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

TL;DR

Mini Amusement Parks (MAPs) is a novel simulator designed to evaluate AI agents' ability to model environments, plan long-term strategies, and make complex business decisions, highlighting current AI limitations.

Contribution

Introduces MAPs, a comprehensive environment unifying multiple decision-making challenges, and provides baseline human and AI performance evaluations.

Findings

Humans outperform AI agents by 6.5x on easy mode and 9.8x on medium mode.

Current AI systems show weaknesses in long-horizon planning and spatial reasoning.

MAPs offers a new platform for benchmarking holistic decision-making in AI.

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 optimizing an open-ended and multi-faceted objective, actively learning environment dynamics from sparse experience, planning over long horizons in stochastic settings, and reasoning over spatial information. Yet existing human--AI benchmarks isolate subsets of these capabilities, limiting our ability to assess holistic decision-making competence. 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 human baselines and a comprehensive evaluation of state-of-the-art LLM agents, finding that humans outperform these systems by 6.5x on easy mode and 9.8x on medium mode. Our analysis reveals persistent weaknesses in long-horizon optimization, sample-efficient learning, spatial reasoning, and world modelling. 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