September 05, 2023 by Coach Training EDU
In Coaching to Flourish #087, CTEDU founder John Andrew Williams brings us a special episode about the book ‘Algorithms to Live By,’ by Brian Christian and Tom Griffiths, exploring how algorithms apply to the human mind and everyday decision-making. John discusses his top 5 insights from the book, and how they apply in a coaching setting. Join us for this illuminating episode!
John Andrew Williams: Welcome everyone to the Coaching to Flourish podcast. Raj is out this week and next week, she's on vacation. So I'm taking over all by myself on this.
And today I thought I would look at ‘Algorithms to Live By,’ by Brian Christian and Tom Griffiths. This is a book that I'm listening to on audio, going back and forth on a couple drives, and thought yeah, this is changing my life. And today, in these 25 minutes or so, I am going to go through and talk about my five biggest insights that I got from this book, and possible coaching questions I think might also be useful to you. So what I'm going to look at is, what's the insight? What's the story behind it, or how do we get a metaphor for this? And two, how is it useful? Are the three questions we're going to go for each of these five insights.
So to begin with, number one. Even just the title, just ‘Algorithms to Live By.’ The whole premise of this book is looking at what computer scientists have learned in their way of looking at the world, and programming computers to do work. And specifically what work is, looking at data inputs, putting them through some sort of mathematical equations, different transformations, and then spitting out the output, which is the actual work or the answer, the prediction, the data point. Whatever we do on a regular basis - and even watching this video, there's thousands of data points being streamed. What color pixel to put on, what pixel is being determined by an algorithm. It's wild.
And one of the biggest things looking at computer science as well, is the idea of thinking about this world, it differs. Where a lot of humans will think, well, what's the best possible scenario? Like, what is the thing that, or even what is the average? What a computer scientist might be more interested in is what is the worst possible case scenario? For instance, how long will this program take to run at the worst possible speed? Because that's when network congestion happens, that's when computers fail, if that's not taken into account. So it's often the worst case scenarios in how to prevent those, from a computer science standpoint, that are in some ways the most interesting.
And so with that, from a life coaching perspective, oh goodness. Looking at getting into, this is what a computer says is the optimal way to make a decision, or this is what a computer says is the optimal way to sort versus search, this is - we have answers. We have a hard data. And so from a life coaching perspective, taking that hard data and working it through, that's what we're doing this morning. So with that introduction, here we go.
Jumping in, number one is, the idea of an optimal stopping point. And so the problem that is being answered is this. When you're, let's say that you have to choose when to stop doing one action and when to move to a different action. What is that stopping point? For instance, you need to choose how you're going to schedule your day. You need to choose which coach training program you're going to work with, or you're going to need to choose which employment opportunity to pursue, or whatever it is.
The optimal stopping point asks the question, how much time should you spend setting your criteria, versus how much time should you spend evaluating the choices that you have? And at what point in time do you stop your evaluation and make a decision? These are the questions. The answer, it turns out, is 37%.
So let's say you have something you have to choose, which coach training company to work with. You should spend 37% of the time evaluating your current criteria, what you wanna see, what's out there. Looking at the whole field and just making assessments, and realizing and getting a sense of, this is where the market is. This is different position statements, different value statements, different things like that. So roughly four out of ten, 37% of the time doing that. And then once you've shifted to the other 67%, or 63%, it's, let me evaluate.
And then as soon as one of those coach training organizations, or one of the things meets your criteria, that's when you say yes, I'm good. This is it. That's the decision made. That is the optimum stopping point for making a decision in your life.
One of the things I really love about this too, is also looking at, you know, when you're looking at optimal stopping points, is you have to set a specific time at which you're going to stop thinking about, or you have made a decision, when you're gonna make the decision. Sometimes they're made for us when we have deadlines. Deadlines are a very useful, useful tool for an optimum stopping point. It has to be done by this point. And so you can work backwards from that deadline, you know, work with yourself to determine what activities need to happen by when, 37%.
