This post is an attempt to better operationalize FDT (functional decision theory). It answers the following questions:

given a logical causal graph, how do we define the logical do-operator?
what is logical causality and how might it be formalized?
how does FDT interact with anthropic updating?
why do we need logical causality? why FDT and not EDT?

Defining the logical do-operator

Consider Parfit's hitchhiker:

A logical causal graph for Parfit's hitchhiker, where blue nodes are logical facts

An FDT agent is supposed to reason as follows:

I am deciding the value of the node "Does my algorithm pay?"
If I set that node to "yes", then omega will save me and I will get +1000 utility. Also I will pay and lose 1 utility. Total is +999.
If I set that node to "no", then omega will not save me. I will get 0 utility.
Therefore I choose to pay.

The bolded phrases are invoking logical counterfactuals. Because I have drawn a "logical causal graph", I will call the operation which generates these counterfactuals a "logical do-operator" by analogy to the do-operator of CDT.

In ordinary CDT, it is impossible to observe a variable that is downstream of [...]

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Outline:

(00:39) Defining the logical do-operator

(04:24) Logical causality

(05:40) Causality as derived from a world model

(06:45) Logical inductors

(07:05) Algorithmic mutual information of heuristic arguments

(08:09) How does FDT interact with anthropic updating?

(09:48) Putting it together: An attempt at operationalizing FDT

(11:22) Appendix: Why bother with logical causality?

(17:51) Acknowledgements

The original text contained 10 footnotes which were omitted from this narration.

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First published:
March 13th, 2026

Source:
https://www.lesswrong.com/posts/RyDkpWGLQsCnABE78/operationalizing-fdt

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Narrated by TYPE III AUDIO.

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Images from the article:

A logical causal graph for Parfit's hitchhiker, where blue nodes are logical facts

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“Operationalizing FDT” by Vivek Hebbar