Multi-Armed Bandits and Robust Budget Allocation: Small and Medium-sized Enterprises Growth Decisions under Uncertainty in Monetization - Liu, Wenwen (European Journal of AI, Computing & Informatics, 2025)
Small and medium-sized enterprises (SMEs) often face significant uncertainty when allocating limited advertising budgets across multiple channels, as the return on investment (ROI) of each channel is typically unknown and volatile. This paper proposes a robust budget allocation framework based on the multi-armed bandit (MAB) model to address this challenge, specifically tailored to the advertising decisions of SMEs. By integrating robustness principles into traditional MAB algorithms, the framework balances "exploration" (testing new advertising channels) and "exploitation" (scaling effective channels) while mitigating the impact of ROI uncertainty. An empirical simulation is conducted using realistic advertising scenarios (including social media ads, search engine marketing, influencer collaborations, and offline promotions) to validate the model. Results show that the proposed robust MAB framework outperforms traditional budget allocation methods (e.g., equal distribution, heuristic allocation) in terms of cumulative ROI, budget efficiency, and risk resistance. This study provides SMEs with a practical, data-driven tool for advertising budget optimization under uncertainty, contributing to sustainable business growth. The findings also enrich the application of MAB models in the advertising domain, particularly for resource-constrained enterprises.