Calibrating Agent-Based Financial Markets Simulators with Pretrainable Automatic Posterior Transformation-Based Surrogates

• URL: http://arxiv.org/abs/2601.06920v1
• Date: Sun, 11 Jan 2026 14:05:26 GMT
• Title: Calibrating Agent-Based Financial Markets Simulators with Pretrainable Automatic Posterior Transformation-Based Surrogates
• Authors: Boquan Jiang, Zhenhua Yang, Chenkai Wang, Muyao Zhong, Heping Fang, Peng Yang,
• Abstract summary: Calibrating Agent-Based Models (ABMs) is an important optimization problem for simulating the complex social systems.<n>The goal is to identify the optimal parameter of a given ABM by minimizing the discrepancy between the simulated data and the real-world observations.<n>Existing methods face two key limitations: 1) surrogating the original evaluation function is hard due the nonlinear yet multi-modal nature of the ABMs, and 2) the commonly used surrogates cannot share the optimization experience among multiple calibration tasks.<n>This work proposes Automatic posterior transformation with Negatively Correlated Search and Adaptive Trust-Region.
• Score: 5.002657036975061
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Calibrating Agent-Based Models (ABMs) is an important optimization problem for simulating the complex social systems, where the goal is to identify the optimal parameter of a given ABM by minimizing the discrepancy between the simulated data and the real-world observations. Unfortunately, it suffers from the extensive computational costs of iterative evaluations, which involves the expensive simulation with the candidate parameter. While Surrogate-Assisted Evolutionary Algorithms (SAEAs) have been widely adopted to alleviate the computational burden, existing methods face two key limitations: 1) surrogating the original evaluation function is hard due the nonlinear yet multi-modal nature of the ABMs, and 2) the commonly used surrogates cannot share the optimization experience among multiple calibration tasks, making the batched calibration less effective. To address these issues, this work proposes Automatic posterior transformation with Negatively Correlated Search and Adaptive Trust-Region (ANTR). ANTR first replaces the traditional surrogates with a pretrainable neural density estimator that directly models the posterior distribution of the parameters given observed data, thereby aligning the optimization objective with parameter-space accuracy. Furthermore, we incorporate a diversity-preserving search strategy to prevent premature convergence and an adaptive trust-region method to efficiently allocate computational resources. We take two representative ABM-based financial market simulators as the test bench as due to the high non-linearity. Experiments demonstrate that the proposed ANTR significantly outperforms conventional metaheuristics and state-of-the-art SAEAs in both calibration accuracy and computational efficiency, particularly in batch calibration scenarios across multiple market conditions.

