423. Unveiling the Biases Behind Misinformation w/ Alex Edmans

Welcome to episode 423 of the Brainy Business Understanding the Psychology of why People Buy. In today's episode, I'm excited to introduce you to Alex Edmonds, author of May Contain Lies. Ready? Let's get started.

You are listening to the Brainy Business podcast where we dig into the psychology of why people buy and help you incorporate behavioral economics into your business, making it more brain brain friendly. Now, here's your host, Melina Palmer.

Hello. Hello everyone. My name is Melina Palmer and I want to welcome you to the Brainy Business Podcast. In today's conversation, I'm joined by Alex Edmonds. Alex is professor of Finance at London Business School. He has a PhD from MIT as a Fulbright Scholar and was previously a tenured professor at Wharton and an investment banker at Morgan Stanley. Alex has spoken at the World Economic Forum, testified in the UK Parliament and given the TED Talk, what to Trust in a Post Truth world and the TEDx talks, the pie Growing Mindset and the Social Responsibility of Business. And these have a combined total of over 2.8 million views. He serves as non Executive Director in the Investor Forum, on the World Economic Forum's Global Future Council on Responsible Investing, on the Royal London Asset Management's Responsible Investment Advisory Committee, and on Novo Nordisk's Sustainability Advisory Council. Alex's book Grow the Pie How Great Companies Deliver Both Purpose and Profit was a Financial Times Book of the year for 2020 and has been translated into nine languages. And he's also a co author of Principles of Corporate Finance. He's won 25 teaching awards at Wharton and LBs and was named professor of the Year by Poets and quants in 2021. His latest book, May Contain Lies, How Stories, Statistics and Studies Exploit Our Biases and what We Can Do about it came out in April of 2024 and is the focus of our conversation today. Really quickly, before we get into the conversation, I want to be sure you know that there are links in the show, notes for everything, including my past episodes and books including May Contain Lies, Ways to get in touch with Alex and more. It's all within the app you're listening to and at the brainy business.com 423. Now let's jump right in. Dr. Alex Edmonds, welcome to the Brainy Business Podcast.

Thanks very much Melina for having me on.

Absolutely. I am so excited to be chatting with you today about your your work and your research and your background. For for everyone who doesn't yet know you, can you share a little bit about yourself and the work that you Do?

Sure. I'm a professor of finance at London Business School. I work on corporate finance, so how companies make decisions. But I also work on behavioral finance, so how biases can cause us to distort our decisions. And while I do some academic research, I also am really passionate about conveying the insights of this academic research to a general audience. I love working with companies, investors, policymakers, the media. And that's why I'm delighted to be on your podcast.

Oh, yes, absolutely. And so I know that we said I want to talk about the work you did in your dissertation research, and I love actually asking people this question, even when they haven't talked about it in their books. But that was actually a big reason we got connected here. Can you share a little bit about the work that you did for your dissertation?

Absolutely. So normally when people talk about their PhD dissertation, it causes people to fall asleep. But I'll just say to the listener, please don't fall asleep, because hopefully this will be somewhat interesting. So what we want to look at is what drives the stock market. So we often think it is traditional factors like profits and dividends and inflation. But I wanted to look at, is it affected by emotions now to do this? It's actually quite difficult because what you need is a measure of emotions that doesn't affect things like profits and dividends. So one thing that affects emotions is the COVID lockdowns. But if you found that the market went down after a COVID lockdown, that might be nothing to do with the emotional effect of the lockdown. It could be that the lockdown stops economic activity and many other things that can affect people's emotions, such as a plane crash or an election result. Those have direct effects on the economy. So what I looked at was the effect of international sports results such as the World Cup. So why is that really a good measure? Because that causes a huge effect on emotion. So studies found that when England lost to Argentina in the 1998 World cup, heart attacks rose. So people actually died because their team lost. And it's not just crazy people in the uk. So at the other side of the Atlantic, in Canada, people commit suicide when the Montreal Canadiens are eliminated from the Stanley Cup. And while Canadians kill themselves, Americans kill each other, unfortunately. So there are studies which find that homicides go up when the NFL team loses in the playoffs. So joking aside, these do mean that sports results are serious. So they can have a serious effect on your mood, and this effect can translate into your trading behavior. So what my co authors and I found was that When a country is knocked out of a major city soccer competition like the European Championships going on right now or the World cup, the market falls by 0.5% the next day, even after controlling for what's going on in the world. And you might think, well, 0.5%, is that a lot? But applied to the UK stock market, that is £10 billion wiped off the market on a single day just because England happens to lose a penalty shootout. Right.