So the question is this. Where in your life do you have to make a decision? Or, what decision do you have to make? How much time are you giving to that decision? And how much time are you giving to setting the criteria, versus evaluating your criteria?
I have to admit a lot of this for myself, as well as I imagine other people, happens on the subconscious level. Where we have an idea where things are at, the criteria we want, things like this. Saying it all out loud and really thinking of that 37 to 63 percent split, it's been helpful in my life.
So next one. How are we doing? Oh, and we have live viewers. Any live viewers, go ahead and ask questions. I love a good q and a.
Point number two, scheduling, and looking at the idea of context switching. And here's a quote by Ellen Oman. “Programmers don't talk because they must not be interrupted. To synchronize with other people, or their representation in telephones, buzzers, and doorbells, can only mean interrupting the thought train. Interrupting means certain bugs. You must not get off the train.” So the question is: how often, or at what point in time, are you protecting your thought train?
Why this is useful, it's - I was working with a coach and he said, every disruption costs money. Every disruption in business. Doesn't matter, big or small, every disruption costs the organization some sort of money. Organizations that are healthy are able to absorb loss. They're able to have a healthy appetite for a non-optimized, we'll call it, number of interruptions. However, healthy organizations also have time and space where people, their people, block out space and time and say, for this two hour stretch, no interruptions. My thought train is going to just go.
Now, from a life coaching perspective, where my life coaching brain goes is, is looking at what are the outside distractions you're allowing, and then what are the inside distractions you're allowing? And what do you want to do with your inside distractions? And I think it's the inside distractions that have an even a bigger impact on the way that we operate and we do things. For instance, let's say you're working on something and then you have a thought. Like, oh, I remembered I need to do this thing. It's really useful to have a bucket or a structure or a way to do that.
Me - I know it's old school - sticky notes. So I've been using sticky notes. Any thought that I have that takes longer than two minutes to execute on, I create a sticky note for it and put it down there. And I have 1, 2, 3, 4, 5, about a dozen sticky notes right now. What happens is I allow Mondays to Tuesdays, I allow sticky note creation. On Wednesdays I consolidate the sticky notes and then make a plan for how I'm going to deal with them on Thursday and Friday.
I've been starting this, I don't know, for a couple weeks now, just to see how it works. I really like it. Before that I had a notebook where I just put all my ideas into of all the things that need to get done. But then it started to run into the, the sort versus search function, you know, and that just became a mess.
So I will get to point number three. However, to wrap all this up, the idea of the context switch is not, it's not without cost. And what that means is every time you switch from one action to the next action, extra work is required to switch the context, to get all the inputs that you need, in order to do the work that needs to get done. So when we stop looking at, when we start putting on guardrails and allowing ourselves to stay focused for longer periods of time, you reduce the context switching. Anytime you can do that, you're also upping the output, and you start getting into a forward feeding system.
Me and my sticky notes, I absolutely love it. Because I know when I'm in that space and time, and I've also been giving myself, I have a little hourglass timer here. It's a 30 minute timer. This is a very, call it, revered object, where if the sand is turned and there's 30 minutes, that is an uninterrupted 30 minute period of time. And I also have another five minute timer, that's my warmup timer. And then any distraction I get, they go on the little sticky note. Wednesday is sticky note cleanup day.
Insight number three. This one is overfitting. The idea of overfitting is when someone's making a decision, or trying to predict what will happen in the future, overfitting is looking at trying to gather so many inputs, so many data points, that the predictive model becomes noise and essentially useless. So for instance, let's say you're planning a vacation, and you want to have the most amazing time. Overfitting would be, trying to gather all the reviews possible of all of the places you're going to stay, all the places you're going to see. Reading as many blogs as possible and trying to determine like the absolute, very best vacation that you could possibly have, and gathering all the data points. That's not possible. And the amount of time and energy that it would take in order for you to achieve that just doesn't really make sense.
