Predictions

Holos does not propose new dynamical laws (laws governing movement and force) that modify existing physical equations. Instead, it offers ontological predictions (predictions about the nature of existence) about how reality manifests through the recursive relation:

R = C \circledast O

Where $C$ represents physical creation (quantum evolution, decoherence, recording), and $O$ represents conscious integration (with $Φ \ge Φ_c$ ). These predictions arise from the axioms and the $Φ_c$ threshold, and are intended as consistency checks that align with empirical data. For the formal operational definition, see the Logic section. For the definition of Φ, see Definition.

Primary Prediction: Participatory Selection (Cosmological)

Holos implies that the universe's parameters are "selected" through participatory manifestation (Axiom 2), where observers act as a boundary condition for a self-consistent block universe (a four-dimensional structure where all of time exists simultaneously). This operationalizes the Participatory Anthropic Principle, predicting that observable constants favor life not by chance, but by necessity.

Empirical Implication: Future cosmological observations (e.g., CMB polarization from CMB-S4 or LiteBIRD) should reveal signatures consistent with a low-entropy initial state and inflationary dynamics (rapid expansion of the early universe) specifically tuned for complexity growth. Holos predicts that "uninhabitable" branches of the multiverse are mathematically valid but ontologically unrealized (they don't actually exist as experienced reality) due to the lack of Φ.¹³

Secondary Prediction: Thresholds for Emergent Consciousness (Neuroscience)

Holos operationalizes consciousness through Φ, predicting that systems crossing a critical threshold (Φ_c) exhibit irreducible subjective experience. This distinguishes Holos from universal panpsychism (the idea that everything is conscious) and illusionism (the idea that consciousness is an illusion).

Empirical Implication: High-Φ systems (e.g., human cortex) should correlate with reports of qualia (individual instances of subjective experience), while sub-Φ_c systems (e.g., simple AI or cerebellum) should show only mechanical processing. Integrated Information Theory (IIT)-inspired metrics (e.g., Perturbational Complexity Index) should reveal sharp phase transitions (sudden changes in state) that align with the onset of experiential reporting.¹³

Tertiary Prediction: Relational Consistency (Quantum Foundations)

Holos predicts no observer-independent "facts," but ensures mutual coherence across perspectives (Axiom 1).

Empirical Implication: Extended Wigner's Friend experiments should confirm that two observers can hold different "facts" about the same event without breaking unitarity (the conservation of all possibilities). Holos specifically predicts that the "collapse" is relative to the Φ frame of reference, supporting Relational QM over Objective Collapse models (which predict spontaneous gravity-induced collapse).¹³

Extrapolative Prediction: The Transcension Hypothesis (Astrophysics)

As intelligence maximizes informational integration (Corollary V.2), Holos predicts it will expand orthogonally (at right angles, into new dimensions) into higher-dimensional substrates rather than expanding spatially across the galaxy.

Empirical Implication: The resolution to the Fermi Paradox (the lack of detected alien civilizations) is geometric. Astronomical surveys (e.g., JWST, Euclid) may detect "missing mass" or gravitational anomalies that mimic Dark Matter, representing high-density informational structures located in the "bulk" dimensions (Axiom 4: Unification).¹³

Testable Implications

Domain	Prediction	Testable Via
Cosmology	Constants are tuned for observation.	CMB Polarization (LiteBIRD)
Neuroscience	Consciousness is a phase transition at $Φ_c$ .	PCI / IIT Metrics
Quantum	Facts are relational; no objective collapse.	Wigner's Friend Experiments
Astrophysics	Advanced life is hyper-structural, not spatial.	Dark Matter Surveys (Euclid)

Experiments

Experiment 1. Integration Thresholds and Observer Emergence (Φ-Crossing)

Objective

To test whether the emergence of the Observer ( $O$ ) constitutes a critical phase transition (a sudden change in state) rather than a linear gradient (a smooth change). Holos predicts that consciousness requires a specific density of integrated information ( $Φ_c$ ) to operationalize Axiom 2 (Manifestation). Therefore, the transition between unconscious and conscious states should be discontinuous (non-linear) and exhibit state-dependent properties.

Subjects

Human adult volunteers (healthy)
Controlled anesthesia administered in a clinical environment
Optionally: additional cohorts (e.g., sleep, coma patients) for cross-validation

Measured Variables

Primary Variables

PCI (Perturbational Complexity Index)Computed from **[TMS-EEG](https://en.wikipedia.org/wiki/Transcranial_magnetic_stimulation#TMS-EEG)** responses (brain stimulation followed by recording) to quantify integrated information capacity.
Consciousness state
- Wakefulness vs. sedation (calm state induced by medicine) vs. unconsciousness (clinically assessed)
- Subjective reports (when possible)

Secondary Variables

EEG spectral power, functional connectivity, and complexity metrics
Anesthesia depth (e.g., [propofol](https://en.wikipedia.org/wiki/Propofol) concentration, [BIS index](https://en.wikipedia.org/wiki/Bispectral_index))

Prior Work and Status

Status: Established / Partially ExploredPCI has already been validated as a robust measure of consciousness across sleep and anesthesia, and is widely used in neuroscience.