And just, I mean, such a, a fascinating thing that we can see in ourselves as most people have an affinity for some type of sport team, be it like you said soccer, as we, you know, say around here in the US or American football or cricket or whatever else, right. That we have those tie ins and we feel the, the lift when our team wins and we feel the, the low when they come down. And I of course know the answer here, but do we get the benefit on the other side? Do we get the lift when people had had a big victory when they win the World Cup?

Let's say, unfortunately not. So unfortunately this is a one sided effect. So sports can only lead to disappointing outcomes. And you might think, well, how can that be? Well, it's because of how biased sports fans are. So if you win, you think, well, we would have won anyway. So you have overconfident expectations about your team's chances of winning. So every couple of years, England fans think they will win the European Championship and the World cup, even though they've not won anything since 1966. So if you win, you're not that elated, but if you lose, you're bitterly disappointed. So it's only an asymmetric result. And so this is something that I got into because, like when I was a teenager and then my team had lost over the weekend, Monday morning was a disaster at school. Like your friends would take the mickey out of you. And then I thought it wasn't just me being a teenager, but when I was working on the trading floor of Morgan Stanley in 2004, this was the European Championship, which you would have thought Americans shouldn't really care about, but many Americans have European heritage as shown by their last name. And they, despite being professional traders, their emotions were hugely affected by what had happened on the soccer field. So I thought this is something that it's plausible to think that there could be a link to the stock market returns.

Definitely. And I think, you know, from reading about it in your book, you know, it was someone that was then gone for, for days because their team had lost right at Morgan Stanley, like couldn't, couldn't bear to be in the office anymore as they were being, you know, dealing with the, the impact of the loss of their, their team. And so what are the, you know, overarching consequences or lessons as we think about what might be impacting, you know, if we go beyond the stock market, we think about our businesses. Right. So we may like to think that the context doesn't matter and what's happening for people at home or other things that they're dealing with, they should be able to compartmentalize that and not have it impact their performance at work. But, you know, your research, of course, shows what many of us know, that that doesn't really work that way.

Right, Absolutely. These are the broader implications that I really want to tease out because somebody might think, well, how is it that MIT can give a PhD for a study on the effect of soccer on the stock market? That doesn't really affect how people make decisions. But by looking at this effect, this suggests that in general, people can't compartmentalize their emotions. So even professional traders can't forget about something like a soccer game, even though that really doesn't affect their family, it doesn't affect their career. But then if even professional traders can't ignore that, there are so many emotional events that may well affect our decisions as investors or as an executives or even as professors and teachers. If you've got a difficult home situation or family situation, these are things that do matter. So we are entire people. We're not just people who just have our work life and a home life. We need to be mindful of our emotions and our biases, our mood, but also hopefully the organizations that we work for, our organizations that we're willing to share this with our boss or with our colleagues. And this is not seen as something which is mentally weak. In fact, we have mental strength to acknowledge when things are difficult for us and then it's better perhaps for us to take a leave from the company and not make some incorrect decisions.

Definitely. Thank you for that. And I think too, you know, knowing with our own finances just having awareness or decisions. So like you're saying it's one is looking inward and seeing it's normal like other people have these type of impacts, even when they should, they have more training and, you know, a professional trader would still be impacted. So I, as just a normal person with normal finance, shouldn't go be checking in and doing a bunch of trades or, or dealing with my finances the day after my team lost, you know, making those sorts of, of decisions and similarly not making those big decisions at, at work, giving myself the grace as well as if, you know, things feel a bit off or you know that a big, there was a big loss for your team or I'm sure with elections and things as these things happen, that we can have awareness that they are going to on the job and we can create space for that so that we don't end up making bad decisions.

