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:

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 [...]

---

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.

---

First published:

Source:

---

Narrated by TYPE III AUDIO.

---

Images from the article:

Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.