# Simulation Goals and Parameter Space Exploration (v2 - P_target Formalism) *(This node supersedes the previous simulation plan [[releases/archive/Information Ontology 1/0099_IO_Simulation_Goals]].)* ## 1. Objective: Testing the Refined Formalism Node [[releases/archive/Information Ontology 1/0100_IO_Kappa_Refinement]] proposed a richer state representation for Information Dynamics (IO) incorporating a **Target Probability Vector `P_target`** to better model Potentiality (κ). Node [[releases/archive/Information Ontology 1/0101_IO_Transition_Rule_v2]] integrated this into a refined unified transition rule (v2). This node updates the simulation plan accordingly, defining the objectives, parameters, metrics, and success criteria for computationally exploring this more sophisticated IO model, as per the refinement strategy [[releases/archive/Information Ontology 1/0094_IO_Refinement_Strategy_v1.1]]. ## 2. Simulation Goals The primary goals remain consistent with [[releases/archive/Information Ontology 1/0099_IO_Simulation_Goals]] but are now pursued with a formalism capable of richer dynamics: 1. **Validate Emergence:** Demonstrate that complex, stable, and potentially diverse structures or patterns can emerge spontaneously from simple initial conditions using the v2 transition rule. 2. **Explore Dynamical Regimes:** Identify different qualitative behaviors (static, chaotic, complex, oscillatory, propagating structures) arising from the interplay of Η, Θ, K, M, CA acting on the refined κ/ε state (`P_target`, `ε`, `Θ_val`). Map the model's phase space. 3. **Investigate Principle Interplay:** Understand how parameters controlling Η, Θ, K, M, CA, and now the dynamics of `P_target` itself, influence emergent structures, stability, adaptation, and information flow. 4. **Search for Analogues:** Look for emergent patterns or behaviors that qualitatively resemble targeted physical or biological phenomena more closely, leveraging the directed potential encoded in `P_target`. 5. **Test Robustness:** Assess the sensitivity of emergent phenomena to specific choices of functional forms and parameter values within the v2 rule set. ## 3. Key Parameters to Explore The parameter space now includes controls for the refined state components and transition rule v2 [[releases/archive/Information Ontology 1/0101_IO_Transition_Rule_v2]]: * **Η Influence:** * `h`: Global entropy drive strength (influencing `f_H`). * **Θ Influence (on `Θ_val` and `P_change`):** * `α`: Sensitivity of resistance `f_Θ` to `Θ_val`. * `ΔΘ_inc`: Rate of stability increase for `Θ_val`. * `Θ_max`: Maximum stability level for `Θ_val`. * `Θ_base`: Reset stability level for `Θ_val`. * **K Influence:** * `K_min` or `γ`: Parameters controlling how local contrast gates interaction probability via `f_K`. * **M Influence:** * `p_M`: Strength of mimetic bias modifying `P_target` based on causal inputs. * **CA Influence (Network Dynamics):** * `Δw_inc`: Rate at which used causal links strengthen (Θ on CA). * `decay_rate`: Rate at which unused causal links weaken. * Initial network topology parameters (if not a simple lattice). * **κ / `P_target` Dynamics (Needs Definition - Pillar 1 Priority):** * Parameters governing the update rule for `P_target(i, S+1)`. This rule is currently underspecified [[0101]]. Placeholder parameters might include: * `p_target_reset_mode`: How `P_target` resets after a state change (e.g., uniform, biased to new ε). * `p_target_theta_influence`: How strongly `Θ_val` biases `P_target` towards the current `ε`. * `p_target_context_influence`: How strongly neighbor states or K/M/CA context directly influence the *intrinsic* `P_target` over time (adaptation of potential). **Exploration Strategy:** Similar to before, start with baseline values and systematically vary key parameters or pairs (e.g., `h` vs `α`, `p_M` vs `K_min`, parameters governing `P_target` dynamics once defined) to map out behavioral regimes. ## 4. Simulation Setup Considerations * **Environment:** 1D/2D grids initially. * **Boundary Conditions:** Periodic or fixed. * **Initial State:** Random `ε` states, low initial `Θ_val`, and crucially, an initial `P_target` distribution (e.g., uniform `P_target = [0.5, 0.5]` for binary ε, or slightly perturbed). * **Network:** Start with fixed nearest-neighbor causal links (`w=1`), then implement dynamic weights `w(j → i, S)`. * **Scale:** Appropriate system sizes and durations to allow for emergence. ## 5. Analysis Metrics and Techniques In addition to the global and local measures listed in [[releases/archive/Information Ontology 1/0099_IO_Simulation_Goals]], new metrics are needed for the refined state: * **`P_target` Analysis:** * Average Shannon entropy of `P_target` vectors across the system (measures average potential uncertainty/specificity). * Distribution of dominant target probabilities (which states are potentially favored?). * Rate and nature of `P_target` vector evolution over Sequence (S). * Spatial correlations between `P_target` vectors of neighboring nodes. * **Transition Analysis:** * Track actual transition probabilities vs. those predicted by `P_modified_target`. * Analyze the influence of K, M, CA parameters on transition outcomes. ## 6. Success Criteria (Connecting to OMF Rule 4) Success criteria remain focused on demonstrating meaningful emergence, but with higher expectations due to the richer formalism: * **Emergence of Non-Trivial Order:** Self-organization into complex, potentially dynamic structures. * **Diverse Dynamical Regimes:** Clear identification of distinct behavioral phases controlled by IO parameters. * **Stable Structures:** Formation of persistent patterns stabilized by Θ, potentially exhibiting more complex internal dynamics enabled by `P_target`. * **Directed Processes:** Evidence of directed information flow or pattern propagation influenced by M/CA acting on `P_target`. * **Qualitative Analogues:** Emergence of behaviors qualitatively resembling targeted phenomena with greater fidelity than simpler models. Failure to observe significant self-organization beyond simple domain growth or noise, even with the richer `P_target` dynamics, would cast doubt on this formalization path. ## 7. Conclusion: Simulating Richer Potentiality This updated simulation plan (v2) aligns the computational exploration strategy with the refined IO state representation incorporating the Target Probability Vector (`P_target`). By simulating the v2 transition rule [[releases/archive/Information Ontology 1/0101_IO_Transition_Rule_v2]], the aim is to investigate whether this richer model of potentiality allows for more complex and realistic emergent phenomena governed by the interplay of Η, Θ, K, M, and CA. A critical prerequisite for meaningful simulation is the further theoretical development of the rules governing the dynamics of `P_target` itself. These simulations are essential for validating the formal approach and guiding the next steps in developing Information Dynamics.