If you're listening to this podcast, you probably aspire to make good decisions. In today's episode of Developer Tea, we're going to talk about 4 ways that smart people can make bad decisions.
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If you're listening to this podcast right now, you probably aspire to make good decisions. You aspire to becoming wiser every day and to learning more and more throughout your career. You're probably a lifelong learner. These are the kinds of people who listen to Developer Tea and yet many of us, myself included, make bad decisions on a regular basis. We make bad decisions on a regular basis. So in this episode, we're going to talk about four ways that smart people make bad decisions. These are things that can happen to all of us. And there are many more than four, but we're going to focus on four for the sake of today's episode. My name is Jonathan Cutrell and you are listening to Developer Tea. My goal on this show is to help driven developers like you find clarity, perspective, and purpose in their careers. We're going to jump straight into this discussion. The first thing that smart people do that results ultimately in a bad decision is not making a decision at all. We'll call this inaction or perhaps a better term or a better mental model for this is inertia. The idea here is that we continue doing whatever it is that we've always done. We don't make a decision because we've already made a good decision in our own minds and perhaps we did make a good decision. And we're relying on that historically good decision to carry us through indefinitely. And there's obviously a lot of problems with inertia. There's a lot of problems with not making more good decisions. One of the most obvious problems is that as things change around you, of course new responses to those changes is necessary. The good decision that you made three days ago could even in comparison to today's situation be a bad decision if you were to make it again. And yet we continue complying with our previously made decisions as if they are kind of slated in gold. They're always going to be good decisions. And this is not only a fallacy, but it's incredibly dangerous because that inertia is self-reinforcing by the time we've built a habit. And we continuously invest in continuing that habit. It's very hard, this is why I'm calling it inertia. Not only is it the unlikeliness to act, the unlikely decision making point, you're not going to come out of nowhere with a new decision unless you are confronted with this. But it's also self-reinforcing because the more times we take that same action without any adverse responses, in other words, everything seems to be going fine, the more we reinforce in our own kind of mental schema that that decision and that ongoing inertia is a good thing. As a quick aside, I'm going to provide kind of an antidote to this. I want you to evaluate the decisions, especially the big ones, the ones that take up a lot of your time, evaluate those decisions on an ongoing basis. Imagine that you had to make that decision again. For example, I might ask myself this weekend, if I was faced with the opportunity to start a new podcast today, would I start one? And it's important to recognize that not all of these questions have obvious answers, and they're only providing a backdrop for conversation. Obviously starting a podcast today is different than starting a podcast five years ago, and things have changed. But if I could snap my fingers and change my reality in a particular way, what would I change? Specifically, as it relates to previously made decisions, if I could make a different decision today, what would it be? So once again, the first thing on our list of four things, four ways that smart people make bad decisions is inertia. The second thing that causes smart people, very smart people, very intelligent and aware, and intentional people like the people who listen to this podcast. The second way that you are likely to make a mistake is by focusing on the outcome in her incredible book, Thinking in Betts, and on the interview that she did with me here on Developer Tea. Annie Duke calls this resulting. The idea of resulting is that you're focusing on the outcomes of your decisions rather than the way that the decision itself was made. And there's a critical difference here, and it's hard to see when you're reviewing a decision. It's intuitive to imagine that the way we determine whether a decision was good or bad is based on what happens as a result of that decision. But we're missing out on a critical component of the equation. We imagine that we have all of the factors necessary to evaluate the quality of the decision by looking at the outcomes, but we're discounting, or perhaps entirely ignoring, the role of many other factors that we don't have control over. We can call all of these factors together randomness. Randomness in this case is not true randomness. There are certainly other kind of decision makers or stakeholders or whatever you want to call them actors in our little scene of a decision, of a given decision, but we don't necessarily have control over them. So we might as well think about those factors as random. They're in the environment that we choose to operate in, but we don't necessarily have control over those factors. I'll give you an exaggerated example of this. Let's imagine that you decide that you want to start working out. You want to become a power lifter. And so you start lifting weights. On the first week that you start lifting weights, you register for a competition. The competition is this fictional competition, allows anybody to register. So you have registered, but it just so happens that a large cohort of the world's greatest power lifters have also registered. Over the course of the next few weeks and ultimately months, you continue working out. You continue going down that path of becoming a power lifter and you make incredible, incredible progress, if you will look at it subjectively. You're