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Neural Network-Based Parameter Estimation for a Labour Market ABM
US & West ·
This study applies a state-of-the-art simulation-based inference framework with neural networks to estimate parameters of a labour market agent-based model. The model is based on job transition networks and is initialized with synthetic and real U.S. labour market data. The approach addresses computational constraints in exploring the parameter space.
Why it matters
The method is important for practical use of agent-based models in decision support, as it enables calibration of complex models on real data.
Original headline
Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model
The authors propose a theoretical and experimental model comparing communities of agents that hold rival theories side by side with communities that fold everything they hear into a single model. The work builds on Feyerabend's idea of theoretical pluralism, where coexistence of incompatible theories helps science work.
Бывший CEO Google Эрик Шмидт советует предпринимателям сосредоточиться на агентном искусственном интеллекте. Он называет текущий момент «агентным периодом» и утверждает, что гонка больших языковых моделей (LLM) выходит на зрелую стадию, а гонка ИИ-агентов только начинается.
