📚 Quinn's Highlights

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A batch of the best highlights from what Quinn's read, .

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

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

The tension between the ambiguity of individuals' goals and large scale collective organization Transcript: Speaker 2 Here's the pessimistic nightmare. It is really good and healthy for human beings to live in an ambiguous environment with a pluralistic set of goals, many of which are in Kuwait. That is an essential tension with the methods of large scale collective organization. If it's true that for an organization to cohere, it needs to have clear policies so it can act coherently, then we should not expect that kind of ambiguity to survive at scale. And I think what you're describing, so I tend to think about since I'm a philosopher like what makes something constitutively coherent. And what you're describing is a kind of evolutionary process. You know, some organizations are going to be more coherent than others and some people are more interested in coherence. And the people that are more interested in following the strict outcome are going to arise in the organization. And the organizations that have clear outcomes are going to be better at achieving those outcomes. And so our world is going to be full of large organizations staffed with people that have very, very clear specifications of outcomes. And there's something inhumane and bad about that for individuals. But that's what happens when we need to organize in large scale collectives.

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

COMPLEXITY: Physics of Life

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