🎤 Jessica Flack: Collective Computation

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Here’s my raw, somewhat structured notes from a conversation Jessica Flack had with Jim Rutt on his podcast.

I felt it was extremely lucid - same as Eric Smith’s appearance on the show.

Everything below is quotes, some modified, from Jessica: my expertise in this subject is not deep enough to attempt a synthesis. If you’re missing context you can find the original here.


Physical Laws and Biological Laws

Why are physical laws easier to see? Information processing comes with errors. Without the errors law like regularities are easy to observe from the observer’s point of view.

Because the system is performing these computations without taking the systems point of view (no ground truth --- so no measurement tools to use), we will never see the laws and in physical systems that is absolutely not required.

What does information processing do a system? My suggestion is that a lot of information processing is erroneous. The components are making mistakes or have restricted compute so they cannot optimally estimate regularities in their environment. So what do they do? They collectively compute - crowdsource - the regularities (coarse-graining).

And in this way the components sort of solve this problem that information processing introduces and the problem is subjectivity. To produce ordered states, they pool votes and get consensus which is not ground truth.

In order to see more laws in biology, we need to understand how the system is processing information through collective computation. And once we have that, we’ll have a better idea of what the relevant macroscopic variables are.

Power distributions are not a ground truth. You’re not recovering a ground truth, you’re recovering a collectively constructed variable that is a result of information processing.

Physics for the most part dominated by concepts like pressure, temperature and entropy that are emergent from fairly simple collective interactions. Physical particles have properties like position, velocity, mass and their collective properties make temperature and pressure and so forth. And you get thermodynamics

Thermodynamics is kind of a theory for a relationship among the macroscopic or aggregate variables at equilibrium. And you get these regularities, like the ideal gas law, which is an equation of state that tells you the amount of gas is determined by its pressure, volume and temperature.

Now biology also makes use of comparable collective concepts as physics does, but in biology these are concepts like metabolism, conflict management and robustness. These are very functional properties with consequences due downward causation and so forth to the system and its components

In biology we don't see many laws yet ordered states are ubiquitous. It seems there’s law-like relations all over the place.

Why adaptive systems have this extra step of information processing, and whether it’s the reason why we see fewer laws in biology are these big open questions that people ask at SFI.

Monkeys, Power Laws and Signaling Circuits

Monkeys coarse-grain fight histories to find the power distribution through unidirectional submission and subordination signals.

Monkeys keep track of fights that happen in their group which determines their power relations and behaviors.

If the effect size, the perceived diff between two monkeys, is small they fight more to get more evidence.

The fights are continuing just so if something changes in the monkeys' fighting skill or circumstances they can reverse the subordination contract

A monkey can emit a subordination signal in non-fight situation preemptively and agree to a subordinate state in the relationship.

The subordination signal is highly unidirectional: It's a circuit that encodes the power distribution and the only leaf node is the monkey perceived most able to win fights

During monkey fights the signal might be temporarily tapping out: submission and not subordination

Subordination signals outside fights is an innovation on how individuals learn the meaning of signals that are temporally and spatially divorced from their reference.

__This is energy minimization because you save yourself the fight through coarse-graining__

The individuals who are even, they often don’t signal at all, and sometimes avoid each other.

There's fluctuations and contextually variable stuff, like how you’re feeling today, or the weather, or the presence of your allies which would take many more fights.

Heavy Tailed Power Distributions leads to new possible functions, like conflict resolution, by the "super rich"

Monkeys who sit out in the tail who are different from the rest of the group, who are perceived by everyone, more or less, as disproportionately powerful. So they’re like our billionaires.

Those individuals, because they’re perceived this way pay almost no costs, avoid taxes and get no aggression response to interventions in fights.

The powerful monkeys can do functions like conflict management that wouldn't be possible if there was a normal distribution or uniform without billionaires. That’s your emergent function. That doesn't necessarily mean that more equality gives less functions...

Energy systems scale sub-linear. Information systems scale super-linear

The industrial evolution was about energy. The digital revolution is about information.

[Metabolic Scaling Theory]
For lots of city variables - patents, STDs, companies - the scaling is super linear. That means it’s an increasing return to scale in contrast to the sub linear three quarter scaling that was found for the metabolic systems. So here’s an interesting observation, in these systems where information processing is important, that team to be collective, you get super linear scaling and in a strongly constrained, energetically constrained systems, you get this sort of sub linear scaling.