Theoretical Framework

This page is the theory text for Information-Time Structure. It is intentionally distinct from the main "Information-Time Structure Theory" landing HUD.

Back to Main HUD Technical Specifications Proof of Concept Code

Author: A. Eis Calder
Preserved by: DIMProductions Experimental Research Archive
Status: Concept / Experimental

0. Fixed Definition

A "time machine" here is not physical time travel, but a receiver-only framework for extracting present-accessible information structures that are more naturally described by future-constrained models than by past-causal ones.

The framework makes zero claims about transmission from the future. It proposes only that if a future entity were to transmit, they would know the receiver coordinates in their past.

1. Core Framework

1.1 Traditional Model

Conventional signal modeling assumes:

                \( x(t) = s_{\text{past}}(t) + n(t) \)
            

Where \(s_{\text{past}}(t)\) represents causal signal from prior events, and \(n(t)\) is unexplained residual ("noise").

1.2 Proposed Reinterpretation

Alternative decomposition:

                \( x(t) = s_{\text{future}}(t) + n'(t) \)
            

Where \(s_{\text{future}}(t)\) is constrained by terminal boundary condition \(C(T)\), and \(n'(t)\) is the residual under this description.

Key insight: The "noise" in traditional models may contain structure better explained by future-constrained descriptions.

2. Detection Protocol

2.1 Time-Reversal Asymmetry

Under time reversal \(t \rightarrow -t\), purely past-causal noise maintains statistical properties. Future-constrained systems exhibit measurable asymmetry.

2.2 Description Length Criterion

Compare information-theoretic complexity:

                \( L_{\text{past}} = -\log P(x | \text{past model}) \)

                \( L_{\text{future}} = -\log P(x | \text{future-constrained model}) \)

If \( \Delta L = L_{\text{past}} - L_{\text{future}} > 0 \), the future-constrained description is more efficient.

2.3 Composite Indicator

                \( I_{\text{TI}} = \alpha \cdot \Delta_{\text{TR}} + \beta \cdot \Delta L \)
            

Where \(\Delta_{\text{TR}}\) quantifies time-reversal asymmetry, and weights \(\alpha, \beta\) are tunable.

3. Falsification Framework

To distinguish from known artifacts, candidate signals must pass:

Endpoint Randomization Test — \(I_{\text{TI}}\) collapses with randomized terminal conditions
Position Sliding Test — Maximum \(I_{\text{TI}}\) occurs at actual endpoint
Multi-Site Correlation — Spatially separated measurements show correlated \(I_{\text{TI}}\)
Null Distribution Deviation — \(z\)-score exceeds \(3\sigma\) relative to synthetic null data

Only signals passing all four tests qualify as candidates for temporal interference.

4. Physical Interpretation

This framework remains agnostic about underlying physics, but is compatible with:

Block Universe — All spacetime moments coexist; future is as determined as past
Wheeler-Feynman Absorber Theory — Time-symmetric electromagnetic solutions
Boundary-Conditioned Dynamics — Systems evolve subject to both initial and terminal constraints

Critical note: The framework does not require belief in any specific interpretation. It operates purely at the level of information-theoretic model comparison.