{"id":39248,"date":"2021-06-09T19:43:48","date_gmt":"2021-06-09T23:43:48","guid":{"rendered":"https:\/\/www.shortform.com\/blog\/?p=39248"},"modified":"2021-06-22T21:48:12","modified_gmt":"2021-06-23T01:48:12","slug":"bayesian-approach","status":"publish","type":"post","link":"https:\/\/www.shortform.com\/blog\/bayesian-approach\/","title":{"rendered":"The Bayesian Approach to Making Smart Decisions"},"content":{"rendered":"\n

Have you heard of the Bayesian approach? Do you know how it can result in making smart decisions?<\/p>\n\n\n\n

The Bayesian approach, also known as Bayesian cognition, involves using assumptions based on your experience of the world to accurately predict outcomes. You may already be using the Bayesian approach without even realizing it. Sharpen this skill to make smart decisions.<\/p>\n\n\n\n

Read more to learn about Bayesian cognition.<\/p>\n\n\n\n\n\n\n\n

What Is the Bayesian Approach?<\/h2>\n\n\n\n

The Bayesian approach (Bayesian cognition) involves using your assumptions about the world and how it operates<\/strong> to make accurate predictions.<\/strong> Your brain forms these assumptions based on experiences that you internalize and patterns that you notice. When you\u2019re faced with a situation that seems to fit a previously observed pattern, you can apply your assumptions to predict, hopefully accurately, how this situation will progress.<\/p>\n\n\n\n

It\u2019s important to note that Bayesian cognition only helps to make your predictions more accurate if your assumptions are accurate. If you base a prediction on a flawed assumption, it follows that the prediction will be flawed, too. <\/strong><\/p>\n\n\n\n

For example, a study required a group of students to predict how much longer an Ancient Egyptian pharaoh would rule if he\u2019d already been on the throne for 11 years. Many students assumed that a pharaoh\u2019s lifespan\u2014and therefore his total time on the throne\u2014would follow the same pattern as the lifespan of a European king from a later period. However, this assumption was flawed. In reality, pharaohs had much lower life expectancies than these kings. Therefore, their rules were much shorter. The students\u2019 incorrect assumption led to bad predictions<\/a>: They predicted that the pharaoh would rule for on average another 23 years. Meanwhile, the data suggested that he would only rule for another 12 years.<\/p>\n\n\n\n

You can increase the accuracy of your assumptions by seeking out as much information about the world as possible. In particular, you should make sure to look out for examples of failure. The brain tends to ignore information about how we and others have failed, and it instead focuses on examples of success. This can lead to your assumptions being unrealistically skewed towards success. You need to actively work to counterbalance this cognitive bias<\/a> if you want your assumptions, and therefore your predictions, to be accurate.<\/p>\n\n\n\n

Benefits of the Bayesian Approach<\/h3>\n\n\n\n

Proponents of the Bayesian approach believe that we can predict the future<\/a> fairly accurately if we make assumptions based on previously observed patterns and information.<\/strong> For example, if you\u2019re asked to predict how much longer a 50-year-old person will live, you can make an assumption based on life expectancy patterns. <\/p>\n\n\n\n

You may already be using Bayesian cognition without even realizing it. The brain constantly gathers data about patterns that exist in the world around you. It uses this data to form assumptions about how the world works. It then instinctively applies these assumptions when it\u2019s time for you to make a prediction.<\/p>\n\n\n\n

Forming accurate assumptions about the world and how it operates can help to make your probabilistic thinking<\/a> more reliable. You may be able to instinctively tell how likely a particular outcome is based on situations and patterns you\u2019ve observed in the past. However, the crucial word here is \u201caccurate.\u201d You need to make sure that your assumptions are correct.<\/strong> If they\u2019re not, issues with Bayesian thinking can arise. If you start out with faulty assumptions<\/a> about the world, any predictions you make based on these assumptions will be inaccurate. <\/p>\n\n\n\n

(Shortform note: To learn more about Bayesian cognition, probabilistic thinking, and making smart decisions, read our summary of Thinking, Fast and Slow<\/em><\/a>.)<\/p>\n\n\n\n

