mlsanityData to 14 Oct 2026

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

arXiv 2511.15830 ↗First posted 19 Nov 2025cs.AIcited by 1

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Abstract (arXiv)

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

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 40 of its 71 references that are in the map. First posted on arXiv 19 Nov 2025.

Builds on

40 of 71 references.

References · 40

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28 more
  1. 2022ScienceWorld: Is your Agent Smarter than a 5th Grader?cited by 327
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026World of Workflows: A Benchmark for Bringing World Models to Enterprise Systemscited by 11

Similar, not cited

Close in wording, no citation either way

  1. 2025PLANET: A Collection of Benchmarks for Evaluating LLMs' Planning Capabilities
  2. 2026EcoGym: Evaluating LLMs for Long-Horizon Plan-and-Execute in Interactive Economies
  3. 2026MAP: A Map-then-Act Paradigm for Long-Horizon Interactive Agent Reasoning
  4. 2021Planning from Pixels in Environments with Combinatorially Hard Search Spaces
  5. 2026CASSANDRA: Programmatic and Probabilistic Learning and Inference for Stochastic World Modeling
  6. 2026MiniCorp: The Last Mile to an AI Agent Corporation
  7. 2025Seemingly Simple Planning Problems are Computationally Challenging: The Countdown Game
  8. 2026AI Gamestore: Scalable, Open-Ended Evaluation of Machine General Intelligence with Human Games