Darmani

# Wiki Contributions

How To Raise Others’ Aspirations in 17 Easy Steps

I'm a certified life coach, and several of these are questions found in life coaching.

E.g.:

Is there something you could do about that problem in the next five minutes?

Feeling stuck sucks. Have you spent a five minute timer generating options?

What's the twenty minute / minimum viable product version of this overwhelming-feeling thing?

These are all part of a broader technique of breaking down a problem.  (I can probably find a name for it in my book.) E.g.: Someone comes in saying they're really bad at X, and you ask them to actually rate their skills and then what they could do to become 5% better.

You want to do that but don't think you will? Do you want to make a concrete plan now?

Do you want to just set an alarm on your phone now as a reminder? (from Damon Sasi)

These are all part of the "commitment" phase of a coaching session, which basically looks like walking someone through SMART goals.

Do you know anyone else who might have struggled with or succeeded at that? Have you talked to them about it? (from Damon Sasi)

Who do you know who you could ask for help from?

I can't say these are instances of a named technique, but they are things you'd commonly find a coach asking. Helping someone look inside themselves for resources they already have is a pretty significant component of coaching.

There's a major technique in coaching not represented here called championing. Champion is giving someone positive encouragement by reinforcing some underlying quality. E.g.: "You've shown a lot of determination to get this far, and I know you'll be able to use it to succeed at X."

$1000 USD prize - Circular Dependency of Counterfactuals I realize now that this expressed as a DAG looks identical to precommitment. Except, I also think it's a faithful representation of the typical Newcomb scenario. Paradox only arises if you can say "I am a two-boxer" (by picking up two boxes) while you were predicted to be a one-boxer. This can only happen if there are multiple nodes for two-boxing set to different values. But really, this is a problem of the kind solved by superspecs in my Onward! paper. There is a constraint that the prediction of two-boxing must be the same as the actual two-boxing. Traditional causal DAGs can only express this by making them literally the same node; super-specs allow more flexibility. I am unclear how exactly it's handled in FDT, but it has a similar analysis of the problem ("CDT breaks correlations").$1000 USD prize - Circular Dependency of Counterfactuals

Okay, I see how that technique of breaking circularity in the model looks like precommitment.

I still don't see what this has to do with counterfactuals though.

$1000 USD prize - Circular Dependency of Counterfactuals I don't understand what counterfactuals have to do with Newcomb's problem. You decide either "I am a one-boxer" or "I am a two-boxer," the boxes get filled according to a rule, and then you pick deterministically according to a rule. It's all forward reasoning; it's just a bit weird because the action in question happens way before you are faced with the boxes. I don't see any updating on a factual world to infer outcomes in a counterfactual world. "Prediction" in this context is a synonym for conditioning. is defined as . If intervention sounds circular...I don't know what to say other than read Chapter 1 of Pearl ( https://www.amazon.com/Causality-Reasoning-Inference-Judea-Pearl/dp/052189560X ). To give a two-sentence technical explanation: A structural causal model is a straight-line program with some random inputs. They look like this u1 = randBool() rain = u1 sprinkler = !rain wet_grass = rain || sprinkler It's usually written with nodes and graphs, but they are equivalent to straight-line programs, and one can translate easily between these two presentations. In the basic Pearl setup, an intervention consists of replacing one of the assignments above with an assignment to a constant. Here is an intervention setting the sprinkler off. u1 = randBool() rain = u1 sprinkler = false wet_grass = rain || sprinkler From this, one can easily compute that. If you want the technical development of counterfactuals that my post is based on, read Pearl Chapter 7, or Google around for the "twin network construction." Or I'll just show you in code below how you compute the counterfactual "I see the sprinkler is on, so, if it hadn't come on, the grass would not be wet," which is written We construct a new program, u1 = randBool() rain = u1 sprinkler_factual = !rain wet_grass_factual = rain || sprinkler_factual sprinkler_counterfactual = false wet_grass_counterfactual = rain || sprinkler_counterfactual This is now reduced to a pure statistical problem. Run this program a bunch of times, filter down to only the runs where sprinkler_factual is true, and you'll find that wet_grass_counterfactual is false in all of them. If you write this program as a dataflow graph, you see everything that happens after the intervention point being duplicated, but the background variables (the rain) are shared between them. This graph is the twin network, and this technique is called the "twin network construction." It can also be thought of as what the do(y | x -> e) operator is doing in our Omega language.$1000 USD prize - Circular Dependency of Counterfactuals

While I can see this working in theory, in practise it's more complicated as it isn't obvious from immediate inspection to what extent an argument is or isn't dependent on counterfactuals. I mean counterfactuals are everywhere! Part of the problem is that the clearest explanation of such a scheme would likely make use of counterfactuals, even if it were later shown that these aren't necessary.

