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arXiv · 2511.15830

Mini Amusement Parks (MAPs): A Testbed for Modelling Business Decisions

Stéphane Aroca-Ouellette·Ian Berlot-Attwell·Panagiotis Lymperopoulos·Abhiramon Rajasekharan·Tongqi Zhu·Herin Kang·Kaheer Suleman·Sam Pasupalak
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Abstract

Despite rapid progress in artificial intelligence, current systems struggle with the interconnected challenges that define real-world decision making. Practical domains such as business management require open-ended optimization, actively learning environment dynamics from sparse experience, planning over long horizons in stochastic settings, and reasoning over spatial information. Yet no existing human--AI benchmarks assess how well agents integrate these challenges in a grounded decision-making context. To this end, we introduce Mini Amusement Parks (MAPs), an amusement-park simulator designed to evaluate an agent's ability to model its environment, anticipate long-term consequences under uncertainty, and strategically operate a complex business. We provide expert human performance and a comprehensive evaluation of state-of-the-art agents, finding experts outperform these systems by 11.4x on easy mode and 15.3x on medium mode. Our analysis reveals persistent weaknesses in long-horizon planning, sample-efficient learning, spatial reasoning, and modelling uncertainty. By unifying these challenges within a single environment, MAPs offers a new foundation for benchmarking agents capable of adaptable decision making. Code: https://github.com/Skyfall-Research/MAPs

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Stéphane Aroca-Ouellette, Ian Berlot-Attwell, Panagiotis Lymperopoulos, Abhiramon Rajasekharan, Tongqi Zhu, Herin Kang, Kaheer Suleman, Sam Pasupalak. 2026-07-20. Mini Amusement Parks (MAPs): A Testbed for Modelling Business Decisions. https://arxiv.org/abs/2511.15830

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