Tourism seasonality is not a minor scheduling nuisance. When demand concentrates around school holidays, summer weather, and headline attractions, destinations suffer overcrowding, strained infrastructure, environmental stress, degraded visitor experiences, and unstable seasonal labour markets, while hotels and restaurants sit half-empty for much of the year. Researchers have long argued that seasonality is a structural feature of destination systems rather than a marketing failure. The central challenge, as Dalir frames it, is not attracting more visitors but redistributing the attention, movement, and spending of the visitors a destination already receives across time and space.

The theoretical heart of the study lies in a distinction borrowed from Herbert Simon’s bounded rationality: the difference between the full choice set — everything objectively available to a traveller at a moment of decision — and the perceived choice set, the small subset a person actually notices and evaluates. Digital platforms appear to expand choice, but they also mediate how choice is encountered. Rankings, recommendation lists, review summaries, filters, and AI-generated itineraries determine which alternatives become cognitively accessible. An off-season festival or a charming peripheral museum may fail not because it lacks value, but because it never enters the traveller’s field of attention. AI, in this framing, is not a neutral information tool but a decision-mediating infrastructure that shapes what becomes visible before any preference is formed.

To test how different forms of algorithmic mediation play out at scale, Dalir built an agent-based model in NetLogo 6.4.0 simulating a destination ecosystem populated by heterogeneous visitor agents and supply agents — attractions, restaurants, accommodations, and souvenir shops. The model was calibrated with multiple data sources, including secondary destination data, online platform data, stakeholder input, and a survey of 400 respondents distributed through Prolific, of which 310 valid responses informed behavioural parameters such as spending, price sensitivity, and revisit probabilities. Visitors evaluate options using a bounded-rational random utility function in which congestion-adjusted quality, price, travel costs, and memory-based expectations combine, and each AI intervention adds a mechanism-specific cognitive term weighted by the visitor’s trust in AI — so effects vary across people rather than assuming uniform responsiveness.

Three AI conditions were compared against a business-as-usual baseline, each intervening at a different point in the decision process. Predictive nudging raises the salience of congestion and timing information, steering visitors toward less crowded options while leaving the choice set intact. Recommender-system filtering, the most aggressive intervention, restricts AI-adopting visitors to a top-k list of attractions ranked by predicted preference fit — directly shrinking the perceived choice set. Fairness-oriented visibility enhancement boosts the attractiveness of low-visibility, underrepresented, or off-peak alternatives without narrowing anything. The full experiment comprised 800 simulation runs, 200 per condition, generating 1,600 season-specific observations that were analysed with two-way ANOVA using intervention condition and season as factors.

The headline results are striking in both directions. All three AI interventions significantly increased spending and visitor numbers compared with business as usual, with large effect sizes. For spending, the intervention main effect was F(3, 1592) = 157.55, p < .001, with a partial eta-squared of .229. Recommender-system filtering produced the highest overall spending mean of roughly 818,793 simulation currency units, followed by predictive nudging at 809,527 and fairness-oriented visibility enhancement at 780,010. Visitor numbers rose from a baseline mean of about 338 to roughly 404–405 under all three interventions. On raw economic performance, AI looks like an unambiguous win.

But the seasonality result is where the study delivers its most important punch. The interaction between intervention and season — the statistical test of whether AI effects differ between high and low season — was not significant for either spending (p = .312) or visitor numbers (p = .655). In fact, the seasonal spending gap widened under every AI condition, growing from 94,597 under business as usual to as much as 127,297 under predictive nudging. AI lifted both seasons, but it lifted them roughly equally, leaving the structural imbalance untouched. The interventions, Dalir concludes, are performance-enhancing rather than seasonality-reducing. The reason is that seasonality is not merely an information problem; it is anchored in climate, school calendars, annual leave, work routines, event schedules, and accommodation availability — conditions that sit outside the platform interface and that no recommendation engine can rewrite.

The mechanism-level analysis reveals why the three designs behave so differently. The recommender condition showed the highest influence rate at 0.547 and cut the perceived choice set from about 25.6 options to 12.3, providing direct evidence of filtering at work. Predictive nudging had the lowest influence rate at 0.169, but every AI-influenced decision under it was attributable to congestion avoidance, confirming its value as a flow-coordination tool. Fairness-oriented visibility enhancement, with an influence rate of 0.396, kept the perceived choice set equal to the full set while spreading attention more evenly. Distributionally, the trade-offs are stark: recommender filtering concentrated demand around highly ranked attractions even as it maximised spending, while fairness-oriented enhancement activated more attractions with lower dispersion — the best profile for provider visibility and destination resilience, even though it was not the strongest earner.

The robustness analysis adds a subtle warning about design parameters. Varying the recommender choice-set size across k = 3, 5, 10, and 15 showed that influence peaks at moderate filtering levels and declines when choice sets become either too narrow or too broad — a non-linear threshold dynamic suggesting an optimal filtering range. Excessive filtering suppresses exploration and reinforces concentration; weak filtering fails to reduce cognitive burden. In other words, settings that look like technical dials are actually behavioural and governance choices with system-level consequences, and small parameter changes near critical regions can produce disproportionate effects on how demand and economic opportunity are distributed.

The broader message extends well beyond tourism. AI-enabled platforms should not be evaluated solely on accuracy, personalisation quality, engagement, or conversion, because systems that perform well by user-level metrics can still generate uneven consequences for providers, destinations, and ecosystems. For destination managers, the implication is to deploy AI with explicit objectives — revenue growth, congestion reduction, low-season stimulation, or provider resilience each demand a different design — and to track dashboards that monitor seasonal gaps and exposure concentration, not just visitor counts. For platforms and policymakers, the study argues for hybrid architectures combining filtering, nudging, and visibility mechanisms, transparency around ranking criteria, and exposure-diversity audits. AI can support more balanced tourism systems, the study concludes, but only when its decision architecture is deliberately designed to redistribute attention, visibility, and demand. Left to its own optimisation logic, it will happily make the busy season busier.

Subject of Research: Agent-based simulation of how AI-mediated decision mechanisms in tourism platforms affect visitor choice, spending, and seasonality

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

Article References: Dalir, S. (2026). Can AI Solve Eco-Economic Challenges? Simulating the Impact of AI Systems on Seasonality Using Agent-Based Modelling. Information Systems Frontiers. https://doi.org/10.1007/s10796-026-10818-z

Image Credits: AI Generated

DOI: 10.1007/s10796-026-10818-z

Keywords: artificial intelligence, tourism seasonality, agent-based modelling, smart tourism, recommender systems, digital choice architecture, bounded rationality, predictive nudging, algorithmic bias, destination management, platform ecosystems, simulation