1. Is the explanation in the "What is a Counterfactual" post linked above circular?
2. Is the explanation in the post somehow not an explanation of counterfactuals?

The key unanswered question (well, some people claim to have solutions) in Functional Decision theory is how to construct the logical counterfactuals that it depends on.

I read a large chunk of the FDT paper while drafting my last comment.

The quoted sentence may hint at the root of the trouble that I and some others here seem to have in understanding what you want. You seem to be asking about the way "counterfactual" is used in a particular paper, not in general.

It is glossed over and not explained in full detail in the FDT paper, but it seems to mainly rely on extra constraints on allowable interventions, similar to the "super-specs" in one of my other papers: https://www.jameskoppel.com/files/papers/demystifying_dependence.pdf .

I'm going to go try to model Newcomb's problem and some of the other FDT examples in Omega. If I'm successive, it's evidence that there's nothing more interesting going on than what's in my causal hierarchy post.

What is a Counterfactual: An Elementary Introduction to the Causal Hierarchy

I'm having a little trouble understanding the question. I think you may be thinking of either philosophical abduction/induction or logical abduction/induction.

Abduction in this article is just computing P(y | x) when x is a causal descendant of y. It's not conceptually different from any other kind of conditioning.

In a different context, I can say that I'm fond of Isil Dillig's thesis work on an abductive SAT solver and its application to program verification, but that's very unrelated.

$1000 USD prize - Circular Dependency of Counterfactuals I'm not surprised by this reaction, seeing as I jumped on banging it out rather than checking to make sure that I understand your confusion first. And I still don't understand your confusion, so my best hope was giving a very clear, computational explanation of counterfactuals with no circularity in hopes it helps. Anyway, let's have some back and forth right here. I'm having trouble teasing apart the different threads of thought that I'm reading. After intervening on our decision node do we just project forward as per Causal Decision Theory or do we want to do something like Functional Decision Theory that allows back-projecting as well? I think I'll need to see some formulae to be sure I know what you're talking about. I understand the core of decision theory to be about how to score potential actions, which seems like a pretty separate question from understanding counterfactuals. More specifically, I understand that each decision theory provides two components: (1) a type of probabilistic model for modeling relevant scenarios, and (2) a probabilistic query that it says should be used to evaluate potential actions. Evidentiary decision theory uses an arbitrary probability distribution as its model, and evaluates actions by P(outcome |action). Causal decision theory uses a causal Bayes net (set of intervential distributions) and the query P(outcome | do(action)). I understand FDT less well, but basically view it as similar to CDT, except that it intervenes on the input to a decision procedure rather than on the output. But all this is separate from the question of how to compute counterfactuals, and I don't understand why you bring this up. When trying to answer these questions, this naturally leads us to ask, "What exactly are these counterfactual things anyway?" and that path (in my opinion) leads to circularity. I still understand this to be the core of your question. Can you explain what questions remain about "what is a counterfactual" after reading my post?$1000 USD prize - Circular Dependency of Counterfactuals

Oh hey, I already have slides for this.

I took the approach: if I very clearly explain what counterfactuals are and how to compute them, then it will be plain that there is no circularity. I attack the question more directly in a later paragraph, when I explain how counterfactual can be implemented in terms of two simpler operations: prediction and intervention. And that's exactly how it is implemented in our causal probabilistic programming language, Omega (see http://www.zenna.org/Omega.jl/latest/ or https://github.com/jkoppel/omega-calculus ).

Unrelatedly, if you want to see some totally-sensible but arguably-circular definitions, see https://en.wikipedia.org/wiki/Impredicativity .

Y2K: Successful Practice for AI Alignment

"Many thousands of date problems were found in commercial data processing systems and corrected. (The task was huge – to handle the work just for General Motors in Europe, Deloitte had to hire an aircraft hangar and local hotels to house the army of consultants, and buy hundreds of PCs)."

Sounds like more than a few weeks.