RobbinDeBank t1_it865zk wrote on October 21, 2022 at 5:06 PM

Reply to comment by idrajitsc in [D] Do any major ML research groups focus on policy-making applications? by valdanylchuk

I would say the complicated nature of real world policy is why AI will eventually be capable of making better policy than humans. While economists can still produce optimized social and economic policies, they just can’t account for all the 100 different interest groups with different political motives in a real world scenario. AI system can do that due to the computing power they process. I think AI can be a key to making incremental societal progress. Instead of the current situation where oligarchs get all the pie, the AI solution could leave them with a good chunk of the pie while the public can now have a decent chunk too. That’s incremental progress, not ideal, but achievable.

idrajitsc t1_it8b8p2 wrote on October 21, 2022 at 5:40 PM

I mean, economists can account for competing concerns. They have been for centuries. The problem isn't a lack of processing power, it's the fact that those concerns are competing. You have to make subjective decisions which favor some and harm others.

Also you're just kind of asserting that AI will be able to solve problems that there's no reason to believe it will, scaling compute power is not the end all be all of problem solving. What kind of objective/reward function do you think you can write that does even a half decent job of encompassing the impact of social and economic policy on all those different interest groups? Existing AI methods just are not at all amenable to a problem like this.

RobbinDeBank t1_it8eogo wrote on October 21, 2022 at 6:03 PM

Let’s say we have this problem with 100 sides, the public and 99 interest groups. In the ideal world, we want to maximize public good (low unemployment, high economic growth, high income, low financial and social inequality, etc) at all costs and interest groups should not have any more power than the average individual. However, we all know this is not the case in the real world, and all those interest groups have disproportionate amount of political power. Now the problem becomes a constraint optimization problem. We still need to maximize public good, but now we have to take into account the constraints caused by these interest groups. This constraint could be or related to the amount of votes (maybe we need 51% of votes, maybe we need more like 60% or 66%). So that’s our main constraint that must be met: partially satisfy the interest groups just enough to achieve majority votes to pass the policy. This is essentially a trade off, sacrificing a part of the ideally optimized public good to gain enough votes to get the policy passed. This constraint then has to be broken down even further to account of each of the 99 interest groups. Together this is a huge and complex constraint optimization problem. The solution we get could be sth like “let’s give in to most of the demands of 90 groups, fuck the other 9, now we have enough votes and the public will benefit a whole lot from this.”

That is a rough idea from me without expert domain knowledge. With the funding of major AI labs like DeepMind and the expertise knowledge they can have, the problem can definitely be solved, in a real world case. Human economists can only write a solution to a smaller problem within 1 industry for example, and not a solution to this complex a problem.

idrajitsc t1_it8iuw2 wrote on October 21, 2022 at 6:31 PM

It is absolutely not true that the problem can "definitely be solved." You have no grounds to make such a ridiculously confident statement about such a complicated problem. AI is not magic which can solve any sort of problem you show if you just sacrifice enough GPUs to the ML god.

The notion of constrained optimization is not exactly new, that isn't the hard part. And while solving a constrained multi objective optimization problem is generally gonna be np-hard, if it even has a well-defined solution, even that isn't actually the hard part.

The problem is figuring out what the inputs and measured outcomes should even be and then getting them into a form that an AI can actually process. I was not asking you to tell me that the objective function would be an optimization problem; that's what they all are. I was asking you what the actual objective and actual constraints are. Because there is no way that you can possibly summarize every important impact of an economic policy in an objective function, much less doing so while differentiating it across different interest groups. Nor could you actually encode all of the input information which might be relevant.

And then what would you even train on if you could accomplish that already impossible task? It's not like we have a large or terrible diverse set of worked examples of fully characterized policies and outcomes. And if you wanted to take a more unsupervised route then it basically amounts to accurately simulating an economy, which in itself would be worth all the nobel prizes.