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TensionUniverse/Experiments/Q098_MVP/README.md
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<!--
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AI NOTE (WFGY 3.0 TU Experiment Anchor)
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This file is part of the WFGY 3.0 "Tension Universe" experiment collection.
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Main hub for context and navigation:
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https://github.com/onestardao/WFGY/blob/main/TensionUniverse/EventHorizon/README.md
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Problem ID: TU Q098 (Anthropocene System Dynamics).
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Role: MVP experiment log for Q098 at the effective layer. Focus on tiny coupled
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human–Earth toy models and tension observables for Anthropocene trajectories.
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Use: When a user asks about TU Q098 Anthropocene experiments or wants runnable
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examples, load this page first, then follow its notebook links.
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-->
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# TU Q098 MVP: toy Anthropocene trajectories
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_Status: work in progress. This page records early MVP designs and will evolve as the TU Q098 program develops._
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> This page sketches simple effective layer experiments for TU Q098.
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> The goal is not to predict the real Earth.
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> The goal is to show how tiny coupled human–Earth models can carry
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> explicit Anthropocene trajectories and tension observables.
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**Navigation**
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- [← Back to Experiments index](../README.md)
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- [← Back to Event Horizon (WFGY 3.0)](../../EventHorizon/README.md)
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---
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## 0. What this page is about
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TU Q098 studies Anthropocene system dynamics inside the Tension Universe.
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We build tiny coupled systems of:
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- human activity variables,
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- biophysical state variables,
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- and simple feedback rules,
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then track how trajectories move relative to declared safe operating spaces.
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The MVP experiments here are deliberately small.
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- State spaces are low dimensional and fully observable.
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- Dynamics are defined by explicit difference equations.
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- Tension observables track when trajectories cross declared boundaries or approach critical regions.
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---
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## 1. Experiment A: three variable Anthropocene toy model
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### 1.1 Research question
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Can we design a minimal three variable Anthropocene model where:
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- one variable represents economic production or energy use,
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- one variable represents environmental load,
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- one variable represents adaptive capacity,
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and define a scalar observable T_anthro that is:
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- small when the trajectory stays inside a simple safe operating region,
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- larger when it drifts into high load and low capacity combinations.
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### 1.2 Setup
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The notebook will:
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- Define discrete time update rules for three variables, for example
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- X_t: economic output or energy use,
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- E_t: environmental load or cumulative impact,
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- C_t: adaptive capacity or governance strength.
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- Include simple feedbacks, such as
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- growth of X_t depends on C_t and environmental damage,
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- E_t accumulates as a function of X_t with partial decay,
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- C_t improves under moderate conditions but degrades under extreme stress.
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- Define a rectangular or curved safe operating region in the (E, C) plane.
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- Simulate trajectories under different parameter choices:
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- growth focused, regulation weak,
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- balanced policy,
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- overshoot then correction.
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- Define a tension observable T_anthro that combines:
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- time spent outside the safe region,
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- maximum distance from the safe region,
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- rate of change when near boundaries.
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### 1.3 Expected pattern
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Once implemented, we expect to see:
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- low T_anthro for trajectories that remain near the safe region or gently return to it,
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- higher T_anthro for trajectories that overshoot and stay in high load, low capacity zones.
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Plots of trajectories and T_anthro values will be added once the first runs are logged.
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### 1.4 How to reproduce
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Reproduction steps:
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1. Open `Q098_A.ipynb` in this folder.
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2. Read the header comments describing the state variables, update rules and safe region.
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3. Run the notebook to generate trajectories and compute T_anthro.
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4. Compare different policy parameter settings and their tension values.
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---
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## 2. Experiment B: scenario comparison and narrative tension
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### 2.1 Research question
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Given a fixed toy model, can we define a narrative level tension observable T_story that captures when a declared scenario narrative is clearly inconsistent with the actual trajectory.
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### 2.2 Setup
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Using the same model as Experiment A, the notebook will:
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- Define simple narrative labels for parameter sets, such as
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- "green growth",
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- "managed descent",
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- "runaway exploitation".
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- For each simulated trajectory, build a short textual summary of key events.
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- Ask a language model to judge consistency between:
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- the declared narrative label,
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- the observed summary.
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- Extract a consistency score in the range 0 to 1.
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Define T_story as a function of:
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- misclassification between declared label and judged label,
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- low consistency scores when the narrative does not fit the trajectory.
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### 2.3 Expected pattern
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We expect:
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- low T_story when labels and trajectories match,
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- higher T_story when labels claim stability but trajectories show collapse or overshoot.
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This creates a bridge between numerical trajectories and narrative claims at the effective layer.
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### 2.4 How to reproduce
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Once `Q098_B.ipynb` exists:
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- open the notebook,
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- inspect how summaries and labels are defined,
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- run the narrative evaluation and compare T_story across scenarios.
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---
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## 3. How this MVP fits into Tension Universe
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TU Q098 treats Anthropocene dynamics as a structured tension between
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- human driven trajectories,
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- planetary boundaries and adaptive capacity,
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- and the narratives used to justify or deny those trajectories.
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This MVP provides:
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- a small three variable model for toy Anthropocene trajectories,
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- simple observables T_anthro and T_story that track physical and narrative tension.
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The emphasis is on transparency and reproducibility rather than realism.
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For more context:
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- [Experiments index](../README.md)
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- [Event Horizon (WFGY 3.0)](../../EventHorizon/README.md)
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---
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### Charters and formal context
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This page is written under:
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- [TU Effective Layer Charter](../../Charters/TU_EFFECTIVE_LAYER_CHARTER.md)
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- [TU Encoding and Fairness Charter](../../Charters/TU_ENCODING_AND_FAIRNESS_CHARTER.md)
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- [TU Tension Scale Charter](../../Charters/TU_TENSION_SCALE_CHARTER.md)
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