Agentic Chunking and Bayesian De-chunking of AI Generated Fuzzy Cognitive Maps: A Model of the Thucydides Trap
The paper discusses a method for generating feedback causal fuzzy cognitive maps (FCMs) using AI agents. It explores how these maps can predict outcomes in scenarios like the Thucydides Trap, where a dominant power faces a rising power. The authors demonstrate the effectiveness of their approach through various simulations and Bayesian updating techniques.
- ▪The study introduces a technique for generating fuzzy cognitive maps from text using large-language-model agents.
- ▪The method involves breaking text into overlapping chunks to create a cyclic FCM knowledge graph.
- ▪The authors applied their model to analyze the Thucydides Trap, predicting potential conflict outcomes.
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Computer Science > Artificial Intelligence arXiv:2605.17903 (cs) [Submitted on 18 May 2026] Title:Agentic Chunking and Bayesian De-chunking of AI Generated Fuzzy Cognitive Maps: A Model of the Thucydides Trap Authors:Akash Kumar Panda, Olaoluwa Adigun, Bart Kosko View a PDF of the paper titled Agentic Chunking and Bayesian De-chunking of AI Generated Fuzzy Cognitive Maps: A Model of the Thucydides Trap, by Akash Kumar Panda and 2 other authors View PDF HTML (experimental) Abstract:We automatically generate feedback causal fuzzy cognitive maps (FCMs) from text by teaching large-language-model agents to break the text into overlapping chunks of text. Convex mixing of these chunk FCMs gives a representative cyclic FCM knowledge graph. The text chunks can have different levels of overlap.
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