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@ -17,7 +17,7 @@ solvers C3). Crypto pump-and-dump ABM C3 (3-agent protocol validated on historic
Pop behavioral models C1.
## 3. Language & location
TBD · `src/economy/sims/sociological/`. **Prolog** — game-theoretic equilibria, replicator
ECLiPSe Prolog · `src/economy/sims/sociological/`. **Prolog** — game-theoretic equilibria, replicator
dynamics, and strategy evolution are naturally expressed as logical relations over population
states; Nash equilibrium search is constraint satisfaction. Needs efficient population iteration,
strategy mutation, PDE solvers for MFG (HJB + Fokker-Planck), and bandit algorithms
@ -81,12 +81,12 @@ strategy mutation, PDE solvers for MFG (HJB + Fokker-Planck), and bandit algorit
- M3 Sims hub — lifecycle management; *stub:* manual init.
## 7. Invariants / laws
- **L1 (C5):** pops are **archetypes, not individuals** — no attempt to model or track real
market participants. The sim models emergent behavior from strategy populations.
- **L1 (C5):** pops are **archetypal individuals, not literao living persons** — no attempt to model or track real
market participants. The sim models emergent behavior from abstracted populations.
- **L2 (C5):** strategies **evolve** — the population distribution shifts over time via
replicator dynamics. No fixed strategy ratios.
- **L3 (C4):** bounded rationality is the **default** — pops satisfice with heuristics, not
optimize with perfect information. Rational-agent models are a special case, not the baseline.
- **L3 (C4):** rationality is ***NOT*** the **default** — pops satisfice with heuristics, not
optimize with perfect information. Rational-agent models are an **abnormal** case, not the baseline.
- **L4 (C4):** **complex contagion requires multiple exposures** — adoption is non-linear in
neighbor count, not simple diffusion. Single-exposure models undercount threshold effects.
- **L5 (C4):** the MFG limit is **valid only for large populations** — below ~100 pops, use
@ -95,7 +95,8 @@ strategy mutation, PDE solvers for MFG (HJB + Fokker-Planck), and bandit algorit
promote → distribute → collapse) has distinct statistical signatures in volume and price.
## 8. Build steps
1. Define pop archetypes and their heuristic strategies.
0. Get ECLiPSe tool chain installed and operational.
1. Define pop archetypes and their various strategies.
2. Implement replicator dynamics (strategy evolution over generations).
3. Implement Hegselmann-Krause bounded confidence opinion model.
4. Implement complex contagion with heterogeneous thresholds.
@ -103,7 +104,7 @@ strategy mutation, PDE solvers for MFG (HJB + Fokker-Planck), and bandit algorit
6. Implement MFG solver (HJB + Fokker-Planck with Newton iteration).
7. Implement pump-and-dump 3-type ABM (Normal, MA, MP) with 4-phase protocol.
8. Wire M2 news/price data → calibration of pop parameters.
9. Implement multi-horizon `BoundedPrediction` output.
9. Implement multi-horizon `BoundedPrediction` outputs.
## 9. Tests
Evolution: dominant strategy shifts when payoff landscape changes. Cascade: sentiment shock
@ -117,7 +118,7 @@ include upper/lower.
## 10. Open items
- Pop archetype catalog (which behavioral types? how many?).
- Network topology for sentiment contagion (small-world? scale-free?).
- Calibration from real market data — how to infer pop distribution from observable price action.
- MFG tensor-train rank $r$ (accuracy vs. compute tradeoff).
- Hegselmann-Krause confidence bound $d$ — fixed or adaptive?
- Cross-sim interaction: do sociological predictions feed into M3c (AMM) or M3d (MEV)?
- Calibration from real market data — how to infer pop distribution from observable price action. >>>We actually use blogs, reddit, and social networks to infer pops<<<
- MFG tensor-train rank $r$ (>>>accuracy<<< vs. compute tradeoff).
- Hegselmann-Krause confidence bound $d$ — fixed or >>>adaptive<<<?
- Cross-sim interaction: do sociological predictions feed into M3c (AMM) or M3d (MEV)? [conditional on prediction accuracy over time]