Absolutely, Melina. And we can certainly broaden this from just sports induced emotions to anything else which will affect our emotions. So let's say we have just lost a lot of money on a particular trade. Often then investor might think, well, let's take even riskier trades in order to try to win this back a bit like how a gambler might just double down in order to try to get back in the black. And so that is a decision that, that is a behavior that people have often seen when they look at the accounts of people with stock trading accounts. And so that's something we need to be careful of. Alternatively, another concern is that what happens if our friends tell us how much money they've made on cryptocurrency? Then we might want to make, try to invest in cryptocurrency ourselves, not realizing, well, why did they make money? It's because the price of crypto has just risen. So if I'm going to come in right now and buy, I may well be buying at the peak of a bubble. So all of these different types of emotions, not just sports results, but in general, our own trading behavior, our friends or our peers trading successes or failures. These are things where we want to make sure that we're not being too swayed by them. We're making a rational decision, a brainy decision, not an emotional, heart driven decision.

Wonderful. Let's you know we're going on this journey then. Now, of course this story has found its way into your new book, but can you talk a little bit about how you got interested in misinformation and unintentional lying and the facts and what's proof and evidence, all of this that you talk about in your new book.

Absolutely, Melina. So as I mentioned at the start, what I'm interested in is not just academic research to stay in an academic journal, but to be applied to practitioners to change the way that executives make decisions or investors entrust their money. And the fields that I work in are highly practical. So they're things like sustainable investing and responsible business and corporate finance. And so I will often try to apply research ideas not just my own, but those by other scholars to practical problems and tell executives and investors about what research suggests. But what I found was how they would respond to data and evidence. And science would be driven by emotion, just like an investor's behavior is driven by soccer. But what if the emotion that I would encounter, it was a different type of emotion. It was confirmation bias. What is that? So people have a view of the world. Let's say they believe sustainable companies always perform better. So if you show them a study which claims that sustainability pays off, they will lap this up uncritically, even if the study itself is flimsy. And if you show them a study which shows that sustainability sometimes does not pay off, they might say, well, that's just an academic study. It has no relation to the real world. Or maybe the authors are climate change deniers or anti woke people. I'm not going to listen to that study. So what that highlighted is how people respond to evidence and data and science based on its appeal, not its accuracy. And so then I thought, well, then what am I doing as a finance professor if for the next 40 years I keep churning out more and more academic research papers, they may not have that big an effect because whether they cut through depends on whether people like what they say. So let me take a step back and let's spend a couple of years actually not just writing academic papers, but writing a book on the dangers of confirmation bias, on how this can lead to distorted decisions, how it can mean that we ignore information which can be critical. It could be Silicon Valley bank ignoring information suggesting that they would go bust if interest rates rose. It could be BP ignoring the tests that suggested that Deepwater Horizon was going to blow up because it wasn't yet safe. It could be the financial crisis, where people thought subprime loans were as safe as houses. So these things matter. They matter, even for really smart people. They can be prone to biases just like professional traders can be skewed by sports results. And I wanted to highlight these biases, but also explain in simple language, in layman's terms and with some practical steps what we can do to address those biases and correctly respond to the information that we see.

Wonderful. And I very much loved that there's a whole chapter there on confirmation bias and of course, how it lays the groundwork throughout the book. I'll be linking to my episode on confirmation bias for everyone who wants additional insights into that. You have another type of thinking that kind of become these, like you say, twin, twin biases that are impactful for us as we are approaching the way we interpret information. So we've talked about confirmation bias. Can you share a little bit about black and white thinking?

