I realized Iām being cheap with books and outsourcing decisions about what I read to Amazon.
I was on a morning walk with my dogs, listening to Charlie Munger on Invest Like the Best, and I remembered the book, āPoor Charlieās Almanackā. Iād had that book on my list for a while. Several people had recommended it to me.
Why hadnāt I read it yet?
Because it had never been on sale on Amazonās Kindle store. š£
I have multiple wishlists on Amazon, collectively about 200 books. Every few weeks Iāll go through the lists, sort them in ascending order by price, and snag any books that are around $3 or less.
I developed that process because I stopped getting books from the library (I wanted to be able to reference them later), and at 100 books per year, I felt like paying $15-22 for each book was too expensive.
I thought I could save money by not being urgent about my reading. Iāll leave it on my wishlist until itās on sale. It brings a $1,700/year hobby down to $300/year.
But thereās an assumption in there thatās not true; that ALL the books eventually go on sale.
They donāt.
I didnāt think about this when setting up my process, but after not buying āPoor Charlieās Almanackā for a year, it finally clicked.
I should not be outsourcing my reading choices to Amazonās sale algorithm. I should chose based on interest and whether itās foundational to my research and knowledge.
In practice, changing my policy is unlikely to cost $1,700/year - more like $1,000 - and Iāll be selecting books that are more relevant to my interests. That seems like a win.
I picked up āPoor Charlieās Almanackā and itās good!
Have you ever done something āsmartā that you later realized had a hidden downside? Tell me at heykev@kevinnoble.xyz.
Have a great week! Kevin
A Quote
ā
...courage, when summoned, is contagious. Dissent can actually increase the likelihood that others will also show courage when faced with consensus in another situation. It is another form of liberation. Dissent can increase the likelihood that we will speak up.
ā Charlan Nemeth in "In Defense of Troublemakers"
Three Things
1 - š§³ Nomad List - This (paid) service provides access to city rankings and other details for ādigital nomads,ā which includes remote workers who arenāt anchored into one city. Maybe when the kids are gone Iāll take advantage of this! Check it out if youāre interested in working around the globe.
2 - šŗ Little Big, Skibidi - I got curious about the oldest reference to skibidi (a kids slang term), and discovered this five year old music video from Little Big, with 751 million views! This is my gift to you, if youāre not already included in those 751M views. If itās your jam, donāt miss the romantic edition. Twist ending?
3 - š Le B Burger, New York - This burger is now on my to-do list the next time Iām in New York - if I can get a seat! Le B (also known as Beatrice Inn) makes nine burgers each night. Chef Angie Mar created this. They make their own ground beef, most of which is 45-day dry aged ribyeye. Itās served with some sort of bloomy-rind cheese and caramelized onions. Video about it here.
Deeper Dive on Decision Trees
Nearly 20 years ago, going into the winter of 2006, my wife and I had a decision to make.
We both worked at Ford Motor Company, and they were going through a tough time. To reduce payroll, they were offering a Voluntary Separation Package to salaried employees.
Volunteer to leave, and youāll get a small severance; I think it was something like one month for each year of employment, so three months pay for us.
If Ford did not get enough volunteers, then theyād have to institute an involuntary layoff. With low tenure, I wasnāt confident weād survive.
Should we put our names in the hat?
If we didnāt put our names in, weād keep our jobs - but there was a risk weād be laid off later with no severance. Not great.
If we did put our names in, weād get a small severance, but weād be without jobs. Not great.
My wife and I would talk about it at length, but it was too complex to keep in my head. Where would we work? Should we move? Will Ford pay our tuition (we had one more semester in our Masters programs)? How long will it take to get a job? What happens if we canāt find a job?
It wasnāt clear what option we should choose.
I needed a way to organize it and get a little mathematical so I could make an effective decision. Excel and decision trees to the rescue!
š¶ Should I stay or should I go now? š¶
ā
What is a decision tree?
Decision trees are a useful and visual way to organize complex decisions. They encourage you to think probabilistically (where things have a chance of occurring) instead of in black and white (where things definitely will, or definitely wonāt, happen).
Let me show you what this looked like for my Ford decision - as a visual created for todayās newsletter. There was no Miro back in 2006; all of these details were done in Excel back then.
To quit or not to quit; that is the question.
The core decision youāre trying to make is called the Root Node. In this case Iām deciding whether to quit working at Ford.
Out of the Root Node grow Branches, which are the possible choices or actions. My Branches arenāt labeled in the visual, but theyāre related to the action of submitting our names into the Voluntary Separation or not.
Next are Decision Nodes, which are decisions that occur subsequent to the Root. In my case, the ādecisionsā are mostly on someone else. Ford was going to choose whether to accept our names or not. Ford was going to choose whether to pay our last semester tuition or not. And future companies were going to choose to hire us or not. Thatās not always the case; many decisions will be ones you can make.
Finally you have Outcome Nodes, which is the final result of the decisions made along that path. In my Ford example, it was sort of a net income measure. There was severance or income coming in, then tuition payment and living expenses going out.
ā
Thinking Probabilistically
One thing not shown in my image, and a key part of true decision trees, is adding probabilities to these various paths and actions. For example, what are the odds that any one of the first four Decision Nodes about the Voluntary Separation Package would happen? I mightāve split it up as follows:
None Accepted - 5% One Accepted - 40% Two Accepted - 40% Laid Off - 15%
I would do the same for every other branch. What are the odds that Ford would pay tuition? Call it 50-50. What are the odds that weād get a job in April versus July? Again, I might call it 50-50.
From there you can calculate Expected Values for every Decision Node.
If you look at the bottom path of getting Laid Off / Not Paid, thereās a 50-50 chance of either outcome.