doubling and tripling your maximum lifts, right? And this progress is incredible. But then you go to the competition and the lifelong power lifters, the ones who have been doing this ever since, they can remember they outpace you substantially. You come in last in the competition. And you're looking back at this decision to become a power lifter and you decide that it was a bad decision because you came in last in the competition. This is obviously exaggerated. We can find the flaws in this particular example very quickly, but a less exaggerated version of this, perhaps a more likely to be mistaken version of this, as if on that first week when you start lifting, you injure your ankle. In this scenario, it's easy to say, well, that was a bad decision. I should have never tried to become a power lifter because I injured my ankle. The truth is very far from this. There are many scenarios, kind of many possible futures, possible pathways where you don't injure your ankle. There's plenty of pathways where you end up becoming a great power lifter. So it's important to recognize when we are discounting or ignoring all of those random factors that we don't have control over when we are evaluating the quality of our decisions. Now remember, the quality of a decision is based on what you know at the moment that you are making the decision. It's not based on the outcome to be clear. We're not going to evaluate the quality of our decisions based on the results of that decision. But instead, the quality of the decision is based on what we know and what we can control at the point that we make that decision. We're going to take a quick sponsor break and then we're going to come back and talk about how very smart people, very intentional people can make terrible decisions. Today's episode is sponsored by Monday.com. Speaking of luck, can you imagine being one of the first people to invest the time to build an app and drop it into the app store only to see it skyrocket after that? Sounds like a once in a lifetime opportunity, but it seems that it's coming around again. Monday.com is an online platform that powers over 100,000 teams daily work and they just launch to contest to build apps that they will include in their marketplace launch. They are giving away prizes that will totally blow your mind and if we weren't advertisers or cells, you'd probably enter. If you want to be one of the first in the Monday apps marketplace, you can start building today. Monday.com is a work operating system that powers teams to run processes, projects, and build custom workflows in one digital workspace with over 100,000 teams on Monday.com. You can imagine this is definitely a flexible and a visually stunning platform and it's not opinionated so you can bring your process to Monday.com. The Monday Apps Challenge is bringing developers around the world together to compete in order to build apps that can improve the way the teams work together on Monday.com. When I say that these prizes are incredible, I'm not kidding, they are some of the best prizes I've seen in a competition like this. There's $184,000 in total prizes. They're actually giving away three Teslas, 10 Macbooks, and plenty more. Go and check it out. Head over to Monday.com slash devt. It's Monday.com slash devta to learn more about the apps challenge. Thanks again to Monday for sponsoring today's episode of Developer Tea. So I want to talk about two more ways that smart individuals, smart people like the people who listen to this show, the ones who actually care very deeply about making good decisions, how they can go wrong and make a bad decision. We've already talked about inertia, inactivity, or choosing not to make a decision, choosing not to have enough decision points, relying on previously good decisions to carry us through. We've also talked about focusing on outcomes rather than focusing on the quality of the decision itself, that idea of resulting that comes from any dukes book. The third thing I want to talk about today is rounding an estimation errors. You might think that this is something that you would save for a math class, but when it comes down to it, many of our decisions are based on some kind of calculation. Whether we put specific numbers to our calculation or not, we are doing some kind of calculation. I'll give an example. Let's say we are trying to decide whether or not we're going to put on our seatbelt. The answer may seem obvious, but we're going to play with the numbers that are underlying this decision to understand how this rounding error can actually have a big impact. Ultimately, putting on our seatbelt is a binary decision. We either do or we do not put on our seatbelt. There are other measures that we might take, but if we're just talking about this one decision in isolation, we either have an activated response or an inactive response. In other words, we either do or we don't. In our minds, you can imagine that we are doing some kind of calculation and we're determining, based on some number of factors, whether we're going to activate that decision pathway. We are going to wear our seatbelt. What are we using to make that decision? Well we might use the length of the drive. The longer the drive, the more likely we are to be exposed to the possibility of an accident and therefore harm. We might also determine it based off of the kind of driving we're planning on doing. If we're going to drive at high speeds on the highway, then that's one type of driving. If we're driving low speeds on a back road, let's say that's a different kind of driving. We might assign, and all of this happens sometimes instantly. We assign these weightings to these various factors and we're putting all of that into our decision-making machine, our mental algorithm for this. Here's the reality. The number of factors that we're taking into account when we make this decision can be enormous. For example, our own experiences, we weigh this against our other information. If we have successfully not worn