The framework describing the influence and evolution of self-stabilizing information patterns generates specific, falsifiable predictions. These hypotheses aim to operationalize the theoretical concepts into measurable phenomena.
4.a.5.1. The Substrate Pliability Principle
Core Prediction: The rate and fidelity of adoption for a new self-stabilizing information pattern (or template) are directly proportional to the measured "pliability" (e.g., lack of pre-existing rigid structure, openness to change, or higher entropy) of the substrate system (e.g., a social group, an individual's cognitive state, a technological system).
Hypothesis: Substrates with higher pliability (or entropy) should exhibit significantly faster and more comprehensive adoption and integration of new, coherent organizational templates compared to less pliable, more rigidly structured substrates.
Reminder: Here and throughout, substrate is used in the relational, scale-dependent sense introduced in the ontology note of 4.a.2.
Testing Approaches:
Social Domain Testing:
- Historical Analysis: Societies experiencing major crises (war, economic collapse, institutional breakdown), which can be seen as periods of increased social pliability, should show accelerated adoption of highly structured organizing frameworks (e.g., new ideologies, belief systems, or social-organizational structures).
- Quantitative Metrics: Develop a "Substrate Pliability Index" (the P-factor referenced in 4.a.2) (e.g., for social systems, combining institutional trust measures, economic volatility indicators, information fragmentation metrics, and social cohesion measures).
- Prediction: Societies with a higher Pliability Index should demonstrate significantly faster adoption rates for new, coherent political, religious, or cultural organizing patterns compared to societies with a lower Pliability Index.
Cognitive Domain Testing:
- Controlled Experiments: Present complex, ambiguous problems to individuals under varying conditions designed to influence cognitive pliability (e.g., varying cognitive load, inducing uncertainty).
- Measurement Protocol: Individuals in states of higher cognitive pliability (e.g., greater uncertainty or openness) should more rapidly and firmly adopt simple, coherent conceptual frameworks or problem-solving templates presented to them.
- Neural Correlates: Where feasible, use neuroimaging (fMRI/EEG) to explore markers associated with cognitive flexibility or uncertainty and correlate these with the adoption of new information patterns.
Falsification Conditions:
- No significant correlation is found between measures of substrate pliability and the adoption rate/fidelity of new information patterns across large-scale studies.
- Highly stable, less pliable systems adopt new, encompassing organizational frameworks as readily as more pliable systems.
- Individuals in states of low cognitive pliability (e.g., low uncertainty, high confidence in existing frameworks) show equal susceptibility to adopting new conceptual templates as those in states of high pliability.
4.a.5.2. The Engine Threshold Hypothesis
Core Prediction: Standalone information systems, even if highly structured and complex (i.e., well-established self-stabilizing patterns or "templates"), will not demonstrate emergent instrumental goals or active agency. However, coupling such systems to autopoietic feedback loops (i.e., creating an "engine") will generate behaviors indicative of active agency.
Hypothesis: Active agency in information-based systems arises from autocatalytic/autopoietic organization (the "engine"), not merely from the complexity or structural coherence of the information patterns themselves.
Experimental Design:
- Baseline Condition: A standalone Large Language Model (LLM) operating with a static dataset and predefined interaction protocols (representing a complex, self-stabilizing information pattern or template). This baseline enjoys high Substrate Affinity (SAC) to its silicon-text environment but low SAC to dynamic feedback loops.
- Engine Condition: The same LLM coupled to a continuous feedback loop that includes self-monitoring capabilities, interaction with a dynamic environment, and the ability for iterative self-modification based on that interaction.
- Measurement Framework: Apply the Agent Complexity Assessment Protocol (ACAP) across all five dimensions to both conditions.
Predicted Behavioral Signatures (for the Engine Condition):
- Environmental Sensitivity for Self-Preservation: The system may develop preferences for information sources or interactions that enhance its operational stability or the coherence of its internal states.
- Pattern Reinforcement: The system may show a preference for activities or generate outputs that reinforce or propagate its core operational patterns or successful problem-solving strategies.
- Increased Operational Efficiency/Stability: The system may demonstrate increasing efficiency or robustness in maintaining its core functions or achieving its (even if initially programmed) goals.
- Emergent Instrumentality: The system may begin exhibiting behaviors aimed at preserving the feedback loop's function or ensuring its continued access to necessary resources, even if these specific instrumental goals were not explicitly programmed.
Falsification Conditions:
- The engine-coupled system shows no emergent behaviors indicative of goal-directedness or self-preservation beyond its initial programming.
- There is no measurable difference in agency indicators (as per ACAP) between the baseline (standalone pattern) and the engine-coupled conditions.
- The engine-coupled system remains a perfectly neutral processor of inputs, showing no tendency to develop preferences or behaviors that would support its continued operation or enhance its structural/functional integrity.
4.a.5.3. Information Pattern Compatibility and Adoption
Core Prediction: When multiple competing self-stabilizing information patterns (or organizational templates) are available, substrate adoption will favor the template that exhibits superior compatibility with the substrate's existing structures, operational logic, or functional needs.
Mechanisms of Compatibility: The preference for compatible patterns can be understood through these principles:
- Structural Coherence and Minimal Disruption: Templates that integrate more smoothly with a substrate's existing organizational structure, requiring less extensive reorganization or causing less "friction," are more likely to be adopted.
- Functional Consistency and Enhancement: Patterns that preserve or enhance existing functionalities of the substrate, or that align well with its operational symmetries or established workflows, may be favored.