Absolutely. And first I'll just go through one bit about confirmation bias so that I can contrast black and white thinking with it. So as I mentioned earlier, confirmation bias is when we have a pre existing view and you might have a pre existing view about sustainability. So there's strong political views about this in the US you might also think people have existing views on say, climate change or gun control or immigration. But there's many topics on which people might not have a pre existing view. And so that is where black and white thinking kicks in. Even if we don't have a pre existing view, black and white thinking suggests that we think that things are either always good or always bad. So it has to be one or the other. So let's give an example. So in terms of diet, people do have pre existing views about protein. That just sounds good. We learned that protein builds muscle. People have pre existing views on fat. Fat must be bad. It's called fat because it makes you fat. But often people are not that ideological about carbs. And so the Atkins diet came along and it preyed on this black and white thinking by saying carbs are really bad. It was black and white in that it said all carbs are bad. It didn't say complex carbs are fine, simple carbs are bad. All carbs were denigrated. Nor did it say carbs are fine as long as you have them in moderation, as long as it doesn't cross 30% of your daily calories. He said minimize carbs. And so why was that so powerful? It played into black and white thinking and it was really easy to action. So as a person, you only need to look at the carbs label on the nutrition information to see whether to avoid something. You don't need to calculate, am I at my 30% level? Also, you didn't need to ask, are these carbs refined sugar or complex carbs? Just avoid it. But notice, had Atkins had completely the opposite diet, one which said eat as many carbs as possible, he may have just been equally as successful. He would have gone viral because that's also a really easy diet to follow. So what black and white thinking means is that to pen a bestseller, you don't need to be right, you just need to be extreme. Mm.

Right. And. And thus in lies the danger, Right?

Yes. And I think you can see this with many other things, right. In politics today, with Say sustainability. There's many people who are completely anti Semitic. Sustainability. There is a lawmaker, I believe in Hampshire, New Hampshire, who wants to punish the consideration of sustainable investing with 20 years in prison. And there's others who say our sustainable investing will change the world. It's the only way to invest. People can be completely pro or completely anti immigration or globalization. If you are a nuanced person suggesting something has both costs and benefits, you are unlikely to go viral and be retweeted and reshared. But if you give a black and white blunt message, then anybody who supports that viewpoint will want to support and reshare and promote that message much more than a nuanced one, which is middle of the road, right?

And then the problem on the other side being if it's very polarizing like that, then people on the other side want to comment and talk about how wrong it is and argue with you and how bad it is, which then boosts the views and algorithms, end up promoting, you know, more of those black and white statements or arguments than say, the stuff in the kind of gray space.

That's right, Melina. And you might think, well, why do people on the other side do this? You might think, okay, they're bad people because you think biases are things that bad people have, but not necessarily because biases in some cases can, can be useful because having a black and white view makes complicated decisions. Simple. So if you go back thousands of years to our ancestors, people needed to make decisions really quickly. So if you see an animal, what do you do? And so people have these black and white rules run from all carnivores, where carnivore has sharp teeth and eyes at the front of the head. Now that can sometimes lead to mistakes such as running from a dog who you could have domesticated and helped with the hunt. Nowadays we don't have as many life and death situations. We don't need to make decisions at such speed, but we do need to make decisions at scale. And black and white rules can typically help us. For example, if you are a sustainable investor, you might have a black and white rule to avoid all fossil fuel stocks. Most of the time that will help you, but sometimes it can lead to wrong decisions because some fossil fuel companies are really investing heavily in climate change. So I think the reason why people follow black and white rules is the world is complex, something with an unequivocal message that's easy to remember, it's easy to put into practice, just like a diet of avoid all carbs. But even though this might help us much of the time. In some cases, it can lead to us making significant errors.

Yeah, definitely. Well, thank you for that background. Now that we've built the foundation, we can put our ladder on top of it. Right. Work our way up. As we go through part two of the book, can you share about those kind of steps of, you know, statements aren't facts and facts aren't data and on and on?

