18 karma · joined August 18, 2016
It's possible that it's something along the lines you say. Another possibility is people just don't have good estimates of popularity among people they don't know, so they overestimate the popularity of opinions represented in their immediate networks.
The model predicts that areas in which there is little evidence one way or the other available are the ones where incorrect beliefs could be sustained, particularly when the belief is held by those around you.
I guess one interesting characteristic of certain superstitions is that people will avoid the scenario that they think will lead to a bad outcome, so they actually don't collect personal evidence discrediting that superstition.
This line of argument suggests that positive superstitions should be less persistent than negative superstitions, though I am not sure if that is true.
I'm kind of shooting in the dark on this one, but Bayesian inference does function as a probabilistic generalization of propositional logic, so in principle it seems like the framework should be able to accommodate at least some kind of scenarios involving logical statements. There are also lots of Bayesian language models.
At the same time, there are impossibility theorems that show beliefs across a group cannot in general be aggregated in coherent ways. (Some of the counter examples look a lot like the one you give.)
So the type of model we are introducing is necessarily only going to be able to apply to certain groups (and perhaps could help characterize collectively rational vs irrational groups).
One other related comment: the case we have dealt with so far is mainly about how groups integrate new information (collective learning), not about the existing prior knowledge knowledge within the group.
The collective intelligence task in this case is for the community on the site to identify who are the best traders to be mimicking. It's not an explicit group goal, but we are treating it as an implicit one.
I wouldn't say we are trying to define collective intelligence per se. e.g., when you offer a model of a dynamical physical process (maybe a ball arching through the air), you can hardly say that your model provides a definition of that physical system. But you can say that the physical model describes the movement of the ball.
In the same way, I'd instead say we are simply trying to grabble with how to think about collective intelligence.
We are offering a model and a class of models that we expect to display collectively intelligent behavior, and we thereby hypothesize that these models might also explain how collective intelligence in human groups might arise.