Difference between revisions of "Filter bubbles"
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| − | Filter bubbles | + | Filter bubbles (sometimes called "echo chambers") are what happens when the information you come across reflects your personal history so well that you stop learning anything new. They're a big problem in [[Knowledge Management]]. |
| − | They reflect self-reinforcing [[positive feedback|positive feedbacks]] | + | They reflect self-reinforcing [[positive feedback|positive feedbacks]] that accelerate as they home in on a fixed view of things. On the plus side, this helps increase precision and split areas of convergence from one another. But from the point of view of a user, like black holes, they're difficult to escape from. |
| − | In natural systems, these positive feedbacks are held in check by opposing mixing processes, which cause divergence instead of convergence, spreading flows out instead of concentrating them. Filter bubbles emerge when those spreading processes are relatively ineffective and so convergence comes to dominate | + | In natural systems, these positive feedbacks are held in check by opposing mixing processes, which cause divergence instead of convergence, spreading flows out instead of concentrating them. Filter bubbles emerge when those spreading processes are relatively ineffective and so convergence comes to dominate information flow. |
Up to a point, you could regard filter bubbles as a natural response to enormous flows passing through parts of the internet. But filter bubbles are not necessarily the most productive or efficient way to accommodate that flow, and clearly they have other important consequences too (e.g. political). | Up to a point, you could regard filter bubbles as a natural response to enormous flows passing through parts of the internet. But filter bubbles are not necessarily the most productive or efficient way to accommodate that flow, and clearly they have other important consequences too (e.g. political). | ||
| − | At least part of the reason for relatively weak mixing on the internet is that [[Algorithms| | + | At least part of the reason for relatively weak mixing on the internet is that [[Algorithms|quantitative methods]] chase marginal improvements in precision, using relatively fixed social and taxonomic data with low degrees of freedom as a conduit. Another is that information providers sometimes build very complex systems (e.g. to include more [[Artificial Intelligence|"intelligence"]]) that simply can't keep up with rapidly changing flows. |
But there are relatively simple ways around some of these things, more in line with balanced natural systems (for example, better use of clickstream data or better design of [[Negative feedback|negative feedbacks]]), so I expect that filter bubbles are not the end of the story. | But there are relatively simple ways around some of these things, more in line with balanced natural systems (for example, better use of clickstream data or better design of [[Negative feedback|negative feedbacks]]), so I expect that filter bubbles are not the end of the story. | ||
| − | [http://www.pontneo.com/ctf/ {!ctf}] is all about balancing divergence and convergence to | + | The [http://www.pontneo.com/ctf/ {!ctf}] extension on this wiki is all about balancing divergence and convergence to help information flow more effectively. |
[[Category:Wiki]][[Category:IT]] | [[Category:Wiki]][[Category:IT]] | ||
Latest revision as of 12:22, 29 November 2018
Filter bubbles (sometimes called "echo chambers") are what happens when the information you come across reflects your personal history so well that you stop learning anything new. They're a big problem in Knowledge Management.
They reflect self-reinforcing positive feedbacks that accelerate as they home in on a fixed view of things. On the plus side, this helps increase precision and split areas of convergence from one another. But from the point of view of a user, like black holes, they're difficult to escape from.
In natural systems, these positive feedbacks are held in check by opposing mixing processes, which cause divergence instead of convergence, spreading flows out instead of concentrating them. Filter bubbles emerge when those spreading processes are relatively ineffective and so convergence comes to dominate information flow.
Up to a point, you could regard filter bubbles as a natural response to enormous flows passing through parts of the internet. But filter bubbles are not necessarily the most productive or efficient way to accommodate that flow, and clearly they have other important consequences too (e.g. political).
At least part of the reason for relatively weak mixing on the internet is that quantitative methods chase marginal improvements in precision, using relatively fixed social and taxonomic data with low degrees of freedom as a conduit. Another is that information providers sometimes build very complex systems (e.g. to include more "intelligence") that simply can't keep up with rapidly changing flows.
But there are relatively simple ways around some of these things, more in line with balanced natural systems (for example, better use of clickstream data or better design of negative feedbacks), so I expect that filter bubbles are not the end of the story.
The {!ctf} extension on this wiki is all about balancing divergence and convergence to help information flow more effectively.
