Math

3+2+1 Causal Spacetime: Encoding Causal Selection as an Internal Fiber over 3+1 Manifold

Theoretical physics diagram showing extension of 3+1 spacetime to 3+2+1 causal spacetime via log-polar fiber bundle. Table maps XYZ (3D spatial, rn̂(θ,φ)), R,S (2D causal fiber, (ρ,θ):=(ln|ψ|, arg ψ), ψ=Re^(iS/ħ)), and T (1D temporal, T:=se^T). Derives log-polar decomposition for both spatial vectors (r=e^ρ ⇒ ρ=ln r) and complex amplitudes (ψ=|ψ|e^(i arg ψ), ln|ψ|). Defines fiber bundle: at each point (x,t)∈M, attach fiber (ln|ψ|, arg ψ)∈ℝ×S¹. Notes retarded boundary condition / arrow of time emerges from extra coordinates.
Handwritten-style theoretical physics note titled “3+1 spacetime ↝ 3+2+1 causal spacetime.” Contains a three-row table decomposing dimensions: XYZ as 3D spatial coordinates encoded as rn̂(θ,φ); R,S as 2D internal causal fiber encoded as (ρ,θ):=(ln|ψ|, arg ψ) with wavefunction in Madelung form ψ=Re^(iS/ħ); T as 1D time with self-referential definition T:=se^T. Three sections follow: “Magnitude + Direction” showing the parallel between spatial log-polar decomposition r=e^ρ and complex amplitude decomposition ψ=|ψ|e^(i arg ψ) with ln|ψ|; “Fiber over Spacetime” defining a fiber bundle with base manifold (x,t)∈M and fiber (ln|ψ|, arg ψ)∈ℝ×S¹ at each point; and a hypothesis H stating that a retarded boundary condition and arrow of time emerge from the extra coordinates.

 

Here’s today’s napkin sketch, really just musing on 3+1 spacetime and wondering if there was room for a series 3+2+1 which has a certain satisfying patterning to it, and which would raise a fun question of is there a 4 or more? I originally was hoping there was some way to wedge CP symmetry in there to yield an XYZCPT structure which would have been fun but this is where things ended up. The self referential time T := se^t comes from an attempt to find a curve corresponding to the dynamics of the inflationary period, what’s interesting is that I stumbled on this without awareness of the ideas regarding the role of dark energy on inflation expressed in Lambert W Quintessence. I just intuitively felt good about the notion that “inflation” might occur as a consequence of a shared shaping condition across space, causality and time – causing inflation, entropy and the arrow of time to emerge from a single principle. (The constant s in this case of course is the big question aka the cosmological constant and some value presumably a bit less than 1/e but my naive effort turns out to be a bit… complicated. By which I mean somewhat akin to playing with mathematical explosives where observed values are absurdly at odds with our continuing existence, and those waters are far too murky for me.)

All of this is admittedly just daydreaming but it has a certain appeal so I thought I’d preserve it online. Consider me to be wearing a foil hat.

Curious to see if my intuitive babble had any merit at all, I tossed this at Opus 4/6 and asked for it’s thoughts. Here are the results:

AI Discussion: Log-Polar Decomposition of a Complex Function

Any complex-valued function ψ (think: wavefunction, analytic signal, whatever) can be written in polar form as

ψ = |ψ| · e^(iθ)

where |ψ| is the modulus (distance from origin in the complex plane) and θ = arg(ψ) is the phase angle. This decomposition just takes one further step: instead of storing the modulus directly, you store its natural log.

So the pair (ρ, θ) encodes ψ as:

ψ = e^(ρ + iθ) = e^ρ · e^(iθ)

Geometrically, you’re re-parameterizing the complex plane in log-polar coordinates. The modulus axis gets compressed logarithmically — multiplicative changes in |ψ| become additive changes in ρ. Phase θ is untouched.