Absolutely. So part one of the book is about the biases that cause us to make mistakes. But then what do we do about these biases? How do we overcome them? So step two, part two of the book talks about all the different types of misinformation out there which can prey on our biases. And before I wrote the book, I read other books on misinformation, and I'm not going to denigrate them thinking they're rival books. I think they're good. In fact, they're so good, they were so comprehensive that they listed, let's say, the 159 types of misinformation out there, which makes it difficult to put these findings into practice because you need to remember all 159. So what I wanted to do was to classify all types of misinformation into just four buckets. And so I use this graphic of a ladder of misinference, because when you start with some facts and then you draw some conclusions, you are climbing up the ladder. But why I call it the ladder of mis inference is that sometimes we make missteps up the ladder. We think that we're on firmer ground than we actually are. So let's go through the steps one by one. So the first misstep is a statement is not fact. It may not be accurate. So let's give an example. So all the time we like to quote some really famous people, and if the quote is pretty black and white, we will believe it. For example, Peter Drucker, a leading management guru, is famously quoted as saying, culture eats strategy for breakfast about how important corporate cultures are. But there's a couple of problems with that. Number one, he never actually said it, but people will quote him all the time without checking. And number two, even if he did say doesn't mean it's true. Has he conducted a study analyzing companies with high culture and seeing whether they're more successful when companies with the great strategy. No. Now, even if he didn't conduct a study, the fact that say, big management guru said it means that it might have some substance to it. But a statement without any evidence is far less convincing and far less important than one which is backed up by a lot of facts. Okay, so how do we avoid the first misstep? Let's check the facts. Check it was actually said. Check the evidence behind it. Now, if you've done that, are you home and dry? Unfortunately not, because there's other steps up the ladder. So the second misstep is a fact is not data. It may not be representative. So what do I mean by this? So even if a fact is 100% accurate, it could be a cherry picked example. It's the exception that may not prove the rule. It's not representative. Give another example. So the third most viewed TED Talk of all time is by Simon Sinek. It sparked his book Start with why Extremely Successful. And that book says, to be successful as a company, as a person, you need a passion, you need a purpose. Those are things we want to be true. What we tell our kids, you can do anything you put your mind to. And he has facts to back him up. So Apple, hugely successful company, nobody can dispute that Wikipedia also had a Y and it has eclipsed the Encyclopedia Britannica as the world's fountain for knowledge. But those are only hand picked examples. Yes, those are organizations that became successful because they started with Y. But there could be tons of other companies that started with Y and they failed. But you never hear about them. Simon Sinek will never tell you about them. Why? Because they don't support his story. So he's only giving you a selected sample. So then you might think, well, isn't the solution just to get the full picture, to look at all companies, those that started with why, who succeeded and those that started with Y and failed. Well, the answer is actually, unfortunately no, because of the third step up the ladder, which is data is not evidence. It may not be conclusive. So even if you have a strong robust correlation between starting with Y and success, correlation is not causation, as many people will know. Is it that starting with Y causes success or maybe something else? Let's say a great CEO chooses to have a why, and that great CEO independently causes the success. So it's the quality of the CEO which drives success. Not having a why statement. The final step up the ladder is evidence is not proof. It may not be universal. So even if conclusively documented a result and shown its causation in one particular setting, it might not apply to other settings. So Malcolm Gladwell has this famous 10,000 hour rule claiming that to be successful, you need to practice for 10,000 hours. Now the evidence on which he based that statement was limited to violin playing, yet he claims this rule is universal. It applies to chess, to neurosurgery. Those are very different contexts. What works in one setting may not work in others.

Yes, thank you so much for the explanation. And of course there's of course, so much more throughout the book on each of the ends, fascinating examples as we're working through and being able to learn along with you and the stories from your own work. I really highly recommend the book for everyone who heard that brief synopsis and thought that is fascinating. Then good news. There's a whole rest in the book that you get to be reading more stories and bits here. Is there an area that you would say that, you know, where most people tend to err and, or should focus their attention on the way up the, the ladder? Is it at, you know, the area of, of facts or of, you know, misrepresenting some, some of the data, intentionally or otherwise? Where would you say is an area? If someone says, I gotta get, I gotta think about doing something here, where should they start?

