Can AI Solve Eco-Economic Challenges? Simulating the Impact of AI Systems on Seasonality Using Agent-Based Modelling

Abstract

Abstract As visitors increasingly rely on AI-enabled apps and recommender systems, AI is becoming an important mediator of tourism choice. Tourism decision-making is cognitively demanding, and visitors rarely evaluate all available destinations, attractions, travel periods, and services equally. Consistent with bounded rationality, they simplify complex choice environments through limited information, heuristics, and platform-mediated evaluation. This creates opportunities for AI systems not only to personalise experiences, but also to influence how attention and demand are distributed across attractions and seasons. This research develops and computationally examines a mechanism-based conceptualisation of how AI-enabled information systems mediate visitor choice and how micro-level decision processes accumulate into tourism-system outcomes. Drawing on digital choice architecture, information filtering, platform ecosystem perspectives, and bounded rationality, the research conceptualises AI as a decision-mediating infrastructure. Using an agent-based model of a tourism platform ecosystem, it compares three visitor-facing AI intervention conditions: predictive nudging, a recommender-system filtering, and fairness-oriented visibility enhancement. The model examines how differences in attention, salience, choice-set formation, evaluation, satisfaction, and subsequent behaviour are associated with system-level outcomes. The findings show that AI-mediated interventions can increase visitor activity and spending, but their effects differ across intervention conditions and do not automatically reduce seasonality. The research contributes to Information Systems and smart-tourism research by showing how AI-enabled decision environments can structure attention, consideration, and visibility, while highlighting the limits of digital interventions when destination outcomes are also shaped by wider structural conditions.