3/ What is CFT? CFT is a theory that asks: "how do experts deal with novelty?"
Some domains are well-structured, like chess. But other domains, like business or medicine, are ill-structured.
CFT is a theory about this second type of domain. It comes from medical education.
4/ CFT has four big ideas. Two of them are the central claims of the theory.
Before we get to those, we need to do some setup.
There are two big ideas that we have to look at first. Then we'll talk about the claims, and then note-taking. 😉
5/ Idea one: CFT is interested in ill-structured domains.
What is an ill-structured domain?
An ill-structured domain is a domain where there are concepts, but the way these concepts show up in reality are HUGELY variable.
6/ Think about a heart attack. A heart attack is a concept. You can study it in a medical textbook. But the WAY a heart attack instantiates in the real world is hugely variable.
Some heart attacks start out as indigestion; others can last DAYS.
7/ Or think about business. Scale economies is a type of competitive advantage, yes?
But the way scale economies show up is HUGELY dependent on the context of the business!
Some businesses enjoy 'simple' unit cost reductions due to scale, others have learning curve effects.
8/ The proper definition for ill-structured is "concept instantiation is highly variable for cases of the same nominal type."
Most fields are a mix of both well-structured and ill-structured. In software, computer programming is the former; software project mgmt the latter.
9/ This leads us to Idea Two: in ill-structured domains, cases are AS if not MORE important than concepts.
This is a tricky idea, so let's slow down a bit.
I think most of us are taught to think that concepts are important, and cases are 'just' examples.
10/ Usually we say something like "it's the PRINCIPLES that are important!"
How did we get here? I think we got here because we are taught to think like this.
In math class, for instance, we are given 1-2 examples but we know it is the formula that is important.
11/ Yes, I know that certain schools teach from cases, not concepts alone.
But if you are trained to think that 'first principles' are important, you will think that the cases are so that you can extract generalised, abstract principles, and that THOSE are primary.
12/ So what does CFT tell us?
CFT tells us that in ill-structured domains, concepts are hugely variable so reasoning from concepts are insanely hard.
In fact, extracting generalisable principles from case studies is close to impossible!
13/ It turns out that experts in ill-structured domains DON'T reason from first principles as much. They tend to reason from past cases instead!
(Sure, they may TALK about concepts and principles, but the concepts are clusters of cases in their heads.)
Read:
14/ As a programmer, I find this difficult to accept. Isn't reasoning by analogy lousier than reasoning from first principles?
But it DOES resolve a question I've always had.
Which is this: why is it that Charlie Munger reasons so much from analogy?
15/ This brings us to Ideas 3 and 4 of CFT, which happen to be the two central claims of the theory.
Recall: the question that CFT attempts to answer is "how are experts able to perform under conditions of novelty?"
We know now that ill-structured domains have a lot of novelty.
16/ CFT tells us that experts do two things:
1. They construct temporary schemas by combining FRAGMENTS of prior cases.
2. They have something called an 'adaptive worldview', which means they do NOT think there is one root cause or framework or model for any event.
17/ So this explains why Munger, like expert doctors, reason a lot by analogy to prior cases.
After all, if businesses are always the result of context-dependent events and factors, then you CAN'T reduce case history into simple principles.
It's just too complex.
18/ Instead, the researchers say that experts do the following (read):
19/ What does this have to do with note-taking?
Well, now that we have the four big ideas, we can invert CFT's claims to get the pedagogical recommendations:
1. Expand the cases you know, so you have a larger set of fragments to draw from. 2. Inculcate the adaptive worldview.
20/ And how do the researchers recommend doing this?
The researchers note that you cannot reduce cases, and real world cases tend to be rich with many concepts. So ... the researchers recommend using a hypermedia system to store cases.
That is: a backlinked note taking system!
21/ Here's what you do: you get the student to store cases, and highlight concepts within the text of each case. Concepts are backlinked. They go to other cases.
There are many variations. Some systems come preloaded with cases, marked up by expert practitioners.
22/ The initial presentation to the student is also chosen carefully. When presenting a concept for the first time, you want to give a student a case, and then give them a different case that is VERY different from the first.
So the student internalises that cases are variable!
23/ Eventually, as the student does concept searches in the CFT system, they begin to overlearn the 'crossroad' cases — that is, the central cases that are the most conceptually rich and therefore the most connected.
