Epistemic Status:Speculative. I am not a physicist. Rather, I am a programmer with a long standing interest in physics. I used an LLM to translate my text to standard physics terminology, but the ideas and concepts are entirely my own. I am looking for where this concept breaks down, to see whether the concept could be made more falsifiable.
The Core Intuition—Gravity as Network Latency
I am proposing a toy model where General Relativity could be modelled as a distributed computing network under load. The central hypothesis is that gravity is not a fundamental force, but an emergent property of computational resource limits.
I’ve been playing with a mental model of physics for the last 8 years. I originally suggested this in my philosophical novel UTOPAI (2017). The book explores AGI, the nature of human thought, and the simulation hypothesis. In the text, while analysing whether reality is a simulation, I proposed that the existence of gravity itself suggests a simulation, because gravity could be an emergent artifact of processing limits.
I want to see more deeply if such a system naturally maps to the geometric effects of gravity (Time Dilation + Spatial Curvature).
The Axioms:
The Universe is a Graph: Spacetime is a collection of interacting local nodes (a causal set or mesh).
Finite Throughput: Each node takes time for “state updates” (processing local physics) and “message passing” (transmitting constraints to neighbors). Similar to any computer network, both state updates and message passing is implemented as something which consumes computational cycles.
Mass = Load: The presence of mass/energy represents a complex state that requires higher computational resources to update. Normally, where there is no data, there is minimal computational cost. However, when there is data, the computational cost is proportional to the amount of data. Please note that this is not rest mass. I am looking at Energy Density (T00)
Speed of Light = Speed of Information Transfer: The speed of light is the speed at which information can be transferred between nodes. This is not a physical constant, but rather a property of the network.
The Hypothesis: Gravity is not a fundamental force or a geometry. it is simply Latency caused by resource contention.
1. Mapping Time Dilation (g00)
In GR, clocks run slower near massive bodies. In this model, a node with high “Mass Load” is computationally bogged down. It takes more system cycles to complete a physical state update (because it has more data to process).
The Result: Relative to a quiet (vacuum) node, the busy (mass) node evolves slower. From the perspective of an external observer, a more massive object appears to do things slower.
The Equivalence Principle: Since all local processes (particle decay, ticks of a clock) are just state updates, everything slows down uniformly. The local observer notices nothing.
This seems to map cleanly to gravitational time dilation. To the local observer, their clock ticks normally. To an external observer, it is different. This mirrors a distributed system where a specific node becomes CPU-bound. Internally, the process executes its logic step-by-step as normal, but to an external node waiting for a response, that node appears to be running in slow motion.
2. Mapping Spatial Curvature (gij)
Time dilation alone only accounts for half of light bending; you need spatial curvature for the rest. The thought is that—“Network Congestion” produces spatial curvature.
Premise: “Transmission” is also a computation. Passing a signal (photon/causality) from Node A to Node B requires some computation at the link.
Momentum Flux (Tij) : High pressure implies that large numbers of particles are traversing the region. This high traffic load saturates the node’s bandwidth, creating a processing bottleneck.
The Result (Metric Stretching): Consequently, the link becomes congested, and latency increases. In physics, distance is defined by time (d=c×t). If a ping between A and B takes longer due to congestion, and c is invariant, then the effective distance between A and B has increased. This is similar to change of spatial curvature.
deffective=c×tcongested
This is quite similar to Refractive Index (n). If the network lag scales with mass density, the optical density of space increases near mass. A photon passing through this region is traversing a region where the cost of a hop is higher. Effectively, the congestion has stretched the fabric of space.
The Questions
I am trying to understand if this intuition holds water (in physics sense).
Is “Congestion” enough for the Spatial Metric? Is there a fundamental difference between spatial curvature in GR and the spatial curvature caused by transmission latency? Is this similar to what Analog Gravity models use?
The Lorentz Invariance Problem: I am not envisioning this as a fixed grid. A fixed grid breaks Lorentz symmetry (preferred frame). However, if we view this as a Random Lattice (like Causal Set Theory) or a dynamic fluid of nodes rather than a crystal grid, does that solve the preferred frame issue? (will still have to think about diffusion though)
Stress-Energy Tensor: GR says pressure and momentum also creates gravity. In this computational model, gravity is considered as the result of depletion of local processing resources. Here, a hot gas (high pressure) requires significantly more “events” (particle crossings/collisions) per second than a cold solid of the same mass, and thus will be considered as having higher gravity.
Has anyone formalized a “Bandwidth/Latency” derivation of the Einstein Field Equations? I am somewhat aware of Wolfram’s Physics Project and Causal Dynamical Triangulations (CDT), but I haven’t seen this specific “Network Congestion” mapping formalized.