To calculate the Expected Value, you multiply the outcomes by the probabilities. That means (0.5 x -11) + (0.5 x -22) = -16.5. I should expect to lose $16.5K down that path.
Itās pretty straight forward with a simple set of decisions and 50-50 probabilities, so you donāt need to (and I didnāt!) do all the calculations. Itās really helpful when things get complicated and youāve got asymmetric probabilities.
Expected Value calculations give you insight into the upsides and downsides of your collection of decisions.
ā
The Importance of Pruning
Since this is real life, we only want to do what is useful. That means itās not only okay, but desirable, that you would not list out every possible permutation in your decision tree.
For example, in my Ford example, the top and bottom paths only have one Decision Node option for Tuition Status. I assumed that if Ford kept us employed theyād pay tuition, and if Ford laid us off, they would not pay tuition.
Itās unnecessary and unhelpful to distract the decision with options that are low probability or would never be chosen by you. Prune them from your map.
Cut branches that don't serve you.
ā
How are decision trees useful?
Decision Trees help in quite a few ways. - Getting things on paper reduces perceived complexity. - Representing things visually makes it easier to interact with. - Forces you to think, and assign, probabilities to scenarios. - Probabilities enable you to think about how to influence those probabilities. - Gets some cold, hard, numbers to balance emotions. - Creates a map for you to create new, previously unseen, options. - Shows you the full spectrum of possible outcomes, exposing downside risk.
You are not outsourcing your decision to a decision tree, but you are getting help in arraying probabilities and outcomes across a complex set of interactions.
By arranging the probabilities and Expected Values youāre getting a really good mathematical model of how things will play out. This mathematical model is a tool that can supplement your judgement and other ways of thinking.
As for how the decision tree helped in my Ford example:
I knew it was better to have Ford pay tuition than not. I didnāt need a decision tree for that! What I did need was a decision tree to help me see the impact of that decision, especially the non-ideal option, alongside all the other decisions.
Same for the other options. Itās obviously better to not be laid off. Itās obviously better (for my bank balance) to find a job in April versus July. Whatās not obvious is the mathematical outcomes of these scenarios.
For one thing, a 15% chance of being laid off is not a low chance! It also carries an expected loss of $16.5K (from our calculation earlier), and thatās something Iād like to avoid.
I could also see there were two paths that had two positive outcomes; Ford accepting one or both of our voluntary separations, with tuition being paid. Those are paths I could be relatively happy going down.
The only path outside layoffs with a double negative outcome is the one where only one of us were accepted, and tuition was unpaid.
That decision tree model was paired with my own subjective desires to give me a fuller picture.
My wife and I had already considered leaving. I had interests that I couldnāt pursue at Ford. The possibility of getting a small severance to leave would be a nice help along that path.
If I was only going based on expected value on the decision tree, then I would have chosen not to submit for severance as that had the highest number.
We chose to submit our names for the voluntary separation.
Saying goodbye to Ford also meant saying goodbye to being a volunteer on the Korn-sponsored race team š
ā
How can decision trees be misused?
Decision trees are a tool, and like any tool, it can be misused. Be conscious of these and avoid them!
Too simple - Trying to over-simplify the complexity of the real situation. You can achieve simplicity, but you lose its ability to be helpful in making a quality decision.
Too complex - Trying to cover all permutations and complexities, you create too many branches and paths. The result is something unwieldy that canāt be used to make a quality decision.
Poor, or no, probabilities - Estimating probabilities is tough, but itās an exercise and a muscle you need to develop. Doing this poorly creates poor outcomes.
Assuming static conditions - The world is constantly moving. If your probabilities, nodes, and branches are not shifting alongside changes in reality, the decision tree wonāt help.
ā
Other Examples
The example Iāve walked you through today about quitting is just one way you can employ a decision tree, but there are many other scenarios, in work and home life, where they can be helpful!
š Buying a Business - Should you buy a business or not? Thatās a good root node. You could map out: - Possible changes in interest rates. - Changes in leadership. - Changes in the competitive landscape. - Changes in consumer behavior. - Getting a loan, and at different terms.
šļø Investing in Real Estate - The root node could be whether to purchase a particular property. You could consider: - Ranges of post-purchase build out (e.g. low, medium, high amount of remodel) - Options for renting it out (how long does it take, whatās your occupancy rate) - What would happen if you just put that money in an index fund for two years instead?
Decision trees can help you decide whether to buy this house.
š± Product Launch - Should you launch a new product? Consider: - Various rates of consumer adoption. - Various rates of churn. - Various marketing channels and strategies. - Cost of delay (e.g. staff salaries, potential bad timing of market).
Thatās just the tip of the iceberg!
Some decisions, like where to go to lunch today, may not need a decision tree (although you could still create one š¤). But for complex decisions with lots of interplay, decision tress are helpful maps.
ā
How did our Ford decision end up?
In case youāre curious, I thought I should wrap up the story on what happened at Ford.
The first thing to say is that a decisionās quality is not the same as a decisionās outcome. Thereās something called outcome bias where you judge a decision based on the outcome; we want to avoid that.
All things considered, with a little bit of luck we got a good outcome AND had a good decision process.
Ford accepted both of our voluntary separations, paid for our final semester of school, and we got jobs relatively quickly - in the brand new state of Texas!
Just kidding, it's nothing like this.
ā
Call to Action
I know youāre going to have something to decide this week. Is your team trying to figure out at uncertain path? Is there something going on in your personal life that you canāt crack?
Give decision trees a shot! If you run into any trouble or just want to share what yours looks like, hit me up at heykev@kevinnoble.xyz.
Enjoy!
Kevin
Thanks for reading! If you loved it, please tell your friends and colleagues to subscribe here: https://kevinnoble.ck.page/ā