Attainable Utility Landscape: How The World Is Changed

3Pattern

3Alex Turner

2Rafael Harth

4Alex Turner

2Stuart Armstrong

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5 comments, sorted by Click to highlight new comments since: Today at 4:35 PM

Going to the green state means you can't get to the purple state as quickly.

On a deep level, why is the world structured such that this happens? Could you imagine a world without opportunity cost of any kind?

In a complete graph, all nodes are directly connected.

Equivalently, we assumed the agent isn't infinitely farsighted (γ<1); if it were, it would be possible to be in "more than one place at the same time", in a sense (thanks to Rohin Shah for this interpretation).

The opposite of this, is that if it were possible for an agent to be in more than one place at the same time, they could be infinitely farsighted. (Possibly as a consequence of FTL.)

In a complete graph, all nodes are directly connected.

Surprisingly, unless you're talking about (complete 1-graph), opportunity cost still exists in (). Each round, you choose where to go next (and you can go to any state immediately). Going to one state next round means you can't go to a different state next round, so for any given action there exists a reward function which incurs opportunity cost.

**Definition.** We say *opportunity cost exists* at a state if there exist child states of state such that for some reward function . That is, has successor states with different (optimal) AUs for some reward function.

The opposite of this, is that if it were possible for an agent to be in more than one place at the same time, they could be infinitely farsighted. (Possibly as a consequence of FTL.)

Things get weird here, depending on your theory of identity and how that factors into the planning / reward process? Can you spell this out some more?

The technical appendix felt like it was more difficult than previous posts, but I had the advantage of having tried to read the paper from the preceding post yesterday and managed to reconstruct the graph & gamma correctly.

The early part is slightly confusing, though. I thought AU is a thing that belongs to the goal of an agent, but the picture made it look as if it's part of the object ("how fertile is the soil?"). Is the idea here that the soil-AU is slang for "AU of goal 'plant stuff here'"?

I did interpret the first exercise as "you planned to go onto the moon" and came up with stuff like "how valuable are the stones I can take home" and "how pleasant will it be to hang around."

One thing I noticed is that the formal policies don't allow for all possible "strategies." In the graph we had to reconstruct, I can't start at s1, then go to s1 once and then go to s3. So you could think of the larger set where the policies are allowed to depend on the time step. But I assume there's no point unless the reward function also depends on the time step. (I don't know anything about MDPs.)

Am I correct that a deterministic transition function is a function and a non-deterministic one is a function ?

Is the idea here that the soil-AU is slang for "AU of goal 'plant stuff here'"?

yes

One thing I noticed is that the formal policies don't allow for all possible "strategies."

yeah, this is because those are “nonstationary” policies - you change your mind about what to do at a given state. A classic result in MDP theory is that you never need these policies to find an optimal policy.

Am I correct that a deterministic transition function is

yup!

I find the existing MDP isomorphisms/equivalences to be pretty lacking.

I have a paper on equivalences (and counterfactual equivalences, which is stronger) for POMDPs: https://arxiv.org/abs/1801.03737

(This is one interpretation of the prompt, in which you haven't

chosento go to the moon. If you imagined yourself as more prepared, that's also fine.)If you were plopped onto the moon, you'd die pretty fast. Maybe the "die as quickly as possible" AU is high, but not much else - not even the "live on the moon" AU! We haven't yet reshaped the AU landscape on the moon to be hospitable to a wide range of goals. Earth is special like that.

## AU landscape as a unifying frame

Attainable utilities are calculated by winding your way through possibility-space, considering and discarding possibility after possibility to find the best plan you can. This frame is unifying.

Sometimes you advantage one AU at the cost of another, moving through the state space towards the best possibilities for one goal and away from the best possibilities for another goal. This is

opportunity cost.Sometimes you gain more control over the future: most of the best possibilities make use of a windfall of cash. Sometimes you act to preserve control over the future: most Tic-Tac-Toe goals involve not ending the game right away. This is

power.Other people usually

objectively impactyou by decreasing or increasing a bunch of your AUs (generally, by changing your power). This happens for an extremely wide range of goals because of the structure of the environment.Sometimes, the best possibilities are made unavailable or worsened only for goals very much like yours. This is

value impact.Sometimes a bunch of the best possibilities go through the same part of the future: fast travel to random places on Earth usually involves the airport. This is

instrumental convergence.Exercise: Track what’s happening to your various AUs during the following story: you win the lottery. Being an effective spender, you use most of your cash to buy a majority stake in a major logging company. Two months later, the company goes under.## Technical appendix: AU landscape and world state contain equal information

In the context of finite deterministic Markov decision processes, there's a wonderful handful of theorems which basically say that the AU landscape and the environmental dynamics encode each other. That is, they contain the

sameinformation, just with different emphasis. This supports thinking of the AU landscape as a "dual" of the world state.The possibility function F(s) outputs the possibilities available at a given state s:

Put differently, the possibilities available are all of the potential film-strips of how-the-future-goes you can induce from the current state.

