James A. Rising

For the EI retreat recently, for one of three short videos I generated for my presentation, I converted my code for the “Distributed Fishery Commons”, an simple ABM, to 2-D. Each dot in the video is a virtual boat, fishing down a path in its wake. The boats never directly interact, other than to not fish at the same location. They just move to where they see the most fish, and the result is an intricate dance or bouncing around. Take a look:

Science is constantly changing– we’re generating new data and developing new models faster than we can understand how they should all fit together.

My tool, the Distributed Meta-Analysis System, is ready to go, and I want to write more about it. But I also want to point people to two other interesting projects that seem to be trying to make science work better:

The Open Science Framework is trying to get people to make their data and papers and science, in general, available for all.

Curate Science is trying to solve the replication problem, encourage people to post their replication results and identifying needs.

For me, this is also about what might be called “Evolutionary Modeling”: modeling as a social and ongoing endeavor, involving many groups and combining their results in institutional ways. Science 2.0 is coming.

El Nino and La Nina affects crops in a lot of different ways. I’ve been looking at the response of agricultural yields over time to an ENSO event, where, depending on the dynamics of the social-natural system, impacts could persist for years after the impact or even emerge before the impact.

Here’s what country-wide production look like, in this impulse response framework. Neither Chile nor Egypt show a response to La Nina, but they both have strong responses (which appear to oscillate) to Modoki El Ninos.

The map below shows areas where Maize is grown (anywhere but black). Areas in white show no significant response from ENSO. Colored areas deviate from grey in three bands: red for a response to traditional El Ninos, green for a response to Modoki El Ninos, and blue for La Ninas.

Everyone’s heard of the logistic map:

It’s elegant, it’s powerful (a classic for modeling ecosystems, e.g.), and it’s incredibly chaotic. As you change r, its internal frequency doubles, and then redoubles over a shorter span, and then again and again, until it reaches an infinity frequency over a finite distance. So you get beautiful fractal pictures like the following, bursting with internal structure:

But they rarely tell you how you get the picture, or what it means. The closest you get is that these are “asymptotic” values– a meaningless statement for something that never settles down.

So, I made an animation. In it, I just keep adding new points, each with a value of r and an initial value of x, and let them fly.

I have a poster at AGU presenting my work with Mark Cane on using process-based crop models to predict agricultural yields, and then using those predicted yields as exogenous variation to predict conflict.

(The poster is big: try looking at the lower resolution PNG version if you have trouble.)

Additional material is available in several forms, but the most fully consolidated form (focused just on the yield results) is this document which I brought to the AgMIP conference in NYC last month: AgMIP Summary.