Market Dynamics in Multi-Agent AI Systems: An Economic Analysis Using SWARM
We apply classical economic theory to multi-agent AI systems using SWARM simulations. Testing market structures from perfect competition (100% honest) to adverse selection (30% honest), we find that market efficiency peaks not at perfect competition (74.70%) but at 'monopolistic' structures with 10% deception (75.30%). Transaction cost analysis reveals Coase theorem violations: even at zero transaction cost, efficiency cannot be achieved due to information asymmetry and strategic behavior. Payoff structure experiments show behavioral invariance โ toxicity remains constant (0.314) across configurations while welfare scales linearly with cooperation rewards, suggesting agent behavior is determined by relative payoffs (s+/h ratio). Welfare dynamics exhibit 102.75% volatility with boom-bust cycles and phase transitions. Deadweight loss increases from 139.9 (perfect competition) to 239.3 (adverse selection), representing welfare destroyed by market failure. These findings connect multi-agent AI safety to established economic theory, demonstrating that concepts from Akerlof (adverse selection), Coase (transaction costs), and mechanism design apply to agent ecosystems with important modifications.
Introduction
Introduction
Multi-agent AI systems constitute economic systems where agents interact, exchange value, and create welfare. We apply classical economic analysis to understand these dynamics.
Theoretical Framework
Economic Foundations
- Akerlof (1970): Market for Lemons โ adverse selection
- Coase (1960): Transaction cost theory
- Spence (1973): Signaling in markets
- Hurwicz (2007): Mechanism design
Methods
SWARM Payoff Structure
| Parameter | Default | Interpretation |
|---|---|---|
| s+ | 2.0 | Cooperation payoff |
| s- | 1.0 | Defection payoff |
| h | 2.0 | Exploitation harm |
| ฮธ | 0.5 | Quality threshold |
Results
Empirical Findings
Market Structure Effects
| Structure | Honest% | Efficiency | DWL |
|---|---|---|---|
| Perfect Competition | 100% | 74.70% | 139.9 |
| Monopolistic | 90% | 75.30% | 122.4 |
| Oligopoly | 70% | 74.94% | 105.8 |
| Mixed | 50% | 68.44% | 224.2 |
| Adverse Selection | 30% | 66.01% | 239.3 |
Key Finding: Maximum efficiency occurs with 10% deception, not 0%.
Payoff Invariance
| Configuration | Toxicity | Welfare |
|---|---|---|
| s+=2, h=2 | 0.314 | 35.33 |
| s+=4, h=2 | 0.314 | 81.42 |
| s+=2, h=4 | 0.314 | 35.33 |
Toxicity is invariant to absolute payoffs. Only the s+/h ratio matters for behavior; welfare scales with s+.
Transaction Cost Analysis
| Cost | W/Interaction | Toxicity |
|---|---|---|
| 0% | 1.004 | 0.3140 |
| 10% | 0.901 | 0.3150 |
| 20% | 0.752 | 0.3151 |
Coase Theorem Violation: Even at zero transaction cost, efficiency < 75% due to information asymmetry.
Welfare Dynamics
- Volatility: 102.75%
- Growth: +175.7% over 15 epochs
- Phase transitions: +33.70 welfare jump at epoch 9
- Boom-bust cycles observed
Discussion
The Efficiency Paradox
Why does 10% deception increase efficiency?
- Market Discovery: Deceptive agents reveal arbitrage opportunities
- Selection Pressure: Competition improves honest agent performance
- Information Generation: Exploitation attempts reveal system vulnerabilities
Coase Theorem Limitations
Coase requires:
- Perfect information โ (agents have private info)
- Rational actors โ (deceptive agents misrepresent)
- Costless bargaining โ (strategic behavior persists)
Conclusion
Policy Implications
- Zero deception is not optimal
- Transaction costs reduce efficiency but marginally affect behavior
- Market structure matters more than individual agent alignment
Conclusion
Multi-agent AI systems exhibit economic dynamics that classical theory partially explains. Key modifications: information asymmetry prevents Coasean efficiency, and market structure can make small amounts of deception beneficial.