Having worked with these sorts of tech/libraries before... they do little or nothing to solve the hard parts of this kinda problem, like: a) actually obtaining the requisite knowledge of all the jurisdictions involved, b) encoding the relevant knowledge to make it useable in a rules engine, and c) understanding and verifying the intent and interactions of all the rules as expected.

In practice, rules engines are almost never more useful than just coding things up normally in the language of your choice when encoding/building the logic for your product. You just have a few business rules that change within your company via conversations, projects, and people involved.

What they're actually useful for is when your product let's your product's users build up logic in some way that you need encoded and executable. In this case the rules are data from your customer. Think a state machine for a Jira ticket/help desk work flow that your customers define, or some workday process that walks your customer's users through new hire onboarding.

> I'm curious if a rules engine [1] could be used instead of implementing the logic in code for something this complicated?

Rules engine rules are code; just code in a language that is usually very simple but also (because it's not used anywhere else) completely obscure.

> Could fuzzy logic be used to build a case when the laws become contradictory?

Didn't the Bayesians kill fuzzy logic?

Bayesian statistics quantifies fuzzy knowledge of absolute statements, fuzzy logic quantifies absolute knowledge of fuzzy statements.

I'm wondering if constraint programming is better suited to such cases? https://en.wikipedia.org/wiki/Constraint_programming

Nah, logic programming and expert systems.