So, models do not actually tell you about the world, they tell you what you currently think about how the world works. But people persist in treating them as if they tell you about the world.

And so yes, the Wall St experience does connect to the problem with climate models.

Pitfalls:
1. Choosing what level of abstraction to model. We don’t have infinite computing (nor the math, nor the physics knowledge) to support creating models which model at the particle level. So, there is always some degree of bias introduced into the system. Maybe you could equate “rules of thumb” to bias.

2. Don’t get hung-up on trying to model macro properties. Those behaviors and properties are in all likely hood one of emergence. In economics Hayek liked to talk about local knowledge of a system (a more micro approach), whereas Keynes would like to look at the world from macro point of view. The interactions amoung local agents in a system is where one should focus their attention. You can’t “fix” a symptom of a system with a solution at the macro level, this sort of top-down approach only indirectly affects the system. You can directly affect the system from bottom-up. Herein lies the power.. the interactions at the micro level.

3. Randomness. What type of random distribution should you assign to your different data sets. And always allow for a freak occurrence to happen, especially when people are involved.

4. Coding errors, bugs, errors in rounding numbers… I think of an example where I was casting from double to float and losing just enough precision to throw off my agent’s Q-learning algorithm (when building a foreign policy model). Or, dare I admit, a bug where an agents movement was slightly off, which when fixed increased my predictive score on the Netflix prize (I used a model to predict movie preference based on spreading likes/dislikes via word of mouth). You show me a perfect program running over 10k LOC, I’ll show you a liar. I don’t care if you are CMM lvl 5.

At the end of the day, my experience with modeling leaves me as a skeptic in the global warming debate.

What a pointless lie. He’s never said anything of the sort. Making up stuff like this only exposes you for the utter charlatan that you are.

(1) Lending huge amounts of money to people (‘no money down’ loans) who didn’t need to demonstrate responsibility and who had little risk of their own, if things turned pear shaped.

(2) Lack of government financial oversight – in fact the opposite – a misguided attempt to encourage home ownership.

(3) Tax and other policies that created a realestate bubble.

Can computer models take into account 1-3? Were they designed to? Should they have been designed to?

As I understand it, the climate modelers do use rules of thumb (AKA “fudge factors” or “plugs”) extensively, because their computer models are too coarse-grained to model clouds, etc. That is, they would need several more orders of magnitude in computer power to just use “clean, objective math” and have anything at all resembling a model of the real world, so they adjust the models empirically. The trouble is, while you have many lakes and rivers to test your empirical models against, climate modelers only have one world climate. For the number that’s most important to them, the annual average temperature for the world, they get just one new number a year! And that number is very noisy with a dubious derivation. So there is no way to tell how good their empirical adjustments are at predicting the future, versus just being adjusted to match past measurement errors, variables and cycles not accounted for, and random variations. We do know that the model predictions presented by Hansen ten years ago were way off – but given the randomness in the data, you need far more than 10 additional data points to test the model’s long term accuracy…

Secondly, what are the chances that any computer program of significant complexity is bug-free? In my experience, zero, unless you have tested the hell out of it, and even then… In my job, I often have to read and understand the software that controls a piece of machinery. This is not very complex software, that has been tested again and again it has been verified that the software will give the specified behavior for every specified condition, and revert to a safe mode for anything unspecified. Yet, in reading the code and comments, I often find places where the path taken to reach the goal clearly isn’t what the programmer expected…

But what happens if the programmer testing the program doesn’t know what the corrrect result is? And that’s the case with climate models – assuming the modeler is actually trying to do scientific work, rather than supporting a predetermining political position. It’s all too easy to tweak the program until the results look like you expected, even though they are wrong. “Hockey stick” Mann is a perfect example of that.

The book also covered not so successful funds, including “Long Term Capital Management” , established by Robert Merton and Myron Scholes, famed for the
“Black-Scholes” equation for determining stock varibility. “Long Term Capital Management” went bust in a very short term, partly because you cannot rely on short term measurements to determine longer term variance, partly because the variance in the companies LTCM invested in were not independent runs, but had a common risk factor. In that regard, it was sort of like the housing market. Models were built showing a small chance of default. . It was assumed that the risks would be reduced by investing in packages of large numbers of subpar loans. Unfortunately for the investors, those loans weren’t varying independently, they all went bust together.- A. McIntire

Similarly, the climatologists and other scientists are the ones who come up with the climate models, but it’s the politicians that ultimately decide what policies to dream up as a result.