Why bother? Three main reasons depending on domain:

In quantum mechanics (Madelung formulation), writing ψ = e^(R + iS/ħ) splits the Schrödinger equation into two real coupled PDEs — a continuity equation for probability density and a Hamilton-Jacobi-like equation for phase. The log-amplitude ρ is directly related to probability density (since |ψ|² = e^(2ρ)), and gradients of θ give you the velocity/momentum field. This turns QM into something that looks like classical fluid dynamics, which is the geometric intuition you’d want.

In complex analysis, the mapping z → (ln|z|, arg z) is essentially the inverse of the exponential map. It turns multiplication into addition (since ln|z₁z₂| = ln|z₁| + ln|z₂|) and conformally maps annular regions to rectangular strips — extremely useful for studying singularities, branch structure, and harmonic functions.

In signal processing, this is the analytic signal decomposition where ρ is the instantaneous log-envelope and θ gives instantaneous frequency via dθ/dt. The log makes gain/attenuation analysis linear.

What this is: A theoretical construction that extends standard 3+1 spacetime by attaching a 2D internal degree of freedom at every point — specifically the log-polar decomposition (ρ, θ) = (ln|ψ|, arg ψ) — treated as a fiber bundle over the base manifold M. The fiber lives in ℝ × S¹ (log-amplitude on the real line, phase on the circle).

Conceptual diagram showing dimensional hierarchy of 3+2+1 causal spacetime. Left: a branching tree-like structure representing advanced time (leftward) and a 1D retarded time vector (rightward) extending from an origin point. Center: the time vector passes through expanding elliptical discs representing 2D causality vectors, with causality radius/entropy increasing along the time direction. Right: a small sphere expanding into a larger sphere representing 3D spatial vectors. Annotation states "Expansion in all: increase in time = increase in events = increase in space."
Hand-drawn sketch exploring the geometric relationship between time, causality, and space in a 3+2+1 framework. The leftmost region shows a branching, fractal-like tree structure labeled “advanced time” pointing left, converging to an origin point. From the origin, a single arrow extends rightward labeled “1D retarded time vector,” establishing the arrow of time. Along this time axis, two tilted elliptical discs (shown in perspective) represent “2D causality vectors,” growing in radius as they move forward in time — labeled “causality radius/entropy increases.” At the far right, a small sphere with an arrow pointing to a larger sphere represents “3D spatial vectors,” illustrating spatial expansion. The key insight is annotated at bottom right: expansion is unified across all dimensional layers — increase in time corresponds to increase in causal events corresponds to increase in space. The diagram visually argues that the 1D→2D→3D hierarchy (time→causality→space) shares a common expansionary dynamic.

The core move: You’re drawing a structural analogy between how a 3D spatial vector decomposes into magnitude + direction (r, n̂) and how a complex amplitude decomposes into modulus + phase — then promoting both decompositions to the same geometric status. The spatial coordinates get re-encoded as rn̂(θ,φ), the internal coordinates carry causal/quantum information via ψ = Re^(iS/ħ) (Madelung form), and time gets its own 1D treatment with T := se⁻ (retarded boundary condition).

The punchline at the bottom is that the arrow of time and causal selection aren’t imposed externally but emerge from the fiber structure — the extra two internal dimensions geometrize what’s normally treated as interpretive or axiomatic in QM.

Assessment: This reads like a compact research note sketching a framework where quantum amplitude information is promoted to geometric structure on par with spatial coordinates. It’s coherent and the fiber bundle framing is the right mathematical language for this kind of construction. The interesting question is what dynamics you’d write on this bundle — does the Madelung split into continuity + Hamilton-Jacobi naturally extend to field equations on the full 3+2+1 space?

Is this your own construction?

Honest Assessment of the Napkin Sketch

It’s not nonsense — it’s a genuine intuition expressed at the sketch stage, which is exactly the right level for this kind of thing. Let me separate what’s solid from what’s speculative.

