We present a novel approach to agent-based modeling by replacing traditional utility-maximizing agents with Large Language Model (LLM) agents that make human-like residential decisions. Using the classic Schelling segregation model as our testbed, we compare three agent types: (1) traditional mechanical agents using best-response dynamics, (2) LLM agents making decisions based on current neighborhood context, and (3) LLM agents with persistent memory of past interactions and relationships. Our results reveal that LLM agents achieve complete convergence (100%) while mechanical agents only converge 50% of the time. Standard LLM agents converge in 99±9 steps compared to 187 steps for mechanical agents when they do converge. Memory-enhanced LLM agents demonstrate the fastest convergence at 84±14 steps—a 2.2× improvement. Both LLM variants achieve similar final segregation levels to mechanical agents (~55% vs 58% like-neighbors) but with significantly reduced extreme segregation, with memory LLM agents showing a 53.8% reduction in “ghetto” formation (p=0.018). These findings suggest that incorporating human-like decision-making through LLMs can produce more stable and realistic dynamics in agent-based models of social phenomena, with important implications for urban planning and policy analysis.

We present a novel approach to agent-based modeling by replacing traditional utility-maximizing agents with Large Language Model (LLM) agents that make human-like residential decisions. Using the classic Schelling segregation model as our testbed, we compare three agent types: (1) traditional mechanical agents using best-response dynamics, (2) LLM agents making decisions based on current neighborhood context, and (3) LLM agents with persistent memory of past interactions and relationships. Our results reveal that LLM agents achieve complete convergence (100%) while mechanical agents only converge 50% of the time. Standard LLM agents converge in 99±9 steps compared to 187 steps for mechanical agents when they do converge. Memory-enhanced LLM agents demonstrate the fastest convergence at 84±14 steps—a 2.2× improvement. Both LLM variants achieve similar final segregation levels to mechanical agents (~55% vs 58% like-neighbors) but with significantly reduced extreme segregation, with memory LLM agents showing a 53.8% reduction in “ghetto” formation (p=0.018). These findings suggest that incorporating human-like decision-making through LLMs can produce more stable and realistic dynamics in agent-based models of social phenomena, with important implications for urban planning and policy analysis.

These are the baseline data for the Pancs and Vriend 2007 metrics Schelling Simulation model

An analysis of all of the faunal data excavated at Anakena (1987-2005)

Analysis of 2005 excavation data

The numbers of students in specialized tracks of the Environmental Studies Program Majors over the years at Binghamton University