Adaptive Digital Marketing: A Systematic Review of Bio-Inspired Reinforcement Learning, Multi-Agent Systems, and Agentic AI for Intelligent Optimisation

Adhikari, Tek Narayan, Sayers, William ORCID: https://orcid.org/0000-0003-1677-4409 and Zhang, Shujun ORCID: https://orcid.org/0000-0001-5699-2676 (2026) Adaptive Digital Marketing: A Systematic Review of Bio-Inspired Reinforcement Learning, Multi-Agent Systems, and Agentic AI for Intelligent Optimisation. Biomimetics, 11 (7). p. 476. doi:10.3390/biomimetics11070476

Preview

Text 16421 Adhikari, T. et al. (2026) Adaptive Digital Marketing - A Systematic Review of Bio-Inspired Reinforcement Learning,.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (642kB) | Preview

Abstract

Background: Digital marketing increasingly functions as a complex adaptive system characterised by non-stationary environments, strategic interaction, and multi-agent competition. Programmatic advertising exemplifies this complexity, where decisions must be made in real time under uncertainty. Under such conditions, traditional static optimisation methods often fail to deliver robust performance. This review synthesises bio-inspired computational approaches, reinforcement learning (RL), multi-agent reinforcement learning (MARL), and agentic artificial intelligence (AI) to develop an integrated theoretical perspective on adaptive optimisation in digital marketing. Methods: Following PRISMA 2020 guidelines, we conducted a systematic search of peer-reviewed research across six databases: Scopus, IEEE Xplore, ACM Digital Library, SpringerLink, ScienceDirect, and arXiv, supplemented by manual reference checking. Each computational paradigm is explicitly grounded in foundational biological literature, including work on evolution, foraging, swarm intelligence, and immune cognition. Reinforcement learning supports adaptive decision-making through mechanisms closely aligned with operant conditioning and foraging behaviour. Multi-agent reinforcement learning extends these principles to interactive marketing ecosystems via decentralised coordination and swarm-based learning. Agentic AI further advances adaptive capability by introducing goal-directed reasoning, memory, and higher-level decision orchestration. Contributions: The review identifies persistent fragmentation across marketing sub-domains and a lack of formal mathematical grounding for widely used bio-inspired analogies. To address these gaps, the study proposes a multi-layer bio-inspired framework and outlines a structured research agenda to guide the development of autonomous digital marketing systems.

Item Type:	Article
Article Type:	Article
Additional Information:	This article belongs to the Special Issue Bio-Inspired Computation and Its Applications
Subjects:	Q Science > Q Science (General) > Q336 Artificial intelligence Q Science > QA Mathematics > QA76 Computer software > QA76.9 Other topics > QA76.9.V5 Virtual computer systems
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Depositing User:	Kamila Niekoraniec
Date Deposited:	09 Jul 2026 11:22
Last Modified:	09 Jul 2026 11:30
URI:	https://eprints.glos.ac.uk/id/eprint/16421

University Staff: Request a correction | Repository Editors: Update this record