# Simulation Goals and Parameter Space Exploration for Unified IO Model ## 1. Objective: Testing the Unified Formalism Node [[releases/archive/Information Ontology 1/0098_IO_Unified_Rule]] presented a synthesized formal transition rule incorporating all core Information Dynamics (IO) principles (Η, Θ, K, M, CA) acting on a state representation capturing κ/ε/Θ aspects within a dynamic causal network. The next crucial step, following the refinement strategy [[releases/archive/Information Ontology 1/0094_IO_Refinement_Strategy_v1.1]], is to computationally implement and explore this unified model. This node defines the specific goals, key parameters, and analysis metrics for these simulations. ## 2. Simulation Goals The primary goals of simulating the unified IO model are: 1. **Validate Emergence:** Demonstrate that complex, stable, and potentially diverse structures or patterns can emerge spontaneously from simple initial conditions (e.g., random states) solely through the interplay of the defined IO principles. 2. **Explore Dynamical Regimes:** Identify different qualitative behaviors (e.g., static/frozen, chaotic/noisy, complex/structured, oscillatory) that arise as key parameters are varied. Map the "phase space" of the model. 3. **Investigate Principle Interplay:** Understand how the relative strengths of Η, Θ, K, M, and CA influence emergent structures and dynamics. For example, how does the Η/Θ balance affect stability and complexity? How does M influence domain growth speed? How does CA structure information flow? 4. **Search for Analogues:** Look for emergent patterns or behaviors that qualitatively resemble physical phenomena (e.g., particle-like localized structures, wave-like propagation, phase transitions [[releases/archive/Information Ontology 1/0067_IO_Complexity_Thresholds]], self-organization reminiscent of life [[releases/archive/Information Ontology 1/0031_IO_Biology_Life]]). 5. **Test Robustness:** Assess how sensitive emergent phenomena are to specific choices of functional forms (e.g., `f_Θ`, `f_K`) and parameter values. ## 3. Key Parameters to Explore The unified rule [[releases/archive/Information Ontology 1/0098_IO_Unified_Rule]] involves several parameters controlling the relative influence of the IO principles. Systematic exploration of this parameter space is essential. Key parameters include: * **Η Influence:** * `h`: Global entropy drive strength. * `p_base`: Baseline intrinsic potential for change. * **Θ Influence:** * `α`: Sensitivity of resistance to `Θ_val`. * `β`: Sensitivity of `p_flip` reduction to `Θ_val`. * `ΔΘ_inc`: Rate of stability increase. * `Θ_max`: Maximum stability level. * `Θ_base`: Reset stability level. * `decay_rate` (for `w`): Rate at which unused causal links weaken. * `Δw_inc` (for `w`): Rate at which used causal links strengthen. * **K Influence:** * `K_min` or `γ`: Parameters controlling how local contrast gates interaction probability (`f_K`). * **M Influence:** * `p_M`: Strength of mimetic bias towards neighbor states. * **Network Structure:** * Initial graph connectivity (e.g., lattice dimension, random graph properties). * Rules for dynamic edge creation/deletion (if implemented). **Exploration Strategy:** Start with a baseline parameter set. Systematically vary one or two parameters at a time (e.g., `h` vs `α`, `p_M` vs `K_min`) while keeping others fixed, observing the impact on emergent behavior. ## 4. Simulation Setup Considerations * **Environment:** 1D or 2D grid/lattice initially for simplicity and visualization, potentially moving to more complex graph structures later. * **Boundary Conditions:** Periodic or fixed boundaries. * **Initial State:** Typically random `ε` states with low initial `Θ_val` and baseline `p_flip`. Explore sensitivity to different initializations. * **Network:** Start with fixed nearest-neighbor causal links (`w=1`), then implement dynamic weights and potentially dynamic topology. * **Scale:** Start with modest system sizes (e.g., 100x100 grid) and simulation durations, increasing as needed and feasible. ## 5. Analysis Metrics and Techniques To characterize emergent behavior, monitor and analyze: * **Global Measures:** * Average state (`<ε>`). * Average stability (`<Θ_val>`). * Average potential (`<p_flip>`). * Overall activity rate (fraction of nodes changing per step). * Measures of spatial correlation or order parameters (e.g., domain size, interface density). * Information-theoretic measures (e.g., Shannon entropy of state distribution, mutual information between nodes [[releases/archive/Information Ontology 1/0049_IO_Information_Measures]]). * **Local Structures:** * Identify and track persistent, localized patterns (clusters, domains, propagating structures). * Analyze their size, stability (lifetime), internal dynamics (oscillations?), and interactions. * **Network Evolution (if dynamic):** * Track average edge weight, degree distribution, clustering coefficient, path lengths. * **Visualization:** Use spatial plots of `ε`, `Θ_val`, `K_local`, and potentially causal network structure over Sequence (S). ## 6. Success Criteria (Connecting to OMF Rule 4) Simulations will be considered successful in validating the potential of the unified IO formalism if they demonstrate: * **Emergence of Non-Trivial Order:** Clear evidence of self-organization into structures significantly more ordered than random initial conditions, across reasonable parameter ranges. * **Diverse Dynamical Regimes:** Identification of distinct behavioral phases (static, chaotic, complex) controlled by IO parameters. * **Stable Structures:** Formation of persistent, localized patterns that resist Η-driven disruption due to Θ/K/M dynamics. * **Qualitative Analogues:** Emergence of behaviors *qualitatively* resembling targeted physical phenomena (even if quantitatively inaccurate at this stage), such as particle-like stability, wave propagation, or phase transitions. Failure to observe significant self-organization or stable structures beyond trivial freezing or noise across wide parameter ranges would challenge the viability of this formal implementation. ## 7. Conclusion: From Formalism to Phenomenology This simulation plan provides the roadmap for testing the unified IO formalism developed in [[releases/archive/Information Ontology 1/0098_IO_Unified_Rule]]. By systematically exploring the parameter space defined by the relative strengths of Η, Θ, K, M, and CA, and analyzing the emergent dynamics using appropriate metrics, these simulations aim to bridge the gap between the abstract principles and observable phenomenology. The results will be crucial for validating whether this IO implementation can indeed generate the kind of complex, structured reality it purports to explain, providing concrete evidence for (or against) its potential as a foundational framework.