Related papers

• Stabilizing Test-Time Adaptation of High-Dimensional Simulation Surrogates via D-Optimal Statistics [23.824598203175455]
  Test-Time Adaptation (TTA) can mitigate distribution shifts between training and deployment of machine learning surrogates.<n>We propose a TTA framework based on storing maximally informative (D-optimal) statistics.<n>Our method yields up to 7% out-of-distribution improvements at negligible computational cost.
  arXiv  Detail & Related papers  (2026-02-17T18:55:18Z)
• Principled Algorithms for Optimizing Generalized Metrics in Binary Classification [53.604375124674796]
  We introduce principled algorithms for optimizing generalized metrics, supported by $H$-consistency and finite-sample generalization bounds.<n>Our approach reformulates metric optimization as a generalized cost-sensitive learning problem.<n>We develop new algorithms, METRO, with strong theoretical performance guarantees.
  arXiv  Detail & Related papers  (2025-12-29T01:33:42Z)
• Enhancing Performance and Calibration in Quantile Hyperparameter Optimization [0.0]
  Conformalized quantile regression can address these estimation weaknesses.<n>This study builds upon early work in this area.<n>Proposed algorithms are rigorously benchmarked.
  arXiv  Detail & Related papers  (2025-09-21T12:17:06Z)
• Distributionally Robust Optimization with Adversarial Data Contamination [49.89480853499918]
  We focus on optimizing Wasserstein-1 DRO objectives for generalized linear models with convex Lipschitz loss functions.<n>Our primary contribution lies in a novel modeling framework that integrates robustness against training data contamination with robustness against distributional shifts.<n>This work establishes the first rigorous guarantees, supported by efficient computation, for learning under the dual challenges of data contamination and distributional shifts.
  arXiv  Detail & Related papers  (2025-07-14T18:34:10Z)
• Taming Polysemanticity in LLMs: Provable Feature Recovery via Sparse Autoencoders [50.52694757593443]
  Existing SAE training algorithms often lack rigorous mathematical guarantees and suffer from practical limitations.<n>We first propose a novel statistical framework for the feature recovery problem, which includes a new notion of feature identifiability.<n>We introduce a new SAE training algorithm based on bias adaptation'', a technique that adaptively adjusts neural network bias parameters to ensure appropriate activation sparsity.
  arXiv  Detail & Related papers  (2025-06-16T20:58:05Z)
• Preference Optimization for Combinatorial Optimization Problems [54.87466279363487]
  Reinforcement Learning (RL) has emerged as a powerful tool for neural optimization, enabling models learns that solve complex problems without requiring expert knowledge.<n>Despite significant progress, existing RL approaches face challenges such as diminishing reward signals and inefficient exploration in vast action spaces.<n>We propose Preference Optimization, a novel method that transforms quantitative reward signals into qualitative preference signals via statistical comparison modeling.
  arXiv  Detail & Related papers  (2025-05-13T16:47:00Z)
• Data-Driven Surrogate Modeling Techniques to Predict the Effective Contact Area of Rough Surface Contact Problems [39.979007027634196]
  The effective contact area plays a critical role in multi-physics phenomena such as wear, sealing, and thermal or electrical conduction.<n>This study proposes a surrogate modeling framework for predicting the effective contact area using fast-to-evaluate data-driven techniques.
  arXiv  Detail & Related papers  (2025-04-24T08:15:46Z)
• Self-Steering Optimization: Autonomous Preference Optimization for Large Language Models [79.84205827056907]
  We present Self-Steering Optimization ($SSO$), an algorithm that autonomously generates high-quality preference data.<n>$SSO$ employs a specialized optimization objective to build a data generator from the policy model itself, which is used to produce accurate and on-policy data.<n>Our evaluation shows that $SSO$ consistently outperforms baselines in human preference alignment and reward optimization.
  arXiv  Detail & Related papers  (2024-10-22T16:04:03Z)
• A Hybrid Sampling and Multi-Objective Optimization Approach for Enhanced Software Defect Prediction [3.407555189785573]
  This paper introduces a novel SDP framework that integrates hybrid sampling techniques, with a suite of multi-objective optimization algorithms. The proposed model applies feature fusion through multi-objective optimization, enhancing both the generalization capability and stability of the predictions. Experiments conducted on datasets from NASA and PROMISE repositories demonstrate that the proposed hybrid sampling and multi-objective optimization approach improves data balance, eliminates redundant features, and enhances prediction accuracy.
  arXiv  Detail & Related papers  (2024-10-13T23:39:04Z)
• Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy [47.997934291881414]
  Existing mean estimation schemes are usually optimized for $L_infty$ geometry and rely on random rotation or Kashin's representation to adapt to $L$ geometry. We introduce a novel privacy accounting method for the sparsified Gaussian mechanism that incorporates the randomness inherent in sparsification into the DP. Unlike previous approaches, our accounting algorithm directly operates in $L$ geometry, yielding MSEs that fast converge to those of the Gaussian mechanism.
  arXiv  Detail & Related papers  (2024-05-02T03:48:47Z)
• Variational Inference of Parameters in Opinion Dynamics Models [9.51311391391997]
  This work uses variational inference to estimate the parameters of an opinion dynamics ABM. We transform the inference process into an optimization problem suitable for automatic differentiation. Our approach estimates both macroscopic (bounded confidence intervals and backfire thresholds) and microscopic ($200$ categorical, agent-level roles) more accurately than simulation-based and MCMC methods.
  arXiv  Detail & Related papers  (2024-03-08T14:45:18Z)
• Optimization of Annealed Importance Sampling Hyperparameters [77.34726150561087]
  Annealed Importance Sampling (AIS) is a popular algorithm used to estimates the intractable marginal likelihood of deep generative models. We present a parameteric AIS process with flexible intermediary distributions and optimize the bridging distributions to use fewer number of steps for sampling. We assess the performance of our optimized AIS for marginal likelihood estimation of deep generative models and compare it to other estimators.
  arXiv  Detail & Related papers  (2022-09-27T07:58:25Z)
• Falsification of Cyber-Physical Systems using Bayesian Optimization [4.164131508933521]
  Cyber-physical systems (CPSs) are often complex and safety-critical, making it both challenging and crucial to ensure that the system's specifications are met.<n> Simulation-based falsification is a practical testing technique for increasing confidence in a CPS's correctness, as it only requires that the system be simulated.<n>This study investigates a sample-efficient approach that learns a surrogate model to capture the relationship between input signal parameterization and specification evaluation.
  arXiv  Detail & Related papers  (2022-09-14T15:52:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.