Discover and grasp the fundamentals and applications of the amazing field of complexity science with Understanding Complexity. Professor Scott E. Page—one of the field's most highly regarded teachers and researchers—shows you how complexity science helps us understand the nature and behavior of financial markets, corporations, native cultures, governments, and other systems that play important roles in our increasingly complex world. By the conclusion of the course, you'll have attained a new lens through which to better view and make sense of your world.
Understanding Complexity
Overview
About
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01: 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.
02: 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.
03: 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.
04: 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.
05: 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.
06: 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.
07: 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."
08: 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.
09: 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."
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.
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.
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.