E105 Complex Adaptive Systems: Ants, Brains, and Economies

Psychology, computer science, economics, biology, and neuroscience
depend upon a deeper understanding of the mechanisms that govern
adaptive systems.  A common feature of these systems is that organized
behavior emerges from the interactions of many simple parts.
Individual cells interact to form differentiated body parts, ants
interact to form colonies, neurons interact to form intelligent
systems, and people interact to form social networks.  The goals of
the course are to: 1) give students an intuitive appreciation for the
behavior of complex adaptive systems, 2) present the student with
specific case studies of these systems, and 3) describe the formal
underpinnings for the complex behavior of these systems. Properties
shared by many complex systems are emergent behavior,
self-organization, adaptation, the development of specialized parts,
patterns of cooperation and competition, and decentralized control.

To address the essential question of “What are the properties of
complex adaptive systems?,” case studies of several systems will be
explored: chaotic growth in animal populations, human learning,
cooperation and competition within social groups, social networks,
cellular automata, the development of stable and globally coherent
perceptual representations, and the evolution of artificial life.  A
central thesis will be that apparently dissimilar systems (businesses,
ant colonies, and brains) share fundamental commonalities. These
commonalities will be described in terms of mathematical and
computational formalisms. Good algebra skills are required, and some
experience with calculus and a computer programming language is
recommended.  During the course, students will become familiar
Netlogo, a high-level computer language for developing complex systems.

The topics will be explored by hands-on use of interactive computer
simulations.  In the first half of the course, students will be
evaluated by their performance on laboratory assignments.  In the
second half of the course, students will execute and describe their
own individual projects.  Projects could involve devising a complex
adaptive system model of a natural phenomenon, fitting an existing
model to data, conducting an experiment, or providing a novel critique
or assessment of a complex adaptive system.  Relevant topics for
individual projects include but are not limited to: dynamical systems,
artificial life, chaotic systems, biological growth and development,
group and collective behavior, bottom-up models of economies, swarm
intelligence, game theory, learning, resource utilization in a
population, pattern formation, pattern recognition, neural networks,
genetic algorithms, emergent organization in social systems, and
evolutionary theory.  Each student is expected to lead one class
period, centered on a specific, well-contained literature (or even a
single article) and/or their own project.