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Judgement-based filtering is what people do.

As we gain skills and knowledge, the amount of information we can process increases. If we invest enough time in learning something, we can reach filter like an expert.

So, the real question is, how do we design filters that let us find our way through this particular abundance of information? And, you know, my answer to that question has been: the only group that can catalog everything is everybody. One of the reasons you see this enormous move towards social filters, as with Digg, as with del.icio.us, as with Google Reader, in a way, is simply that the scale of the problem has exceeded what professional catalogers can do. But, you know, you never hear twenty-year-olds talking about information overload because they understand the filters they’re given. You only hear, you know, forty- and fifty-year-olds taking about it, sixty-year-olds talking about because we grew up in the world of card catalogs and TV Guide. And now, all the filters we’re used to are broken and we’d like to blame it on the environment instead of admitting that we’re just, you know, we just don’t understand what’s going on.

There can also be expert networks – in some sense that is what the original search engines were, and what mahalo.com is trying now. The problem that the original search engines encountered is that the amount of information available on the web expanded so quickly that it outstripped the ability of the network to keep up with it. This led to the development of google’s search algorithm – an example of one of the versions of mechanical filtering: algorithmic.

heingold also provides a pretty good description of the other form of mechanical filtering, heuristic, in his piece on crap detection. Heuristic filtering is based on a set of rules or routines that people can follow to help them sort through the information available to them.

Filtering by itself is important, but it only creates value when you combine it with aggregating and connecting. As Rheingold puts it:

The important part, as I stressed at the beginning, is in your head. It really doesn’t do any good to multiply the amount of information flowing in, and even filtering that information so that only the best gets to you, if you don’t have a mental cognitive and social strategy for how you’re going to deploy your attention. (emphasis added)

That complexity demands non-simple responses. Each of the techniques sketched above offers one way of helping groups to think through these emergent forces and to apprehend the future. Crowdsourcing, scenarios, prediction markets, the Delphi method, and environmental scanning are complementary strategies. Using several of these methods can teach us to learn about the future in more sophisticated, pro-active ways. If the methods appear strange, resembling science fiction, perhaps that is a sign of their aptness for the future, since the future often appears strange just before it becomes ordinary—or, in our case, just before it becomes a campus reality. As higher education budgets clamp down and the future hurtles toward us, we need these methods and techniques as allies that can help us to survive . . . and to learn.

Crowdsourcing, scenarios, prediction markets, the Delphi method, and environmental scanning are complementary strategies. Using several of these methods can teach us to learn about the future in more sophisticated, pro-active ways. If the methods appear strange, resembling science fiction, perhaps that is a sign of their aptness for the future, since the future often appears strange just before it becomes ordinary—or, in our case, just before it becomes a campus reality. As higher education budgets clamp down and the future hurtles toward us, we need these methods and techniques as allies that can help us to survive . . . and to learn.

to apprehend the future. Crowdsourcing, sce