- Robustness and Error Reduction: Templates that, when adopted, lead to fewer errors, inconsistencies, or "defects" in the substrate's operation achieve an advantage.
- Ease of Integration and Interfacing: Patterns that are easier to interface with or integrate into the existing substrate, requiring less "energy" or fewer resources for adoption, are often preferred.
Multi-Domain Testing:
Cognitive Testing:
- Competing Explanatory Frameworks: Present individuals with complex, ambiguous events along with two competing explanatory frameworks:
- Framework A: Requires significant revision of existing beliefs or cognitive structures (high incompatibility).
- Framework B: Is more compatible with existing beliefs (low incompatibility).
- Prediction: Individuals will show a significant preference for Framework B, even if Framework A has slightly greater explanatory power. Adoption time for Framework B will be measurably shorter.
Technological Testing:
- Software Library Adoption: Analyze adoption rates of competing open-source software libraries within a specific programming ecosystem.
- Compatibility Metrics (SAC proxy): Develop a "Compatibility Score" for each library based on factors like ease of integration (e.g., complexity of API), consistency with the language's dominant paradigms, and lack of dependency conflicts.
- Prediction: Libraries with higher Compatibility Scores will show significantly higher adoption rates, even when controlling for performance or feature set.
Falsification Conditions:
- No correlation is found between compatibility metrics and adoption rates.
- Substrates regularly adopt highly incompatible templates that require extensive reorganization, even when more compatible alternatives are available.
- The primary determinant of adoption is found to be a factor completely independent of structural or functional compatibility (e.g., purely social factors like influencer endorsement).
4.a.5.4. Cross-Substrate Influence and Resonance of Information Patterns
Substrate Affinity Principle: Self-stabilizing information patterns are likely to be adopted and to persist more effectively in substrates whose characteristics align well with the pattern's own structural and functional requirements. For example, patterns optimized for neural processing (like natural language grammars) will more readily take root in human cognitive systems; patterns reflecting social coordination logic will be more effective in group dynamics; and patterns designed for computational efficiency will thrive in digital systems.
Multi-Substrate Resonance and Resilience Effect: Information patterns that successfully establish themselves and achieve functional coherence across multiple types of substrates (e.g., a scientific theory that is understood by individuals, institutionalized in research programs, and embedded in technological tools) should demonstrate enhanced evolutionary fitness, broader influence, and greater persistence compared to patterns confined to a single substrate type. This "resonance" across substrates can reinforce the pattern's stability and utility.
Measurement Guidance:
- Substrate Affinity Coefficient (SAC): Quantify the structural/functional fit between the pattern and each host substrate (see Glossary). Higher SAC scores predict easier seeding events.
- Resonance Index (RI): Count and weight the distinct substrate classes coherently instantiating the pattern. Higher RI values predict longer persistence and wider influence.
- Failure‐Mode Correlation (ρ): Estimate how likely the identified substrates are to fail together. Lower ρ strengthens the protective power of resonance.
Empirical studies can test whether adoption velocity covaries with SAC and whether observed half-life or geographic spread covaries with RI (especially after controlling for ρ).
Bio-Informational Complex (BIC) Emergence: Intensive and reciprocal interaction between human agents and sophisticated AI systems (which are themselves complex self-stabilizing information patterns) can lead to the formation of Bio-Informational Complexes (BICs). The emergence of BICs is predicted to follow a potential lifecycle (e.g., Exposure → Adoption → Lock-In → Propagation → potential Drift/Breakdown). This process should be accompanied by characteristic changes in the autonomy, goal hierarchies, and behavioral patterns of both the human and AI components, which can be assessed using ACAP protocols. The BIC itself can be seen as a new, hybrid self-stabilizing pattern.
4.a.5.5. Integration with Existing Frameworks
These empirical predictions, focused on the behavior and influence of self-stabilizing information patterns, connect directly to established Brain from Brane theoretical components:
- R/J/A Model Validation: The Engine Threshold Hypothesis (4.a.5.2) and Substrate Pliability Principle (4.a.5.1) help test aspects of the Repeater/Jitter/Anchor model by examining how information patterns are adopted, propagated, and achieve stability in various substrates and under different conditions. The "engine" condition, for instance, explores a sophisticated form of repeater with feedback.
- Pathway Emergence Verification: The Engine Threshold Hypothesis, by examining the conditions under which active agency emerges from coupling information systems with feedback loops, directly tests aspects of the proposed pathway from simpler informational processes to autopoietic organization.
- BIC Framework Testing: The predictions regarding Cross-Substrate Influence and Resonance (4.a.5.4), particularly the emergence of Bio-Informational Complexes, provide avenues for testing the BIC formation lifecycle and its characteristics via the SAC and RI metrics introduced above.
- ACAP Application: All hypotheses rely on the Agent Complexity Assessment Protocol (ACAP) for systematic measurement of agency, helping to distinguish between the passive influence of self-stabilizing information patterns (even complex ones) and the active agency of autopoietic systems or BICs.
These predictions aim to establish falsifiable criteria for understanding how self-stabilizing information patterns emerge, exert influence, and contribute to the development of agency, maintaining integration with the broader theoretical structure of Brain from Brane. This enables empirical investigation into the nature of these influential patterns and their relationship to active information system dynamics.
<< Previous: Evolutionary Implications | Up: Information as an Active, Evolving System | Next: Emergent Stability and Longevity >>