I would like to discuss an idea about how simple forecasting tournaments could be used as a public participation tool to 1) increase the transparency and accuracy of public funding, 2) increase citizen´s trust in governments, 3) improve the quality of political debate by building a database of forecasts and arguments, and 4) improve national foresight on emerging policy priorities in a medium to long-term horizon.

With a broad version of this idea in mind, I and my colleagues in Czech Priorities have managed to get two grants for applied research (Use of forecasting tournaments in policymaking and a Methodology for early identification of megatrends). The Czech public institutions are interested in the outcomes of both projects and they should provide useful data and practical insights on their own (see future posts). But this set-up also seems like a nice opportunity to maximize the exploration value by validating additional research questions, e.g. regarding the feasibility of the idea outlined below.

We can do minor adjustments to the experimental design of both projects until March 2021. So the main goal of this post is to find EAs/Rationalists, who would be interested in further exploring this idea with us in the next ca. 2 months, to better understand what evidence would be the most useful, pre-register questions and/or write a paper.

Short theory: Deliberation & Participation

Let's consider forecasting tournaments a method of public deliberation. A deliberation is a useful tool that may improve policymaking in various direct and indirect ways, but it is complicated to design the process robustly. Forecasting tournaments can be useful in structuring the process and providing better incentives to deliberate well, but it has its own problems. Nonetheless, there seems to be a large space for exploration.

Representativeness of the population is important for the legitimacy of deliberation, but even with sortition or mini-publics methods, it is difficult to create truly representative groups. Nationwide participation methods such as referenda or public budgeting, which are more likely to be representative, often encounter little interest or give suboptimal results, because citizens are neither incentivized to be honest nor to do their research to be right.

Proposals such as Democracy Dollars (each person gets $200/year to give to a party or a cause of their choice) improve the incentives (and I think they are likely to be trialed on a national level somewhere soon), but they have the same problem as public budgeting - citizens are not incentivized to think too hard about how they spend, so when applied to complex nationwide causes, it might increase public participation (an improvement over the status quo), but won´t otherwise produce much valuable info.

Proposal: Forecasting of Priorities

I suggest another mechanism, primarily for prioritization between precisely these complex societal causes. I´m describing here an ambitious version, where the organizer is a national government and participants are all eligible voters (though participation is voluntary). However, the mechanism should work on smaller samples too, and so it should be able to be scaled up from small groups all the way to large corporations and nations, where the benefits over the status quo would be, I think, substantive.

The core of the mechanism is a forecasting tool, where each citizen receives a virtual credit (let's say $200) each year (on their birthday, so that the participation is spread in time). After login, they see a long-list of 20-40 public causes and challenges (such as longevity, corruption, legalization of drugs, mental health, better roads, etc). Citizens can (anonymously) allocate credit anytime within a year, using quadratic voting, to any causes that they consider a priority, and explain why or specify it further in a public comment. They can use different strategies to do that, which I will describe below. Once they allocate the credit, the amount actually goes to solving the cause (i.e. funding research and implementation of the solutions) as funding by the government.

To motivate due diligence, the citizens receive financial rewards if they allocate the credit to priorities that turn out to be considered priorities by an expert panel 3 years later (3y seems to be the longest horizon for both forecasting and financial motivations to work). More specifically, a few % of participants with the best foresight (whose distributions of priorities in year Y was the closest to the distribution of priorities of an expert panel in year Y+3) receive a substantial financial reward (by the government, kind of like a bounty for having caused a positive impact 3 years ago). As a resolution (in time Y+3) a panel of experts simply gets asked, what are the most important priorities (probably in a real-time Delphi-like method, but more on that later).

As a hypothetical result, the government is happy because it effectively harnesses a lot of inputs about what to fund and only pays rewards to the most visionary inputs 3y later. Non-populist politicians are happy because they can tell their voters “your opinion matters” and now it's believable. The citizens are happy because they feel directly involved in policymaking and get educated in the process. NGOs hoping to improve public discourse are happy because there is a growing structured database of weighted arguments that get checked for accuracy 3 years later. Media are happy because now there is a constantly updating and easily understandable aggregate of what citizens actually think and want.

Two strategies

Now imagine, that you just received $200 credit to allocate to causes. Depending on what you want, you can decide on the spectrum between two main strategies, both of which should be beneficial for both you and the government. My intuition is, that around 70% of citizens in the western societies, who decide to participate, would go for the "Activist" strategy and 30% for the "Forecaster" strategy, but there is no evidence.

“Activist“ strategy

• Your selfish motivation is to get benefits for yourself.  You think of what you, your family, or friends most need from the government to fund, and allocate credit to that. In a comment, you specify what exactly you´d prefer to be solved within the cause. You are instrumentally rational. That's great.
• You also want to solve today's problems. To your friends, you explain, that you are the kind of person that cares about problems that are here and now. They see it as a virtue. You get mainly social rewards. The government gets informed about your real needs.

