As I'd posted this poll a week or so back, general direct-dialed telephony, a/k/a PSTN (public switched telephone networks) seem to be in trouble. I think we could be within five years of their total collapse. And no, not just land lines (already about 25% of their peak in much of the US), but _all_ direct-dialed phones: mobile and VOIP included.
The problem, as I'd written before, is paradoxically the _low_ cost of calls. This is inducing tremendous volumes of junk calls, with one ...
@dredmorbius It's the #@*&#@$ topology - any can reach any in one step is a nightmare. An absolute nightmare. That's what's going to take us down. We need something like web of trust to put a distance metric back in; anything that doesn't have one is going to implode.
This is the ball of radius one and all other radii are infinite topology. It works great, if people are willing and familiar that they need to get introductions from other humans in order to connect. Given the success some networks have had with FOAF type layers, I think this can be leveraged and improved upon.
@kensanata @feonixrift @dredmorbius Well, I'd distinguish FOAF as a network strategy from FOAF as a communications strategy. FOAF for networking is, I hope, more likely to be a scale-tolerant and resilient system. But I've also had abysmal experiences of FOAF comms.. Retroshare, for example: once you hit FOAF connectivity sufficient for global routing.. there's this one guy who spams all the fora with antisemitist screeds. And anonymous, censorship-resistant FOAF networks can't deal with that. 🤷
This is why I think we need a toy network - a sandbox of completely simulated 'nodes' and 'links' and 'posts', purely statistical, not involving real servers just simulations - on which to try these things out! Then we would be able to work from at least simulated data when hashing out which methods might work, rather than pure imagination.
@cathal Reputational accountability for introductions and recommendations _is_ something that I think should scale _somewhat_ better than raw content-recommendations cases. Though given the disasters (and susceptibility to manipulation) of the latter, we may want to dial up the skepticism fully.