Example: An Experiment on Bayesian Cognition<\/h4>\n\n\n\n

In the 1990s, professors at the Massachusetts Institute of Technology conducted a study on how people are able to make predictions when they have limited data on a topic. They asked students to make predictions based on one small piece of information\u2014for instance, asking how much longer a U.S. congressman will serve if he\u2019s already served for 15 years. The situations that the students were asked to predict predominantly followed strict patterns. However, the students weren\u2019t given information about these patterns in advance.<\/p>\n\n\n\n

The study found that a lot of the time, the students\u2019 predictions were spookily accurate. In some cases, they made predictions that were within 10% of what the data indicated was the \u201ccorrect\u201d answer. The students couldn\u2019t always explain why they\u2019d made these accurate predictions; they often claimed that their answer had just \u201cfelt\u201d right. They\u2019d intuitively used their prior assumptions and knowledge to come to the right conclusion. This was strong Bayesian cognition in action.<\/p>\n\n\n\n

However, there was one situation in which the students failed to predict the answer accurately. <\/p>\n\n\n\n

They were asked to predict the total reign of a pharaoh in Ancient Egypt if he\u2019d already ruled for 11 years. The students predicted that the pharaoh would rule for on average another 23 years. However, the data suggested that the pharaoh would only rule for another 12 years.<\/p>\n\n\n\n

The students were unable to predict this answer correctly because they\u2019d begun with a faulty assumption. They\u2019d based their predictions on the assumption that a pharaoh\u2019s lifespan\u2014and therefore his total time on the throne\u2014would follow the same pattern as the lifespan of a European king from a later period. These kings tended to survive until they were elderly if they made it past middle age, leading to a lengthy reign. <\/p>\n\n\n\n

However, the students were missing a key piece of information. They didn\u2019t realize that in the time of the pharaohs, people generally had much lower life expectancies compared to the kings of later eras. For this reason, the average length of time a pharaoh spent as a ruler was significantly shorter than that of European kings. Because they didn\u2019t have this information, the students were unable to make an accurate prediction. They were unable to apply the Bayesian approach effectively.<\/p>\n\n\n\n

How Can We Ensure Our Assumptions Are Accurate?<\/h3>\n\n\n\n

To ensure that your assumptions are accurate, you need to take in as much information about the world as possible.<\/strong> You should seek out experiences and perspectives that will widen your knowledge of the patterns that surround you. <\/p>\n\n\n\n

Easy, right? Unfortunately, not quite. To make sure you take in the right kind of information, you need to be aware of certain biases that the brain has when it comes to the information it seeks out. <\/p>\n\n\n\n

A major example of such a cognitive bias is the way we internalize success and failure<\/a>. Our minds tend to place greater importance on times we\u2019ve succeeded than on times we\u2019ve failed. Sometimes, we omit failures from our memory entirely. For this reason, we tend to have an inflated view of the odds of success and forget to factor in the likelihood of failure. As a result, our assumptions become inaccurate. We end up predicting success too often.<\/p>\n\n\n\n

To counteract this, we need to actively search for information on times we and others have failed. We should build up a picture of the factors that can increase the likelihood of failure, where we and others have gone wrong in the past, and why these failures occurred. It\u2019s not fun to contemplate why you\u2019ve failed, or to ask others why they\u2019ve failed. But, it\u2019s necessary in order to improve your likelihood of making smart decisions in the future. <\/p>\n\n\n\n

Another way to strengthen your assumptions is to consider situations in which you were certain things would turn out one way but were ultimately proved wrong. Why were your Bayesian instincts so incorrect? Did you work on a faulty assumption? What information do you need to seek out to stop this from happening again?<\/p>\n\n\n\n

Example: Probabilistic Thinking and the Bayesian Approach in Poker<\/h4>\n\n\n\n

Poker is all about making predictions; predicting what cards are going to come up, and what your opponents are going to do. Players can strengthen these predictions by using probabilistic thinking and Bayesian cognition. <\/p>\n\n\n\n

