class: center, middle, inverse, title-slide .title[ # Getting Started - Overview ] .subtitle[ ## How To Think - Week 1 ] .author[ ### Fernando Alvear ] .institute[ ### University of Missouri ] .date[ ### Jan 18 ] --- # Overview .center[ <img src="assets/course-image-small.jpeg" alt="" height="300"/> ] This class is called "How to Think: Logic and Reasoning for Everyday Life." In this class, we will discuss some fundamental concepts and techniques required for good reasoning. --- # What does "good reasoning" mean? Good reasoning has to do with forming _accurate_ beliefs about reality and the world. Here are some true statements: - 2 + 2 = 4 - My son is called "Nico." - You are enrolled in PHIL 1200. - It's not the case that the earth is flat. - Humans set foot on the moon at least one time. If you believe any (or all) of these statements, you have accurate beliefs about reality and the world. --- # What does "good reasoning" mean? However, good reasoning is not only about _accurate beliefs_. The way we get to the truth matters too. We want to use _reliable_ methods to form beliefs. A *reliable belief-forming method* is one that results in more true beliefs than false ones over time. Sometimes, reliable belief-forming methods can lead to _inaccurate_ beliefs. Compare these two belief-forming methods: - _Wishful thinking_: I wake up and form the belief that today will rain, but only because I want that it rains today. - _Weather forecast_: I wake up and look at the weather forecast in my phone. It says that it will not rain. I believe that today it will not rain. Suppose further that it rains today. Which method was more _accurate_? Which method is more _reliable_? --- # What does "good reasoning" mean? While wishful thinking was a more accurate method this time, this doesn't make it reliable. Over time, it will not deliver a preponderance of true beliefs over false ones. In contrast, checking the weather forecast is a more reliable method, even if sometimes fails (as it happened with today's weather). Good reasoning is about **accuracy** and **reliability**. --- # How we will learn about good reasoning? We will cover several concepts and topics related to good reasoning. It's useful to think of this class as roughly divided in three parts: 1. The psychology of reasoning 2. Deductive reasoning 3. Probabilistic reasoning --- # The psychology of reasoning In order to reason better, we need first to know how we _actually_ reason. For the most part, and for simple matters, we form beliefs reliably. However, in certain conditions, we fail spectacularly. We will learn about these conditions in which we become unreliable believers, and about the best attitudes to promote good thinking. Some of the disciplines interested in this are cognitive psychology and behavioral economics. --- # Deductive reasoning When it comes to more complex matters, we use arguments to back up our beliefs, or to understand others' beliefs. Consider this argument: 1. If the economy is in recession, inflation is high. 2. The economy is in recession. 3. Therefore, inflation is high. This is an argument. It involves a series of statements interconnected with each other. In addition, it's a good argument, because if the premises are true, the conclusion is _guaranteed_ to be true. Consider this other argument 1. If the economy is in recession, inflation is high. 2. Inflation is high 3. Therefore, the economy is in recession. This is not a good argument, as the premises do not guarantee the truth of the conclusion. The fact that inflation is high doesn't secure that there's a recession. According to this argument, _if_ the economy is in recession, _then_ inflation is high, not the other way around. --- # Deductive reasoning We will learn the mechanics of these kinds of arguments. The discipline that studies these kinds of arguments is called **logic**, and it's usually studied by philosophers, mathematicians, and computer scientists. --- # Probabilistic reasoning When it comes to yet more complex matters, arguments cannot guarantee the truth of its conclusions. But still, there are some ways to get accurate beliefs reliably, even in these not-so-perfect conditions. > Suppose you want to know what is the perception of Mizzou students regarding the legalization of cannabis. You go to speakers' circle and interview five students, four of which tell you that they are deeply concerned about this issue, and that they don't agree with the legalization of cannabis. You go to your house thinking that around 80% of all Mizzou students are against the legalization of cannabis. Would this be a good reasoning process? --- # Probabilistic reasoning What if you interviewed 10 students? Or 50? Or 100? Or 1,000? How many students _are enough_ for you to get a good answer to your question? Even though we can't interview all students, there is a way to get a good answer to this question without having to interview all students. This topic pertains to probabilistic reasoning. The discipline that studies probabilistic inferences in detail is **statistics**, which is practiced by all natural sciences. Probabilistic reasoning is also important for making good decisions. Surprisingly, gamblers, investors, and insurance companies are also very interested in probabilities, as they are crucial to make good investments. We will discuss some important concepts in this area by learning the fundamentals of decision theory.