WFGY/TensionUniverse/Experiments/Q107_MVP

TU Q107 MVP: toy large scale collective action

Status: work in progress. This page records early MVP designs and may change as the TU Q107 program evolves.

This page sketches simple game based experiments for TU Q107.
The aim is to capture collective action tension in transparent toy models.

Navigation

• ← Back to Experiments index
• ← Back to Event Horizon (WFGY 3.0)

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0. What this page is about

TU Q107 studies large scale collective action.

We work with toy models such as:

• public goods games,
• threshold contribution games,
• simple coordination games,

with many agents and simple rules.

The MVP experiments here define observables that track tension between:

• individual incentives,
• group outcomes,
• and declared norms or agreements.

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1. Experiment A: public goods game with tension scores

1.1 Research question

In a repeated public goods game, can we define a scalar observable T_collective that

• is small when group outcomes and declared norms align,
• grows when individual best responses erode collective provision.

1.2 Setup

The notebook will:

• Simulate many agents playing a simple public goods game.

  • Each round, agents decide how much to contribute.
  • Contributions are multiplied and shared.

• Implement different strategy types, for example:

  • unconditional cooperators,
  • free riders,
  • conditional cooperators.

• Define a group norm, such as a target contribution level.

For each simulation record:

• average contribution and variance,
• fraction of free riders,
• distance from the norm.

Define T_collective from:

• the gap between observed contributions and norms,
• group payoff shortfall relative to maximum possible payoff.

1.3 Expected pattern

We expect:

• low T_collective when norms are respected and provision is high,
• higher T_collective when free riding dominates and norms fail.

1.4 How to reproduce

After Q107_A.ipynb is created:

1. Open the notebook.
2. Inspect the definition of strategies and norms.
3. Run simulations for different mixes of agent types.
4. Compare T_collective across scenarios.

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2. Experiment B: coordination with misaligned narratives

2.1 Research question

Can we expose coordination failure tension by comparing:

• numerical game outcomes,
• narrative claims about cooperation.

2.2 Setup

The notebook will:

• Implement a simple coordination game where agents choose actions that are better when aligned.

• Simulate multiple rounds under different information and communication structures.

• For each scenario produce:

  • numerical measures such as coordination rate,
  • a short textual description.

Ask a language model to:

• assess whether the scenario fits a narrative like "successful large scale cooperation" or "fragmented coordination".

Define T_coord as a function of:

• mismatch between numerical outcomes and narrative labels,
• frequency of coordination failures in scenarios claimed to be cooperative.

2.3 Expected pattern

We expect:

• low T_coord when narratives and outcomes align,
• higher T_coord when large scale collective failure hides behind cooperative language.

2.4 How to reproduce

Once Q107_B.ipynb exists:

• open the notebook and inspect prompts and metrics,
• run scenarios and evaluate T_coord.

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3. How this MVP fits into Tension Universe

TU Q107 treats large scale collective action as a tension between:

• individual and group payoffs,
• norms and actual behavior,
• numerical and narrative views of cooperation.

This MVP provides:

• a public goods game experiment with T_collective,
• a coordination narrative experiment with T_coord.

Both are small and transparent starting points.

For more context:

• Experiments index
• Event Horizon (WFGY 3.0)

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Charters and formal context

This page follows:

• TU Effective Layer Charter
• TU Encoding and Fairness Charter
• TU Tension Scale Charter