I would say it's the third misstep. So I think all missteps are really important. But why do I focus on the third, the first misstep of checking the facts that people typically do know now? So in 2016, Oxford Dictionaries named Post Truth the word of the year. And so we've known for at least eight years that you should check the facts. If somebody claims that Barack Obama is, is not a natural born US Citizen, you can dispute that by looking at his birth certificate. But why the second and third and the fourth missteps are so serious is they highlight that just checking the facts is not enough. So even if the facts are 100% accurate, they may be misleading. So the second misstep is just having only a few facts. But the third one is so dangerous because people think, well, if I have a huge data source here, then I must have proven something and you haven't. So let's go back to that third misstep. I said it's data is not evidence. It may not be conclusive. So what is the difference between data and evidence? Because we often use these terms interchangeably. So data is just a collection of lots and lots of facts. What is evidence? So where do we hear the word evidence most? It's in a criminal trial. So evidence is evidence that points towards one particular suspect. If the evidence suggests that Tom or Dick or Harry could have killed Sarah, that is not evidence because it's consistent with multiple suspects. And so the danger is that even if we have lots of data, there could be multiple different explanations. But if we have a certain view of the world, we will latch onto our preferred explanation and ignore the alternative ones. Just like a police officer with a hunch will interpret every bit of information as being consistent with that suspect and be blind to alternative suspects. Again, as an example, let's say we find large scale data that sustainable companies perform better. Now, I want that to be true. A lot of my workers are the benefits of sustainability. So I claim sustainability causes better financial performance. But is it the opposite? Is it once the company is already performing really well, then then it can invest in sustainability? Or maybe there are these common causes which cause both. A good CEO improves sustainability. A good CEO also improves performance. Or maybe an industry, if you're in the tech industry, you perform well in terms of sustainability. You're not burning fossil fuels. And also the tech industry has independently performed very well because it's an industry of the future. And so often we think that if there's loads of data, it must be true. We often browbeat readers into submission by highlighting how much data we have. But no matter how much data you have, if that data is consistent with multiple explanations, it will be a correlation and not a causation.

Definitely. Well, thank you for that. I appreciate the further explanation and helping people get, you know, more to wrap their, you know, arms around this idea as they're looking at their own information for themselves and in their companies and communities. Which of course brings us into part three of the book, which is how we can be using these insights for us individually, organizations and as a society. Can you share a little bit of your tips from that part of the book?

Yeah, absolutely. So as individuals, I think to be aware of our own biases and to make sure that if we are chomping at the bit to believe in something, let's try to make sure that we're going to apply the same criticism as we would do if we didn't like the finding. So let me again give you a practical example to illustrate this. So let's say we find that breastfed babies have higher IQ than non breastfed babies. Now, we want that to be true, right? We think that breastfeeding is better than bottle feeding. Why? Because breast milk is natural, formula is fabricated by a giant corporation. But whenever you want to believe something, one really useful tip is imagine the opposite result. Imagine the result that we didn't want to be true. So let's say breastfeeding was correlated with lower iq, we would immediately want to shoot down that result. We would say, well, there's alternative explanations. Maybe the mothers who breastfeed are poor, they can't afford formula, and it's that poverty which is leading to the low child IQ because they can't afford as much in terms of books and other things. Now that we've alerted ourselves to the possibility of alternative explanation, ask whether those same alternative exhalations still apply even though the result is in our favour. So the result was that breastfeeding is linked to higher iq. Now, could poverty, could family affluence be a driver? And the answer is yes. Maybe the women who are breastfeeding are wealthier. They might have a supportive partner at home, they might have help outside the house. Why? Breastfeeding is really tough. It's hard to do without home support. And it could be that home support is leading to the improvements in iq. So why I think this is so enlightening is we often think, well, the solution to misinformation is we need a PhD in statistics or we need to check every single footnote and look up every reference. And this is just not plausible for the average reader or the person who is time constrained. But what I'm trying to highlight is we already have the skills for discernment within ourselves. Whenever I see a study posted on LinkedIn that people don't like the findings of, there is no shortage of comments as to why correlation is not causation, why the data might be cherry picked. We know how to knock down studies we don't like, so we need to apply the same discernment to studies that we do like and that we're tempted to listen to. So whenever you hear a finding that you really want to be true, imagine it had the opposite result and see how you might knock it down.

