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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

Perverse Incentives Select for People Who Are the Best at Exploiting a Given System Summary: The original deans and administrators burn out due to their dislike of the US News and World Report rankings and are replaced by individuals driven by ranking success. This shift reflects a difference in mentality between valuing money as a means of support versus valuing money as the sole purpose of life. Similarly, pursuing publications and citations for a job versus making them the ultimate goal shows a significant distinction. However, these differences are connected through a temporal dynamic where initially people adapt their behavior to succeed in a flawed system. The system then filters out those who can best exploit it, resulting in the selection of individuals with specific values. Transcript: Speaker 2 What happens later on, the original deans and administrators burn out because of how much they hate the US news and rule report rankings, and they get replaced by people who are all it. They think the only point is to rise in the rankings. And those people don't hold back. They only have one target. I think something similar is the difference between so realizing I need a lot of money in order to a decent amount of money to support my family, but not thinking money is the point of life. And similarly, realizing that getting a decent number of publications and citations is necessary for a job versus thinking the goal of my life is to max out citations. And for me, there's a huge gulf between those things. Speaker 1 Well, here's where I think they're connected because I see the difference and I understand the difference you're talking about. But I think the difference is that is this temporal dynamic, right, where you start out with, let's say, perverse incentives and people saying, well, I don't necessarily value these Things, but I have to shape my behavior in order to succeed in this system. But the thing is, the system being the way it is creates a filter. And the people who are the best at figuring out how to operate in that are the ones that then end up being successful. And they're the ones that teach the next generation or emulated by the next generation. And over time, the people for whatever reasons, psychologically or behaviorally or ever their path is, are best able to exploit the system are going to be able to thrive in it. And I think that because of that, you end up selecting for people with certain kinds of values, because they're going to be the people who there's always exceptions, but are going to Be best able to thrive in this kind of thing.

Paul Smaldino & C. Thi Nguyen on Problems With Value Metrics & Governance at Scale

COMPLEXITY: Physics of Life

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

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