What Is Chance?” — A Philosophical Chain of Reasoning Reconstructed with LLMs and HellixMap

Abstract

In this presentation, we explore an experimental protocol combining generative AI, philosophical reasoning, and causal modeling. Sixteen major philosophers, from Rousseau to Nietzsche, are each embodied by a different Large Language Model (LLM) and tasked with answering the same question: What is chance?

Rather than responding in isolation, each AI-generated philosopher critiques the definition given by the previous one, then proposes a refined or opposing view. The result is a conceptual relay of 16 reformulations, where disagreement becomes a method of philosophical construction.

To analyze this evolving structure, we used HellixMap to generate:

• A Semantic Flowchart tracing the argumentative progression across the chain, and
• A Causal Semantic Diagram synthesizing key notions (epistemic vs ontological chance, determinism, divine order, scientific modeling, etc.) into a unified conceptual graph.

This project demonstrates how LLMs can emulate structured philosophical discourse, and how semantic graph tools like HellixMap can make complex idea networks visible, navigable, and analyzable.

About the Presenter

Dr. Lionel Jouffe is co-founder and CEO of France-based Bayesia S.A.S. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has worked in Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks. After co-founding Bayesia in 2001, he and his team have been working full-time on the development of BayesiaLab. Since then, BayesiaLab has emerged as the leading software package for knowledge discovery, data mining, and knowledge modeling using Bayesian networks. It enjoys broad acceptance in academic communities, business, and industry.