Wonderful. It is. It's so quick and easy to share, right when we like the thing and say, oh yes, share, love this, boop, boop, boop, easy, easy. And so I love that kind of mental block to say, well, what if? Right? And we can use that quick little mental flip to ask a couple of those questions before we're so quick to, in that case, potentially, you know, inadvertently share some misinformation. So that's the individual level, which will apply with organizations, I'm sure as well. But you then have some specific tips for, for companies that are wanting to be using this information to improve their work as well. What's one thing you know that organizations should be thinking about based on this?

Absolutely. It's for companies to try to encourage as much dissent and cognitive diversity as possible. So there's a lot of focus nowadays on diversity, equity inclusion, which is in terms of demographic diversity, which is certainly important. But what is even more important is diversity of thinking. And this requires not just hiring people who think different ways. So you might have scientists and psychologists and people with humanities backgrounds, but also creating the space for them to share their different viewpoints without being reprimanded. And so one simple example might be, let's say we have a meeting and that meeting is to decide are we going to take strategy X. And let's say one person, Emma, disagrees with this and she raises her concerns and people are polite and people listen to the concerns and they discuss them and they say, actually we still think strategy X, the benefits outweigh the costs. So what the chair of the meeting can do afterwards is go to Emma privately and say, emma, I really wanted you to know that we valued you expressing these concerns. I recognize that it didn't change the decision, but we're still going to make strategy X. But when we implement this, we are going to bear your concerns in mind. So why is that so important? Because for Emma it probably took a lot of courage for her to speak up. Maybe the people who were proposing strategy X might be a bit miffed, maybe everybody else would be miffed because she might be delaying the meeting by another half an hour and so she might think I went through all of this and I spent a lot of political capital and it still didn't change the decision. So next time I've got some concerns, I'm going to self centre. So this is really helpful just to highlight what the organization values is somebody who's willing to express different viewpoints. And the chair could do this perhaps even publicly. But if that might be a bit awkward just to do this privately, just to highlight what is the type of person the organization values. It's not somebody who just follows everything the boss suggests, but it's somebody who's willing to think for themselves and to share when they have different viewpoints.

Definitely. And I loved the example here in the book we were talking about and I think it was the CEO of ge but by saying, okay, we're all in agreement here and they say, and yes and say, okay, well then we better table this until we can come up with some dissenting or disagreement, you know, different viewpoints on this. Because if we have nothing to disagree, we haven't thought about it enough. Some very poorly paraphrased, but that story stood out for me.

No, no, that's absolutely right, Molina. So this was Alfred Sloan when he was CEO of not ge, but gm. But he closed the meeting by saying this. And so he recognized that if nobody had dissented, it's not because he's some superhuman, amazing leader who comes up with flawless strategies. It's just there wasn't time for people to think about reasons for why that might go wrong. So he wanted to give time and space for that to happen. And notice now, well, that Alfred Sloan was in charge many decades ago. Nowadays we have technology who might do that for us. So there was a Singaporean bank called dbs, which has a thing they call the Raccoon. W R E C K O O N. Why? Because their mascot is the Raccoon. So in a PowerPoint presentation, sometimes randomly, this raccoon would appear and say, what are the assumptions that we've made here, which we haven't actually made explicit? What could go wrong? What are the blind spots? What have we failed to consider? And so this will just sometimes ask questions. So if people are unwilling to ask themselves, then the reckon will do this. But also more generally, that can catalyze people to ask questions because it highlights that. What do we want in the meeting? A successful meeting is one where there is challenge.

