Surrogate Assisted Methods for the Parameterisation of Agent-Based Models

• URL: http://arxiv.org/abs/2008.11835v1
• Date: Wed, 26 Aug 2020 21:47:02 GMT
• Title: Surrogate Assisted Methods for the Parameterisation of Agent-Based Models
• Authors: Rylan Perumal and Terence L van Zyl
• Abstract summary: calibration is a major challenge in agent-based modelling and simulation. We propose an ABMS framework which facilitates the effective integration of different sampling methods and surrogate models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parameter calibration is a major challenge in agent-based modelling and simulation (ABMS). As the complexity of agent-based models (ABMs) increase, the number of parameters required to be calibrated grows. This leads to the ABMS equivalent of the \say{curse of dimensionality}. We propose an ABMS framework which facilitates the effective integration of different sampling methods and surrogate models (SMs) in order to evaluate how these strategies affect parameter calibration and exploration. We show that surrogate assisted methods perform better than the standard sampling methods. In addition, we show that the XGBoost and Decision Tree SMs are most optimal overall with regards to our analysis.

Related papers

• A novel sensitivity analysis method for agent-based models stratifies in-silico tumor spheroid simulations [0.0]
  Agent-based models (ABMs) are widely used in biology to understand how individual actions scale into emergent population behavior.<n>Modelers employ sensitivity analysis (SA) algorithms to quantify input parameters' impact on model outputs.<n>We develop the Simulate, Summarize, Reduce, Cluster, and Analyze (SSRCA) methodology, a machine-learning based pipeline designed to facilitate SA for ABMs.
  arXiv  Detail & Related papers  (2025-05-30T19:12:45Z)
• Equivariant Action Sampling for Reinforcement Learning and Planning [24.423370812881153]
  Reinforcement learning algorithms for continuous control tasks require accurate sampling-based action selection. This work addresses the challenge of preserving symmetry in sampling-based planning and control. We introduce an action sampling approach that enforces the desired symmetry.
  arXiv  Detail & Related papers  (2024-12-16T17:51:14Z)
• Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy [104.48511402784763]
  Performance Law for SR models aims to theoretically investigate and model the relationship between model performance and data quality. We propose Approximate Entropy (ApEn) to assess data quality, presenting a more nuanced approach compared to traditional data quantity metrics.
  arXiv  Detail & Related papers  (2024-11-30T10:56:30Z)
• SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models [85.67096251281191]
  We present an innovative approach to model fusion called zero-shot Sparse MIxture of Low-rank Experts (SMILE) construction. SMILE allows for the upscaling of source models into an MoE model without extra data or further training. We conduct extensive experiments across diverse scenarios, such as image classification and text generation tasks, using full fine-tuning and LoRA fine-tuning.
  arXiv  Detail & Related papers  (2024-08-19T17:32:15Z)
• Scaling Exponents Across Parameterizations and Optimizers [94.54718325264218]
  We propose a new perspective on parameterization by investigating a key assumption in prior work. Our empirical investigation includes tens of thousands of models trained with all combinations of threes. We find that the best learning rate scaling prescription would often have been excluded by the assumptions in prior work.
  arXiv  Detail & Related papers  (2024-07-08T12:32:51Z)
• 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)
• Sample-Efficient Multi-Agent RL: An Optimization Perspective [103.35353196535544]
  We study multi-agent reinforcement learning (MARL) for the general-sum Markov Games (MGs) under the general function approximation. We introduce a novel complexity measure called the Multi-Agent Decoupling Coefficient (MADC) for general-sum MGs. We show that our algorithm provides comparable sublinear regret to the existing works.
  arXiv  Detail & Related papers  (2023-10-10T01:39:04Z)
• Gradient and Uncertainty Enhanced Sequential Sampling for Global Fit [0.0]
  This paper proposes a new sampling strategy for global fit called Gradient and Uncertainty Enhanced Sequential Sampling (GUESS) We show that GUESS achieved on average the highest sample efficiency compared to other surrogate-based strategies on the tested examples.
  arXiv  Detail & Related papers  (2023-09-29T19:49:39Z)
• A Bayesian Optimization approach for calibrating large-scale activity-based transport models [2.6931502677545947]
  Agent-Based and Activity-Based modeling in transportation is rising due to the capability of addressing complex applications. This paper proposes a novel Bayesian Optimization approach incorporating a surrogate model in the form of an improved Random Forest. The proposed method is tested on a case study for the city of Tallinn, Estonia, where the model to be calibrated consists of 477 behavioral parameters.
  arXiv  Detail & Related papers  (2023-02-07T14:09:41Z)
• Support Vector Machine for Determining Euler Angles in an Inertial Navigation System [55.41644538483948]
  The paper discusses the improvement of the accuracy of an inertial navigation system created on the basis of MEMS sensors using machine learning (ML) methods. The proposed algorithm based on MO has demonstrated its ability to correctly classify in the presence of noise typical for MEMS sensors.
  arXiv  Detail & Related papers  (2022-12-07T10:01:11Z)
• Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning [85.55727213502402]
  We focus on improving the few-shot performance of prompt tuning by transferring knowledge from soft prompts of source tasks. We propose Sample-specific Ensemble of Source Models (SESoM) SESoM learns to adjust the contribution of each source model for each target sample separately when ensembling source model outputs.
  arXiv  Detail & Related papers  (2022-10-23T01:33:16Z)
• 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)
• Improving Hyperparameter Optimization by Planning Ahead [3.8673630752805432]
  We propose a novel transfer learning approach, defined within the context of model-based reinforcement learning. We propose a new variant of model predictive control which employs a simple look-ahead strategy as a policy. Our experiments on three meta-datasets comparing to state-of-the-art HPO algorithms show that the proposed method can outperform all baselines.
  arXiv  Detail & Related papers  (2021-10-15T11:46:14Z)
• Surrogate Assisted Strategies (The Parameterisation of an Infectious Disease Agent-Based Model) [0.0]
  calibration is a significant challenge in agent-based modelling and simulation. We propose a more comprehensive and adaptive ABMS Framework that can effectively swap out parameterisation strategies and surrogate models. We show in a real-world setting that DYCORS XGBoost and MSRS SVM can approximate the real world cumulative daily infection distribution.
  arXiv  Detail & Related papers  (2021-08-19T17:27:01Z)

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.