@phillipdewet The "put enough people and it will always go altruistic" notion has one severe issue:

Survivorship bias.

A large polity can do one of two things: survive, or fail.

The large polities which fail ... aren't around any more.

So QED, the only large polities which we have to examine are those which have sufficiently addressed the challenges of society-at-scale.

Note that "survive" does not equal "thrive", and there are plenty of large polities which exist at the threshold of failure. Think of any megacity with slums and barrios, of regimes best described not as liberal democracies but autocracies, or kleptocracies, of warring city-states (e.g., Afghanistan, Syria, Somalia, Iraq), as narco-states, or as regions with raging endemic disease (HIV/AIDS in much of sub-Saharan Africa, malaria, TDR-TB (a particularly ... interesting case), or "diseases of modernity" such as diabetes, heart failure, lead poisoning (and other heavy-metals contamination), asthma, etc.

One consequence of marginal-benefit theory is that systems tend to develop right up to that margin, be it Degree of Website Suck, level of product satisfaction, or the balance of social function against crime, vice, corruption, disease, economic exploitation, environmental degradation.

Metcalfe's Law is a popular, but incorrect, model of the "value" of a network: V ~= n2 . That is, value is proportional to the square of the nodes, call it population size or membership in the case of a social network or city.

A correction to that was proposed by Andrew Odlyzko and Ben Tilly in 2005 is that the value is proportionate to the log of the nodes: V ~= log(n) https://www-users.cse.umn.edu/~odlyzko/doc/metcalfe.pdf (PDF)

That's ... better ... but still inaccurate.

In reality, each additional node contributes both value and cost to a network, and on average that cost function can be assumed to be fixed, at least for a given point in time. So:

V ~= log(n) - kn

Where k is some fixed cost value.

If we presume marginal-benefit, that is, a network will grow to the point that the marginal value of the next node is equal to the marginal cost, then the size of the network is governed by the cost function.

Put another way: The reason Facebook has succeeded in scaling to 3 billion MAU is because it's managed to keep that cost function low.

But there's a catch: k is not constant over time.

That is, as a network grows, new pathologies co-evolve with it. Scammers and sociopaths arrive. And you tend to lose the highest-value contributors. Both result in a death spiral of a failing network (social, communications, trade, marketing, whatever).

See "Geeks, MOPS, and Sociopaths in subculture evolution" for a narrative explanation: https://meaningness.com/geeks-mops-sociopaths. Kyle Harper's The Fate of Rome describes how infectious disease co-evolved with the empire due to the very characteristics of that empire, which is a fascinating exploration.

You can also see this in, e.g., the development of cities. Rome was the first Western city to reach ~1 million population, a mark not met until London surpassed that number in the 19th century. (There may have been larger cities in China and India, I'm hazy on this.) 19th century London was a death mill. The city had to import fresh blood because its mortality rate exceeded live births. Life expectancy of a newly-arrived (immigrating, not born) resident was less than a decade. Epidemics were legion, and killed by the tens of thousands. It wasn't until sewerage, fresh water, solid waste, and basic hygiene standards and systems were imposed that this ceased. New York City followed a similar trend.

(And that's not even diving into issues of corruption, exploitation, crime, vice, and the rest.)

A huge challenge of developing a new network is that there are two very fundamentally different phases: growth, where the goal is to get big enough to sustain critical mass, at any cost, and maturity, where the chief issues is to manage emerging pathologies, to stem defections, and to subvert any newly-emerging rivals. I call that latter "hygiene factors", which relates to an #TechOntology you might want to look up.

FACEBOOK IS INTIMATELY FAMILIAR WITH THIS GAME AND HAS PLAYED IT WITH GREAT SKILL TO DATE. And THAT is a chief reason I'm quite concerned with its arrival here.

So: no, scaling isn't an automatic guarantee of success. There are plenty of social networks which outgrew their own capacities, or succumbed to 'k', if you will. Often that occurs through out-migration as more viable opportunities present (Friendster to MySpace, MySpace to Facebook, Digg to Reddit), though it's also possible to implode without a clear successor.

#MetcalfesLaw #OdlyzkoTilly #KyleHarper #GeeksMopsAndSociopaths #Networks #NetworkCostFunction

@pnathan I've run across TRIZ before, though only given it a fairly cursory look.

https://en.wikipedia.org/wiki/TRIZ

Thanks for the reminder.

Altshuller's four components of systems ("Any working system must have 4 parts: the engine, the transmission, the working unit (working organ) and the control element (organ of steering). The engine generates the needed energy, the transmission guides this energy to the working unit, which ensures contact with outside world (processed object), and the control element makes the system adaptable.") reminds me of my own ontology of technological mechanisms, with nine components:

#TRIZ #GenrichAltshuller #LTSE #systems #TechOntology

@dlovell Incidentally, on what technology is, in terms of what its operating mechanisms are, I've arrived at a nine-element list:

Early development here:
https://ello.co/dredmorbius/post/klsjjjzzl9plqxz-ms8nww

I've written more on this at https://old.reddit.com/r/dredmorbius and here under the #TechOntology tag.

Again, information is specifically included as a technological mechanism. This includes gestures, speech, writing, maths, logic, mechanical storage and retrieval, computing, printing, telecoms, broadcast, etc. All of these have profound impacts on the societies and cultures into which they emerge.

@kensanata
An alternative is the traditional "chinese doctor payment model": you pay the doctor when you're well, the doctor's incentive is to keep you well, and to restore your health at minimal cost and time.

Keep in mind that this can still be a market-based mechanism. What's changed, though, is the notion of what specificially the good or service being sought is, where the value lies, and what constitutes cost.

Though it might also be considered a state (or other collective) interest, and that the healthcare sector is delivering a service (a healthy and capable population) to the community as a whole.

(Education and other social services might be similarly considered, though here, education as a service to employers in delivering a capable workforce is another interpretation --- not without its own set of implications.)

#RobertKMerton #Hygiene #CovertFunctions #ManifestFunctions #TechOntology

3/end/

@kensanata
Because of numerous aspects of market function, we tend to compensate based on service or product delivery rather than based on achieved results. There's also a challenge in measuring or assessing hygiene interventions, simply because they're long-term, indirect, and in general covert rather than manifest.

The sociologist Robert K. Merton came up with (or substantially developed) the notions of manifest & covert functions as well as intended and unintended consequences. He makes a strong argument that covert functions are conceptually more significant knowledge simply by fact that they're less evident or obvious.

#TechOntology #ManifestFunctions #CovertFunctions #Hygiene #RobertKMerton

2/