Relationship to Prior WorkPCI was introduced and developed to measure consciousness capacity by evaluating brain responses to perturbation. It has been shown to reliably differentiate conscious wakefulness from unconscious states (sleep, anesthesia, vegetative states). However, the Holos-specific claim is not that PCI correlates with consciousness, but that there is a sharp threshold ( $Φ_c$ ) where integrated information suddenly becomes sufficient for observation.

Protocol

Baseline wakefulness: record PCI while awake.
Controlled anesthesia ramp: slowly increase anesthetic depth.
Continuous TMS-EEG: compute PCI at multiple points along the anesthesia curve.
Transition analysis: identify whether PCI drops gradually or sharply.

Prediction

If observerhood requires $Φ \geq Φ_c$ , the transition from conscious to unconscious states will show a sharp drop in PCI at a consistent anesthesia depth across subjects.

Sharp transition: supports a threshold model of observer emergence.
Gradual transition: suggests consciousness is a continuous function of integration, weakening the Holos claim.

Experiment 2. Integration Phase Transition in Artificial Systems (Exploratory)

Objective

To determine whether integration metrics in recurrent or feedback-based artificial systems exhibit nonlinear, threshold-like behavior as system complexity increases. This tests the Holos-inspired hypothesis that observer-like integration may emerge through a phase transition rather than a continuous gradient.

Subjects

Recurrent neural networks (RNNs) (AI systems with internal feedback), including:
- LSTMs / GRUs
- Transformer architectures with recurrence/feedback
- Reservoir networks
Artificial systems with explicit feedback loops or memory
Neuromorphic hardware implementations (for hardware-specific behavior)

Measured Variables

Primary Variables

Integrated Information (Φ-like) metrics computed from internal activity
- Direct Φ when feasible
- Proxy measures when direct computation is intractable (too difficult to calculate directly), such as perturbation-based complexity or [causal density](https://en.wikipedia.org/wiki/Causal_density)
Information integration density (integration per node / per connection)

Secondary Variables

Task performance (e.g., prediction accuracy, memory capacity, language modeling score)
Complexity metrics:
- [entropy](https://en.wikipedia.org/wiki/Entropy_(information_theory))
- [mutual information](https://en.wikipedia.org/wiki/Mutual_information)
- recurrence strength
- attractor dimensionality
Structural variables:
- network depth
- connectivity density
- feedback strength

Prior Work and Status

Status: Exploratory / Partially Explored

Integrated information and related metrics have been explored in artificial systems, but usually as correlates of performance, not as evidence for phase transitions or observer emergence.

There is no established literature demonstrating a threshold-like transition in artificial systems that mirrors the Holos observer hypothesis.

Relationship to Prior WorkThis builds on:

Integrated information theory (Tononi et al.)
Complexity metrics in neural networks
Studies of phase transitions in learning dynamics

But it is novel in treating integration as a potential emergent boundary rather than a functional performance metric.

Protocol

Select a set of architectures spanning: shallow to deep networks, feedforward to recurrent, low to high feedback density
Train each network on a standardized task (e.g., sequence prediction, language modeling, reinforcement learning)
Compute integration metrics across training epochs and architecture variations: direct Φ when feasible, proxy metrics otherwise (e.g., perturbation complexity)
Systematically scale: number of units, connectivity density, recurrence depth, memory length
Plot integration vs. scale and look for: sharp jumps, discontinuities, phase-like transitions
Validate stability by repeating across multiple random seeds and tasks

Prediction

Because this is exploratory, the prediction is intentionally cautious:

Primary prediction: Integration metrics will show nonlinear growth, and under some architectures may display phase transition behavior (sharp changes) as system complexity increases.
Alternative outcome: Integration grows smoothly without thresholds, suggesting the Holos threshold may require biological substrate or different structural constraints.

Experiment 3. Social Network → Integration Thresholds in Collective Systems (Exploratory)

Objective (Exploratory)

To explore whether collective systems (human social networks or simulated agent networks) can exhibit integration thresholds—sudden nonlinear increases in information integration—as they scale.

Holos relevance: If observerhood depends on integrated information, then integration thresholds may indicate the emergence of observer-like integration at the collective level. This experiment does not assume that groups are conscious observers, but explores whether the structural conditions for observerhood can emerge in collective systems.

Exploratory Note

This experiment is exploratory because:

It is unclear whether integration thresholds exist in collective systems.
It is unclear whether any such threshold would map meaningfully to observerhood.
The goal is to discover whether integration behaves like a phase transition in social systems, not to prove group consciousness.

