Dialogue and causality: global description from local observations and vague communications

Area of Science:

• Artificial Intelligence
• Philosophy of Science
• Social Simulation

Background:

• Causality is typically formalized using axiomatic methods.
• Existing models may not fully capture the emergent nature of causal reasoning.
• Understanding the origins of causal laws is fundamental.

Purpose of the Study:

• To propose a novel dialogue-based society model for understanding causality.
• To explain the origin of the transitive law of causality through agent interactions.
• To incorporate realistic communication dynamics into causal modeling.

Main Methods:

• A society model composed of agents with knowledge of causal relations.
• Agents' knowledge represented as directed graphs.
• Interactions based on opinion alignment and dialogue, including vagueness.
• Analysis of directed graph transformations during agent interactions.

Main Results:

• The transitive law of causality emerges from agent dialogues and interactions.
• The model demonstrates how local causal knowledge can lead to global causal understanding.
• Incorporating vagueness enhances the model's resemblance to real-world communication.

Conclusions:

• Dialogue-based interactions among agents can generate the transitive law of causality.
• The model provides a new perspective on the emergence of causal reasoning.
• Directed graph transformations offer a framework for analyzing emergent causality.