In early 2025, a twenty-year-old computer science student in China named MiroFish released an open-source agent-based simulation framework. Within ten days, it had accumulated 33,000 GitHub stars and attracted 4 million dollars in funding. The concept was simple: populate a simulated world with AI agents that have distinct personalities, knowledge, and behavioral patterns, then observe the emergent dynamics. Applied to financial markets, the simulation populates a virtual exchange with agents representing different investor archetypes and observes how they react to specific catalysts such as earnings surprises, regulatory changes, or supply disruptions.¹

The intellectual lineage goes back further. Joshua Epstein and Robert Axtell's Growing Artificial Societies (1996) demonstrated that complex social phenomena, trade, migration, conflict, could emerge from simple behavioral rules applied to a population of heterogeneous agents. Leigh Tesfatsion's work on agent-based computational economics showed that market dynamics that are intractable in standard equilibrium models arise naturally from agent interaction.²

The six archetypes. A minimal market simulation requires at least six distinct agent types, each embodying a different decision-making framework. The Value Investor buys assets trading below intrinsic value and sells above it, with a long time horizon and tolerance for short-term losses. The Momentum Trader follows price trends, buying what is rising and selling what is falling, with a short time horizon. The Macro Strategist responds to regime changes: interest rate shifts, inflation surprises, geopolitical events. The Corporate Insider has operational knowledge of specific industries: supply chain bottlenecks, customer dynamics, competitive shifts. The Retail Participant responds to narratives, social media buzz, and the behavior of other retail participants. The Short Seller actively searches for overvaluation, deterioration, and fraud signals.

When a catalyst is introduced, a new fact or a constraint graph prediction, each archetype responds according to its rules. A simultaneous "buy" signal from five or six archetypes constitutes a high-confidence convergence. A three-three split indicates a contested catalyst where the outcome depends on which archetype's framing proves correct. The simulation does not predict what will happen. It predicts how different market participants will react to what happens. This is a distinct and valuable layer of prediction that sits on top of the physical simulation described in Article 10.

The practical implementation runs entirely on local compute. Each archetype is a local language model with a 200-word system prompt constraining its perspective. Six parallel calls, one per archetype, complete in under three seconds across a ten-node cluster. The cost is zero beyond electricity. The output is structured: action (buy/sell/hold), conviction (1-10), reasoning, and time horizon. Unanimous signals are rare and valuable.