Donald Hoffman’s Conscious Realism models the universe as a network of Markovian Agents. However, a fully synchronized network of deterministic phase oscillators reaches a state of minimum entropy, preventing further computation. We introduce relativistic latency ($\tau$) and non-equilibrium thermal fluctuations (Langevin dynamics) into the agent network to prove that strict bounds on information propagation (the speed of light) are required to maintain the stochastic transitions necessary for a functioning Markovian network. By modeling the network via a Fokker-Planck equation, we demonstrate that relativistic delay acts as an effective thermodynamic reservoir, preventing the computational "freezing" of the phase-space and ensuring the persistent exploration required for complex agent behavior.
A network of interacting agents seeking phase alignment will trivially collapse into a global synchronized state (a Kuramoto limit cycle). Once synchronized, state transitions halt. To map such a network to Hoffman’s Conscious Realism (Hoffman & Prakash, 2014)—which requires continuous probabilistic state updates—an explicit source of stochasticity and frustration must exist.
The probability density $P(\vec{\theta}, t)$ of the network states evolves according to the corresponding Fokker-Planck equation. The introduction of the delay $\tau_{ij}$ structurally alters the energy landscape (Hamiltonian) of the network. The delay induces multistability and phase-frustration, preventing the probability density from collapsing into a single delta function.
Spacetime and a finite speed of light are not arbitrary properties of a "desktop interface"; they are non-equilibrium thermodynamic requirements. Without relativistic latency and thermal noise, the Markov kernel of a Conscious Agent would converge to a deterministic identity matrix, and the universe would cease to compute.