I'm going to write a review of functional decision theory, I'll use the two papers.

It's going to be around as long as the papers themselves, coupled with school work, I'm not sure when I'll finish writing.

Before I start it, I want to be sure my criticisms are legitimate; is anyone willing to go over my criticisms with me?

My main points of criticism are:

Functional decision theory is actually algorithmic decision theory. It has an algorithmic view of decision theories. It relies on algorithmic equivalence and not functional equivalence.

Quick sort, merge sort, heap sort, insertion sort, selection sort, bubble sort, etc are mutually algorithmically dissimilar, but are all functionally equivalent.

If two decision algorithms are functionally equivalent, but algorithmically dissimilar, you'd want a decision theory that recognises this.

Causal dependence is a subset of algorithmic dependence which is a subset of functional dependence.

So, I specify what an actual functional decision theory would look like.

I then go on to show that even functional dependence is "impoverished".

Imagine a greedy algorithm that gets 95% of problems correct.

Let's call this greedy algorithm f'.

Let's call a correct algorithm f.

f and f' are functionally correlated, but not functionally equivalent.

FDT does not recognise this.

If f is your decision algorithm, and f' is your predictor's decision algorithm, then FDT doesn't recommend one boxing on Newcomb's problem.

EDT can deal with functional correlations.

EDT doesn't distinguish functional correlations from spurious correlations, while FDT doesn't recognise functional correlations.

I use this to specify EFDT (evidential functional decision theory), which considers P(f(π) = f'(π)) instead of P(f = f').

I specify the requirements for a full Implementation of FDT and EFDT.

I'll publish the first draft of the paper here after I'm done.

The paper would be long, because I specify a framework for evaluating decision theories in the paper.

Using this framework I show that EFDT > FDT > ADT > CDT.

I also show that EFDT > EDT.

This framework is basically a hierarchy of decision theories.

A > B means that the set of problems that B correctly decides is a subset of the set of problems that A correctly decides.

The dependence hierarchy is why CDT < ADT < FDT.

EFDT > FDT because EFDT can recognise functional correlations.

EFDT > EDT because EFDT can distinguish functional correlations from spurious correlations.

I plan to write the paper as best as I can, and if I think it's good enough, I'll try submitting it.

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