On the other side of that, it might be just flipping a coin and saying, you know what, if I go this way, if the coin lands here we stay this place, if it goes there, you go to that place. That might be it. And it could just be as simple as a coin flip, is actually the more optimal way of choosing where to go. Because the overfitting way might take so much time that you're taking time away from other decisions that need to be made. Where if you just made a coin flip, because there's no model that can predict which place is gonna be better, you're just saving yourself lots of time, and minimally putting in the effort required to make those two decisions.
This coincides with the book that I've been reading my little ones sleep at night, just a wildly technical science book that talks about using algorithms and their predictive nature, and just the nature of algorithms data in general. I've been diving into statistics and number theories and things like this, mostly from an interest in positive psychology and what some of the studies are saying, the statistics are saying, the statistical analysis, things like this. Super geeky things, which I enjoy, and puts my little ones to sleep in like 10 seconds, you know, within minutes.
One of the things I’m in love about it is this idea that there are certain algorithms, there are certain functions. Let's say you have an infinite grid of squares, and they can either be cream or gray. And the question is, whether any one individual square is going to be cream or gray, right, depending on the algorithm. Now there are some algorithms that if they run, you can predict with a simpler algorithm if any particular square is going to be cream or gray.
For instance, let's say the algorithm, you know, every even square is gray, every odd square is cream. That's a pretty easy algorithm. You could give a number and it would instantly determine whether or not that individual square is going to be cream or gray. There are other algorithms, however, that you cannot determine whether any particular individual square is gonna be cream or gray unless you run the algorithm. What this means is that this algorithm is the simplest thing possible to determine whether or not that's gonna be cream or gray.
Now this doesn't seem, it might not seem so earth shattering. But in the sense of, is there an algorithm that can determine which is the best, the very best place for you to stay in on your vacation? No. There's not. There is no possible way to determine that ahead of time. The only way to determine it is to do a trial and error. Try it out and see if it works well.
Now this, for me, earth shattering. Why this is useful and why I get excited about it, is I as a business owner, as someone planning vacations, as someone trying to figure out as a human being, what's gonna be the best possible thing? So much thought, mental/emotional energy goes into that question. But the answer is, that's not knowable. You cannot determine what often, what's gonna be the best thing for us. And a lot of science backs this up, the idea of, human beings are pretty not great at choosing, determining whether or not they're going to like something months, years later down the road.
We put so much time and energy into trying to determine that. Maybe that's a waste of time. Maybe we should spend more time doing trial and error and just trying it out, and seeing what we learned from it, and having a relationship where we allow ourselves to get more comfy with failure. Where we allow ourselves to accept and have an appetite, even a welcoming, for failure, knowing that it's part of the trial and error system that is the optimal strategy for addressing some of life's most challenging questions.
You don't know whether the square is gonna be cream or gray. You don't. So the only way to do it is to jump in and find out. That's what this means. And so for me, it's looking at the idea of overfitting is asking myself, am I thinking too much about this? Yes. Good, coin flip, and we're done. You know, that's how it goes. Or even better, let's try it out. If it fails, it fails. Okay, move on next time. It's an internal game of not feeling that shame of not getting it perfectly done the very first time.
This is something that I feel like we need to bring in almost - I don't even know how you would bring this into a school system, but if there were a class that you would get graded not on whether or not you got the right answer, but if you just tried a certain number of answers, or just tried a certain number of things, that would be the grade. That would be amazing.
Point number three, or four. On to four - is relaxation. What is the technical computer definition of relaxation? Now, when I was in the car and I was listening to this on audio - and I did not preview it ahead of time, so I was listening to it on audio, got about two thirds of the way through the book, decided to buy the book. Which I had always planned to do, but it then became a thing. So I didn't preview it ahead of time, any of the chapters. And when I was in the car listening and chapter, I think it's nine, came on, and said ‘relaxation from a computer standpoint,’ I just let out a hoop and holler of joy thinking, yes, we are going to get to the computer definition of relaxation? This is going to, this is cool. And it did not disappoint.
