Carissa Véliz

speaker

349 appearances 4 recordings 4 series first heard Apr 2026 last heard 12 Jun

Carissa Véliz’s voice in public audio — every appearance, attributed to the second.

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recordings per month · last 12 months

Recordings per month over the last 12 months — 4 in all, peaking in Apr 2026 with 2.

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Something You Should Know · What Your Attachment Style Reveals & The Trouble with Predictions · 20 Apr 2026 podcast

But when I make a social prediction, that's very different because it changes the expectations of people if people believe me and that changes that which I'm predicting.

Thank you for having me, Mike.

The only thing that is more dangerous than being blind is thinking that you can see something when in fact what you're seeing is an illusion.

And often predictions make us have the feeling of safety, but it's an illusion and we're not seeing the actual risks.

So that's something to bear in mind.

I also want to make a very important distinction between predictions about things or natural phenomena and predictions about people or social predictions.

If I predict that it will rain tomorrow, it will have no effect on whether it actually rains.

The clouds aren't listening to my prediction.

They don't have agency to do otherwise.

But when I make a social prediction, that's very different because it changes the expectations of people if people believe me.

And that changes that which I'm predicting.

So social predictions have a tendency to become self-fulfilling prophecies or to act like magnets.

They bend reality towards themselves.

So if I tell a student, I think you're going to do great at this exam, it might encourage them and make them do better.

Whereas if I tell them, I think you're going to flunk this exam, it might be so discouraging that they might do worse than they otherwise would have.

So we need some guidance as to which kinds of predictions are the ones that actually enlighten us and are helpful to navigate the world, and which kinds of predictions might be leading us astray at best or at worst, creating incredible kinds of injustice and then covering them up.

That is true, but there's a lot of nuance about it.

For example, in the case of making decisions about loans or about jobs, we never collect the data of the person who didn't get the job.

So data can be very misleading sometimes.

And sometimes, in other cases in which we're looking at events that we've never seen before, for example, climate change, there is no database about it, about the future.

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