our seatbelt on this particular short drive, then that reinforces that it is safe to not wear seatbelts. This may have very little to do with rationality. This is where rounding errors come into play. And estimation errors come into play, we can imagine that our estimates were our understanding of risks. For example, in this case, risk of not wearing our seatbelt for a one mile drive, we could have drastically different pictures of the level of risk than match's reality. If we had the corrected inputs, if we knew the risks were higher or even lower than what we expected, we might make a different decision. By the way, we also have other factors like whether or not this decision causes pain or is difficult to make. For example, there's very little pain involved with putting on our seatbelts. And this becomes incredibly important because as we increase the difficulty to make a good decision, in this case, by the way, if it's not abundantly clear, wearing your seatbelt is a good idea. As we increase the difficulty to making that good idea decision, for example, preventive care, preventive habits like exercise, as the difficulty increases the adherence or the likelihood of us making a good decision is going to decrease. So if you look at all of these factors together, this is where rounding errors and estimation errors can come into play. I may believe that putting on my seatbelt is going to be more uncomfortable than it really will be as an example. And so I may be less likely to wear my seatbelt because the hassle in combination with all of those other factors isn't worth it. And so that particular decision pathway is not activated. I don't wear my seatbelt and I accept risk that I don't even realize that I'm accepting. This is how we make bad decisions, even when we think we're making good decisions. But finally, I want to talk about a fourth way, a fourth way that smart people tend to make bad decisions. And I'm going to call this permanent marker decisions versus pencil decisions. This is a very simple distinction. All right. Having a permanent marker decision means it's a very difficult to reverse type of decision. It's difficult to get permanent marker off of a piece of paper. You might strike through it, but it's going to leave a mark. It's going to be a lot of effort to go a different direction once you've made this kind of decision. The second kind of decision, the pencil decision, is much easier to reverse. Often we make permanent marker decisions when we could make pencil decisions. Why is this important? Well, let's say you have 100 decisions to make. And all of them carry the same level of risk, let's say 5%, that your first instinct or your first decision is going to be wrong. Well, that means 5 out of 100 of those decisions are going to want to change. And so in the case that you choose pencil decisions 100% of the time, that means easy to change. Then those five decisions that were bad, those five gut instinct or whatever the reason is that that first 5% is bad is easy to change. It rests the other 95%, you don't need to change them necessarily, but that's okay. There's nothing lost by having a pencil decision, but if you go with a permanent marker decision, those 95, they're never going to change, but those five out of 100, they still need to be changed. And the amount of effort necessary to change those decisions is difficult. I want to say a little bit more about pencil versus permanent marker decisions. We have some psychological attributes, some biases as humans that cause us to believe that our decisions are always permanent marker decisions. For example, adhering to our own belief structures. When we choose a belief, and especially if we share what that belief is in some public or social atmosphere, we are very reticent. To change that, to go back and erase it and say, well, actually I believe something new, something different now. This is very similar to our previous discussion on inertia. The idea that you're carrying through from your previous decisions and that whatever work to before is going to work indefinitely. But when we view our own decisions as permanent marker decisions, when in fact, they're easy to change, when in fact, they are more like pencil decisions, then we lock ourselves in an invisible cage. So it's just as important to ask before you make a decision, whether that is a permanent marker or pencil decision, as it is to ask after you've already made the decision. Am I viewing this previously made decision as a permanent decision, when in fact, it's not, but in fact, it's easily changed and the obstacle to that change is my own mindset. Ultimately, making good decisions isn't easy. Making good decisions requires a lot more intentionality and study than we give a credit for. Humans tend to make decisions that keep them alive, but we can go beyond that. I encourage you to take time to think about these four ways that perhaps you have made a bad decision. You can even give yourself a timeframe. What is one of the ways that was talked about on this episode? How did it play out in your life in the past year? Thanks so much for listening to today's episode of Developer Tea. Thank you again, of course, to Monday.com, go and learn more about how you can win one of those $184,000 worth of prizes. That's an incredible competition that they're running over at Monday.com slash devt. That's Monday.com slash DEVT-A. Thanks so much for listening to this episode of Developer Tea. If you enjoyed this episode, or if you think one of your friends or colleagues would appreciate it, I encourage you to share it with them or leave us a review on iTunes, whatever platform you are using to listen to this podcast. These kinds of endorsements from you, your voice, is the voice that matters the most. And so sharing this podcast or reviewing this podcast, that is how we will continue to make the podcast a reality. This episode of Developer Tea was produced by Sarah Jackson. My name is Jonathan Cutrell, and until next time, enjoy your tea.