenge and the German nuclear weapons program on the time that the primary nuclear weapon was examined in 1945.
The within view overwhelms the surface view. For instance, the second transatlantic telegraph cable was a lot, significantly better than the primary. Utilizing our methodology, it was practically as giant an development over the primary cable as the primary cable was over mailing by ship. However we lose rather a lot by viewing these advances solely when it comes to their deviation from the earlier pattern. The primary cable had extraordinarily poor efficiency, whereas the second carried out about in addition to a typical excessive efficiency telegraph cable did on the time. If we had been making an attempt to foretell future progress on the time, we’d give attention to questions like “How lengthy do we expect it’s going to take to get a standard cable working?” or “How quickly will it’s till somebody is prepared to fund the subsequent cable laying expedition?”, not “If we draw a line by way of the factors on this graph, the place does that take us?” (Although that exterior view should be value consideration.) Nonetheless, if we’re simply making an attempt to describe how the metric developed over time, then the right factor to do is to simply draw a line by way of the factors as finest we are able to and calculate how far off-trend the brand new development is.
Causes for focusing extra on the descriptive method for now
Each of those questions are vital, and we are able to’t actually reply one whereas completely ignoring the opposite. However for now, we now have centered extra on describing what occurred (that’s, answering query 1).
There are a number of causes for this, however first I’ll describe some benefits to specializing in simulating historic predictions:
- It mitigates what could also be some deceptive outcomes from the descriptive method. See, for instance, the outline of the transatlantic telegraph above.
- We’re making an attempt to do forecasting (or allow others to do it), and actually good solutions to those questions is likely to be extra priceless.
However, for now, I believe the benefits to specializing in description are higher:
- The outcomes are extra readily reusable for different initiatives, both by us or by others. For instance, answering a query like “How a lot of an enchancment is a typical main development over earlier expertise?”
- It doesn’t require us to mannequin a hypothetical forecaster. It’s exhausting to foretell what we (or another person) would have predicted if requested about future progress in weapons expertise simply earlier than the invention of nuclear weapons. To me, this course of feels prefer it has a variety of shifting components or not less than a variety of subjectivity, which leaves room for error, and makes it more durable for different folks to judge our strategies.
- It’s simpler to construct from query 1 to query 2 than the opposite means round. An outline of what occurred is a reasonably cheap start line for determining which forecasting strategies would have labored.
- It’s simpler to check throughout applied sciences utilizing query 1. Query 2 requires taking a extra inside view, which makes comparisons more durable.