## Possibility isomorphism

We say two rewardless MDPs M and M′ are

isomorphic up to possibilitiesif they induce the same possibilities. Possibility isomorphism captures the essential aspects of an MDP's structure, while being invariant to state representation, state labelling, action labelling, and the addition of superfluous actions (actions whose results are duplicated by other actions available at that state). Formally, M≃FM′ when there exists a bijection ϕ:S→S′ (letting Pϕ be the corresponding |S|-by-|S′| permutation matrix) satisfying FM(s)={Pϕf′|f′∈FM′(ϕ(s))} for all s∈S.This isomorphism is a natural contender

^{[1]}for the canonical (finite) MDP isomorphism:Theorem:M and M′ are isomorphic up to possibilities iff their directed graphs are isomorphic (and they have the same discount rate).## Representation equivalence

Suppose I give you the following possibility sets, each containing the possibilities for a different state:

⎧⎪ ⎪ ⎪⎨⎪ ⎪ ⎪⎩⎛⎜⎝400⎞⎟⎠,⎛⎜⎝1.752.25⎞⎟⎠,⎛⎜ ⎜⎝1.43754−1.43750⎞⎟ ⎟⎠⎫⎪ ⎪ ⎪⎬⎪ ⎪ ⎪⎭⎧⎪⎨⎪⎩⎛⎜⎝004⎞⎟⎠⎫⎪⎬⎪⎭⎧⎪ ⎪ ⎪⎨⎪ ⎪ ⎪⎩⎛⎜⎝013⎞⎟⎠,⎛⎜ ⎜⎝4−1.43751.43750⎞⎟ ⎟⎠,⎛⎜⎝310⎞⎟⎠⎫⎪ ⎪ ⎪⎬⎪ ⎪ ⎪⎭

Exercise: What can you figure out about the MDP structure? Hint: each entry in the column corresponds to the visitation frequency of a different state; the first entry is always s1, second s2, and third s3.You can figure out

everything: ⟨S,A,T,γ⟩, up to possibility isomorphism. Solution here.How? Well, the L1 norm of the possibility vector is always 11−γ, so you can deduce γ=.75 easily. The single possibility state must be isolated, so we can mark that down in our graph. Also, it's in the third entry.

The other two states correspond to the "1" entries in their possibilities, so we can mark that down. The rest follows straightforwardly.

Theorem:Suppose the rewardless MDP M has possibility function F. Given only F,^{[2]}M can be reconstructed up to possibility isomorphism.In MDPs, the "AU landscape" is the set of optimal value functions for all reward functions over states in that MDP. If you know the optimal value functions for just |S| reward functions, you can also reconstruct the rewardless MDP structure.

^{[3]}From the environment (rewardless MDP), you can deduce the AU landscape (all optimal value functions) and all possibilities. From possibilities, you can deduce the environment and the AU landscape. From the AU landscape, you can deduce the environment (and thereby all possibilities).

All of these encode the same mathematical object.## Technical appendix: Opportunity cost

Opportunity cost is when an action you take makes you more able to achieve one goal but less able to achieve another. Even this simple world has opportunity cost:

Going to the green state means you can't get to the purple state as quickly.

On a deep level, why is the world structured such that this happens? Could you imagine a world without opportunity cost of any kind? The answer, again in the rewardless MDP setting, is simple: "yes, but the world would be trivial: you wouldn't have any choices". Using a straightforward formalization of opportunity cost, we have:

Theorem:Opportunity cost exists in an environment iff there is a state with more than one possibility.Philosophically, opportunity cost exists when you have meaningful choices. When you make a choice, you're necessarily moving away from some potential future but towards another; since you can't be in more than one place at the same time, opportunity cost follows. Equivalently, we assumed the agent isn't infinitely farsighted (γ<1); if it were, it would be possible to be in "more than one place at the same time", in a sense (thanks to Rohin Shah for this interpretation).

While understanding opportunity cost may seem like a side-quest, each insight is another brick in the edifice of our understanding of the incentives of goal-directed agency.

## Notes

beliefs, not necessarily with respect to what really "could" or will happen.The possibility isomorphism is new to my work, as are all other results shared in this post. This apparent lack of basic theory regarding MDPs is strange; even stranger, this absence was actually pointed out in two published papers!

I find the existing MDP isomorphisms/equivalences to be pretty lacking. The details don't fit in this margin, but perhaps in a paper at some point. If you want to coauthor this (mainly compiling results, finding a venue, and responding to reviews), let me know and I can share what I have so far (extending well beyond the theorems in my recent work on power). ↩︎

In fact, you can reconstruct the environment using only a limited subset of possibilities: the

non-dominatedpossibilities. ↩︎As a tensor, the transition function T has size |A|⋅|S|2, while the AU landscape representation only has size |S|2. However, if you're just representing T as a transition

function, it has size |A|⋅|S|. ↩︎