The idea of relaxation from a computer is, you look at the constraints. You look at what you're asking it to do, and then you dial that down one by one to see if it spits out a different answer, or if it creates a solution that you think, yeah, that might work. For instance, let's say that you are a sports scheduler, a professional sport game scheduler. And the professional sport organizations have all sorts of requirements: We need the teams with the highest audiences to play in prime time, we can't have the teams be overscheduled too many, you know, too many weeks in a row. Each team needs to have so many home games, each team needs to make sure they play the people in their division certain number of times, the TV networks, they have their own requirements that they want to have.
There are are dozens, hundreds of requirements that a pro sports scheduler will have to use when scheduling a pro sports calendar. Now you can imagine the pressure of the pro sports scheduler to meet all of these expectations, but it might not be mathematically possible. There is not a mathematically optimal schedule that would adhere to all of those constraints. So the best they can do is start the relaxation process, which is to start relaxing a constraint and say, well, it's best if a team doesn't have, you know, too many weeks in a row off, or you know, that plays in prime time, not prime time. But if we can relax that requirement a little bit and let the algorithm run again, it might come up with a different scenario, a different schedule, and that one might be acceptable. Or you tell the TV network, we can do X, Y, and Z, but not B, C? Okay, let's go ahead and this is gonna be the schedule.
So what this means for a human level is, what are the expectations you have of yourself? What are the requirements? What are the things that you are asking of yourself? And then what happens when you relax one or two of them? Or you have an expectation of the way a situation needs to go. What happens when one of those expectations is relaxed? Or how can you dial it down, and then is that still acceptable? It's a lovely way of looking at, how can you relax whatever is happening in your life, or your internal world?
It's a very technical and concrete, tangible way to look at the requirements and then dial them down. Maybe dial them up. But for me the question is, what can you change about your expectations? And the idea of relaxation allows us to accept certain limitations. Were you realize, okay, I'm just really only able to do four meaningful work units in a day. At my very optimum I could do maybe six. But if we're looking at ‘life happens,’ we have, you know, bodies that need hydration and nutrients and things like that. And that takes time too. So maybe six at optimum, but four? Okay. And then you can just start to realistically plan on four. What can you get done in four work units.
And then especially if those work units, you protect them with not being interrupted or avoid interruptions, oh, now you're cooking. And how many of those work units can you protect, two? So what would two protected work units a day look like in your life? This is how a life coach takes ‘Algorithms to Live By’ and then works 'em out.
How are we doing everyone? Five minutes left. Randomness. Last chapter I'll look at.
Wow. Random. I'm just gonna read this little section here. This is page 203. “Being randomly jittered, thrown out of the frame and focused on the larger scale, provides a way to leave what might be locally good, and get back to the pursuit of what might be globally optimal.” Wow. Okay, okay. I'll read it again and break it down a little bit, little by little bit.
To me, when I was listening to this, it felt like such a affirmation of the field of coaching, and some of what I've learned to do on a practical basis as a life coach to be effective as a coach. And what we do train people, especially in advanced 2.0 to do, which is to trust your intuition. We even have a whole training session on using randomness and silliness and fun, and how that operates on a life coaching level. This chapter, I feel like gives a solid theoretical foundation for why that is effective, and it's tied to this sentence.
So I'll go through it again. “Being randomly jittered” - so what this looks like from a coaching perspective is when you're a coaching session, you're working with a coach, you have an agenda, something that you wanna work on, your coach is listening deeply to you, your coach might have a question that seems random. For instance, think of a challenge you have in your life. Okay, I have one, hope you have one too. And then you can ask yourself, the question is - what would the banana perspective be on it? Or, what would it look like if you were to think about, uh, what would your best friend from high school say about it? What would, if you were to go on a five mile run, what would you wanna say at the end of a five mile run, if you were thinking about this thing the whole entire time you're running those five miles?
