📚 Quinn's Highlights

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

The Factors That Hinder Knowledge Transfer Are Often Structural Summary: Barriers to knowledge transfer or knowledge sharing are often structural, rather than merely the result of skill set limitations. Lessons can be transferred through storytelling, lessons with direction reviews and debriefs, analysis and research, and by allocating workload strategically. Placing the workload at the top, instead of burdening lower-level employees with excessive reading, is crucial. It is essential to identify the structural barriers causing the hindrance and address them. Transcript: Speaker 1 And if something is learnt at one end of the state, I need to transfer that to the other. Now you can do that with story, but you can also do that with lessons without direction reviews and debriefs. You can do that through analysis and research. And you can do that by putting the workload where it should be, you know, up at the top, rather than on the poor people down below expected to read 160 documents a year about everything Because they're going to remember that really, they're going to remember that, not in my experience. So what we have to do is take those really important lessons and then think, well, what are the structures that are causing that to happen? Speaker 2 And now we've already kind of hindered around this. What are the barriers to knowledge transfer or knowledge sharing? And is it just the skill set of listening and conversation? Is that the biggest barrier? Speaker 1 I think a lot of the barriers that we deal with are structural.

Organizational Structures That Enable Knowledge Flow With Stuart French

Because You Need to Know Podcast ™

Have we overshot the scale at which humans can effectively coordinate? Summary: We need Jim Rutt to join the conversation to discuss whether we have exceeded our ability to coordinate effectively. The slow progress of science and the population growth curve are related to this question. Sam Bowles and his work on behavioral engineering and the return of civil society are also important in this discussion. We are currently witnessing a clash between institutions and individuals, and something has to give. Transcript: Speaker 3 We need Jim Rutt on this conversation right because ultimately this is about have we actually overshot the scale at which we can effectively coordinate and all these studies like you Know this I know it's controversial but like the slowed canonical progress of science these kinds of questions they seem related in a way to the sigmoidal curve of population growth. Have we risen above a level at which intelligibility can actually happen and if so where was that level. I mean I remember you know Sam Bowles is another person who has been looming large for me over this whole conversation not only for his work on the problems of viewing humans as agents That can be governed through behavioral engineering via incentive but also because of the paper that he wrote with Wendy Carlin the article he wrote in Vox EU in 2020 on the battle for The COVID-19 narrative which talked about the return of the civil society you know meaning that the Mesoscopic world of guilds and church groups and sports clubs and pubs and neighborhood Organizations mutual aid networks and all of these other human scale sub-done bar number structures that we found ourselves suddenly very much in need of and yet were eroded by the Radical success of both state power and market power in every way it feels like we are in a kind of clash of the titans right now we're like you know we watch institutions going up against Large institutions and people are struggling to remain unpolverized underfoot. At some point something has to give right.

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

COMPLEXITY: Physics of Life

While Algorithmic Decision-Making Does Suffer From Bias, It Offers the Potential for Unparalleled Transparency In the Decision-Making Process Summary: Algorithms offer a transparent and accountable way for decision making. They can detect bias and perpetuated patterns, but must be transparent, independently audited, and not proprietary or snake oil. Transcript: Speaker 1 And then the response comes back saying yes but if you're basing it on historical data then you're feeding in biases of the past which you're going to propagate into the future there Is a kind of new attitude about all this which is kind of orthogonal to these two axes which I personally find pretty compelling and it's come up in from a couple of different places independently I could drop a few names but let me just say that the attitude is that algorithms at their best offer a new way for decision making to be transparent and accountable that's at their best So you know if an algorithm is something that everyone understands how it works everyone understands why we are chose to use this algorithm how it was trained and it's something which Can be independently audited it's even something which could be tinkered with to see if it could be made more fair and more accurate that kind of algorithm could raise the standard of Decision making in many areas and let us detect bias where it crops up and also help us detect where historical patterns are being perpetuated and what we might do to fix that but the big But is they have to be transparent they have to be independently audited they can't be proprietary and opaque and hidden behind veils of intellectual property and they also can't just Be snake oil right so there is a lot of snake oil out there there's a lot of products being put out to market which have not in any sense been independently verified or validated and where Their users and customers frankly don't really know whether their results ought to be interpreted the way they ought to be interpreted and so there needs to be a lot more critical thinking Aimed at these

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

COMPLEXITY: Physics of Life

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