The following is a new version of the About Page. Gradually getting this website organized the way I want it.
I have always been fascinated with prediction.
As an undergraduate I made stock predictors on the first PCs and lost money in 1987.
Studied maths, statistics and started a PhD in ecological prediction.
Developed betting systems and lost money.
Studied algorithms for predicting species distributions and developed GARP which other people used for cool things like finding new species of chameleon in Madagascar.
Developed automated trading systems for FOREX in 2002 and lost money.
So I know a few things about prediction, and more about how not to do prediction. In addition, in this blog I hope to pass on a few, and help people to predict better. Like predicting the risk to poultry from Bird Flu using GIS spatial analysis. Or monitoring the health of different types of hydrocoral polyps on reefs. The possibilities are endless.
There is a new thing in business called the Predictive Enterprise, based around not only system management, but proactive management, with decisions based on predicting potential demand. There are prediction markets, where groups trade on events such as when Microsoft will release Vista, or the number of hurricanes in a season.
There are grand prediction enterprises, such as predicting climate a hundred years into the future and thousands of years into the past. One of these, the reconstruction of temperatures for the last 2000 years, has just seen a major reversal, with the release of a National Academy of Sciences report on page 107 diplomatically stating:
“Some of these criticisms (by McIntyre and McKitrick) are more relevant than others, but taken together, they are an important aspect of a more general finding of this committee, which is that uncertainties of the published reconstructions have been underestimated.”
We have yet to see what the flow-on will be to that other grand enterprise, the prediction of future temperatures due to burning fossil fuels.
Overall, I would say people generally have exaggerated confidence in their predictions. I have been working on ways of validating models that include more of the uncertainties, such as Monte Carlo methods of normalizing for known biases. However, generally, overconfidence is the norm.
There is a blog.
There is prediction software.
And because prediction means being aware of the present, a controversial topics aggregator.
Finding technologies to do better is the goal. Breaking them into pieces is fun. Subscribe for email postings and enjoy.