Like, do you feel those questions? They're just sort of out there. They don't make a ton of sense, they're not really logical, they just push you in and out of these things. But the sheer randomness jitters you out of your normal, emotional, mental thought pattern. Those patterns, for me, when I'm coaching other people, listening to clients, you can almost feel their thought patterns. And one of the huge advantages of coaching is being jittered out of it, having to be challenged to think outside of that normal rut that we find ourselves in. So that's what being jittered out of it looks like from a coaching session.
“Thrown out of the frame and focused on a larger scale.” So this is scaling up. So going back to looking at narratives, and how our minds work in story - when you're looking at ancient Homeric, you know, Homer, the Odyssey, things like that, Homeric poems, which I studied in college, looking at these things. Homer does a really amazing job of somehow focusing in on the details, let's say of a bronze sword with little silver studs. Or taking this big scope out, and looking at the whole view of the whole armies that are coming together, like wheat fields, you know, like shafts of wheat blowing in the wind. And you can almost see this whole big amazing scale.
Coaching does the same thing, where you can ask a question that looks very much specific on the details. Like right here, right now, when you notice you take a breath, what emotion-state do you feel in your chest? Right. Does it feel happy? Does it feel heavy? Does it feel joyful? Grateful? Like, get detailed with it. What if you were to assign a color to it, what color would just get to it? I mean, all these questions that get very detailed and force your mind to put tangibility to what mental thought and emotion might just be floating there. That's getting detailed.
What this is, it’s focusing, being thrown out of the frame and focus on a larger scale, it forces you to chunk up and scale way back. Take the bird's eye view of your life and go, okay, from a bird's eye view of your life, how are you doing? How are things going? What level of happiness, on that bird's eye view, on a scale of one to ten do you feel? What ups it two, three levels? What makes it go down two, three levels? You know, what are the chances it goes up? The chances it goes down? Oh, the questions are endless once you get into that space.
We'll keep going. So, “Being thrown out of a frame and focused on a larger scale” - there we are, a larger scale - “provides a way to leave what might be locally good.” Let's talk about locally good. The idea, and this talks about some of the concepts earlier in the book as well. The idea of locally good means that, given our little worlds or our bubbles of awareness, that we have optimized within our bubble of awareness, a certain activity or idea or object or emotion, or habit. But that local good might be terrible when looking at the larger global picture of what's possible.
So the question then is, how much time do you spend optimizing for a local good, or seeking out a global optimum that you can then bring back and make that a local good, knowing that you're also running with, in tune with, a global optimum? It sounds like we're back to the very first question of the optimal stopping point, which we now know the answer is 37%. So if you're looking at, how much time should you spend trying to optimize your local good versus the global optimum, it's probably 63%.
And the last little point here is, “provides a way to leave what might be locally good and get back to the pursuit of what might be globally optimal.” There we go again. The key word in that is ‘pursuit.’ And the idea is that sometimes you don't know unless you try it out. And trial and error is a lovely strategy.
And my main takeaways from this book is, looking at increasing trial and error as a valid strategy to try something out, gathering data from it, trying something else, is a valid strategy. Looking at search, the idea of you can go out and see what's - you can go out and search and look for the things that are happening around you in this world, and get where you want to be, and then bring that back to your local good. That is an optimal strategy as well.
So thank you so much. We're at time for, I guess is my first book report, or my book Coaching to Flourish, looking at life coaching to live by, looking at algorithms to live by. Appreciate you all for watching, being a part of this community, and hope to see you next week. We're gonna look at some business building things. I think I might come up with the top five business ideas or concepts that have helped me the most through my career building, my independent life coaching practice.
Again, thanks so much for watching. I'll see you next week. Bye.
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