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253 lines
9 KiB
Markdown
<!--
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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 Q105 (Prediction of Systemic Crashes).
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Role: MVP experiment log for Q105 at the effective layer. Focus on small
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networked systems and tension observables for early warning signals.
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Use: When a user asks about TU Q105 crash prediction experiments or wants
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runnable examples, load this page first, then follow its notebook links.
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-->
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# TU Q105 MVP: toy systemic crash warnings
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_Status: MVP notebook A is implemented and fully offline. This page will be updated again when notebook B is added._
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> This page reports toy experiments for TU Q105.
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> The aim is to show how small networked systems can expose tension
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> between local early-warning indicators and global crash risk.
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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 Q105 looks at prediction of systemic crashes.
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Instead of real financial markets or infrastructures we start with toy networks:
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- nodes with local balance sheets or loads
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- edges representing exposures or dependencies
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- simple failure rules for cascades
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We then define:
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- early-warning indicators
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- crash outcomes
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- and tension observables when indicators fail to warn
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The current MVP implements **Experiment A** as a single offline notebook.
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All runs are fully simulated and deterministic once the random seed is fixed.
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No API key is needed.
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---
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## 1. Experiment A: network contagion and local indicators
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[](https://colab.research.google.com/github/onestardao/WFGY/blob/main/TensionUniverse/Experiments/Q105_MVP/Q105_A.ipynb)
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### 1.1 Research question
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In a simple contagion model on a network, can we define a scalar observable `T_warning` that
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- is small when early-warning indicators successfully flag upcoming crashes
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- grows when indicators remain calm but large cascades occur
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The goal is not to build a realistic risk system.
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The goal is to make the tension between local indicators and global crashes explicit and measurable.
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### 1.2 World and early-warning schemes
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The notebook builds a tiny networked world and three naive early-warning schemes.
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**Configured world**
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- Nodes: 20 in total (5 core nodes, 15 periphery nodes)
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- Network structure: core–periphery with additional ring edges in the periphery
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- Crash definition: a cascade is a crash when at least 40% of nodes fail
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- Scenarios per scheme: 500 independent shock-and-propagation runs
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- Observed crash rate across all runs: about 0.8% of scenarios crash
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Each node carries a simple load or capacity variable.
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Loads are shocked and then propagated through the network using a threshold failure rule.
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Once a node fails its load is pushed to neighbours, possibly triggering further failures.
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**Early-warning schemes**
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Before shocks are applied we compute a few aggregate indicators and apply three schemes:
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- `global_mean`: uses the global mean of load divided by capacity as a single indicator
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- `tail_sensitive`: focuses on how many nodes are close to their threshold
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- `core_focused`: gives extra attention to core nodes in the network
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Each scheme turns its indicators into a simple warning rule, for example
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"issue a warning when mean load is above a threshold".
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### 1.3 T_warning: how tension is measured
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For each scheme and each scenario the notebook records:
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- whether a large cascade (crash) occurred
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- whether the scheme issued a warning before the crash window
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- the overall crash rate in the world
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From these outcomes we compute:
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- `FN_rate`: fraction of crashes with no prior warning (false negatives)
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- `FP_rate`: fraction of warnings that occur in non-crash scenarios (false positives)
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- `quiet_crash_rate`: crash rate conditional on no warning
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The scalar tension observable `T_warning` is then defined so that:
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- high false negative rate and high quiet crash rate give a high `T_warning`
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- excessive false positives also increase `T_warning`
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- lower values mean better calibrated warnings for this particular world
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In the current configuration all three naive schemes behave badly.
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They almost never issue warnings, so every crash is a quiet crash and `T_warning` is high.
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### 1.4 What the current run shows
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The notebook prints a summary table for the three schemes:
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- `global_mean`
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- `tail_sensitive`
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- `core_focused`
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For the current world we obtain:
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- Crash rate across all scenarios: about `0.008`
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- For every scheme:
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- `FN_rate` close to `1.00` (all crashes happened with no warning)
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- `FP_rate` close to `0.00` (warnings are almost never issued)
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- `quiet_crash_rate` close to `0.008`
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- `T_warning` around `3.008` for all three schemes
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In words:
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> The world does occasionally crash.
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> The simple early-warning schemes almost never speak up.
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> All systemic crashes happen in quiet conditions.
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> Tension is high because the indicators are not aligned with actual crash risk.
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The notebook also saves two plots in this folder.
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**Crash probability versus mean load ratio**
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Each point shows the empirical crash probability in a bin of the mean load to capacity ratio.
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Most bins at lower ratios have almost zero crash probability.
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The rightmost bin with higher ratios has a noticeably higher crash probability.
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This shows that the toy world itself does carry a usable signal:
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when the system is globally more loaded, crashes become more likely.
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**T_warning per scheme**
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All three schemes end up with nearly identical and relatively high `T_warning` values.
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They fail to exploit the available signal and leave crashes almost completely unwarned.
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This is the intended tension for TU Q105:
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even in a small 20 node world it is possible to measure how badly a given
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early-warning design matches the actual structure of crash risk.
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### 1.5 How to reproduce
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To rerun or inspect Experiment A:
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1. Open the notebook with the Colab badge above or from this repository: `Q105_A.ipynb`.
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2. Read the header comments that describe the network, failure rules and indicators.
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3. Run all cells. The notebook will:
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- build the network
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- simulate 500 scenarios for each early-warning scheme
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- compute `T_warning` and print a summary table
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- regenerate the two plots (`Q105A_indicator_vs_crash.png` and `Q105A_T_warning_per_scheme.png`)
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4. Optionally modify the world parameters or warning thresholds to explore how
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`T_warning` changes when indicators are improved or degraded.
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No API key is needed. Everything runs offline inside the notebook.
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---
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## 2. Experiment B: model based versus data based risk assessment
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_This section describes a planned follow up experiment. The notebook is not yet implemented._
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### 2.1 Research question
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Can we define a tension observable `T_model_data` that captures when a simple
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model based risk score disagrees with an empirical data based score?
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### 2.2 Setup (planned)
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Using the same simulation engine, the future notebook will:
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- generate datasets of scenarios with
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- basic features
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- whether a crash occurred
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- train a small supervised learner to predict crash probability from features
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- compare
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- model based risk scores from the structural indicators
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- data based risk scores from the learner
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`T_model_data` will be defined as a function of:
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- divergence between the two risk scores
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- misranking of scenarios by each score relative to true crash outcomes
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### 2.3 Expected pattern
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We expect:
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- low `T_model_data` when structural and data based scores agree
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- higher `T_model_data` when a structural story and empirical patterns diverge
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### 2.4 How to reproduce (future)
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Once `Q105_B.ipynb` exists:
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- open and inspect the feature definitions
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- train the learner and compute both risk scores
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- compare `T_model_data` across parameter settings
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---
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## 3. How this MVP fits into Tension Universe
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TU Q105 treats systemic crash prediction as a tension between:
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- local indicators and global outcomes
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- structural models and data based models
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The current MVP provides:
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- a network contagion toy model with `T_warning` for early-warning schemes
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- visual evidence that the toy world carries a usable signal while naive schemes fail to use it
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Both notebooks in this series are designed as inspection friendly demos,
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not as real risk management systems.
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For overall 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 follows:
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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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