Are good ideas hard to find? Evidence shows that yes, getting harder.

Pointer from: Friends
Place digested: Running in Dogpatch
Time digested: Oct 21, 2018




exponential growth in the economy: growth is getting harder and harder to achieve
nowadays, have to throw more and more scientists + R&D budget at any industry-relevant real world problem

hard to see, because: research productivity is falling dramatically, while # scientists rising. i.e. increasing # scientists who are offsetting exponential decrease in productivity

compared to 1970s need 20x more effort

don’t have a preferred explanation; this paper just documenting the fact

potential explanations though!
distance to the pit-face (mining metaphor) to shovel the newest bit of gold is a lot further away, so the amount more knowledge need to get to the frontier much greater
  • hold old to get patent
  • how long to get PhD
so for individuals, spend more time studying, or spend time on narrower and narrower fields
which means might have less good insights
^specialization!

general purpose technology waves of change
last 40-50 years benefitting from computer, before that electricity
within a given wave
first strike oil… spurts out, super easy… but over time gets harder and harder
need to find next big oil field (AI, biology, etc)

innovation exhaustion
fishing out story
fewer of them, have taken the low hanging fruit (consistent with what find but not implied by it)

plausible that a lot more R&D on averting bad outcomes
loads more spending on expensive valves for oil & gas industry, that doesn’t show up in GDP. you just see the disaster
fewer low probability events
if you could run history a bunch of times, and took average, then maybe would see the improvement
don’t see the bad thing happening but do see the money going into it

comparing huge innovation that startups do vs. big company… follow on innovations
because now larger # firms and fewer # startups
expect more incremental stuff vs. more foundational new innovations

Ideas alluded to...

Mokyr on propositional vs. prescriptive knowledge
Mokyr differentiates between two types of useful knowledge: what he calls "propositional knowledge," which focuses on how nature works; and "prescriptive knowledge," which focuses on how to use techniques. The former is not embodied just in science but in all kinds of knowing about how the world works. An example would be the development of the laws of thermodynamics. The latter is embodied in technical manuals and other "cookbooks," but also in the technologies themselves. An example would be the technical knowledge needed to build a working steam engine.

Romer’s definition of ideas
To understand this diagram, first consider what we mean by "ideas". Romer (1993) divides goods into two categories: ideas and objects. Ideas can be thought of as instructions or recipes, things that can be codified in a bitstring as a sequence of ones and zeros. Objects are all the rivalrous goods we are familiar with: capital, labor, output, computers, automobiles, and most fundamentally the elemental atoms that make up these goods. At some level, ideas are instructions for arranging the atoms and for using the arrangements to produce utility. For thousands of years, silicon dioxide provided utility mainly as sand on the beach, but now it delivers utility through the myriad of goods that depend on computer chips. Viewed this way, economic growth can be sustained even in the presence of a finite collection of raw materials as we discover better ways to arrange atoms and better ways to use the arrangements