Darmani

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Extortion beats brinksmanship, but the audience matters

Very interesting post. I was very prepared to praise it with "this draws some useful categories for me," but it began to get less clear as I tried more examples. And I'm still trying to come up with a distinction between brinksmanship and extortion. I've thought about the payoff matrices (they look the same), and whether "unilateral attack vs. not" is a distinguishing factor (I don't think so). I still can't find a clear distinction.

 

Examples:

(1) You say that releasing nude photos is in the blackmail category. But who's the audience?

(2) For n=1, m large:  Is  an example of brinkmanship here a monopolistic buyer who will only choose suppliers giving cutrate prices? It seems to have been quite effective for Walmart decades ago, and effective for Hollywood today ( https://www.engadget.com/2018-02-24-black-panther-vfx-models.html ).

Two Alternatives to Logical Counterfactuals
In this possible world, it is the case that "A" returns Y upon being given those same observations. But, the output of "A" when given those observations is a fixed computation, so you now need to reason about a possible world that is logically incoherent, given your knowledge that "A" in fact returns X. This possible world is, then, a logical counterfactual: a "possible world" that is logically incoherent.

Simpler solution: in that world, your code is instead A', which is exactly like A, except that it returns Y in this situation. This is the more general solution derived from Pearl's account of counterfactuals in domains with a finite number of variables (the "twin network construction").

Last year, my colleagues and I published a paper on Turing-complete counterfactual models ("causal probabilistic programming"), which details how to do this, and even gives executable code to play with, as well as a formal semantics. Have a look at our predator-prey example, a fully worked example of how to do this "counterfactual world is same except blah" construction.

http://www.zenna.org/publications/causal.pdf