“Achieving insight, innovative design, and informed decision-making through systems thinking.”

1. Dr. Margaret (Maggie) J. Eppstein Associate Professor in Computer Science Director, Complex Systems Center B.S. Biology (after astrophysics, anthropology, biochemistry, …) M.S. Computer Science Ph.D. Environmental Engineering “Achieving insight, innovative design, and informed decision-making through systems thinking.”

36. These examples are different in many ways: biotic/abiotic, natural/engineered, molecular/cosmic spatial scale, nanosecond/cosmic timescale… so what is similar about them? • Lots of independently acting component parts (no global control); • Components interact locally (in space and time); • Results in Self-organization (in space and time); • “Emergence” – qualitatively different system properties arising at higher scales in time and/or time; • Nonlinear dynamical systems: • Semi-stable states correspond to attractor basins (fixed point or periodic); • Negative feedbacks are stabilizing and tend to keep system in the same basin of attraction (homeostasis) • Positive feedbacks are destabilizing and can move system to a “tipping point” where it leaves the current basin of attraction. • Dynamics may be chaotic (bounded non-repeating fluctuations, sensitivity to initial conditions, difficult to predict into the future) • These are the hallmarks of “Complex Systems”

38. Example of a negative feedback loop (stabilizing)

39. Example of a positive feedback loop (destabilizing): Earth’s Albedo

40. Why Study Complex Systems?: • For basic science, e.g., • Understanding brain function, intelligence • Origins of life, evolution • Earth science • For our health, e.g., • Cancer biology • Epidemiology • To interface with them, e.g., • Engineer orthopedic devices • Rational Drug Design 1) To learn about them • For our planet, e.g., • Engineer sustainable systems • Understand impacts of interventions (intentional or unintentional) • To exploit them, e.g., • Engineer bioremediation schemes • Generate efficient algae-based biofuels

41. In artificial intelligence, e.g., • Artificial neural networks • Evolutionary Computation • Swarm intelligence • In molecular nanotechnology, (use self-organization as a design principle), e.g., • Molecular motors • Smart materials • Nanobot swarms • DNA computing Why Study Complex Systems?: 2) Steal ideas/components from them • In designing distributed control of systems, e.g., • Substation control in electrical grid • Wireless sensor networks • to copy them, e.g., • Copy insect flight for mini UAVs • Copy gecco wall-gripping technology

42. Fortunately, many principles of complex systems (as well as methods for studying them) apply to systems at widely different scales in space and time. “Universality”; very different underlying mechanisms can result in very similar system behaviors. e.g., scaling laws, LALI, phase transitions, self-organization, etc. Many diverse complex systems can be modeled and analyzed using the same types of mathematical and computational abstractions.

43. Modeling self-organized complex fluids at multiple scales; e.g., blood coagulation, synovial fluids Yves Dubief, Mechanical Engineering Program, UVM

44. Modeling self-organized particulate flows with particle collision and adhesion at multiple scales: e.g., for algae biofuels, blood flow through artificial heart valves, gastro-intestinal flow, particle clogging of vehicle cooling systems, and sediment entrainment modeling Jeff Marshall, Mechanical Engineering Program, UVM

45. Synthetic Biology is a new branch of Bioengineering: Engineers are learning how to “program” DNA itself, with the goal of designing and building engineered biological systems that process information, manipulate chemicals, fabricate materials and structures, produce energy, provide food, and maintain and enhance human health and our environment. The Registry of Standard Biological Parts now includes ~3200 genetic parts (DNA sequences that code for specific actions) that can be mixed and matched to build synthetic biology devices and systems New UVM bioengineering faculty, Mary Dunlop, plans to sponsor a UVM Synthetic Biology Team and wants to engineer Genetic Regulatory Networks for efficient bio-fuel conversion. A light programmable biofilm made by the UT Austin / UCSF team during the 2004 Synthetic Biology competition

46. Engineering “intelligent systems”: Try to engineer flexible systems that can adapt on-the-fly to changing conditions. e.g., Robot uses an evolutionary algorithm to artificial neural network to create an internal “self-image” of how itself; if a limb is removed, the robot can figure out alternative ways to locomote using remaining body parts. Josh Bongard, Computer Science Program, UVM

47. Engineering swarms: Borrow ideas from self-organizedswarming behaviors and evolution of cooperation in nature for applications ranging from animation (e.g. battle scenes in Lord of the Rings) to swarms of cooperating autonomous robots or UAVs.

48. Modeling chaotic systems: e.g., improving weather prediction, solar system orbits, blood coagulation, population dynamics, etc. Chris Danforth, Mathematics Program, UVM

49. Studying Complex Social Networks; e.g., information exchange, robustness of organizational networks, “social contagion”, etc. Peter Dodds, Mathematics Program, and Maggie Eppstein, Computer Science, UVM

50. Evolutionary Algorithms: Borrow ideas from evolution to create computer programs that “evolve” good solutions to a wide range of engineering problems. (e.g., designing optimal management of stormwater run-off, evolving strategies for robot maneuvering, evolving architecture of neural networks for environmental sound recognition, etc.) Maggie Eppstein and Josh Bongard, Computer Science Program, UVM