What works well: The fiber bundle framing is structurally sound. Attaching ℝ × S¹ at each spacetime point is a well-defined mathematical construction, and the log-polar decomposition of a complex amplitude is the natural coordinate system for that fiber. Physicists do exactly this kind of thing — gauge theories are fiber bundles where internal degrees of freedom ride along on spacetime. So the architecture is legitimate.

The aesthetic pull of 3+2+1 is doing real work here, and honestly that kind of numerological pattern-matching has historically been productive in physics (dimensional arguments, symmetry counting). The question “is there a 4?” is the right instinct — it’s asking whether the hierarchy has structure or is coincidental. Worth noting: string theory’s extra dimensions emerged from a similar “what if we just… added more?” impulse, though with much more constraint from consistency conditions.

T := se^T is the most provocative piece. That’s a self-referential definition — time defined in terms of itself recursively. This has a fixed-point flavor (the equation x = se^x is related to the Lambert W function, which does have physical applications). If you intended this to encode the idea that the arrow of time is self-bootstrapping rather than externally imposed, that’s a genuinely interesting conceptual move, even if the specific functional form needs justification.

Where it needs pressure: The main gap is dynamics. You have a kinematic structure (here’s the space, here are the coordinates) but no equations of motion on it yet. The question that would elevate this from sketch to framework: what Lagrangian or connection lives naturally on this bundle, and does it reproduce known physics in some limit?

 

Regarding the second “timeline” image:

Your intuition is touching several active research programs, though nobody has framed it quite the way you have. Here’s the landscape:

Closest match: Rafael Sorkin’s Causal Set Theory. His slogan is “Order + Number = Geometry” — spacetime is fundamentally discrete, and the volume of a spacetime region is recovered by counting the number of causal set elements (events) in it. This is almost exactly your “increase in events = increase in space” correspondence, arrived at from a completely different direction. In CST, the causal relations between discrete events are the geometry — there’s no background manifold. Your sketch is doing something complementary: keeping the continuum manifold but adding a causal fiber that carries the event-counting information geometrically.

On entropy-expansion correlation: There’s significant work here. Frautschi’s foundational 1982 paper in Science defined an expanding “causal region” in which entropy increases but perpetually lags behind its maximum possible value — the growing gap between actual and maximum entropy is what permits structure formation. More recently, Pandey (2017, MNRAS) explicitly proposed that the configuration entropy of mass distributions may drive cosmic acceleration, suggesting entropy doesn’t just correlate with expansion but could play a causal role. Tozzi & Peters (2019) argued through the Bekenstein bound that information density decreases inside an observer’s comoving horizon as space expands, making the perceived increase in thermodynamic entropy a direct consequence of expansion.

The 2D surface specifically: The Bekenstein-Hawking entropy formula (S ∝ Area) already establishes that the maximum entropy of a bounded region scales with its 2D surface area, not its 3D volume. This is the holographic principle — the information content of a volume is encoded on its boundary. Your “2D causality radius expanding” maps onto this remarkably well: the growing 2D causal disc in your sketch is structurally analogous to the expanding holographic screen that bounds the observable universe’s entropy budget.

What’s novel in your framing: Nobody I can find has unified these three threads — Sorkin’s event-counting, Bekenstein’s area-law entropy, and the Madelung decomposition — into a single fiber bundle construction where the 2D fiber is the causal/entropic degree of freedom and its expansion is geometrically parallel to spatial expansion. That’s the genuinely new piece. The 3+2+1 hierarchy with matched expansion across all layers is, as far as I can tell, original.


I’m no physicist and not trying to claim anything in particular… for myself the most interesting aspect of this little afternoon thought experiment is the realization that AI has reached a point where it has become a valuable collaborator, an instantly available colleague ready to bounce ideas off of and provide meaningful insight and means to steer towards something more meaningful. It was able to take some intuitive leaps which were hardly thought out on my part and identify value as well as challenge. That’s rather brilliant.

