Predictable Identities: 5 – Outgroup Homogeneity

This entry is part 5 of 15 in the series Predictable Identities

There are more ways for someone to be different from you than to be similar. But psychologically, it works the other way around. We perceive those like us as uniquely distinct and the outgroup as undifferentiated. It’s called the outgroup homogeneity effect.

This effect extends to physical appearance (e.g. faces of other ethnicities looking the same) and mental traits (e.g. people of the other gender all supposedly wanting the same things). Surprisingly, the effect is unrelated to the number of ingroup and outgroup members one knows; it’s not about mere exposure.

I recently wrote about a remarkable case of the outgroup homogeneity effect: Ezra Klein’s strange attempt to make the case that liberaltarian podcaster (and Klein’s co-ethnic) Dave Rubin is a reactionary.

Klein starts by looking at the network graph of podcast appearances which links Rubin to several unsavory reactionaries. But Klein himself is just two podcasts removed from Richard Spencer, so that’s not great. He then defines “reactionaries” narrowly as those who seek “a return to traditional gender and racial norms”. Of course, most of Rubin’s flagship positions (gay marriage, drug legalization, abortion access, prison reform, abolishing the death penalty) have to do with gender and race norms. Specifically: changing them.

I think what happened is that Ezra Klein picked up intuitively on the one important similarity between Rubin and conservative reactionaries: they both strongly dislike him.

People legislate the distinction between pies and tarts and between plums and nectarines, but only geologists care about telling apart inedible rocks. Same with people: it’s important to keep track individually of potential cooperators, reciprocity relationships, etc. But once you model someone as a defector, you don’t need more detail to predict that they’ll defect. The outgroup is good for writing snarky articles about. For this, you don’t have to tell them apart.

Predictable Identities: 4 – Stereotypes

This entry is part 4 of 15 in the series Predictable Identities

How do we predict strangers?

Humans evolved in an environment where they rarely had to do this. Practically everyone a prehistoric human met was familiar and could be modeled individually based on their past interactions. But today, we deal with ever-stranger strangers on big city streets, in global markets, online…

We need to make quick predictions about people we’ve never met. We do it using stereotypes.

Early research on stereotypes focused on their affective aspect: we dislike strangers, but less so as we get to know them. But new studies have looked at the content of stereotypes, finding that groups are judged independently on the dimensions of warmth/competitiveness and status/competence. For example, Germans see Italians as high-warmth low-competence, i.e. lovable buffoons; Italians see Germans as the exact opposite, i.e. mercenary experts.

These dimensions are primarily about predicting someone’s behavior and capability. In game theory terms: Will that person cooperate? And can I safely defect on them or do I have to play nice?

Contrary to the well-meaning wishes of most stereotype researchers, there is robust empirical evidence on stereotype accuracy. The short of it is that people’s stereotypes are, in fact, quite accurate, especially stereotypes of gender and ethnicity. Whether stereotyping is good or bad, it cannot simply be wished “educated” away. It is universal because it’s very useful for prediction.

A smart person noted that problems arise from having too few stereotypes, not too many. If you have a single stereotype for “Jews” you’re doing a bad job of modeling Jewish people, and are likelier to mistreat them due to prejudice. If you have separate stereotypes for Hasidic Jews, Brooklyn conservative Zionists, liberal Jewish atheists, secular Israelis etc. you’re one step closer to treating (and modeling) unfamiliar Jews as individuals. Studying cultures is about acquiring many useful stereotypes.

Predictable Identities: 3 – Prisoner’s Dilemma

This entry is part 3 of 15 in the series Predictable Identities

Much of human interaction is shaped by the structure of the prisoner’s dilemma. We put in place institutions and norms to enforce cooperation. We tell shared stories to inspire it. We evolved moral emotions to achieve cooperation on an interpersonal level: empathy and gratitude to assure cooperators of our cooperation, anger and vengefulness to punish defectors, tribalism and loyalty to cooperate with those we know well.

