Decision frameworks: “How to approach solving problems is itself a problem to solve.”
/By Duncan Anderson. To see all blogs click here.
“For the person with a hammer every problem looks like a nail.’
One sentence summary: each problem is likely different, therefore the approach to solving each problem should be different.
Jingle: How to approach solving problems is itself a problem to solve.
Here are some ‘small’ frameworks for y’all! One day I’ll get around to making blogs about medium and large frameworks :)
Problems I see:
Problem 1: no problem solving done at all
Problem 2: treat all decisions as irreversible
Problem 3: do not balance solution confidence vs decision sufficiency threshold to make a decision
Problem 4: do not take into account how much change a decision is and / or how controversial it is
Problem 5: make decision only in ‘theoretical land’. IMO most decisions about whether to go ahead with something should have data gathered from ‘practical land’.
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Delicious Details:
Problem 1: no problem solving done at all
Context:
Often one’s inclination is to go with the first thing that comes to mind. This is not problem solving.
I often try to class problems into three sizes: small, medium or large. For a medium or large problem I think you need to put forward multiple options for what to do.
A small decision size is often for something that is very similar to what you have done before, so you have high confidence in your proposed solution will work.
Solution: for medium or large problems put forward multiple options for your solution.
Problem 2: treat all decisions as irreversible
Context
“One common pitfall for large organizations – one that hurts speed and inventiveness – is “one-size-fits-all” decision making. Some decisions are consequential and irreversible or nearly irreversible – one-way doors – and these decisions must be made methodically, carefully, slowly, with great deliberation and consultation. If you walk through and don’t like what you see on the other side, you can’t get back to where you were before. We can call these Type 1 decisions. But most decisions aren’t like that – they are changeable, reversible – they’re two-way doors. If you’ve made a suboptimal Type 2 decision, you don’t have to live with the consequences for that long. You can reopen the door and go back through. Type 2 decisions can and should be made quickly by high judgment individuals or small groups. As organizations get larger, there seems to be a tendency to use the heavy-weight Type 1 decision-making process on most decisions, including many Type 2 decisions. The end result of this is slowness, unthoughtful risk aversion, failure to experiment sufficiently, and consequently diminished invention. We’ll have to figure out how to fight that tendency. And one-size-fits-all thinking will turn out to be only one of the pitfalls. We’ll work hard to avoid it… and any other large organization maladies we can identify.”
This I thought was cool, thanks Mr Bezos.
Solution: before you make a decision, figure out if it’s reversible and as such adjust the level of confidence you need to move ahead with the decision.
Problem 3: do not balance solution confidence vs decision sufficiency threshold to make a decision
Context:
Decision making confidence needed = 1. Reversibility of decision * 2. Confidence of solution being correct.
Solution: A nice 2 x 2 for y’all!
Problem 4: do not take into account how much change a decision is and / or how controversial it is
Context:
Decision making confidence needed = 1. Amount of change proposed new proposal is * 2. How controversial new proposal is
Solution: (aside: this is sometimes referred to as the ‘Overton Window’)
Problem 5: make decision only in ‘theoretical land’. IMO most decisions about whether to go ahead with something should have data gathered from ‘practical land’.
“In theory 100% of my proposed solutions make sense, in practice 50% actually work out.”
AKA “In theory I’m a genius, in practice I’m profoundly fallible :).” DA
Sub Problem 1
Problem: people can spend an excessive amount of time in theoretical problem solving land when the only way to know if something is going to work is to actually try the solution.
Solution: I’ve found that it’s possible to try basically 100% of things in a ‘soft’ way. And that you can only really know if something is a good solution when there is actual hard data it is working.
Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days is a wonderful book that shows you how to prototype and get meaningful feedback on many things like ‘hardware’ in 5 days.
Sub Problem 2:
Problem: people often don’t kill solutions after they go live (ie in practical land, ie after leaving theoretical land) because of ‘the sunk cost fallacy’.
Solution: you only decide on if you are actually going ahead with a solution after a requisite amount of real world ‘practical’ data has been collected for the solution. You don’t commit to an ongoing solution in the theoretical problem solving phase.
Examples:
Hiring people
What I think is the way to approach things:
Have ~2x interviews - theoretical data collection
Have 1x audition - practical data collection
This is typically half a day of actual work that the person would be doing
Have a review after 3 and 6 months where the actual decision about if you keep someone is made - practical data collection
Netflix decision variable = if you wouldn’t fight to keep someone then you should push them out
Edrolo decision variable = knowing all that you know now would you rehire the person.
So the actual decision point about keeping someone is at 6 months of practical data collection. It is not assumed they are going to be around after the interview stage (ie zero practical data collection has been done). This is one reason why businesses have probation periods .
A way to do things suboptimally.
You only hire based on interviews. Ie theoretical information.
Sometimes you might have an excessive amount of interviews like 6x interviews.
The decision point about keeping someone is done at interview stage. You only get rid of people after this if they are in bottom quartile of performance. Ie the decision point is after zero theoretical information.
While this may sound ‘harsh’ I actually think it is the most ‘humane’ way to go about things.
Continuum: “Ruinous empathy” ⇔ “fair” ⇔ “harsh”
IMO you want to try and be as ‘fair’ as possible, ‘ruinous empathy’ is no good for anyone.
I doubt there is a job that is a good fit for every single person in the world. Having a person in a job that isn’t good for them is: 1. Bad for that person, 2. Bad for the team, 3. Bad for the company and 4. Bad for management.
I’ve found that having the standard of ‘knowing all that you know now would you rehire the person’ is a much better way to get a ‘fair’ outcome.
Making product
At Edrolo one product we are now making is ‘textbooks’.
We are trying to move the game forward and as such do things that have not been done before with our textbooks. If you do something that has not been done before there is a risk that it will not work.
What we do is make a small sample of say 5x lessons of our new ‘recipe’ and then trial this with 5-10 schools who we believe are representative of the broader school body.
How to do this wrong:
No matter how many people you speak to in theoretical land (eg teachers and students) we always always find there are things you only find out when people are actually using tangible product.
So you wouldn’t make an entire textbook before you get real feedback on a sample of the product.
Internal processes
What not to do
You have decided to run monthly ‘all hands meeting’ for your company and come up with a format for what they should be.
Before you have run one all hands you have decided on the format should be and stick with this going forward.
What to do
You decide to run monthly ‘all hands meetings’ for your company. You come up with a proposed format for the 1st all hands.
However you are only going to commit to a format after gathering feedback after the first three all hands. Ie there is a non-trivial amount of ‘real world practical data’ taken into account for your decision.
Comment
This is basically lean product methodology.
For some reason I find that people can be ‘lean’ when looking at product, but they aren’t ‘lean’ when they think about other areas like hiring, internal processes etc.
How much can you learn in theory vs in practice? For many decisions you can only know if it’s right after practical data. So, don’t overcook time on theoretical problem solving and don’t make a decision before you have sufficient practical data :).