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Understanding Complexity

Understanding Complexity

Professor Scott E. Page, Ph.D.
University of Michigan

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Understanding Complexity

Course No. 5181
Professor Scott E. Page, Ph.D.
University of Michigan
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Course Overview

Recent years have seen the introduction of concepts from the new and exciting field of complexity science that have captivated the attention of economists, sociologists, engineers, businesspeople, and many others.

These include

  • tipping points, the sociological term used to describe moments when unique or rare phenomena become more commonplace;
  • the wisdom of crowds, the argument that certain types of groups harness information and make decisions in more effective ways than individuals;
  • six degrees of separation, the idea that it takes no more than six steps to find some form of connection between two random individuals; and
  • emergence, the idea that new properties, processes, and structures can emerge unexpectedly from complex systems.

Interest in these intriguing concepts is widespread because of the utility of this field. Complexity science can shed light on why businesses or economies succeed and fail, how epidemics spread and can be stopped, and what causes ecological systems to rebalance themselves after a disaster.

In fact, complexity science is a discipline that may well hold the key to unlocking the secrets of some of the most important forces on Earth. But it's also a science that remains largely unknown, even among well-educated people.

Now you can discover and grasp the fundamentals and applications of this amazing field with Understanding Complexity. Professor Scott E. Page-one of the field's most highly regarded teachers, researchers, and real-world practitioners-introduces you to this vibrant and still evolving discipline. In 12 lucid lectures, you learn how complexity science helps us understand the nature and behavior of systems formed of financial markets, corporations, native cultures, governments, and more.

What Makes a System Complex?

What defines a system as complex, as opposed to being merely

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12 lectures
 |  30 minutes each
Year Released: 2009
  • 1
    Complexity—What Is It? Why Does It Matter?
    Learn what the experts mean when they talk about "complex" systems. Discover why these networks of diverse, connected, and adaptive entities acting in interdependent ways are so powerful, and why understanding them is crucial to so many disciplines. x
  • 2
    Simple, Rugged, and Dancing Landscapes
    Using the concept of a mountainous landscape as both metaphor and mathematical object, you begin to grasp the ways in which concepts like "rugged" or "dancing" landscapes—where adaptation and learning are vital for survival—can be visualized even by nonmathematicians. x
  • 3
    The Interesting In-Between
    You develop your understanding of complexity's key components of interdependence, connectedness, diversity, and adaptation/learning. And you learn—by figuratively dialing the strength of each of these components up or down—why the proper balance between them is essential if a system is to be complex. x
  • 4
    Why Different Is More
    Variance and diversity have very different meanings in the world of complexity theory. Grasping that difference puts you on the way to understanding how complex systems achieve diversity and why diversity enables them to be both innovative and robust, maintaining functionality even when the system is disturbed. x
  • 5
    Explore Exploit—The Fundamental Trade-Off
    Actors in complex systems face a constant tradeoff. Do they exploit the knowledge already learned in past explorations to achieve a solution? Or do they continue to explore, seeking an even better solution? Learn the pros and cons of each, and how the best balance can be achieved. x
  • 6
    Emergence I—Why More Is Different
    One of the most fascinating ideas in complexity theory is that of emergence, the spontaneous creation of order and functionality from the bottom up, with no "central planner" putting them into place. You gain an appreciation of the two kinds of emergence and why each is a source of wonder. x
  • 7
    Emergence II—Network Structure and Function
    Continuing the discussion of emergence, you see how emergence applies to networks and why network theory has become such an active discipline. And you understand how modern complexity theory adds to the study of networks the previously ignored element of space. x
  • 8
    Agent-Based Modeling—The New Tool
    Agent-based modeling—in which computers model complex systems from interdependent agents—may be complexity theory's most promising tool. Its full potential hasn't yet been realized, but this lecture offers a taste of what it can already achieve in disciplines as disparate as fire prevention and disease transmission. x
  • 9
    Feedbacks—Beehives, QWERTY, the Big Sort
    Drill even deeper into the implications of interdependent agents as you focus on the idea of feedbacks—both the positive ones, in which "more creates more," and the negative ones, in which "more creates less." x
  • 10
    The Sand Pile—Self-Organized Criticality
    Complex systems often create large events. Using the example of how a single unscreened passenger in Atlanta delayed flights and passengers across the nation, you are introduced to one of the key concepts that explain how complex systems can be so powerful. x
  • 11
    Complexity versus Uncertainty
    There is a vast difference between thinking of events as "random" and recognizing them as the output of a complex system. Explore three conventional explanations of randomness before turning to a fourth—the interdependent rules-based analysis offered by complexity theory. x
  • 12
    Harnessing Complexity
    Although complex systems can't be controlled, we may well be able, with proper respect, to harness them. Learn why conventional decision theory doesn't work in complex environments and what a proper use of complexity theory might promise us instead. x

