The big question for this segment is, what is this course about? And what will you get from it? [MUSIC] Welcome to this course on big history, evaluating problems. Whether you're an amoeba or the CEO of a major company, you have to solve problems. If you can't solve problems, you're in big trouble. There are two parts to problem solving. Analyzing problems and finding solutions. So, how first do we analyze problems? How do we know what's true or false? This question lies in the area of philosophy known as epistemology or the theory of knowledge and truth. Then, the second issue is, how do we use what we know to get the outcomes we want? Whether we want a bit more food, or larger annual profits or a sustainable world. It would be wonderful if this course could give you the algorithm to solve all problems. If we could do that, we'd surely get a Nobel prize. So, I should confess immediately that we can't do that. No one can. Nevertheless, in field after field, scientists, lawyers, statisticians, artists, students, engineers, and even simple organisms such as amoeba, have actually made some progress in solving problems to some extent, and some of the time. So we do have some idea of how to solve problems, though we'd love to be better at it. What makes this course different is that, as with the other courses in this specialization, we will look at problem solving through the lens of big history. Big history tells the history of the entire universe. From the moment 13.82 billion years ago, when our universe was created in the Big Bang, to the creation of stars then of exotic new chemicals that could be used to create planets, moons, and asteroids, then to the creation of life on Earth, and the evolution 4 billion years later of the extraordinarily complex human dominated societies of today. Now, let's distinguish between the problems and the solvers. When thinking of the problems, we can borrow from the course on complexity. Because problems always imply some level of complexity. Less complex problems should be easier to understand. Complex systems, we've seen, can be divided into complex physical systems such as chemical molecules whose components are not alive, and complex adaptive systems such as ecosystems whose component parts are alive or seem to be alive. The natural sciences are often concerned with complex physical systems, whose components are relatively well-behaved. But, biology and the humanities disciplines deal with complex adaptive systems, whose components such as amoeba or human beings are unpredictable, and sometimes, downright perverse in their behavior. By and large, the task of understanding seems to get more difficult as we move from complex physical systems to complex adaptive systems. There are more moving parts, more surprises, more that is unknown. As for the solvers, they arise at a very specific point in the big history story, the point where complex adaptive systems appear. That's when we get complex entities that seem to have some sense of purpose, so that they actively try to solve problems. That the stars don't have purpose in the sense nor the planets. So it doesn't make sense to think of them in solving problems. But, every bacterium solves problems, every detective, and even every musician looking for the perfect ending to a melody. Even trees solve problems. The stomata, the tiny hole through which leaves breathe in carbon dioxide and shed excess water, expand and contract, depending on carbon dioxide levels, and aridity. And computers? They have purposes built in by their designers and they certainly solve problems. But do computers or even networks of computers, count as complex adaptive systems? Hm, I may have to pass on that one. Problem solving is about truth and control. To solve problems, solvers need good information, a good model of their surroundings. And, they need a plan for controlling their environment. Really complex problems are unpredictable and we never seem to have quite enough information to solve them. So, the ideal problem solver needs more than the forensic skills of a Sherlock Holmes or the logic and imagination of a great mathematician or even the chemical sensitivity of a tree leaf. When the problems are really complex, you also need the care and quick thinking of a parent with a baby, the aesthetic precision and patent recognition skills of a great composer, and the intuition and sensitivity of a horse whisperer. There are no simple answers here. But what we can do is get more familiar with the territory of problem solving, as if it were a landscape. Because the big history story crosses so many disciplines, from cosmology, to biology, to history, and economics, that it provides the perfect way of doing this. It shows us many different types of problems and solutions in different contexts and at different scales. And that's important because problems despise discipline boundaries, and they're interconnected and nested within each other. Today's weather is a product of yesterday's weather. But also, of the work of photosynthetic organisms that created our oxygen rich atmosphere over several billion years. So to understand how climates change, you need to be able to think across multiple time scales, and you need to think across disciplinary boundaries that the big problems don't respect. This course will ask how scientists solve complex problems about the nature of the universe, or how did living organisms solve the problem of getting food and protecting themselves? Or how do engineers, managers, and politicians go about the task of building buildings or running companies or managing entire societies? We will ask how good brains are at problem solving, why amoebas are remarkably good at it, and how good computers are at it. Big history allows us to look at problem solving through many different lenses. Rather as if problem solving were a complex crystal that we were slowly turning, as we study facet after facet in order to gain a deeper and deeper appreciation of problem solving and the many different forms it can take. But in big history, we're also looking at different scales, as if we were zooming in on what mathematicians call a fractal, a pattern that has interesting details at many, many different scales. So, when a quantum physicist talks of truth, do they mean the same thing that Newton meant, or the Greek philosophers meant? Is an engineers idea of solving problems the same as a musicians? Are there rules for solving problems? Or will problem solving always involve creativity, a bit of luck, and a dollop of fuzzy thinking? The first module in this course raises fundamental problems about logic and cognition. What is logic, why is it important? How do simple organisms solve problems? How did brains evolve? And why are they such extraordinarily powerful problem solvers? And why are brains sometimes so bad at problem solving? The next two modules look at problem solving in the sciences. Is modern science a uniquely successful way of solving problems? Or does it, too, have limitations? And how does scientists and engineers actually go about solving problems? The fourth module asks about problem solving where there's a lot of uncertainty. This is where statistical methods come into their own. The fifth module is about the peculiarly fuzzy problems we find in the humanities and arts where intuition and pattern recognition may be as important as logic and mathematics. Finally, we look at a real mega example of a complex problem that is present today, the problem of the Anthropocene. Today, we humans dominate change on the surface of planet Earth. We dominate the biosphere. That is a system about as complex as anything we can imagine. And it's changing fast and the results of uncertainty. The results will matter to future generations. Can we solve the mega problem of managing it well? Whatever the problems you are tackling, we hope you will find that this tour of the many forms that problem solving can take will enrich your understanding of the related challenges of finding out the truth about our surroundings and solving the problems they pose. [MUSIC]