“Forecaster” strategy

• Your selfish motivation is to win money and a forecaster status. You think of what your society as a whole needs most to invest in, and allocate credit to that.  In a comment, you explain why this cause should be prioritized. You are epistemically rational. That's great.
• You want to solve tomorrow's problems. To your friends, you explain that you are the kind of person that cares for others and for the best possible future. They see it as a virtue. As a result, you get both social and financial rewards (if you are actually right). The government gets your insights about the future.

Note, that it is hard to discredit either of the two strategies as bad or stupid. The worst strategy would probably be to allocate all of your credit to whichever one cause, but quadratic voting makes sure that the impact of these strategies gets limited. Since you can comment only while allocating credit, you can write hoaxes to actively cause harm, but you actually have to fund (give some of your credit to) the causes that you want to harm.

It is desirable to provide a constructive critique, though. It is treated in the same way as endorsements (e.g. others could see how you are weighting those arguments by how much credit you allocate). You are just incentivized to provide critique on causes that you think are still important (since you know you fund them at the same time), you just maybe want to criticize the methods of solving them. It will be read by others and probably also by the organizations that received the funding, so it can have an impact.

Discussion: Fixed point & Self-fulfilling prophecy

I have many raw pages on other specifications (e.g. how to choose recipients of the funding), possible tweaks (e.g. a cheaper amplifying research tool instead of a Delphi as a resolution), other potential benefits (e.g. CSR or donor funds could match the best forecasters), possible failures (e.g. clientelism, fake recipients of the funding) and explanations, why this design should be robust to those failures (e.g. the causes are too broad for insider trading, citizens can´t decide who receives the funding or which solutions get implemented, $200/year is not enough incentive for bad cause advocates to run large campaigns all year).

In the remainder of this post, however, I would like to consider two predict-o-matic problems that could be important when we consider a nationwide application. I think they will turn out to be a feature rather than a bug, but tell me if this is wrong.

1) Fixed point problem

You are a rationalist using the Forecaster strategy. Your line of thought goes: "Well, it's obvious, that the cause X will be a priority 3y later, but many people already think the same -> they will give credit to it now -> it will get a lot of funding and get largely tackled in 3 years -> it will not be a priority. But this thinking would be wrong, if:

• The cause X is too big to be solved in 3 years (AI safety, climate change, fake news, wealth redistribution, animal suffering, air pollution, even a new highway, etc.)
• The amount of funding, even if prioritized heavily (and matched by external actors), is not large enough to cause the cause to be solved rapidly, and
• You think that many other people with similar priorities have the same line of thought and act on it, so NOT acting on it would be a better strategy.

The first two conditions should apply here - the causes need to be quite general for other reasons too (comprehensibility, keeping the debate on a level of values, not of the feasibility of solutions, etc.) and even if applied in many countries, the funding would not be sufficient to solve them in 3 years.

Regarding the third condition, the reasonability of this thinking depends on the assumed number of citizens in these four categories:

1. Those who are wrong about the importance of X - not prioritize X
2. Those who are right about X, but not have this line of thought - prioritize X
3. Those who are right about X, have this line of thought, act on it - not prioritize X
4. Those who are right about X, have this line of thought, think that many others act on it too, not act on it - prioritize X

This could be a problem with YES/NO answers or one-on-one elections,  but in this mechanism, you will always prioritize between many various causes, so there should always be more people who don't prioritize X (1+3) than those who do (2+4). Then, the best strategy should always be to prioritize what you actually believe.

2) Self-fulfilling prophecy

You distribute your credits, but before submitting, you think about whether it makes sense to invest time into writing good explanations and arguments in comments.

If you do, you might help others to be good forecasters (especially if you already have a forecaster status), which lowers your chances to get rewarded 3y later. But the benefits are much higher (even for yourself), precisely because of the self-fulfilling prophecy.

Others (who use the forecaster strategy) will be persuaded by your arguments -> the cause will gain traction and get more funding -> more people will get involved -> more articles will be written -> experts in Delphi will prioritize it 3y later -> you win.

The beauty of this is that this should work especially with new, emerging causes, that take 3 years before they enter the Overton window and can be seriously considered by policy experts. Wouldn't it be great to have precisely these causes funded 3y earlier?

Next steps: Research questions

Starting March 2021, we will test, whether a diversified group of 150-250 people with financial motivations and the basics of forecasting (representing the citizens who would choose the “forecaster” strategy) are actually able to predict the top 5-10 megatrends / grand societal challenges (from a long-list of 20-40), that will be prioritized 3-5 years later by experts in a Delphi study.

The same Delphi will be done already in 2021 (by us) and then every 3-5 years (by whoever wins the Czech public tender, but using our methodology). With this in mind, I'm confident there are other research questions (with earlier resolution) that we could ask, but cooperation with people experienced in designing research would be useful.

Interested EAs/Rationalists, feel free to contact me (jan@ceskepriority.cz). Any other comments (especially on why similar mechanisms won't work) would also be great.