I think this misunderstands forecasting or mischaracterises decision making.

Your brain absolutely does decide when to make a left-hand-turn at a busy intersection based on a probabilistic estimate. Your brain is just pretty reliable at making said judgments that you assume some binary choice was made. Subconciously you are weighing up the likelihood of various risks based on your senses observing them and concluding that there is a very low (or an appropriately low) probability of it going wrong. The easy way to test this is go to a zip line, one with a drunk operator and one with a sober operator. The zip line may look identically safe but your brain will (hopefully) consider the former more risky - not because the impact of something going wrong is different between the zip lines (both falls would hurt, if not kill you), but because the likelihood of something going wrong is different (as you'd calculate that a drunk operator may not be as cognitively aware of what they are doing). That's a probabilistic assessment.

The difference is, most decisions we deal with in the world don't need us to sit down and do a formal prediction process to ensure good decision making. However, most organisational, and especially those on policy and governance etc., do require it. They are concretely two predictions you are making:

1. What will the world be? (through the lens of what you care about, e.g. will there be an oil crisis?)

2. What influence on the world will my policy have? (e.g. if I send Trump a friendly email will he avoid attacking another petrostate?)

Viewing forecasting as just the probabilistic estimate is missing the entire benefit. Forecasting is the entire process. Stating your view probabilistic just allows you to understand your own and others uncertainty (and to provide a purity of accountability and incentive alignment). It only adds decision fatigue if you have not put in the right processes to interpret the result (such as upfront thresholds for action). If you are following an optimal predictive decision making process, you should be making your assumptions and your weighing up of information explicit. This is how you determine what is relevant, what isn't relevant etc. 

Dominic Cummings statement misses that everything he said there IS forecasting. What he's actually saying is "the point estimate from forecasters was not useful - it was the explicit reasoning about the causal chain etc. from forecasters that was useful". 

However, benchmarking against accuracy ensures the incentives are correct and that decisions aren't manipulated by elements that decrease the end outcomes efficacy.

If you conclude that it'd be better if decision makers had: 1) the most accurate view of the world; and 2) the most accurate view of how their actions may influence that world towards their objectives. Then I would stand by the statement that it's sad. 

So my experience is that identifying/specifying/generating the right questions is at least 50% of the benefit, if not higher. There are lots of reasons organisations struggle with this, from: organisational incentives; incoherent steers and views from senior managers; lack of accountability and ownership; to simply not recognising that they are trying to do a prediction.

This is why forecasting funding that has focused on improving forecasting accuracy is flawed, because it doesn't matter how accurate you are if your question isn't of use to the decision making process. 

The problem exists at both those "levels", but the most important one to solve for an organisation is the first one. Issues with resolution criteria etc. decrease accuracy but as long as people's rationale's are explicit you can bridge that gap (i.e. I know why one person was higher and another was lower, it was because they both took the resolution to mean something slightly differently). But if the question/problem you are trying to solve in the first instance is wrong, then the whole thing is a waste of time and energy.

We do a monthly public forecast at the Swift Centre (swiftcentre.org) - just a chance for us to make predictions and insights on some topical events.

As it’s EAG London month I thought I’d ask what topics/questions are on people’s mind so we can potentially throw them into the mix.

Comment below!

I agree with the premise but we shouldn’t be using philanthropic funds to try to patch over what is a market problem. 

The route here should be projects that enable less friction for trade and investment, rather than creating a company that tries to bypass the fundamental issues. Philanthropic funding here should focus on systemic change to have compounded impact. 

This obviously assumes Marcus has a sufficient level of experience to justify the claims. Which I think, given other comments, can be adequately challenged.

It would be good to know what metric/threshold/examples would be taken as forecasting delivering adequate impact to justify funding. From examples in this thread alone, we can see senior government decision makers in both the U.K. (including Ministerial teams and critical committees) and US, frontier labs safety teams, and philanthropic funds moving tens of millions of dollars a year) have utilised forecasting (either the process or the outputs) to inform their decisions.

The argument of it only shifting a decision 1-2% is totally fair. But to keep consistent I’d expect the same people who make that argument to also be highly sceptical of the vast majority of research funding.

(Caveat - I read the premises and skimmed the rest)

Yes - AI research is useful and does help highlight specific advancements or potential risks. However, I fear it is being focused on by many because of personal interest in the topic, rather than the best route to reduce catastrophic and existential risks. 

For better or worse, advocacy, policy, and communications are the most likely routes to reduce p(doom) - unless you believe alignment is a plausible and concrete thing. 

Yeah 40pp, though 40% difference may also be informative depending on the question and distribution. 

Regardless, “40” was just a random number. Basically the interesting thing are the areas of greatest difference, not the probability itself. 

We could “forecast” the likelihood of that haha.

I can’t get into specifics. But if you believe activities like evaluations of models to test for dangerous behaviour etc. is net negative, then that may give credence to your assumption. As an extra data point of whether we’d do work we thought was net negative, I was Head of Policy at ControlAI and co-authored narrowpath.co, and our forecasters have done numerous AI safety focused projects (with and outside of the Swift Centre, including AI 2027).

Sort of, but that also doesn’t capture the significant accuracy and efficiency benefits the process of structured reasoning and communication that forecasting enables. There’s substantial risks and issues of “just looking into an issue yourself” - especially when you are more confident in your judgement (because that’s a clear risk of confirmation bias/overconfidence).

The main use of forecasting is in utilising the core scientific benefits it can bring as above into, to help real world decision makers. But fundamentally, that hasn’t been funded - instead we’ve funded tournaments and research.

I don’t disagree with some of the fundamentals of this post. Before diving into that, I want to correct a factual error:

“the Swift Centre have received millions of dollars for doing research and studies on forecasting and teaching others about forecasting”

The Swift Centre for Applied Forecasting has not received millions in funding. The majority of our earnings have been through direct projects with organisations who want to use forecasting to inform their decisions.

On your wider argument. I think forecasting has probably received too much funding and the vast majority of that has misallocated on platforms and research. I believe some funding (hundreds of thousands) to maintain core platforms like Metaculus as a public good of information. Though, services like Polymarket can probably fill most of this need in the future (but many useful, informative markets would never reach the necessary volume to be reliable).

Where I think we disagree most is in the application of forecasting and some of the achievements. We’ve worked with frontier AI labs to inform their decisions, are currently advising a U.K. Minister’s team on a central piece of their policy, and are about to start a secondment where I will be advising one of the most influential decision making committees in the country to help improve their scenario analysis and forecasting.  Forecasting, and specifically, the science of decision making that it is built on, has the ability to structurally improve decisions in institutions. Significantly better than asking two or three of your smartest friends. That was just never funded, so instead we conclude forecasting is not useful.