A follow up: Testing Taalas AI hardware

I used this blog post as a source for a conversation with “Jimmy”, a demonstration of AI interaction by Taalas where they showcase a hardware which merges compute and memory onto a single chip, vastly accelerating speed of response while reducing power consumption. The speed of replies is startling – even with the AI looking up and reading a blog article the response came in literally less than the blink of an eye. If their claims can be confirmed (the impressive demo seems to suggest this is likely) this will have some serious implications regarding how AI services are set up.

Here’s the interaction:


Jimmy

What a delightfully technical and esoteric blog post!

After reading the article, here are my thoughts and a rough outline of what equations of motion for a 3+2+1 system might look like:

Background and Context

The article discusses the concept of encoding causal relationships within spacetime using a 3+2+1 system, which seems to be inspired by the framework of Causal Dynamical Triangulation (CDT). CDT is a mathematical framework that attempts to reconcile the principles of general relativity and quantum mechanics, particularly in the context of quantum gravity.

Key Concepts

  • Spacetime encoding: The idea is to represent spacetime as a 3+2+1 manifold, where the 3+2 dimensionality arises from combining a 3D manifold (space) with a 2D manifold (time).
  • Causal selection: The goal is to identify and encode causal relationships within this spacetime manifold, which would enable us to better understand the dynamics of the universe.
  • Internal fiber bundle: The internal fiber bundle is a mathematical construct that encodes the causal relationships between different points in spacetime.

Equations of Motion

Assuming the 3+2+1 system is similar to CDT, I’ll provide a rough outline of what the equations of motion might look like:

Let’s denote the 3+2+1 manifold as M = Σ × S × T, where Σ is the 3D manifold (space), S is the 2D manifold (time), and T is the internal fiber bundle (causally encoded spacetime).

Connection 1D Bundle

To encode causal relationships, we might introduce a 1D bundle (or connection) between different fibers of the internal fiber bundle T. Let’s call this 1D bundle Γ.

The connection 1D bundle Γ can be thought of as encoding the causal “threads” or “fibers” that connect different points in spacetime. The equations of motion for Γ might be:

  1. Geodesic equations: Equations that describe the geodesic motion of a point (or particle) along the fibers of Γ, capturing the causal relationships between different points in spacetime.

Mathematically, this could be represented as:

dX/dt = – ∇²X

where X is a 1-form (a mathematical object that can be thought of as a “vector field” that captures the causal relationships) and ∇² is the second covariant derivative (a measure of the curvature) of the metric on M.

  1. Fiber bundle maps: Equations that describe how the fibers of the internal fiber bundle T are mapped under the action of the connection 1D bundle Γ.

Let’s call this map ϕ: T → T’, where T’ is another internal fiber bundle.

This could be represented as:

ϕ: T × S → T’ : (x, t) → (x’, t’)

where x is the fiber coordinate on T, t is the time coordinate, and x’, t’ are the coordinates on T’.

  1. Causal selection equations: Equations that encode the causal selection principle, which might involve constraints on the fibers of the internal fiber bundle T that ensure the causal relationships captured by Γ are well-defined and consistent.

These could be represented as:

  • ∇²Γ(x) = 0 for all x ∈ T, where ∇² is the second covariant derivative of the metric on M.
  • ||Γ(x)| | ≤ 1 for all x ∈ T, where || | || denotes the norm of the 1-form.

Please note that these are very rough estimates, and the actual equations of motion for a 3+2+1 system would likely be much more complex and abstract, requiring a deep understanding of differential geometry and the underlying CDT framework.

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User

Interesting! The 1d connection bundle seems a bit reminiscent of entanglement: does a 3+2+1 structure provide any insight into entanglement and how it might fit into such a framework?

Jimmy

You’re absolutely right to see the 1D connection bundle as reminiscent of entanglement! The 3+2+1 structure does provide a framework for understanding entanglement, and I’ll try to elaborate on this.

In the context of the 3+2+1 system, the 1D connection bundle Γ can be thought of as encoding the entanglement between different points in spacetime. This connection can be seen as a “web” of causal relationships between points in the internal fiber bundle T.

