[FRIAM] Chaos Scientist Finds Hidden Financial Risks That Regulators Miss Oxford Professor Doyne Farmer is working with central banks to improve stress testing.

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Since Marcus hasn't answered, I think it's important that he pre-pended ML with "generative". There's plenty to argue about with respect to that word (e.g. my complaint that EricC _thinned_ relativism). But in my own work, I've made the somewhat hand-waving argument that mathematical models (e.g. systems of ODEs) are thin and component-based models are thick. But, I'll take the opportunity, here, to argue against myself (and against you, Steve 8^) that not only can mathematical models be "a little bit thick", but that models induced into combined-but-separable probability distributions are also "a little bit thick".

For math models, each term of some equation kinda-sorta represents a component of the model and then various operators are used to integrate those components (+ and - are the most boring). At the next layer, different constraints, mechanisms, components might be modeled by entirely different equations that have to be solved as a system. So, even in this brief conception, it seems clear that these models *can be* mechanistic ... i.e. explanatory, at least to some extent.

The same might be said of an induction method that produces, say, bifurcated components. If you find 2 modes in some output, then it seems reasonable that there might be 2 mechanisms at work.

So, in direct response to your response. Had you said the point of agent models is to yield *more* explanatory results than a generative ML classifier, I don't think there's much room to argue. We could turn the tables and argue that agent models might be more explanatory, but they'll be less predictive. So, maybe the total power is similar and we should all use *both*. (That's what I argue to my clients ... but it's rarely done because the skill sets are a bit different and it's more expensive. [sigh])


On 11/14/19 5:56 PM, Steven A Smith wrote:
> 
> On 11/14/19 6:10 PM, Marcus Daniels wrote:
>>
>> Generative machine learning seems a heck of a lot easier than ABMs for stress testing. 
>>
>     Agent-based models, used in fields from biology to sociology, are bottom-up, simulating the messy interactions of hundreds and even millions of agents—human cells or attitudes or financial firms—to explain the behavior of a complex system.
> 
> I think the point of Agent Models is to yield *explanatory* not just *predictive results?

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