Quantifying the Lifelong Impact of Resilience Interventions via Agent-Based LLM Simulation

TL;DR

This paper introduces LALS, a simulation framework using large language models to estimate the long-term causal effects of resilience interventions on life outcomes, revealing critical developmental windows and socioeconomic disparities.

Contribution

The paper presents a novel agent-based simulation method that enables precise long-term causal inference of psychological interventions using LLM-generated digital clones.

Findings

Intervention at age 6 more than doubles wealth impact compared to age 18.

Resilience training reduces mortality and dementia incidence.

Low-socioeconomic agents benefit most from early interventions.

Abstract

Establishing the long-term, causal impact of psychological interventions on life outcomes is a grand challenge for the social sciences, caught between the limitations of correlational longitudinal studies and short-term randomized controlled trials (RCTs). This paper introduces Large-Scale Agent-based Longitudinal Simulation (LALS), a framework that resolves this impasse by simulating multi-decade, counterfactual life trajectories. The methodology employs a "digital clone" design where 2,500 unique LLM-based agent personas (grounded in a curated corpus of 3,917 empirical research articles) are each cloned across a 2x2 factorial experiment. Specifically, the simulation models the efficacy of extended psychological resilience training (Intervention vs. Control) either in childhood or as a young adult (age 6 vs. age 18). Comparing digital clones enables exceptionally precise causal inference. The simulation provides a quantitative, causal estimate of a resilience intervention's lifelong effects, revealing significant reductions in mortality, a lower incidence of dementia, and a substantial increase in accumulated wealth. Crucially, the results uncover a crucial developmental window: the intervention administered at age 6 produced more than double the positive impact on lifetime wealth compared to the same intervention at age 18. These benefits were most pronounced for agents from low-socioeconomic backgrounds, highlighting a powerful buffering effect. The LALS framework serves as a "computational wind tunnel" for social science, offering a new paradigm for generating and testing causal hypotheses about the complex, lifelong dynamics that shape human capital and well-being.