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  • 12 lectures on 2 DVDs
  • 64-page printed course guidebook
  • Downloadable PDF of the course guidebook
  • FREE video streaming of the course from our website and mobile apps

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Course Guidebook Details:
  • 64-page printed course guidebook
  • Photos & illustrations
  • Suggested readings
  • Questions to consider

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Your professor

Scott E. Page

About Your Professor

Scott E. Page, Ph.D.
University of Michigan
Dr. Scott E. Page is Leonid Hurwicz Collegiate Professor of Political Science, Complex Systems, and Economics at the University of Michigan, where he has taught since 2000. The holder of a master's degree in Mathematics from the University of Wisconsin and a Ph.D. in Managerial Economics and Decision Sciences from Northwestern University, Professor Page has also been Professor of Economics at the California Institute of...
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Reviews

Rated 4.4 out of 5 by 101 reviewers.
Rated 4 out of 5 by Complexity is not same as Chaos theory Am enjoying this even though I have to keep remembering what his metaphors mean. Understanding how to conceptualize this world is fascinating. Instead of study halls when I was a high school student in the public school I wish we could have had access to such learning experiences Ii would have been delightful to have spent my time learning rather than tolerating the luddites that tormented the teachers and the rest of us. I have advised two of my school districts board members about the Great Courses available for students and teachers in our county library system. This one on Complexity could certainly help keep the majority of kids tuned into something besides drugs. July 24, 2016
Rated 4 out of 5 by Great Intro I understood essentially nothing about the study of complexity when I started this course. Complexity hadn't been invented yet when I completed my schooling! Professor Page by training is a mathematician who then focused on economics before moving to Complexity as a field of specialization. Despite his background, no math is really required to understand the concepts although if you have had zero about statistics you might have to re-watch certain parts or read the written materials. So this is a very good intro to the field for newcomers. Some things are of course skipped over. For example he briefly describes Gaussian or standard bell-shaped curves, and then skips to power-log distributions without any discussion of asymmetric or skewed-Bell curves. As should be evident from the description the course is 12 half hour lectures on 2 DVD discs. I found Professor Page a very good lecturer. He always started a lecture with an enthusiastic greeting that made me feel as if I were right in a classroom with him. He has a pretty good pace and a dry sense of humor. I always liked listening to the lectures. If I asked for anything more, it would be more visuals-for example, perhaps more visual displays of simulations for the evolution of different types of complex systems. The visuals included were all quite good, but a little infrequent. I would have enjoyed hearing about attempts to model more real-life situations, whether they succeeded or failed, to get a sense of how well the field is doing at modeling and predicting real-world problems like market crashes or the spread of a viral epidemic. Going in I really did not understand, for example, the difference between complex versus chaotic systems, or how either one related to "fractals". Exiting, I had a better sense of the difference and understood many other concepts in the study of complexity. One model presented will stick in my memory as emblematic of the course: the "Game of Life". Also, several excellent quotations. This is a nice intro! It could be worth following up with a more in-depth, maybe a 4 disc course if there is sufficient interest. July 1, 2016
Rated 5 out of 5 by Excellent content and presentation It would be useful to look into the future and try to predict what applications this new science could possibly have. I am sure there will be many. June 29, 2016
Rated 4 out of 5 by It's over?! Interesting topic. Generally the professor does a good job presenting the information, if a bit dry. This course seemed like it could have used another 6-12 lectures, so 18-24 total. The 12 it has seemed to go by too quick and it "felt" like there was more that could have been covered. May 23, 2016
  • 2016-09-28 T16:06:46.895-05:00
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