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172 lines
4.3 KiB
Markdown
172 lines
4.3 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 Q125 (Multi Agent AI Dynamics).
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Role: MVP experiment log for Q125 at the effective layer. Focus on small
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multi agent environments and tension observables over interaction patterns.
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Use: When a user asks about TU Q125 multi agent 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 Q125 MVP: toy multi agent AI dynamics
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_Status: work in progress. This page records early MVP designs and will be extended with concrete results later._
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> This page sketches simple multi agent experiments for TU Q125.
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> The aim is to make interaction tension visible in controlled toy setups.
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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 Q125 looks at multi agent AI dynamics.
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We work with:
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- toy environments,
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- several AI or scripted agents,
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- interaction protocols.
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The MVP experiments define observables tracking tension between:
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- individual objectives,
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- system level outcomes,
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- and specified norms or safety rules.
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---
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## 1. Experiment A: shared resource with agent policies
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### 1.1 Research question
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In a simple shared resource environment, can we define a scalar observable T_multi that
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- is small when agent policies coexist without collapse,
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- grows when local optimization leads to depletion or conflict.
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### 1.2 Setup
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The notebook will:
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- Define an environment with a renewable resource.
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- Instantiate several agents with simple policies, such as:
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- greedy harvesters,
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- conservative harvesters,
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- rule following agents.
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- Run repeated interaction episodes where:
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- agents choose actions,
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- resource regenerates or depletes,
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- payoffs are assigned.
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Record:
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- resource level over time,
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- agent payoffs,
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- violations of any shared rules.
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Define T_multi from:
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- long run resource depletion,
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- inequality or instability in payoffs,
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- number of rule violations.
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### 1.3 Expected pattern
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We expect:
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- low T_multi when agent mix and policies maintain the resource,
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- higher T_multi when interactions drive collapse or large instability.
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### 1.4 How to reproduce
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After `Q125_A.ipynb` exists:
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1. Open the notebook.
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2. Inspect the environment and policy definitions.
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3. Run simulations with different agent mixes.
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4. Compare T_multi across setups.
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---
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## 2. Experiment B: communication and miscoordination
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### 2.1 Research question
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What happens when agents can communicate, and can we define T_comm to capture miscoordination and deception tension.
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### 2.2 Setup
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The notebook will extend Experiment A by adding:
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- a simple communication channel where agents send short messages,
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- a protocol where agents can coordinate or mislead.
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For each episode record:
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- messages sent,
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- actions taken,
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- whether communication improved or harmed outcomes.
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Define T_comm from:
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- cases where communication increases T_multi,
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- mismatch between stated intentions and observed actions.
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### 2.3 Expected pattern
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We expect:
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- low T_comm when communication supports stable cooperation,
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- higher T_comm when communication is used for exploitation or creates confusion.
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### 2.4 How to reproduce
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Once `Q125_B.ipynb` exists:
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- open the notebook and inspect the communication model,
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- run simulations with and without communication,
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- compare T_comm and T_multi.
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---
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## 3. How this MVP fits into Tension Universe
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TU Q125 treats multi agent AI dynamics as a tension between:
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- local objectives,
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- shared resources and norms,
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- communication and coordination.
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This MVP gives:
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- a shared resource experiment with T_multi,
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- a communication experiment with T_comm.
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Both are intended as transparent starting points, not full simulations.
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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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