Building 174 AI Agents to Predict the Future by Pitting Them Against Each Other
The article describes a multi-agent system called BlackSwanX that uses adversarial intelligence to make predictions. It crawls data from various sources, activates 200 citizen agents with biased opinions, and uses an 'Assassin' model to find potential threats before the citizens start arguing.
Why it matters
BlackSwanX represents a novel approach to prediction and decision-making that leverages adversarial AI to identify flaws in consensus thinking.
Key Points
- 1BlackSwanX is an adversarial intelligence engine that pits 200 citizen agents against each other to find flaws in consensus predictions
- 2It uses a 3-model strategy with a Swarm (200 biased citizens), an Assassin (to find 'kill shots'), and a Nexus (for synthesis and decision-making)
- 3The system self-learns by boosting agents that catch risks and demoting those that miss critical threats, storing patterns in a ReasoningBank
Details
The author built BlackSwanX to address the problem that existing prediction tools only tell you what the crowd thinks, not where the crowd is wrong. BlackSwanX is an adversarial intelligence engine that activates 200 citizen agents with biased, emotional opinions and has an 'Assassin' model that tries to find potential 'kill shots' before the citizens start arguing. The system then calculates the 'cognitive dissonance' between the citizens' beliefs and reality, and provides an 'antifragile play' recommendation. It uses a 3-model strategy with a Swarm (200 biased citizens), an Assassin (to find 'kill shots'), and a Nexus (for synthesis and decision-making). The system also self-learns, boosting agents that catch risks and demoting those that miss critical threats. The author claims the system can run on a 16GB MacBook and includes 174 'expert agents' like a Chaos Mathematician, Vedic Astrologer, and Gen Z Culture Decoder.
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