Adaptive Diffusion-Based Generative Algorithm For Synthetic Business Scenario Simulation And Strategic Planning

Abstract

Developing high-fidelity synthetic business scenarios is a key issue when it comes to strategic decision-making, organizational resilience testing, and management education. Current techniques based on rule-based systems, Monte Carlo simulation, or generative adversarial networks (GAN) suffer from mode collapse, low diversity, and poor temporal coherence. In this paper, AdaptDiff-BSim is presented, a new Adaptive- Diffusion-Based Generative Algorithm to support the generation of synthetic business scenarios and assist with strategic planning. AdaptDiff-BSim combines a score-based diffusion backbone with a hierarchical contextual encoder that can encapsulate domain-specific business ontologies, macroeconomic covariates, and competitive landscape representations. In addition, an Adaptive Noise Schedule (ANS) is introduced, which dynamically adjusts the denoising paths of diffusion based on the complexity of the scenario and the length of the planning horizon. Extensive experimentation using three industry-standard benchmarking datasets (StratSim-2500, EnterpriseScenario-DB, and GlobalMacro-Scenarios) demonstrates that AdaptDiff-BSim provides a 18.3% increase in scenario fidelity (as measured by Fréchet Scenario Distance, FSD), an increase of 22.7% in the overall strategic diversity score, and an average reduction of 31.4% in the rate of inconsistency of planning across the three datasets when compared to state-of-the-art baselines. Developed through user testing with 84 strategy consultants, this demonstrates that AdaptDiff-BSim provides both measurable benefit to users and offers narrative coherence in generated synthetic business scenarios. The code and datasets are publicly released at https://github.com/adaptdiff-bsim.