Introducing a Complexity Science Course

I’m outlining a new college course to teach at Kenyatta University that incorporates everything missing from my own science education (BS, PhD). These are the emerging topics that I believe will form the foundation of scientific fields in the next decade to come:

Complexity Syllabus (still a work in progress)

  • Visualizations: Fractals in nature
    • Fractal cities in Africa (TED talk)
    • Fractal computer game landscapes
    • Fractal intelligence?
    • Visualizing complex information: InformationIsBeautiful.net examples
  • Fractals as a mathematical concept
  • Nonlinear statistics
    • Vs. Hypothesis testing
    • Assuming normal distribution – how to verify assumption?
    • Looking at sample diversity through meta analysis instead
    • Rigorous experimental error analysis (physical chemistry calculations)
      • Useful in thinking about what part of the problem is creating the uncertainty
  • Emergence
    • How nobody knows the answer, but collectively everyone does the answer
    • And human Crowd-sourcing
    • Social networks
    • Chaos, Complexity, and hidden order as emergence phenomenon
    • Neural networks and insurance actuarial modeling
  • Prediction models (reliable guesses at future outcomes)
    • Prediction markets (intrade) vs. Opinion polling
    • Facebook as a one-question marketing survey concept
    • Predicting future epidemics using the friends paradox to map social networks (TED).
    • An actuary predicts financial impact of risk and uncertainty
      • Actuaries (using neural networks) already include global warming / climate change in prediction – i.e. the debate is over
  • Iterative systems
    • Fractals as an iterative math function
    • Recursive logic (Douglas Hoffstadler)
      • Self-replicating sentences
    • Recursive clustering algorithm for self-sorting information
    • System Dynamics and causal-loop diagrams of complex processes
  • The basis of intelligence
    • The “Turing test” game
    • Neural Networks and insurance companies
    • Self-referential systems and mathematical recursion (why human intelligence cannot be programmed by a computer yet.)
      • Computers vs human intelligence (serial vs parallel processing)
  • Feedback loop systems
    • In learning (predicting medical outcomes & diagnosis)
    • In the brain (how neurons use multiple feedback loops to self-regulate activity)
    • In international development
  • Behavioral Economics
    • Examples from Dan Ariely – predicting political economy
    • Examples: Curbing smoking (the New York City Model)
    • Perverse incentives and collapse of the housing market (The Big Short, Too Big to Fail)
  • Game Theory
    • Decade of the Game, follows upon the Decade of Social Media and Decade of the Brain. Will allow groups to solve problems that were previously unsolvable
    • Think about Incentives
    • Crowd sourcing
      • GlobalGiving network
      • World Bank Development Marketplace
      • YELP, EBAY
    • Predictions from a moving reference frame
    • Competition to solve social problems
      • Innocentive
      • GlobalGiving Open Challenge

Synopsis

Briefly, these topics will provide scientists with an understanding of how to deal with real-world non-linear systems, and mechanisms for generating more accurate predictions of future outcomes. Both skills will prepare the next generation of great thinkers to tackle the world’s problems outside of the laboratory. These non-linear methods are drawn from chaos theory, complexity theory, game theory, and behavioral economics, and I will teach by illustrating several examples of each in a real-world setting. Prediction methods include several forms of emergence, fractal math, prediction games, neural networks, and iterative learning systems (feedback loops). Where possible, I will try to demonstrate the interface between each of these concepts and the conventional material in a scientific textbook. We do our students a disservice when we present science and a static and self-contained set of ideas (as most textbooks are written) – and I aim to highlight what we don’t fully understand in the course of teaching one of the core sciences.

Teaching Goals:

Students will acquire some body of knowledge within Biology or Chemistry, but also experience methods first hand that they will one day use to extend that knowledge, innovate as entrepreneurs, and generally solve the world’s problems in some rational systematic way.

My Teaching Philosophy:

Good instruction begins with breaking down the walls between the classroom and daily life. Students would attend regular lectures, but also work in groups to identify and attempt to solve problems in the real world that relate to classroom concepts. One of every 2-3 lectures would be devoted to the “special topics” in this complexity syllabus. I will use real-time SMS polling during lectures to enable student feedback to be aggregated and visualized on the screen during lectures. This interactive, technology-aided approach should keep them engaged, and allow us to practice some of the techniques of crowd-sourcing, emergence, and prediction games that I am teaching.

Within each subject, my goal is to:

  1. Define the problem
  2. Provide the best scientific understanding or principle currently available to explain it
  3. Illustrate how the principle relates to an existing real-world problem
  4. Emphasize how the researchers’ thinking and assumptions evolved during the process of developing more refined theories.
  5. Foster debate and predictions about future scientific discoveries in this area.

Employers and Entrepreneurs value these capacities above all others:

  1. Capacity to learn and adapt to new situations
  2. Capacity to apply knowledge in practice
  3. Capacity for analysis and synthesis

Where does this fit in science overall?

Current Science courses split the natural world into subdisciplines based mostly on how each approaches research:

  • Phsyics – the study of forces in nature
  • Biology – the study of life
    • Medicine
  • Chemistry – the study of molecules
    • BioChemistry / Molecular Biology – study of molecules unique to life

There are many subdivisions within these three sciences, but they employ similar methods to one of these three core sciences. These concepts overlap with each of the core sciences and with some math courses. There is no logical place for it to fit, but it reflects aspects of nature that are not covered in these areas.

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