I love that too. And it's not. It's not me. It was the raccoon. Right? The raccoon had the question for. For us to get us thinking a little bit differently. And I know in the book you talk about the value of pre. Pre mortems, which we've had a few people talk about on the show as well. A really great practice. Whether it's asking. Ask, just asking those questions, like you said, what could go wrong? What might we be missing things to kind of get out of our own way, Whether you have a mascot randomly pop up in the meeting or some other tactic to help you be on path for that. So as we put a bow on the episode and the conversation we've had, we made. We've made it through essentially a little bit about everything in the book, except. Except for your tips for thinking about all of this as a society. So going in that next phase, what's one little thing that you would share for us to think about in that grander scale?

It's to develop critical thinking in schools. So we often think about teaching literacy, and sometimes people think about AI literacy or IT literacy. But I'd highlight the importance of statistical literacy. And so some listeners might think, well, that is quite wishful thinking because don't you need to be 16 years old and be doing high school maths to understand statistics? But no, right? There has not been a single equation in this podcast. There's not a single equation in the entire book. Statistical literacy is all just about being discerning with data and recognizing there were alternative explanations. So already when kids read murder mysteries, they learn that the most likely suspect is probably not the person who did it. There's alternative suspects. And so statistical literacy is realizing there's alternative explanations of the same facts. And so what I have in the book is a couple of brain teasers that you can teach to your kids. Just about not going for the most obvious answer. And I think if we do this, then we set up our kids for a lifetime of critical thinking.

Wonderful. Well, I know everyone should now hopefully be so curious, even if it's just for those brain teasers. Right? You want to go learn a little bit more to understand more about this topic and to dig in. And I will of course have a link to the book in the show. Notes for everyone. Where else should people be going to to learn more about you and your work, to connect anything along those lines? What's the best path for them to do so?

Thanks for asking, Melina. So I'm very active on LinkedIn. I'm a LinkedIn top voice under A. Edmonds, that's a Ed M for Mike, a N for November, S for Sugar. I also am on Twitter or X under the same. My website, alexedmonds.com gives you my more general research on many, many topics, including the link between football and the stock and the stock market. Another thing, music and the stock market, which we haven't talked about, but all of those things and I try to make this accessible for a general audience.

Oh, fascinating. Well, I can't wait to read more about music in the stock market. I'm sure everyone as well. So we'll be linking to your Twitter, X, LinkedIn, the book, your website, all the things for those resources. So thank you again, Alex for joining me on the show. It was really delightful to chat with you today.

Thanks, Melina. I really enjoyed the conversation.

Thank you again to Dr. Alex Edmonds for joining me on the show today. What got your brain buzzing in today's conversation? For me, I love the way he takes us up the ladder of misinformation and misinference and showcasing that a statement is not fact, a fact is not data, data is not evidence and evidence is not proof. For one thing, I love that this leaves my brain with this feeling of what's even real. And in a lot of ways, I think that's the point. You know, everything is more complex than we give it credit for. There's more than one way to look at a problem, more than one way to share the numbers, to read the numbers, to understand them. And while people do unfortunately use this for unintentional ill, sometimes, hopefully being exposed to these ideas can help us all to be more discerning and sniff out when someone's being manipulative, intentionally or unintentionally. And it can also be used for good to not share blindly, to not accept everything as we see it, to challenge our status quo a little more. To be more thoughtful about a problem or project or opportunity. Because even if you are right, it may not be the only right answer, the only right way to see something, or even the best way. This is a book that made me think and keep thinking and wondering. I like that. And I'm guessing since you're here, you like that too. So don't forget to get your own copy of May Contain Lies and check out the other top resources for the episode which are waiting for you in the show notes. Those are found within the app you're listening to and@the brainybusiness.com 423 and thank you again to Alex Edmonds for joining me on the show today. It was a delight to chat with and learn from you. Join me Tuesday for another Brainy episode of the Brainy Business Podcast. It's going to be a lot of fun. You don't want to miss it. Until then, thanks again for listening and learning with me. And remember to be thoughtful.

Thank you for listening to the Brainy Business Podcast. Molina offers virtual strategy sessions, workshops and other services to help businesses be more brain friendly. For more free resources, visit thebrainybusiness.com.