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