Probabilistic Thinking<\/h5>\n\n\n\n

In the 2004 Tournament of Champions, former psychology student Annie Duke was up against some of the world\u2019s best poker players, including Greg \u201cthe FossilMan\u201d Rayner. In a crucial turn, Rayner pushed all of his chips into the pot. This suggested he had a very strong hand. Duke needed to decide whether she should match this bet, or fold. <\/p>\n\n\n\n

Duke thought of all of the possible futures that could come from this hand and the variables that affected these futures\u2019 odds. Duke had a fairly strong hand. If this hand turned out to be stronger than Rayner\u2019s, she could end up winning big and becoming the frontrunner of the tournament. <\/p>\n\n\n\n

However, Rayner could have a stronger hand than she did. If Duke matched Rayner\u2019s bet in this scenario, she would end up losing everything and being knocked out of the tournament. If Duke folded, she would lose a lot of the progress she\u2019d made in this game, but would be able to carry on in the tournament.<\/p>\n\n\n\n

Ultimately, Duke needed to figure out how likely it was that Rayner had a strong hand. Rayner could be bluffing and actually have a terrible hand. He could have gone all-in simply to scare Duke off and encourage her to fold, knowing that this was the only way he\u2019d emerge from the round victorious. Or, Rayner could genuinely have a strong hand. He could have gone all-in because he\u2019d confidently predicted that he would beat Duke.<\/p>\n\n\n\n

Throughout the tournament, Rayner had proven to be a very rational player. He\u2019d never gone all-in without having a good reason to do so. Based on this, Duke theorized that it was most likely that he\u2019d bet as if he had a strong hand because he actually did have a strong hand. He wasn\u2019t bluffing or taking a massive risk; he was acting rationally based on the hand he\u2019d been dealt.<\/p>\n\n\n\n

Duke folded. Later, Rayner told her that she\u2019d made the right choice; he\u2019d had a stronger hand than she did.<\/p>\n\n\n\n

Bayesian Cognition<\/h5>\n\n\n\n

Probabilistic thinking isn\u2019t the only cognitive approach that can help you in poker. The Bayesian approach can come in handy, too. <\/p>\n\n\n\n

According to Annie Duke, observing certain traits about your opponent can help you to make Bayesian assumptions about how they\u2019ll play. For instance, Duke\u2019s experience has taught her that 40-year-old businessmen usually only care about the prestige that comes with playing poker against experts; they don\u2019t actually care about winning. Because of this, they\u2019re likely to take a lot of risks. Using this assumption, Duke can make strong predictions about how a game against someone in this demographic will pan out and play in a way that maximizes her chances of winning.<\/p>\n\n\n\n

However, for Duke, coming up with these initial assumptions isn\u2019t enough. She updates her assumptions as she begins to observe how her opponents actually play. For instance, she may find that a 40-year-old businessman is able to make excellent bluffs. This would suggest that he is a cautious and skilled player after all, and that she shouldn\u2019t underestimate him. Duke wants to keep her assumptions as accurate as possible so that her predictions regarding her opponents\u2019 actions remain accurate, too.<\/p>\n\n\n\n

Is the Bayesian approach in your cognitive toolbox? The more you use it, the more you’ll be able to make smart decisions.<\/p>\n","protected":false},"excerpt":{"rendered":"

Have you heard of the Bayesian approach? Do you know how it can result in making smart decisions? The Bayesian approach, also known as Bayesian cognition, involves using assumptions based on your experience of the world to accurately predict outcomes. You may already be using the Bayesian approach without even realizing it. Sharpen this skill to make smart decisions. Read more to learn about Bayesian cognition.<\/p>\n","protected":false},"author":9,"featured_media":39260,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[9,43],"tags":[387],"class_list":["post-39248","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-psychology","category-self-improvement","tag-smarter-faster-better","","tg-column-two"],"yoast_head":"\nThe Bayesian Approach to Making Smart Decisions - Shortform Books<\/title>\n<meta name=\"description\" content=\"The Bayesian approach uses assumptions based on experience to accurately predict outcomes. 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