Artificial Intelligence (AI) has witnessed significant advances with the emergence of powerful neural network (NN) models. Examples include large language models [1] and image generation models such as DALL-E [2], Imagen [3], and Parti [4]. Each has achieved previously unseen capabilities as powerful individuals through recent technical breakthroughs. On the other hand, the biological evolutionary strategy focuses more on the direction of collective intelligence compared to individual ability, especially for species living in populations [5]. Unlike individual intelligence, which deals with challenges independently, collective intelligence necessitates the ability to process information, operate in a decentralized manner, and adaptively integrate information based on context. This distinction is evident in social insects, such as ants and bees, where collective behavior with role differentiation emerges not from highly complex individuals but through simple interactions among members.

We study the emergence of agency from scratch by using Large Language Model (LLM)-based agents. In previous studies of LLM-based agents, each agent's characteristics, including personality and memory, have traditionally been predefined. We focused on how individuality, such as behavior, personality, and memory, can be differentiated from an undifferentiated state. The present LLM agents engage in cooperative communication within a group simulation, exchanging context-based messages in natural language. By analyzing this multi-agent simulation, we report valuable new insights into how social norms, cooperation, and personality traits can emerge spontaneously. This paper demonstrates that autonomously interacting LLM-powered agents generate hallucinations and hashtags to sustain communication, which, in turn, increases the diversity of words within their interactions. Each agent's emotions shift through communication, and as they form communities, the personalities of the agents emerge and evolve accordingly. This computational modeling approach and its findings will provide a new method for analyzing collective artificial intelligence.

This paper presents a new artificial market simulation platform, PAMS: Platform for Artificial Market Simulations. PAMS is developed as a Python-based simulator that is easily integrated with deep learning and enabling various simulation that requires easy users' modification. In this paper, we demonstrate PAMS effectiveness through a study using agents predicting future prices by deep learning.