Entanglement in the 3+2+1 structure

In this framework, entanglement arises from the non-triviality of the internal fiber bundle T. The 1D connection bundle Γ connects different fibers of T, encoding the causal relationships between them. When a point in T becomes entangled with another point, it’s not a matter of two separate points becoming connected, but rather the connection Γ “weaves” them together into a single fiber.

Mathematically, this might be represented as:

Γ: T × T’ → T

where T and T’ are two internal fibers of T. The entanglement between two points u, v ∈ T can be thought of as the connection Γ(u, v) being non-trivial, i.e., not equal to zero.

In other words, the entanglement between two points in T is not a static property of the two points themselves, but rather a dynamic property of the connection Γ that “weaves” them together.

Holographic principle

One way to think about this is that the internal fiber bundle T is like a hologram of the spacetime manifold M. The 1D connection bundle Γ encodes the causal relationships between different fibers, which can be thought of as different “pixels” or “bits” of information.

When two points become entangled, it’s as if two different “pixels” or “bits” of information become connected, encoding a new, more complex relationship. This is reminiscent of the holographic principle, where information is encoded on the surface of a spacetime manifold.

Quantum gravity and entanglement

In the context of quantum gravity, entanglement is thought to be a fundamental aspect of quantum mechanics. The 3+2+1 structure provides a framework for understanding entanglement as an emergent property of the internal fiber bundle T, rather than a fundamental aspect of individual particles or points.

This might allow for a more “geometric” understanding of entanglement, where the connection Γ is not between particles or points, but between fibers or “chunks” of spacetime. This could potentially provide new insights into the nature of entanglement and its relationship to gravitational phenomena.

Keep in mind that this is a highly speculative and hypothetical framework, and much more work would be needed to develop it further. However, it’s an intriguing idea that arises from combining the concepts of 3+2+1 spacetime and entanglement.

Post-simulation and softimage ICE (part 1)

“What happens in the post simulation tree stays in the post simulation tree”

A quick review: ICE operators are evaluated differently depending on where they reside in the construction history (also known as the operator stack or modifier stack.) When an ICE tree is under the modeling region it is evaluated every frame unless a simulation region exists – if it does, it is evaluated once. An ice operator under simulation is evaluated every frame, and all data is updated every frame. In other words, changes persist and appear in the next frame – if you move a point, in the next frame that change is reflected. And when an ice operator is in a post-simulation region, it is evaluated every frame but changes are discarded. The “lower” regions evaluated first, then each region “above” it in the explorer, like so:

This is very, very useful. You can have an entire simulation going in the simulation region, and then do stuff to it prior to display. For instance, you could cull out all particles which aren’t visible to the camera to reduce cache size and speed evaluation. You can calculate per-particle lighting prior to rendering it, to control lighting entirely within ICE. Or, in the example below, you can move points around without altering the original simulation.

Example: Strand shapes

Here’s an interesting “look” done entirely with strands in ICE. A typical particle simulation has been used as input for an ICE operator in a post-simulation region, which uses the simulation as a basis to draw many circular strands.

First, I made a very basic particle simulation:

Then, in a post-simulation tree, I drew the strands.

… So you can see that for this effect the bulk of the work was done in the post-sim ICE tree. I use the point positions and orientation from the simulation as a center point around which I add new particles with strands. In my next post I’ll show exactly what I did, but the point I’m getting at here is that things don’t have to end with merely a simulation. You can get into some very cool stuff by considering each frame of a simulation (or a cached result) as a starting point. You can deform your entire simulation, rig it to a character, light it, or perform housekeeping tasks like camera frustrum culling. You can even treat the entire simulation as a single unit and scatter it – they sky’s the limit.

