Toward An Honest Consensus

Star Trek original series featured a smart computer that mostly only answered questions; humans made key decisions. Near the start of Nick Chater’s book The Mind Is Flat, which I recently started, he said early AI visions were based on the idea of asking humans questions, and then coding their answers into a computer, which might then answer the same range of questions when asked. But to the surprise of most, typical human beliefs turned out to be much too unstable, unreliable, incoherent, and just plain absent to make this work. So AI research turned to other approaches.

Which makes sense. But I’m still inspired by that ancient vision of an explicit accessible shared repository of what we all know, even if that isn’t based on AI. This is the vision that to varying degrees inspired encyclopedias, libraries, internet search engines, prediction markets, and now, virtual assistants. How can we all coordinate to create and update an accessible shared consensus on important topics?

Yes, today our world contains many social institutions that, while serving other functions, also function to create and update a shared consensus. While we don’t all agree with such consensus, it is available as a decent first estimate for those who do not specialize in a topic, facilitating an intellectual division of labor.

For example: search engines, academia, news media, encyclopedias, courts/agencies, consultants, speculative markets, and polls/elections. In many of these institutions, one can ask questions, find closest existing answers, induce the creation of new answers, induce elaboration or updates of older answers, induce resolution of apparent inconsistencies between existing answers, and challenge existing answers with proposed replacements. Allowed questions often include meta questions such as origins of, translations of, confidence in, and expected future changes in, other questions.

These existing institutions, however, often seem weak and haphazard. They often offer poor and biased incentives, use different methods for rather similar topics, leave a lot of huge holes where no decent consensus is offered, and tolerate many inconsistencies in the answers provided by different parts. Which raises the obvious question: can we understand the advantages and disadvantages of existing methods in different contexts well enough to suggest which ones we should use more or less where, or to design better variations, ones that offer stronger incentives, lower costs, and wider scope and integration?

Of course computers could contribute to such new institutions, but they needn’t be the only or even main parts. And of course the idea here is to come up with design candidates to test first at small scales, scaling up only when results look promising. Design candidates will seem more promising if we can at least imagine using them more widely, and if they are based on theories that plausibly explain failings of existing institutions. And of course I’m not talking about pressuring people to follow a consensus, just to make a consensus available to those who want to use it.

As usual, a design proposal should roughly describe what acts each participant can do when, what they each know about what others have done, and what payoffs they each get for the main possible outcomes of typical actions. All in a way that is physically, computationally, and financially feasible. Of course we’d like a story about why equilibria of such a system are likely to produce accurate answers fast and at low cost, relative to other possible systems. And we may need to also satisfy hidden motives, the unacknowledged reasons for why people actually like existing institutions.

I have lots of ideas for proposals I’d like the world to consider here. But I realized that perhaps I’ve neglected calling attention to the problem itself. So I’ve written this post in the hope of inspiring some of you with a challenge: can you help design (or test) new robust ways to create and update a social consensus?

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