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People's Understanding of Others' Lives Is Biased Based on the Structure of Their Social Network
Transcript:
Speaker 1
So there's something in that that I found really interesting about this social sampling, which is that as you mentioned, like if you happen to be worse off and everyone else is worse Off, as is the case with like income, for example, then being worse off, you're going to project your bias into that general population more accurately than if you're better off in some Situation for which the most of the population is worse off. And that these biases are not all created equal. Yes. It has to do with how they stand relative to the broader population. So what we show is that this kind of biases of judgments of the broader population can be explained by the structure of social network and not by some cognitive deficit or motivational, Motivational bias, some desire to be better than others or that or some idea that everybody's like me or some cognitive deficit that people cannot, that people are too stupid to understand How other people live. It's really determined by the context of memory, that by the content of one's memory, which comes from one social circle.
Mirta Galesic on Social Learning & Decision-Making
COMPLEXITY: Physics of Life
Risk tolerance in org change: When 1 bad thing happens, don't pave over it with rules
Transcript:
Speaker 1
Two is, and Sam, like you and I have talked about this a million times, like there is such a bias around risk where it's like this thing happened once. Now we make a rule for it. Now we have to uphold that rule in perpetuity. And that is what we call work debt. We don't even know if that one thing would ever happen again. But now we have hours and time and money and cycles and cycles and cycles probably forever because let's be honest, we're never going to unwrite that rule for what might have been a one Off. And so on the one hand, I think like that person's perspective is really valid. On the other hand, I'm like, if you want to be strategic, you have to learn to look at risk a little bit differently, which is that you can never eliminate it. You are always doing stuff with the issue in the rear view.
The Future of HR — Building Your Capabilities, Pt. 1 - Getting to Level 3
At Work with The Ready
The Danger of Incorrectly Mapping Between Scientific Measures and Truth
Transcript:
Speaker 1
And it's a problem when scientific culture tolerates too much ambiguity. There's always a caveat there, which is that at the early stage of theory development, sometimes you need ambiguity because you don't actually know really what you're talking about Yet. And so you need to allow for multiple interpretations to be possible until you can figure out what you mean. But a mature theory should be minimally ambiguous. This is at odds with things like metrics in terms of let's say how to evaluate something because people think, oh, well, it's scientific. Therefore, I want to use this to then therefore impose a value judge on something. It's better because it has a higher score on it. But that's not what science is actually able to do. Science can say, it has this score and it measures this thing because what it measures is this. If you say what it measures is this, and therefore it means this other thing, that's a problem because that's a false mapping. And it's not really about ambiguity versus precision. It's about, I think, the imprecision of the mapping between the measure and the term. So if you want to measure something like happiness or economic prosperity, you can say, well, we'll measure the genie coefficient, we'll measure GDP. But those are rigorous, clearly unambiguous measures. They have a meaning. This is what they are. This is how we measure them. We can compare things on this measure. And that's not problematic until you then say, and it is better to have a higher GDP full stop.
Paul Smaldino & C. Thi Nguyen on Problems With Value Metrics & Governance at Scale
COMPLEXITY: Physics of Life
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