Brooking Gatewood
Social Science 240B
Professor Doug White
Review Paper SocSci 240 Networks and Complexity Videoconferences
3/7/2010
This paper reviews Carl Simon’s May 2007 talk “Simplicity in Complexity:
Complex Systems Approaches Across the Disciplines”. I chose this talk because I am
interested in interdisciplinary research and in the use of systems thinking in
different disciplines so thought it might provide a useful overview.
Simon, Professor of Economics and Public Policy at University of Michigan,
offers a useful framing for this talk in which he lays out the 5 basic assumptions of
simple systems-based modeling and decision-making and suggests that the opposite
condition or relaxing of each of these assumptions offers a working
conceptualization of complexity science.
The following table illustrates these 5 parallel and opposing assumptions:
SIMPLE MODEL ASSUMPTIONS
COMPLEX SYSTEMS ASSUMPTIONS
1. Homogeneity (representative agent)
2. Equilibrium (no or simple dynamics)
3. Random mixing (no organization/structure)
4. No feedback (no learning, adaptation)
5. Deterministic
6. No link between micro/macro level
1. Heterogeneous agency/diversity
2. Nonlinear Dynamics
3. Contact structures, networks, organization
4. Feedback (adaptation, learning, evolution)
5. Stochastic (w/ concern for “tails”)
6. Emergence (micro  macro phenomena)
Simon gives examples of how these left-hand column assumptions have
offered useful heuristics and insights about connections in simple systems, such as
in microeconomics. But he offers more examples of failures that resulted from a lack
of systems thinking: the DDT story; antibiotic resistance as a result of using the
strongest antibiotics in unnecessary contexts; adding a lane to address highway
congestion; bulldozing old neighborhoods to solve urban decay problems; raising
prices to increase profits.
We can think of systems thinking as a more effective way of solving
problems, suggests Simon. Most simply it is about looking at the ways in which what
you are studying connects to everything else. It helps us find unanticipated
consequences and connections, that is, how a change in one part of a system of
interest might affect other parts of the system. Predator-prey and disease models
offer simple examples of the deeper understanding we gain from thinking about our
problems in this way.
Simon suggests that, across disciplines, we can really think of systems as
networks – about connections and relationships, and about how structure
determines outcomes. And we can find different kinds of networks with different
kinds of patterns: random, rectangular grids, small world, and power-law networks,
to name some of the primary categories. He suggests that the arsenal of techniques
in use today for complex systems analysis at U-Michigan include: networks, agentbased modeling, game theory/dynamical systems, genetic algorithms, cellular
automata, computational social and decision science, thresholds, tipping points.
The talk concludes with a number of examples of these techniques in action
in research by the complex systems group at U-Michigan. Simon is using complexity
logic to show the flaws of neoclassical economic assumptions, to address long-term
transportation sustainability challenges, to analyze literature-culture co-evolution,
and antibiotic resistance. He closes with an extended example of the use of genetic
algorithms for strategic decision-making.
Though this talk was indeed a review of the differences between simple and
complex systems approaches to problem solving and research, three points were
particularly notable for me. First of all, I find the framing of the difference between
simple and systems thinking being one of assumptions very elegant and useful. This
was particularly striking to me in his project on the classical assumptions of
microeconomics modeling. My pre-graduate training was in environmental science,
particularly ecological economics, and I have been railing against the fundamental
assumptions of neoclassical economics for years. My arguments however lacked this
framing – that it is not just a matter of false assumptions, but of lack of systems
thinking, per se.
Secondly, and similarly, the discussion of sustainable transport is one I’ve
thought about a great deal as well. I appreciate that in Simon’s work on this
modeling he includes and highlights the systems problem in terms of behavior and
decision-making and not just engineering and planning. This problem of short-term
incentives in our pricing and planning systems is pervasive in many social science
challenges. My tool-kit for addressing it had been grounded in anthropology,
psychology, and cultural ecology, but to be able to include this behavioral element in
these kinds of economic and planning model gives me a lot of hope.
Finally, a small piece of critique: Simon mentioned early in his talk that the
difference between simple systems assumptions and complex systems assumptions
is what separates high school education from college education. First of all, I wish
that were true, but as far as I can tell the average American student can graduate
from college without any grounding in systems thinking. They may be exposed to
the problems that result from failures in systems thinking, but not to the logic of
thinking systemically per se. It’s my personal belief that, speaking of systemic
solutions, one of the most effective ways we can plan now for a sustainable future is
to begin teaching the fundamentals of systems thinking throughout the entire
education process - starting in preschool, not college! If we’re going to be able to
solve the profound societal challenges we’re addressing we need an entire society
that is capable of understanding unintended consequences, the value of diversity, of
learning processes, and of choosing long-term best interest over short-term interest.