Chris van Merwijk

Moloch games

Maybe. I actually don't think the term "Moloch" is very important. What I think is important is getting a good conceptual understanding of the behavioural notion of "what society wants", behavioural in the sense that it is independent of idealized notions of what would be good or what individuals imagine society wants but depends on how the collection of agents behaves/is incentivized to behave. I view the fact that this ends up deviating from what would be good for the sum of utilities, as essentially the motivation for this topic, but not the core conceptual problem. So I'd want to nudge people who want to clarify "Molochs" to focus mostly on conceptually clarifying (1) and only secondarily on clarifying (2).

Secondarily, just to push back against your point that "Moloch" is historically more connotated with (2). This is sort of true, but on the other hand, what does the concept of "Moloch" add to our conceptual toolbox, above and beyond the bag of more standard concepts like "collective action problem" and "externalities" and so forth? I'd say that it is already well-understood that collections of individuals can end up interacting in ways that is globally pareto-suboptimal. I think the additions to this analysis made in SSC are *something like*: conceptualizing various processes as optimizing in a certain direction/looking at the system-level for optimization processes. The core point to get clarity on here is I think (1), and then (2) should fall out of that.

Moloch games

Yeah I suppose that you're taking an essential property of a Moloch to be that it wants something other than the sum of utilities. That's a reasonable terminological condition I suppose, but I'm addressing the question of "what does it even mean for 'society' to want anything at all?" Then whatever that is, it might be that (e.g. by some coincidence, or by good coordination mechanisms, or because everyone wants the same thing) what society wants is the same as what would be good for the sum of individual utilities. It seems to me that the question of "what does society want?" is more fundamental than "how does that which society want deviate from what would be good for its individuals?"

Finite Factored Sets: Conditional Orthogonality

I think a subpartition of S can be thought of as a partial function on S, or equivalently, a variable on S that has the possible value "Null"/"undefined".

Finite Factored Sets: Orthogonality and Time

I just want to point out some interesting properties of this definition of time: Let time_C refer to the classical notion of time in a dynamical system, and time_FFS the notion defined in this article.

1. Suppose we have a field on space-time generated by a typical differential dynamical law that satisfies time_C-reversal symmetry, and suppose we factorize its histories according to the states of the system at time_C t=0. Then time_FFS doesn't make a distinction between the "positive" and "negative" part of the time_C. That is, if x is some position (choose a reference frame), then the position (x,2) in space-time (i.e. the value of the field at position x at time_C 2) is later in time_FFS than (x,1), but (x,-2) is also later in time than (x,-1). In this sense, the time_FFS notion of time seems to naturally capture the time-reversal symmetry in the laws of physics: Intuitively, if we start at the big bang, and go "backward in time" we are just as much going into the future as we are if we would go "forward in time". Both directions are the future.

2. However, more weirdly, time_FFS also allows a comparison between the negative-time_C and positive-time_C events. Namely, (x,1) happens before_FFS (x,-2) while (x, -1) happens before_FFS (x,2). I am not sure what to make of this, or whether we should make anything of it.

3. Suppose a computer is implemented in the physical world and implements a deterministic function , AND we restrict to the set of histories in which this computer actually does this computation. Now let x denote the variable that captures what input is given to this computer (meaning, the data stored in the input register at one particular instance of running this algorithm), and y similarly denote the variable that captures what the output is, then y occurs (weakly) earlier_FFS than x, even though the variable x is defined to be earlier than y (more precisely, to directly apply the definitions of x and y to check their value in a particular history h would involve doing a check for x at a time_C that is earlier_C than the check for y). I'm not sure what to make of this, though it kind of seems like a feature not a bug. If we don't restrict to the set of histories in which the computer does the computation, I'm pretty sure this result disappears, which makes me think this is actually a desirable property of the theory.

Finite Factored Sets: Orthogonality and Time

In the proof of proposition 18, "part 3" should be "part 4".

Finite Factored Sets: Orthogonality and Time

Can't you define for any set of partitions of , rather than w.r.t. a specific factorization , simply as iff ? If so, it would seem to me to be clearer to define that way (i.e. make 7 rather than 2 from proposition 10 the definition), and then basically proposition 10 says "if is a subset of factors of a partition then here are a set of equivalent definitions in terms of chimera". Also I would guess that proposition 11 is still true for rather than just for , though I haven't checked that 11.6 would still work, but it seems like it should.

How to Throw Away Information

I might misunderstand something or made a mistake and I'm not gonna try to figure it out since the post is old and maybe not alive anymore, but isn't the following a counter-example to the claim that the method of constructing S described above does what it's supposed to do?

Let X and Y be independent coin flips. Then S will be computed as follows:

X=0, Y=0 maps to uniform distribution on {{0:0, 1:0}, {0:0, 1:1}}

X=0, Y=1 maps to uniform distribution on {{0:0, 1:0}, {0:1, 1:0}}

X=1, Y=0 maps to uniform distribution on {{0:1, 1:0}, {0:1, 1:1}}

X=1, Y=1 maps to uniform distribution on {{0:0, 1:1}, {0:1, 1:1}}

But since X is independent from Y, we want S to contain full information about X (plus some noise perhaps), so the support of S given X=1 must not overlap with the support of S given X=0. But it does. For example, {0:0, 1:1} has positive probability of occurring both for X=1, Y=1 and for X=0, Y=0. i.e. conditional on S={0:0, 1:1}, X is still bernoulli.

Violating the EMH - Prediction Markets

Is there currently a way to pool money on the trades you're suggesting? In general it seems like there is some economies of scale to be gained by creating some kind of rationalist fund

I suggest renaming this to "countably factored spaces". Countably being a property of the factorization rather than the space.

Also I suggest adding an actual self-contained definition of countable factored space to make it more readable.