LUIS A. NUNES AMARAL
KELVIN H. LEE
Ithaca, New York
In this brief introduction to complex systems, we distinguish between complicated and complex systems. We also give an overview of the challenges posed by the study of complex systems and a brief description of the collections of concepts and techniques being used to uncover some of their properties
More than the usual session title, this one begs for clarification. For example, when do we not study complex systems? Or, at least, very complicated systems? Isn’t engineering what engineers do? To clarify what this session is about, it is useful to first understand the differences between complex problems and complicated problems.
Consider the January 2004 announcement by President Bush of a new initiative to send humans to Mars. A lot of heated discussion ensued, but images of a human colony on the Moon, and later on Mars, have made their way into funding agencies, such as DARPA (Defense Advanced Research Projects Agency) and NASA (National Aeronautics and Space Administration), the media, and the collective imagination.
As scientists and engineers, we are trained to check if a problem is likely to have a feasible solution based on present day knowledge before we embark on solving it. The announcement by President Bush prompts at least two important questions. First, will we be able to send humans to Mars? Second, will we be able to build a viable colony on Mars?
Sending humans to Mars poses a number of very complicated problems: protection from radiation, including solar wind, during the long flight; the physi-
ologic effects of the absence of gravity; and the landing of a heavy load on a planet with a significant atmosphere and gravitational pull. All of these problems are quite complicated. However, we can work on solving each one separately using, mostly, present day technology and knowledge. In short, we can break the problem into small pieces, solve each piece, and then put the solutions together.
What about building a viable colony on Mars, that is, a colony that can produce its own food, oxygen, energy, and so on? The most natural way to produce food and oxygen is by means of an ecosystem—a stable collection of different species that interact. Building an ecosystem is a complex problem. Its solution cannot be broken into small pieces because an ecosystem only exists in its entirety.
Although it may be difficult to come up with an all-encompassing definition of a complex system, let us attempt it. A complex system is a system with a large number of elements, building blocks, or agents capable of interacting with each other and with their environment. Interactions between elements may occur with immediate neighbors or distant neighbors; agents can be identical or different from each other; they may move in space or occupy fixed positions; and they can be in one of two states or multiple states. The common characteristic of all complex systems is that they display organization without any external organizing principle being applied. The whole is much more that the sum of its parts.
Studies of complex systems have raised some of the most elusive and fascinating questions being investigated by scientists today: how consciousness arises out of the interactions of neurons in the brain and between the brain and its environment; how humans create and learn societal rules; or how DNA orchestrates processes in our cells.
We do not yet have a framework for understanding, designing, or engineering complex systems, such as ecosystems. The difficulty of such undertakings was brought home by the failure of Biosphere 2. Built in the 1980s at a cost of $150 million, Biosphere 2—named in deference to Biosphere 1, the Earth—was originally designed as a closed system environment. From 1991 to 1993, eight “biospherians’” sealed themselves in the glass-enclosed dome and tried to survive on sustainable agriculture and recycling. For reasons still unknown, low crop yields and a 25-percent decrease in oxygen supply quickly led to the failure of the experiment.
The speakers in this session will provide an overview of the theoretical and experimental tools that are enabling us to tackle challenges posed by complex systems in a systematic way. On the experimental side, new high-throughput techniques are revolutionizing our understanding of processes at the cellular level, and large computers are being used to analyze vast amounts of social, economic, and financial data. On the theoretical side, a new tool—network analysis—has been added to a tool kit that already contains nonlinear dynamics, statistical physics, and discrete (agent-based) modeling. Network analysis has al-
ready led to significant advances in the modeling and characterization of complex systems.
Talk 1. Complex Networks: Ubiquity, Importance, and Implications, by Alessandro Vespignani
What do metabolic pathways and ecosystems, the Internet, and the propagation of HIV infection have in common? Until a few years ago, the answer would have been very little. In the last few years, a different answer has emerged—they have similar network architectures. Seemingly out of nowhere, in the span of a few years, network theory has become one of the most visible pieces of the body of knowledge that can be applied to the description, analysis, and understanding of complex systems.
Talk 2. Engineering Bacteria for Drug Production, by Jay Keasling
Biological systems are among the most complex systems we know. Even “simple” organisms have evolved elegantly complex chemistry to carry out a variety of functions. Using the tools of modern biology, one can study, create, transfer, and manipulate parts of the chemistry of life. An important goal of this research is the practical application of these techniques to the creation of organisms with new and useful properties to benefit society.
Talk 3. Population Dynamics of Human Language, by Natalia Komarova
Hallmarks of complex systems are adaptability and emergence. Consider how ants find food sources and how their communication methods efficiently solve the problem of the search for and transport of food. A particularly exciting realm in which emergence is of great importance is language acquisition, a topic to which this author has made important contributions.
Talk 4. Agent-Based Modeling as a Decision Making Tool, by Zoltán Toroczkai
Agent-based models are being used to study diverse systems, from ant colonies to the behavior of traders in financial systems to traffic patterns and urban growth to the spread of epidemics. Traffic modeling is one of the most successful applications so far. Indeed, a number of cities now use traffic models to predict and attempt to control traffic patterns.