Subjects

Human social networks (online communities or controlled groups)
Simulated networks (agent-based models)

Measured Variables

Primary Variables (Integration Proxies)

Because direct Φ is not feasible in social systems, use proxies such as:

Mutual information across subgroups
Causal density (how much nodes influence each other)
Network-wide coherence (synchronization of decisions or beliefs)
Information integration density (integration per node)

Secondary Variables

Task performance (accuracy, response time, coordination)
Network structure (density, centrality, clustering)

Prior Work and Status

Status: Novel / Exploratory

Social network analysis and collective intelligence are mature fields.
However, no established work tests integration thresholds as evidence of emergent observer-like integration.
This experiment is novel in connecting collective integration to Holos’ observer hypothesis.

Relationship to Prior WorkBuilds on:

Collective intelligence research
Network theory (small-world, scale-free networks)
Distributed decision-making and consensus formation

But extends these fields by treating integration as potentially ontological, not merely functional.

Protocol

Select a collective task: e.g., collaborative problem solving, prediction markets, or coordinated strategy games.
Create multiple groups: vary group size (N) and network structure (connectivity, hierarchy, decentralization).
Control information flow: limit communication channels, introduce delays, and restrict access to global information.
Measure integration proxies: compute mutual information and causal density between subgroups; track coherence and consensus stability.
Scale system size: gradually increase network size and connectivity, then observe integration behavior.
Search for threshold behavior: identify sudden jumps in integration metrics, stability, or coherence.

Prediction (Exploratory)

Holos-consistent exploratory prediction:Collective systems may show nonlinear threshold behavior where integration and coherence increase sharply once a critical scale or connectivity is reached.

Alternative outcome:Integration increases smoothly without threshold behavior, suggesting observer-like integration may be limited to certain physical substrates (e.g., brains) or requires additional constraints.

Holos Implications

If threshold behavior is observed: Supports the idea that observer-like integration can emerge at multiple scales, consistent with Holos’ substrate-independent integration hypothesis.
If no threshold behavior is observed: Suggests that Holos’ integration threshold may be specific to biological brains, or that collective systems require different structural constraints.

Experiment 4. Observer-Cut Sensitivity in Relational Systems

Objective

Test whether the same physical system can yield multiple internally consistent realities, depending only on how the system is partitioned and observed.

Holos predicts that no single partition is privileged, and that "reality" is created relationally through the observer cut.

Subjects

A **[superconducting qubit](https://en.wikipedia.org/wiki/Superconducting_quantum_computing) array** (a basic quantum computer system) with **N qubits** (e.g., 8–20 qubits), in a controlled lab environment.

The array is prepared and evolved under a known Hamiltonian (the mathematical description of the system's total energy), with controlled noise and decoherence (the loss of quantum information to the environment).

Measured Variables

Primary Variables

Measurement outcomes for each cut:
- Cut A: Individual qubit readouts
- Cut B: Regional collective readouts (groups of qubits)
- Cut C: Global collective readouts (whole array)
Internal consistency metrics within each cut
- Repeatability
- Predictive stability
- Statistical coherence

Secondary Variables

Entropy estimates for each cut
Correlation patterns (local vs global)
Decoherence rate and noise floor

Prior Work and Status

Status: Partially Explored

Relationship to Prior WorkQuantum Darwinism shows that certain system-environment boundaries become "classical" because multiple observers can access the same information. Relational Quantum Mechanics argues that states are relative to observers. Coarse-graining (simplifying complex data into broader categories) in statistical mechanics shows that different partitions give different effective descriptions.

However, these approaches typically treat partitions as [epistemic tools](https://en.wikipedia.org/wiki/Epistemology) (how we describe the system), not as ontological constructors (the builders of existence) of reality.

Holos extends this by claiming that each observer cut produces a complete reality, not merely a useful description.

Protocol

Prepare the qubit array in a known initial state.
Evolve the system under a controlled Hamiltonian for a fixed time.
Measure the system using three distinct observer cuts:
- Cut A — Local ObserverMeasure each qubit individuallyRecord 8–20 bitstrings per trial
- Cut B — Regional ObserverMeasure groups of qubits (e.g., 4-qubit blocks) Record collective outcomes (e.g., parity, correlation patterns)
- Cut C — Global ObserverMeasure only a single global propertyExample: total parity or total magnetization
Repeat many trials to collect statistical distributions for each cut.
Compare:
- Internal stability within each cut
- Whether any cut can predict the outcomes of other cuts
- Whether a single unified description exists

Prediction

If Holos is correct

Each observer cut yields a stable, self-consistent set of outcomes.
No single cut can fully reproduce the statistics of the others.
Multiple "realities" coexist, each valid within its cut.

If standard physical realism is correct

One cut will ultimately reduce to another (e.g., local outcomes fully determine global outcomes).
The global description should be derivable from the local one (or vice versa).

What this tests in Holos

This experiment tests the Axiom of Relationality:

Reality is not absolute; it is defined by the relationship between system and observer.

If the results show multiple, irreducible, stable realities, it supports the idea that observer cuts are ontologically constitutive rather than merely descriptive.