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The Map is not the Territory Summary: Humans often confuse maps with territories, despite evidence from various disciplines. We wrongly assume that what we measure is what matters, but our values may not have quantifiable metrics. Biometric data can oversimplify complex discussions on health. This conundrum becomes more significant when considering governance on a larger scale. How do we count and operate a nation state wisely? Can social science inform smarter political economies? We must escape the false clarity of information systems that lack collective wisdom. Transcript: Speaker 3 There are maps and there are territories and humans frequently confuse the two. No matter how insistently this point has been made by cognitive neuroscience, epistemology, economics, and a score of other disciplines, one common human error is to act as if we know What we should measure and that what we measure is what matters. But what we value doesn't even always have a metric and even reasonable proxies can distort our understanding of and behavior in the world we want to navigate. Even carefully collected biometric data can include the other factors that determine health or can oversimplify a nuanced conversation on the plural and contextual dimensions Of health, transforming goals like functional fitness into something easier to quantify but far less useful. This philosophical conundrum magnifies when we consider governance at scales beyond those at which homo sapiens evolved to grasp intuitively. What should we count to wisely operate a nation state? How do we practice social science in a way that can inform new, smarter species of political economy? And how can we escape this seductive but false clarity of systems that reign information but do not enhance collective wisdom?

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

COMPLEXITY: Physics of Life

Perspectives on Organizational Strategy & Coordination: Optimizing for Few Coherent Goals v.s. Many Incoherent Goals Transcript: Speaker 1 I think one of the things where the corporate world is actually much better at this than the academic world or the educational world, because their goal is profit. So it's very clear. It's much harder to say what the goal of an educational institution is. It feels like it should be obvious, but within the general goal of like we want to produce successful, well-rounded people, there's a lot of disagreement about what the goals are. And so shaping the institutional incentives around those goals becomes extremely difficult, because not only do we have to worry about perverse incentives, but we have to worry about Vigorous disagreement about the kinds of things that are valued in the first place. And I think exactly what you're talking about, T, is something that if you went to a bunch of university administrators, let's say, or medical school administrators or doctors, and You said, what is the point to what you're doing? Is it to produce wise, well-rounded people? Is it to minimize costs to insurance companies? Is it to increase donor contributions? What is it? And there are all these competing goals. And so there's this constant infighting about among different people who have different versions of what the best version of their institution is, and it's so difficult to articulate What that is. Speaker 2 I wonder if we're in different sides of this, because are you like worried about the hardness of it? It sounds like you think it's a problem that it's hard to come to agreement and articulate a goal, where I actually prefer the university that disagrees, has many incuit and plural goals, And worry that when it articulates an outcome clearly and starts orienting around that outcome, that's when it starts shedding a lot of what was good about the kind of pluralistic more. So let me just give you this is like from my life, right? So a university I've been employed at has started moving toward orienting everything around student success, where student success is defined as graduation rate, graduation speed, Salary after graduation. When you define that outcome, it becomes really easy to target, and the people that are targeting it, as you say, the people that target it well tend to rise, people that are willing to Go all in on targeting that stuff instead of caring about all the other weird shit that education might be for, tend to have better recordable outcomes and tend to rise in the university Structure. So I actually am happier for something as complicated with education, in which different groups have different conceptions of values about what they're doing, and we don't actually Try to settle it, and we don't hold them all to a high articulability constraint, because I think the business school and the CS department have more easily articulable outcomes than The creative writing department, art history department. A lot of the stuff that I'm writing right now is about like this defense of the inarticulable. Speaker 1 It's a hard question to answer because I think that there are multiple levels of organization going on here. There's like a top administrator level, because these institutions tend to be pretty hierarchical. I think at the top of the hierarchy, there has to be some sort of reasonably well-defined goal, even if it doesn't specify what every individual component of the organization or institution Would do it. And I think that that trickles down to those levels though, and creates incentives. Regardless of whether or not it's a good thing, I think there has to be some sort of coherence at the very top level, even if it doesn't dictate what each individual component is doing.

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

COMPLEXITY: Physics of Life

The need for transparent and democratic decision-making: Human bullshit and algorithmic bullshit are two sides of the same coin Summary: Data and algorithms are not inherently bad, but they should be used in a transparent and democratic way that empowers everyone. Instead of arguing about whether computer or human decision-making is better, we should focus on accountable and transparent decision-making. This means avoiding human biases and stereotypes as well as naive machine learning without considering its real-world implications. Transcript: Speaker 1 So the point is that it's not that data and algorithms are bad it's that they need to be applied in a way which is transparent and which is democratic and which empowers all of us to carry On these debates rather than simply being tools which accurately or inaccurately are being used to buy the powerful to control the rest of us it's silly to argue about which is better You know computer decision making or human decision making that's really not the point I mean the point is we should have accountable transparent decision making instead of bs there's Human bs which comes in the form of stereotypes in ideology and there's algorithmic bs which comes in the form of naive machine learning without thinking enough about its applications

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

COMPLEXITY: Physics of Life

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