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New methods improve Bayesian inference for agent-based models

By PulseAugur Editorial · [2 sources] ·

Researchers have developed new methods for Bayesian inference in agent-based models (ABMs), which are commonly used in economics and social sciences. The first paper introduces a framework called sequential neural posterior estimation test-time training (SNPE-TTT) to adapt pre-trained neural posterior estimators to new tasks with limited simulations. The second paper proposes Double-Flow Neural Posterior Estimation (DF-NPE), which uses learned time-series summaries and two conditional normalizing flows to significantly reduce the number of direct simulations required for training, achieving lower posterior discrepancies and runtime compared to standard methods. AI

IMPACT These advancements in neural posterior estimation could accelerate research and applications in economics and social sciences by making complex agent-based models more computationally tractable for parameter inference.

RANK_REASON Two research papers published on arXiv detailing novel methods for neural posterior estimation in agent-based models.

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New methods improve Bayesian inference for agent-based models

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Two research papers published on arXiv detailing novel methods for neural posterior estimation in agent-based models.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Junyao Zhang, Jinglai Li, Junqi Tang ·

    Adapting Neural Posterior Estimators for Simulation-Based Inference in Agent-Based Models

    arXiv:2601.07413v2 Announce Type: replace Abstract: Agent-based models (ABMs) are widely used in economics and social sciences. They represent heterogeneous personal behaviors and their interactions by using simulations, whereas their likelihoods are often intractable for paramet…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Junqi Tang ·

    Simulation-Efficient Double-Flow Neural Posterior Estimation for Agent-Based Models

    Neural posterior estimation (NPE) enables Bayesian inference of the posterior parameter in agent-based models (ABMs) with intractable likelihoods, but simulating training data can be computationally expensive. We propose Double-Flow Neural Posterior Estimation (DF-NPE), which com…

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