But the crux of the prisoner’s dilemma is that defection is always better for the defector. We try to get others to cooperate with us, but we also try to defect as much as we can get away with. We want our peers to pay their taxes, admit mistakes, share credit, and stay faithful. We also fudge our taxes, shift blame, boast, and cheat.

There are many strategies for dealing with PD, and some of them can be formalized in code and entered in competitions with other strategies. The simple strategies are named and studied: tit-for-tat responds to each play in kind, tit-for-two-tats forgives one deception in case it was a mistake, Pavlov changes tacks after being defected against and so on and so forth. Which strategy works best?

It turns out that the success of each strategy depends almost entirely on the strategies played by opponents. Each approach can fail to reach cooperation with others or under-exploit opportunities to defect; even a strategy of always defecting is optimal if enough other players always cooperate. If you only knew what your opponent is playing, you could always choose the best option.

And this brings us back to predicting other humans. If we can model their strategies, if we know who will be forgiving and kind, who will be vengeful and dangerous, we can play optimally in any situation. Predicting well is the unbeatable strategy.

Predictable Identities: 2 – Active Inference

This entry is part 2 of 15 in the series Predictable Identities

One sentence recap of Part I: our brains are constantly trying to make true predictions about the world. We do it in two ways:

  1. Assembling good models that make accurate predictions.
  2. Changing the world to match our predictions.

When Apple released a buggy version of Maps, The Onion joked that “Apple is fixing glitches in Maps by rearranging Earth’s geography”. That’s exactly what our brains do.

Actions are driven by predictions propagating across different levels. We shoot a basketball by forecasting the flight of the ball, which leads to predicting that we will lift the ball and push it, culminating in precise anticipations of the required tension in the arm muscles. We are satisfied when the ball flies according to our projection and upset when it doesn’t

That’s why predicting well is so important to our evolved brains – when we predict well we know how to act to achieve our goals. A predictable environment is an exploitable environment.

Of course, basketballs are not a big component of our milieu, and our ability to predict them isn’t crucial. What is vital for us to model above all else are people, from faraway strangers to neighbors and friends. Also ourselves: prediction happens at different parts of the brain simultaneously, and each module has to predict what the others would do, now and in the future.

Predictive processing expert Sun Tzu observed:

If you know the enemy and know yourself, you need not fear the result of a hundred battles.

We observe other minds, interrogate them, and push them to conform to our models of them as best we can – all to maximize our predictive power and capacity to act effectively. This is a powerful lens through which to observe how we interact with others, and how we build our own predictable identities.

Predictable Identities: 1 – Guess What’s Coming

This entry is part 1 of 15 in the series Predictable Identities

Can you predict the last word in this sentience? It’s not “sentence”. The last word in sentience research is that most of what our brains do is try and predict the signals they’re about to receive, like the words you read on a page. Prediction shapes our perception, which is why that word appeared as “sentence” the first time you read it.

Our brains implement predictive models at multiple levels, from general worldviews to detailed patterns. When reading a text, your brain starts predicting the language and theme based on its model of the publication. This drives the prediction of sentences based on a model of grammar, prediction of how words should be spelled, and finally a detailed prediction of how characters should display.

Consider:

  1. This looks weird.
  2. This looks wiedr.
  3. Looks weird this.
  4. Your mom looks weird.

Our brains optimize for predicting incoming signals over our entire lifetime. This is achieved in two ways: doing a good job of predicting inputs right now, and learning new models that will allow us to make great predictions in the future. Does reading the post so far feel unpleasantly confusing? That’s because the content was too unpredictable, and contradicted too many of your existing models of how brains work. Did it feel awesomely mind-blowing? That’s the joy of acquiring a new model that offers a condensed explanation of what you already know, and thus a promise of better predictions to come.

In either case, you should learn more about the predictive processing paradigm of cognition from this series of articles or this book review; this blogchain is mostly done covering the established science. Instead, we’re going to forge forward irresponsibly and use predictive processing to explain political polarization, identity, war, and adjunct professorship.

Do you think you know what’s coming?