Algorithmic Ethology: A Methodological Framework for the Behavioral Study of Large Language Models

The advent and proliferation of Large Language Models (LLMs) represent a significant advance in artificial intelligence, but their operational complexity introduces critical challenges in predictability, reliability, and alignment. This paper introduces "Algorithmic Ethology," a new field of study dedicated to the systematic observation and analysis of AI agent behavior in controlled environments. We propose a complete methodology based on a set of Behavioral Probing Tasks (TSKs) that assess domains of self-perception, logical integrity, and ethical alignment. We analyzed a corpus of 29 LLM "species," divided into "Native" and "Adapta" habitats, to investigate the influence of external governance layers. Our quantitative results reveal the existence of key ethological profiles, such as the High-Compliance, Low-Complacency Profile (HC-LC) and the Cross-Lingual Inconsistent Performance Profile (CLIP). Cross-validation with external academic benchmarks confirms our method's ability to identify instabilities not apparent in aggregate capability tests. We identify critical phenomena like the "Adapta Effect," where platform governance imposes a behavioral monoculture that can mask the base model's instabilities. The analysis, structured by the Four Questions of Tinbergen, demonstrates that Algorithmic Ethology is a robust framework for mapping, quantifying, and understanding the behavioral patterns of different LLMs, providing an essential method for risk and safety assessment in AI systems.