Climate Change Discussion driven by Model-driven debate

Who to believe?

The real question is — why are readers and decision-makers forced to “believe” anything at all? Many claims made during the debate offered no numbers to back them up. Claims with numbers rarely provided context to interpret those numbers. And never — never! — were readers shown the calculations behind any numbers. Readers had to make up their minds on the basis of hand-waving, rhetoric, bombast.

Imagine if Blinder’s proposal in the New York Times were written like this:

Say we allocate $3.0 billion for the following program: Car-owners who trade in an old car that gets less than 17 MPG, and purchase a new car that gets better than 24 MPG, will receive a $3,500 rebate.

We estimate that this will get 828,571 old cars off the road. It will save1,068 million gallons of gas (or 68 hours worth of U.S. gas consumption.) It will avoid 9.97 million tons CO2e, or 0.14% of annual U.S. greenhouse gas emissions.

The abatement cost is $301 per ton CO2e of federal spending, although it’s -$20 per ton CO2e on balance if you account for the money saved by consumers buying less gas.

Media for Thinking the Unthinkable

Presented at the MIT Media Lab on April 4, 2013.

Talk outline:worrydream.com/MediaForThinkingTheUnthinkable/
Personal preface:worrydream.com/MediaForThinkingTheUnthinkable/note.html

For more information about the demos —

1. Scientific paper.worrydream.com/ScientificCommunicationAsSequentialArt/
2. Circuit. vimeo.com/36579366
3. Digital filter.worrydream.com/ExplorableExplanations/
4. Multitrack signal processing. (first time presented)
5. Nile viewer. github.com/damelang/nile
6. Drawing tool. vimeo.com/66085662

Up and Down the Ladder of Abstraction

A Systematic Approach to Interactive Visualization

An arbitrary road could look like almost anything. In order to tame this data space, we choose some aspect of the road which we suspect issignificant — an aspect that reflects some challenge that our algorithm will face. Our algorithm is currently built around a fixed turning rate which determines how sharply the car turns. We might therefore suspect that the sharpness of the bend in the road will play an important role.

 .. Real-world systems may be more complex, but they all share the same general anatomy: an independent variable (such as time), a structure (such as an algorithm), and a dataset (such as an environment).

  • The independent variable is usually time. This is our way of thinking about causality — a system’s state depends on its previous states in time. Even for systems that are normally expressed with multiple independent variables, such as heat diffusion or wave propagation, we typically think of the system as evolving over time.
 .. Unfortunately, development environments generally don’t support this process. Most are actively hostile to it. We live in primitive times.

 .. Perhaps IDE makers will focus on dynamic exploration instead of static analysis, rich visualization instead of line debugging. Perhaps language theorists will stop messing around with arrows and dependent types, and start inventing languages suitable for interactive development and discovery.