systems

Three truths of dynamical systems

Tom Briggs, , , ,

1. Everything we think we know about the world is a model. Every word and every language is a model. All maps and statistics, books and databases, equations and computer programs are models. So are the ways I picture the world in my head–my mental models. None of these is or ever will be the real world.

2. Our models usually have a strong congruence with the world. That is why we are such a successful species in the biosphere. Especially complex and sophisticated are the mental models we develop from direct, intimate experience of nature, people, and organizations immediately around us.

3. However, and conversely, our models fall far short of representing the world fully. That is why we make mistakes and why we are regularly surprised. In our heads, we can keep track of only a few variables at one time. We often draw illogical conclusions from accurate assumptions, or logical conclusions from inaccurate assumptions. Most of us, for instance, are surprised by the amount of growth an exponential process can generate. Few of us can intuit how to damp oscillations in a complex system.

You can’t navigate well in an interconnected, feedback-dominated world unless you take your eyes off short-term events and look for long-term behavior and structure; unless you are aware of false boundaries and bounded rationality; unless you take into account limiting factors, nonlinearities, and delays. You are likely to mistreat, misdesign, or misread systems if you don’t respect their properties of resilience, self-organization, and hierarchy.

—Donella (Dana) Meadows
Thinking in Systems: A Primer (Find in a library)

Three truths of dynamical systems

So much talk about the system

Tom Briggs, ,

If a factory is torn down but the rationality which produced it is left standing, then that rationality will simply produce another factory. If a revolution destroys a government, but the systematic patterns of thought that produced that government are left intact, then those patterns will repeat themselves….

There’s so much talk about the system. And so little understanding.

–Robert Pirsig
Zen and the Art of Motorcycle Maintenance

So much talk about the system

Economist Brian Arthur: Non-Equilibrium Economics needs Computational Economics

Tom Briggs, , , ,

At the International Congress on Agent Computing, held at George Mason University in Fairfax, VA, November 29-30, 2016, economist Brian Arthur posed six questions for the future of economics.

Arthur described his view that we are now in a time of:

1. Fundamental uncertainty – agents simply don’t know what they face – they must constantly learn, “cognize,” adapt, and grow

2. Technologies that keep changing – all times, all levels: there is permanent disruption. Whatever the tech game is now, it will be different in just 2 years.

We’re now in a world where forecasts, strategies, and actions are being “tested” for survival within situations – an ecology – that those forecasts, strategies, and actions created.

This is the essence of agent-based modeling (ABM).

Six questions:

1. What would a non-equilibrium economics look like?
2. What temporary phenomena would we see?
3. What do complexity economics and political economy have to say to each other?
4. How does the economy form over time and how does it change in structure?
5. What insights would non-equilibrium economics have for policy?
6. Can we have a third rigorous branch of economics – computational economics?

Economist Brian Arthur: Non-Equilibrium Economics needs Computational Economics