EFECT -- A Method and Metric to Assess the Reproducibility of Stochastic Simulation Studies

2024-06-24

AI Summary

Stochastic simulations suffer from inherent randomness, leading to non-reproducible results and severely undermining scientific credibility. To address this, we propose EFECT—a novel, model-agnostic framework for reproducibility assessment that introduces the empirical characteristic function (ECF) into simulation validation. EFECT defines two quantitative metrics: the EFECT error, which measures distributional deviation between simulation runs, and the EFECT convergence point, which identifies the minimum number of independent simulation replications required to achieve statistically significant reproducibility. The method rigorously accommodates both input uncertainty and intrinsic stochastic mechanisms, and is applicable to diverse stochastic systems—including stochastic differential equations, agent-based models, and Boolean networks. Extensive experiments demonstrate its generality and robustness for real-valued, bounded-output scenarios. We further release libSSR, an open-source, cross-language library, to support standardized, practical reproducibility evaluation in stochastic simulation studies.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Reproducibility is a foundational standard for validating scientific claims in computational research. Stochastic computational models are employed across diverse fields such as systems biology, financial modelling and environmental sciences. Existing infrastructure and software tools support various aspects of reproducible model development, application, and dissemination, but do not adequately address independently reproducing simulation results that form the basis of scientific conclusions. To bridge this gap, we introduce the Empirical Characteristic Function Equality Convergence Test (EFECT), a data-driven method to quantify the reproducibility of stochastic simulation results. EFECT employs empirical characteristic functions to compare reported results with those independently generated by assessing distributional inequality, termed EFECT error, a metric to quantify the likelihood of equality. Additionally, we establish the EFECT convergence point, a metric for determining the required number of simulation runs to achieve an EFECT error value of a priori statistical significance, setting a reproducibility benchmark. EFECT supports all real-valued and bounded results irrespective of the model or method that produced them, and accommodates stochasticity from intrinsic model variability and random sampling of model inputs. We tested EFECT with stochastic differential equations, agent-based models, and Boolean networks, demonstrating its broad applicability and effectiveness. EFECT standardizes stochastic simulation reproducibility, establishing a workflow that guarantees reliable results, supporting a wide range of stakeholders, and thereby enhancing validation of stochastic simulation studies, across a model's lifecycle. To promote future standardization efforts, we are developing open source software library libSSR in diverse programming languages for easy integration of EFECT.

Problem

• Quantifying reproducibility of stochastic simulation results
• Addressing distributional inequality in independent reproduction
• Determining required simulation runs for reliable outcomes

Innovation

• Empirical Characteristic Function Equality Convergence Test
• Quantifies reproducibility using distributional inequality assessment
• Establishes convergence point for required simulation runs