How do I know when some trend isn’t made of S-curves? How do S-curves help me make predictions, or, alternately, tell me when I shouldn’t try predicting? Is this falsifiable?
Innovation research is notoriously hard to falsify and subject to just-so stories and post-hoc justifications.
One of the things I find compelling about S-curves is just how frequently they show up in innovation research coming from different angles and using different methodologies.
Some examples:
Everett rogers is a communication professor trying to figure out how ideas spread. So he finds measurements for ownership of different technologies like television and radio throughout society. Finds S-curves.
Clayton Christensen is interested in how new firms overtake established firms in the market. Decides to study the transistor market because there’s easy measurements and it moves quickly. Finds S-curves.
Carlotta Perez is interested in broad shifts in society and how new innovations effect the social context. She maps out these large shifts using historical records. Finds S-curves.
Genrich Altshuller is interested in how engineers create novel inventions. So he pores through thousands of patents, looks for the ones that show real inventiveness, and tries to find patterns. Finds S-curves.
Simon Wardley is interested in the stages that software goes through as it becomes commodotized. Takes recent tech innovations that were commodotized and categorizes the news stories about them, then plots their frequency. Finds S-curves
> How do S-curves help me make predictions, or, alternately, tell me when I shouldn’t try predicting?
> How do I know when some trend isn’t made of S-curves?
I think understanding how to work with fake frameworks is a key skill here. Something like S-curves isn’t used in a proof to get to the right answer. Rather, you can use it as evidence pointing you towards certain conclusions. You know that they tend to apply in an environment with self-reinforcing positive feedback loops and constraints on those feedback loops. You know they tend to apply for diffusion and innovation. When things have more of these features, you can expect them to be more useful. When things have less of these features, you can expect them to be less useful. By holding up a situation to lots of your fake frameworks, and seeing how much each applies, you can “run the Bayesian Gauntlet” and decide how much probability mass to put on different predictions.
I think you’ll always be working in S-curves if you’re in a finite system. The trick is to be able to detect the rate-limiting factor. That’s the factor that marks the inflection point between exponential growth and the beginning of the slowdown. For classic examples like bacterial growth that might be nutrients, space, elimination of waste, etc.
The hard part is determining whether you’ve considered all the rate-limiting factors involved. Going back to bacterial growth, if you think food is the rate-limiting factor and you predict your culture will continue to grow for six hours, you might be surprised when you hit an inflection point after three hours because waste products start killing bacteria off. This same principle can be applied in technology and elsewhere, where people often aren’t even looking for rate-limiting factors and appear to assume exponential growth in a finite system.
Agree, recognizing constraints before they limit you is key if you’re looking to create growth (or alternatively, adding multiple constraints if you’re looking to slow growth).
If you want to slow growth, pick any limiting factor and apply pressure. One will do.
Sometimes a trend continues growing exponentially for a long time before bumping up against a limiting factor. The thing to remember about an S-curve is that if you plot it on a log scale the first half of the curve looks like a straight line all the way backward. That’s because it’s exponential growth at the beginning, so every new observation dwarfs all those that came before. Sometimes we spend a lot of time in exponential growth phase and people write articles about how it’ll go on forever, and The Singularity, and whatnot. When you don’t know the limiting factor, it’s very tempting to fit your model to exponential growth, only to get burned later on.
Agree, this misconception (and seeing it everywhere) is one of the things that made me wrote the article (particularly the part about “exponentional growth vs. s-curves).
The other side of it is when people think that trends are made of a single s-curve, and think that when growth is slowing down, that means that the trend is done forever, rather than simply the start of another s-curve when the constraint is defeated.
Yes, I think it’s an excellent article, especially the observation about constraints. If we can correctly identify which elements are constraining a system we have a path to return to exponential growth.
Still, we’ll see articles lamenting that “despite how we’ve overcome Constraint X, growth hasn’t returned.” The world is multi-causal/multi-factoral, though. More than one factor can constrain growth. It is often an engineering problem, and focusing on the system as driven by rationally understandable forces is important. Otherwise the default seems to be to view trends as ‘magical growth’ and make illogical predictions based on that thinking.
In the case of growth in the computer hardware industry, where you have a veritable army of engineers focused on the problem, is it any wonder we continuously overcome constraints?
How do I know when some trend isn’t made of S-curves? How do S-curves help me make predictions, or, alternately, tell me when I shouldn’t try predicting? Is this falsifiable?