These cases begin to be evoked from even small fragments.
24/ Once this happens, the student enters something called 'epitome mode', where case comparisons happen at the speed of thought.
In other words, they have a set of representative cases in their heads, available for on-the-fly schema assembly in the real world.
25/ Ok, I'll wrap up. A couple of days ago, I said that it's probably worth it to dig for cognitive science results in the tools for thought space.
26/ If you enjoyed this thread, you should read the full essay, which goes into way more detail on how to create a CFT system for yourself: commoncog.com/blog/how-note-…
27/ I also write a newsletter: commoncog.com/blog/subscribe… where you can subscribe for updates on essays like this. I mostly write about better business and career decision making.
28/ Finally, you can follow me on Twitter, where I write about expertise acceleration from time to time. Here's a thread on that:
I want to call out an example of some remarkable thinking that I've had the privilege of observing up close.
About 2 years ago, @vaughn_tan started a project to come up with better thinking around 'uncertainty'. This MIGHT be important to business! MIGHT! But I was unconvinced.
Vaughn had noticed that our collective ability to deal with uncertainty was compromised by bad language. Because we do not have good language for uncertainty, we are forced to borrow words and concepts from risk management.
But this is bad: risk is VERY diff from uncertainty!
I was in good company in my scepticism, though. Vaughn's friend, the notable VC Jerry Neumann, told him that he was sceptical Vaughn's project would be very useful.
Neumann argued that it wasn't important to know what types of uncertainty exist — merely how to use it.
I once had an intern do an internship with me because she wanted to see how I approached 'startup things'. At the end of the summer, she was surprised that I didn't have a set of hypotheses to test.
"Doesn't this go against the data-driven approach you talked about?" she asked.
I didn't have the language for it then, but I think I do now.
When an initiative / product / project is too new, there is too much uncertainty to form useful hypotheses.
Instead, what you want to do is to just "throw shit at the wall and see what sticks."
This sounds woefully inefficient, but it's not, not really. A slightly more palatable frame for this is "take action to generate information."
But what kind of information?
Actually I was looking for answers to the following four questions:
A gentle reminder that if you want to speed up expertise intuition, you will do a lot better if you have an actual mental model of what expert intuition *is*.
The most useful model is the one below:
It gives you more handles on how to improve.
The name of the model is the 'recognition primed decision making' model, or RPD.
The basic idea is simple: when an expert looks at a situation, they generate four things automatically:
1. Cues 2. Expectancies 3. Possible goals 4. An action script.
You can target each.
For instance, if you're a software engineer and you want to get better from the tacit knowledge of the senior programmers around you, ask:
- What cues did you notice?
- What were your expectancies?
- What was your action script?
1. DP is a sleight of hand research paradigm, and only claims to be the best way to get to expertise in fields with a good history of pedagogical development. (See: The Cambridge Handbook, where they point out that pop stars and jazz musicians become world class but not through DP)
2. Most of us are not in such domains.
3. Therefore we cannot use DP, and tacit knowledge elicitation methods are more appropriate.
The counter argument @justinskycak needs to make is simple: math is a domain with a long history of pedagogical development, therefore DP dominates.
Justin says that “talent is overrated” is not part of the DP argument.
I’m not sure what he’s read from Ericsson that makes him think that.
Hambrick et al document the MANY instances where Ericsson makes the claim “DP is the gold standard and therefore anyone can use DP to get good, practice dominates talent.”
Ericsson spends the entire introduction of Peak arguing this. When Ericsson passed, David Epstein wrote a beautiful eulogy but referenced his being a lifelong proponent of the ‘talent is overrated’ camp, which frustrated him and other expertise researchers to no end.
Now you may say that DP has nothing to say on talent, but then you have to grapple with the man making the argument in DECADES of publications — both academic and popular! If the man who INVENTED the theory sees the theory as a WAY TO ADVANCE his views on talent, then … I don’t know, maybe one should take the man at his word?
“Oh, but his views have NOTHING to do with the actual theory of DP” My man, if you’re talking to anyone who has ACTUALLY read DP work, you need to address this, because they’re going to stumble into it. Like, I don’t know, in the INTRODUCTION CHAPTER OF THE POPSCI BOOK ON DP.
Anyway, strike two for reading comprehension problems. But it gets worse …