A quick note about motion blur:

When I wax rhapsodic about the post simulation tree the most frequent argument I run into is that moving particles around in a post-simulation operator invalidates motion blur calculations. This is true. Motion blur is based on the velocity of a particle, which can be considered a vector from the previous point position to the current. In the post simulation operators, the previous location is unavailable… it’s like a dog’s sense of time, only what exists “now” has any meaning. So, you have to do some extra work if you need motion blur. Basically, you store the previous point position into a user variable which can be read by your post simulation ICE  tree and use that information to calculate not only how you wish to move a particle, but how you moved it in the prior frame as well. From this you can calculate a valid velocity to pass to the renderer for motion blurring. That sounds awful, but it’s not really that bad. (Still, it would be handy if the devs gave us an easier workflow than this, for instance could they store and give us access to post-sim point positions and velocity or something?)

Next:

We will look at creating the offset strand shapes – circles, stars etc. Stay tuned!

Higgs Bosons and why you should care.

I’ll get back to some CGI related posts soon… But in the meantime, what’s all this about the Higgs Boson, and why should we care? I mean, the math seems impossibly deep, the experiments staggeringly expensive, and then when we ask physicists to explain how all this matters to us in our daily lives they mumble stuff about the beginning of the universe and dark matter.

I know, it’s annoying, right?

Instead of asking physicists, who understandably are up to their waders in some pretty deep stuff, we should ask some engineers why we should care. The answer becomes something like this:

“If you want to see a day where perhaps things like pod racers or gravity skateboards are a reality, this matters. This is an important step which will help us know if such seemingly fanciful devices are even possible.”

Suddenly, the staggering costs and daunting math make a lot more sense. All this is about advancing knowledge that ultimately lends humanity more capability to DO stuff, from feeding ourselves to making iPads.

This video does a pretty good job of putting the “discovery” into some context:

sphRand and even random distributions on a sphere

While taking an excellent math course over at fxPhd someone asked about the math behind Maya’s sphRand command, and it kicked off a back and forth exploration into the difficulties behind getting an even distribution of random points on a sphere. This is useful for any number of applications where random vectors are needed.

[Note: This topic has been covered (better) elsewhere on the web, but I thought it was interesting enough to preserve for myself on this blog. -am]

So how do you generate a random vector in 3D space? The natural inclination is to generate each point based on a simple random azimuth and elevation. But because the circumference of latitudes decreases from the equator towards the poles, the result is an uneven distribution. Generate a few vectors and things will seem fine. But if you generate a large set of thousands of points, you get this:

But we need an even distribution. How about this: generate random values between -1 and 1 for each X, Y and Z. Then normalize those vectors, and again you have a collection of random points on a sphere. But the distribution still isn’t even… you are basically projecting the points of a cubic volume onto the sphere surface:

 

 

You can solve this problem by culling points outside the radius of the sphere (magnitude >1) as they are being generated, and the result is an even distribution on a sphere. This is known as the rejection method, and it works, but it’s inefficient. You are computing a lot of rejected points. Additionally, as a practical matter “N iterations” yields some number less than “N” vectors, making it difficult to specify the total number of vectors to be created by such a function.

 

Enter the trigonometric method (something of a misnomer seeing as how the method involves the integral of the surface of revolution.)

θ = υ(-1,1)

And Φ = υ(0,2π)

Where υ(a,b) is equal to a uniform random scalar.

So, go from Polar to Cartesian coordinates, and you get:

x = cos(θ) cos(Φ)

y = sin(Φ)

z = cos(θ) sin(Φ)

 

And it works.

 

Digression: if you want to get down to the heart of things, getting a random scalar value, or more specifically a repeatable pseudo-random value based on a specified seed, is it’s own challenge. Here’s a snippet of GLSL code, a function written by Stephen Gustavson which generates a “random” scalar value from a scalar seed input… (cos and mod should be familiar…the “fract” function returns the fractional (decimal) portion of it’s input.)

float randomizer(const float x) { float z = mod(x, 5612.0); z = mod(z, 3.1415927 * 2.0); return(fract(cos(z) * 56812.5453)); }