> Is this falsifiable?
Innovation research is notoriously hard to falsify and subject to just-so stories and post-hoc justifications.
One of the things I find compelling about S-curves is just how frequently they show up in innovation research coming from different angles and using different methodologies.
Some examples:
Everett rogers is a communication professor trying to figure out how ideas spread. So he finds measurements for ownership of different technologies like television and radio throughout society. Finds S-curves.
Clayton Christensen is interested in how new firms overtake established firms in the market. Decides to study the transistor market because there’s easy measurements and it moves quickly. Finds S-curves.
Carlotta Perez is interested in broad shifts in society and how new innovations effect the social context. She maps out these large shifts using historical records. Finds S-curves.
Genrich Altshuller is interested in how engineers create novel inventions. So he pores through thousands of patents, looks for the ones that show real inventiveness, and tries to find patterns. Finds S-curves.
Simon Wardley is interested in the stages that software goes through as it becomes commodotized. Takes recent tech innovations that were commodotized and categorizes the news stories about them, then plots their frequency. Finds S-curves
> How do S-curves help me make predictions, or, alternately, tell me when I shouldn’t try predicting?
By understanding the separate patterns, they can give you an idea of the most likely future of different technologies. For instance, here’s a question on LW that I was able to better understand and predict because of my understanding of S-curves and how innovations stack.
> How do I know when some trend isn’t made of S-curves?
I think understanding how to work with fake frameworks is a key skill here. Something like S-curves isn’t used in a proof to get to the right answer. Rather, you can use it as evidence pointing you towards certain conclusions. You know that they tend to apply in an environment with self-reinforcing positive feedback loops and constraints on those feedback loops. You know they tend to apply for diffusion and innovation. When things have more of these features, you can expect them to be more useful. When things have less of these features, you can expect them to be less useful. By holding up a situation to lots of your fake frameworks, and seeing how much each applies, you can “run the Bayesian Gauntlet” and decide how much probability mass to put on different predictions.
I think you’ll always be working in S-curves if you’re in a finite system. The trick is to be able to detect the rate-limiting factor. That’s the factor that marks the inflection point between exponential growth and the beginning of the slowdown. For classic examples like bacterial growth that might be nutrients, space, elimination of waste, etc.
The hard part is determining whether you’ve considered all the rate-limiting factors involved. Going back to bacterial growth, if you think food is the rate-limiting factor and you predict your culture will continue to grow for six hours, you might be surprised when you hit an inflection point after three hours because waste products start killing bacteria off. This same principle can be applied in technology and elsewhere, where people often aren’t even looking for rate-limiting factors and appear to assume exponential growth in a finite system.
Agree, recognizing constraints before they limit you is key if you’re looking to create growth (or alternatively, adding multiple constraints if you’re looking to slow growth).
If you want to slow growth, pick any limiting factor and apply pressure. One will do.
Sometimes a trend continues growing exponentially for a long time before bumping up against a limiting factor. The thing to remember about an S-curve is that if you plot it on a log scale the first half of the curve looks like a straight line all the way backward. That’s because it’s exponential growth at the beginning, so every new observation dwarfs all those that came before. Sometimes we spend a lot of time in exponential growth phase and people write articles about how it’ll go on forever, and The Singularity, and whatnot. When you don’t know the limiting factor, it’s very tempting to fit your model to exponential growth, only to get burned later on.
Agree, this misconception (and seeing it everywhere) is one of the things that made me wrote the article (particularly the part about “exponentional growth vs. s-curves).
The other side of it is when people think that trends are made of a single s-curve, and think that when growth is slowing down, that means that the trend is done forever, rather than simply the start of another s-curve when the constraint is defeated.
Yes, I think it’s an excellent article, especially the observation about constraints. If we can correctly identify which elements are constraining a system we have a path to return to exponential growth.
Still, we’ll see articles lamenting that “despite how we’ve overcome Constraint X, growth hasn’t returned.” The world is multi-causal/multi-factoral, though. More than one factor can constrain growth. It is often an engineering problem, and focusing on the system as driven by rationally understandable forces is important. Otherwise the default seems to be to view trends as ‘magical growth’ and make illogical predictions based on that thinking.
In the case of growth in the computer hardware industry, where you have a veritable army of engineers focused on the problem, is it any wonder we continuously overcome constraints?