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Psychologist Richard Wiseman created a study using waiters to identify what was the more effective method of creating a connection with strangers: mirroring or positive reinforcement. One group of waiters, using positive reinforcement, lavished praise and encouragement on patrons using words such as “great,” “no problem,” and “sure” in response to each order. The other group of waiters mirrored their customers simply by repeating their orders back to them. The results were stunning: the average tip of the waiters who mirrored was 70 percent more than of those who used positive reinforcement.

Never Split the Difference

Chris Voss and Tahl Raz

Prediction Markets Are Built on the Principle of Adverserial Engagement Transcript: Speaker 2 There the first is what you're describing is precisely the reason why i am a bit of a skeptic of prediction markets not to say that they don't have a role but i don't think that they are nearly The solution that many believe they are and it's because they set us up in an adversarial relationship with regards to determining the truth it's not at all the say i don't think incentives Have a role or that it isn't worth a listening information for me i believe in all those things but the notion that the way that we should do it is betting against each other so that we want Everyone else to be as wrong as possible so we can be right and we want to get like one big payoff for like the person who's most right and anything that can be like too easily analogized To some sort of like dick measuring contest is not something that like excites me as a mechanism for like coming to good social outcomes and i think that prediction markets have an important Element of that

Glen Weyl & Cris Moore on Plurality, Governance, and Decentralized Society

COMPLEXITY: Physics of Life

Explore v.s. Exploit: Finding Solutions Quickly Can Get You Stuck in a Local Optimum Transcript: Speaker 1 So when I started doing the work in AI, one of the really, very, very general ideas that comes across again and again in computer science is this idea of the explore, exploit trade on. And the idea is that you can't get a system that is simultaneously going to optimize for actually being able to do things effectively. That's the exploit part. And being able to figure out, search through all the possibilities. So let me try to describe it this way. I guess we're a podcast. So you're going to have to imagine this usually I wave my arms around a lot here. So imagine that you have some problem you want to solve or some hypothesis that you want to discover. And you can think about it as if there's a big box full of all the possible hypotheses and all the possible solutions to your problem or possible policies that you could have, for instance, Your reinforcement learning context. And now you're in a particular space in that box. That's what you know now. That's the hypotheses you have now. That's the policies you have now. Now what you want to do is get somewhere else. You want to be able to find a new idea, a new solution. And the question is how do you do that? And the idea is that there are actually two different kinds of strategies you could use. One of them is you could just search for solutions that are very similar to the ones you already have. And you could just make small changes in what you already think to accommodate new evidence or a new problem. And that has the advantage that you're going to be able to find a pretty good solution pretty quickly. But it has a disadvantage. And the disadvantage is that there might be a much better solution that's much further away in that high dimensional space. And any interesting space is going to be too large to just search completely systematically. You're always going to have to choose which kinds of possibilities you want to consider. So it could be that there's a really good solution, but it's much more different from where you currently are. And the trouble is that if you just do something like what's called hill climbing, you just look locally, you're likely to get stuck in what's called a local optimum.

Alison Gopnik on Child Development, Elderhood, Caregiving, and A.I.

COMPLEXITY: Physics of Life

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