Monthly Archives: May 2020

Cognition Is Social

Oddly enough, this stylized fact bears fruit on several different levels.

E pluribus unum

Intelligence emerges non-additively from the interaction of many less-intelligent agents. A group can have intelligence greater than any of its individual parts, or even their sum. Of course, the opposite can be true as well.

This phenomenon has many forms and names:

  1. Brian Eno’s idea of scenius: “an ecology of talent…supporting each other, looking at each other’s work, copying from each other, stealing ideas and contributing ideas.” (see takes from Kevin Kelly and Austin Kleon).
  2. Marvin Minsky’s Society of Mind modeled human intelligence as a society of agents competing for resources and power, and collaborating to achieve shared goals.
  3. Hayek’s point that computation is difficult and information scattered, so to solve the social optimization problem, our only hope is to use markets to aggregate many local decisions.
  4. The hive mind of twitter (the cool kids are calling it an “egregore”), the world of memes, of the tyranny of ideas.
  5. Individual minds often work better in a group setting. We respond to rewards, social rewards are powerful, and if you are rewarded for “good” thoughts then you generate more of them.

Counterpoint: “nine women can’t make a baby in one month.”

E unibus pluram

The human intelligence explosion can’t be explained by normal processes of natural selection. What new threats or opportunities emerged in the savannah to create such an intense selection pressure? Answer: other humans.

Runaway evolutionary processes are usually explained by an arms race of some sort. Humans are intensely social creatures, and in our evolutionary environment the most interesting thing is other people. Being able to anticipate what other people will do and incorporate it into your actions is an immense edge. All of our social actions (flattery, threats, flirtation, requests, manipulation) rely on having accurate mental models of other people. So success depends on two traits: the ease with which you can simulate other people, and the difficulty of being simulated. The arms race goes exponential because of the “coincidence” that these two are both grounded in “intelligence”.

You can tap into this with a simple technique: Decide who you would ask for help, and imagine what they’d say.

[epistemic status for this section: shakier than I’d like. I didn’t invent this idea, but I haven’t done the work to firmly ground it in the originating research.]


Find a scene, think in public, work with the garage door up, use your social imagination productively.

Stability and Play

I often think about how heavy my head is. When I let it rest on my hand, or lie down, I feel its weight and think about how much work my neck is doing to keep it upright throughout the day. The neck is, generally, an incredible system which keeps the head stable and allows for fully three-dimensional movement (pitch, roll, yaw) through a fairly wide range.

When I’m stressed or unhappy I usually feel that emotion to be physically located in the back of my neck, and sometimes treating the physical symptoms can alleviate the emotional distress. Which is to say in the normal course of events my neck is working hard.

The other day I was doing some yoga with a youtube video, and I got to a part of the session where I was told to move my head in circles to stretch out my neck. The instructor said something like “remember to keep your core activated”, I tightened up, and felt something completely new.

I became aware of a freedom in my neck that I had never felt before, a looseness that let me stretch farther, with less risk and worry, than I ever had. The muscles relaxed, the low-grade worry about keeping my head from toppling over dissipated, and I could playfully explore all kinds of new movements.

This all came about, of course, because I had given myself more physical stability with the rest of my body. This is a general pattern: there is a certain amount of stability necessary across every system, and stability in one place can ripple out, allowing other parts of the system to relax and play freely. Stability here is not static, but a control loop of sensing the outside world and making microadjustments to maintain whatever equilibrium is important.

Note that this isn’t a “nerve center” controlling the reaction of different components: stability within one component can trickle to the rest without direct control.

There are other kinds of stability we can think about, too.

Corporate research labs are kind of like this: Xerox PARC, Bell Labs, Google X. Only a company that is absolutely printing money, whose core business is rock-solid, can allow for such an unstructured approach to R&D. Here stability is basically cashflow; play is expensive.

Another is the advice given to startups that they shouldn’t try to innovate on corporate structure, because their core mission is already so difficult. The scarce resources here are time and attention for the people involved, but also some sense of risk. If your central idea is already likely to fail, you want as little risk in the rest of the system as possible.

Similarly, young writers often try to live bohemian lives, but are usually advised to build routine in their lives so they can be reckless in their art. There’s been an analogous idea floating around the techno-rationalist worlds that getting married early frees you to do greater career work, and I think the hypothesis is similar. (The econ blogosphere has been talking for a while about the marriage premium but the emphasis there is slightly different.)

Stability Mapping

When you are trying to understand, explore, or improve a system, why not try “stability mapping”? Which parts of the system generate stability, and which use it up or rely on it? Which kinds of stability are in surplus, and which are in short supply? How does stability percolate from one part of the system to the next?

Right now, organizations of every sort are reeling with VUCA (new term for me, see slide 14 here) . This analysis suggests one promising approach: identify the sources of stability that still remain and build around the kinds of stability they give you.