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Related Reading

Computational Law

https://legalese.com/⁠

⁠

It's worth reading their beautiful manifesto.

They rely on µ-calculus, which I don't yet understand

As a side note, this page on holocratic compensation

https://github.com/legalese/legalese.github.io/blob/static/doc/company.org#2-roadmap-and-where-we-are-now⁠

is just really interesting

Also, particularly insightful is Meng's quote: "We need to catch errors at compile-time, because run-time errors are called _litigation_."

https://groups.google.com/a/lists.legalese.com/g/talk/c/p7frI1r-_MM⁠

⁠

A programming language for law:

https://arxiv.org/abs/2103.03198⁠

⁠

Of particular interest is the use of Default Logic:

https://en.wikipedia.org/wiki/Default_logic⁠

⁠

This demonstrates the surprising utility of a Non-Turing Complete language

Causal Modeling and machine learning

⁠

https://www.kdnuggets.com/2021/04/machine-learning-struggles-causality.html⁠

⁠

https://www.causalens.com/⁠

⁠

The problem with prescriptive law

⁠

https://en.wikipedia.org/wiki/Wikipedia:Chesterton%27s_fence⁠

⁠

Proof assistants:

⁠

https://leanprover.github.io/⁠

⁠

https://coq.inria.fr/⁠

⁠

https://isabelle.in.tum.de/⁠

⁠

Graph based programming languages:

⁠

http://www.cs.columbia.edu/~sedwards/classes/2014/w4115-fall/proposals/GPL.pdf⁠

⁠

Decentralized betting and prediction markets:

⁠

https://manifold.markets/⁠

⁠

https://www.metaculus.com/questions/⁠

⁠

https://polymarket.com/⁠

⁠

https://augur.net/⁠

⁠

Collective sensemaking projects

⁠

https://www.kialo.com/my⁠

⁠

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