Toy Model: Gravity as Network Latency in a Computational Graph.
Epistemic Status: Speculative. I am not a physicist. Rather, I am a programmer with a long standing interest in physics. I used an LLM to translate my text to standard physics terminology, but the ideas and concepts are entirely my own. I am looking for where this concept breaks down, to see whether the concept could be made more falsifiable.
The Core Intuition—Gravity as Network Latency
I am proposing a toy model where General Relativity could be modelled as a distributed computing network under load. The central hypothesis is that gravity is not a fundamental force, but an emergent property of computational resource limits.
I’ve been playing with a mental model of physics for the last 8 years. I originally suggested this in my philosophical novel UTOPAI (2017). The book explores AGI, the nature of human thought, and the simulation hypothesis. In the text, while analysing whether reality is a simulation, I proposed that the existence of gravity itself suggests a simulation, because gravity could be an emergent artifact of processing limits.
I want to see more deeply if such a system naturally maps to the geometric effects of gravity (Time Dilation + Spatial Curvature).
The Axioms:
The Universe is a Graph: Spacetime is a collection of interacting local nodes (a causal set or mesh).
Finite Throughput: Each node takes time for “state updates” (processing local physics) and “message passing” (transmitting constraints to neighbors). Similar to any computer network, both state updates and message passing is implemented as something which consumes computational cycles.
Mass = Load: The presence of mass/energy represents a complex state that requires higher computational resources to update. Normally, where there is no data, there is minimal computational cost. However, when there is data, the computational cost is proportional to the amount of data. Please note that this is not rest mass. I am looking at Energy Density (T00)
Speed of Light = Speed of Information Transfer: The speed of light is the speed at which information can be transferred between nodes. This is not a physical constant, but rather a property of the network.
The Hypothesis: Gravity is not a fundamental force or a geometry. it is simply Latency caused by resource contention.
1. Mapping Time Dilation (g00)
In GR, clocks run slower near massive bodies. In this model, a node with high “Mass Load” is computationally bogged down. It takes more system cycles to complete a physical state update (because it has more data to process).
The Result: Relative to a quiet (vacuum) node, the busy (mass) node evolves slower. From the perspective of an external observer, a more massive object appears to do things slower.
The Equivalence Principle: Since all local processes (particle decay, ticks of a clock) are just state updates, everything slows down uniformly. The local observer notices nothing.
This seems to map cleanly to gravitational time dilation. To the local observer, their clock ticks normally. To an external observer, it is different. This mirrors a distributed system where a specific node becomes CPU-bound. Internally, the process executes its logic step-by-step as normal, but to an external node waiting for a response, that node appears to be running in slow motion.
2. Mapping Spatial Curvature (gij)
Time dilation alone only accounts for half of light bending; you need spatial curvature for the rest. The thought is that—“Network Congestion” produces spatial curvature.
Premise: “Transmission” is also a computation. Passing a signal (photon/causality) from Node A to Node B requires some computation at the link.
Momentum Flux (Tij) : High pressure implies that large numbers of particles are traversing the region. This high traffic load saturates the node’s bandwidth, creating a processing bottleneck.
The Result (Metric Stretching): Consequently, the link becomes congested, and latency increases. In physics, distance is defined by time (d=c×t). If a ping between A and B takes longer due to congestion, and c is invariant, then the effective distance between A and B has increased. This is similar to change of spatial curvature.
deffective=c×tcongested
This is quite similar to Refractive Index (n). If the network lag scales with mass density, the optical density of space increases near mass. A photon passing through this region is traversing a region where the cost of a hop is higher. Effectively, the congestion has stretched the fabric of space.
The Questions
I am trying to understand if this intuition holds water (in physics sense).
Is “Congestion” enough for the Spatial Metric? Is there a fundamental difference between spatial curvature in GR and the spatial curvature caused by transmission latency? Is this similar to what Analog Gravity models use?
The Lorentz Invariance Problem: I am not envisioning this as a fixed grid. A fixed grid breaks Lorentz symmetry (preferred frame). However, if we view this as a Random Lattice (like Causal Set Theory) or a dynamic fluid of nodes rather than a crystal grid, does that solve the preferred frame issue? (will still have to think about diffusion though)
Stress-Energy Tensor: GR says pressure and momentum also creates gravity. In this computational model, gravity is considered as the result of depletion of local processing resources. Here, a hot gas (high pressure) requires significantly more “events” (particle crossings/collisions) per second than a cold solid of the same mass, and thus will be considered as having higher gravity.
Has anyone formalized a “Bandwidth/Latency” derivation of the Einstein Field Equations? I am somewhat aware of Wolfram’s Physics Project and Causal Dynamical Triangulations (CDT), but I haven’t seen this specific “Network Congestion” mapping formalized.