Mini Amusement Parks (MAPs): A Testbed for Modelling Business Decisions
Mini Amusement Parks (MAPs) is a testbed for modeling business decisions, with experts outperforming AI by 11.4x.
Key Findings
Methodology
MAPs is a simulated amusement park environment to evaluate AI's ability in long-horizon planning and complex business operations under uncertainty. Benchmarked using ReAct and PPO algorithms.
Key Results
- Experts outperform AI by 11.4x in easy mode and 15.3x in medium mode, highlighting AI's deficiencies in long-term planning and uncertainty modeling.
- PPOFull and PPOSimple show significant performance differences under different observation and action space settings.
- GPT-5 performs best in research strategies but still falls short of human levels.
Significance
This study provides a new benchmark for AI applications in complex business decisions, revealing current AI deficiencies in integrating multiple capabilities and advancing adaptable decision-making agents.
Technical Contribution
MAPs unifies challenges of long-horizon planning, sample-efficient learning, spatial reasoning, and uncertainty modeling, offering a comprehensive AI testbed.
Novelty
MAPs is the first to integrate these challenges in a single environment, providing a unique benchmark for evaluating AI's adaptable decision-making capabilities.
Limitations
- AI still struggles with long-term planning and uncertainty, especially in complex environments.
- Current models show significant gaps in spatial reasoning and sample-efficient learning.
Future Work
Future research could explore enhancing AI's long-term planning and uncertainty modeling capabilities, particularly in complex business environments.
AI Executive Summary
Despite significant advances in artificial intelligence across various tasks, challenges remain in complex business decision-making. Current systems underperform in integrating multiple capabilities, particularly in long-horizon planning and uncertain environments. To address this, researchers introduce Mini Amusement Parks (MAPs), a simulated amusement park environment designed to evaluate AI's ability in long-horizon planning and complex business operations under uncertainty. Compared to human experts, AI systems achieve only 11.4% performance in easy mode and 6.5% in medium mode. These results highlight AI's deficiencies in integrating multiple capabilities, particularly in long-term planning and uncertainty modeling. MAPs provides a new benchmark for AI applications in complex business decisions, advancing adaptable decision-making agents. Future research could explore enhancing AI's long-term planning and uncertainty modeling capabilities, particularly in complex business environments.
Deep Analysis
Background
Recent years have seen breakthroughs in AI across many fields, especially in specific tasks and exams. However, these successes often occur in environments with clear objectives and minimal uncertainty, far removed from complex real-world tasks like business management. Business management requires coordinating interdependent decisions, involving staffing, infrastructure development, R&D investment, and market research.
Core Problem
Current AI systems underperform in integrating multiple capabilities, particularly in long-horizon planning, sample-efficient learning, spatial reasoning, and uncertainty modeling. Success in business management depends on long-term reasoning about noisy, partially observable customer behavior, posing significant challenges for existing AI systems.
Innovation
MAPs is the first to integrate challenges of long-horizon planning, sample-efficient learning, spatial reasoning, and uncertainty modeling in a single environment, providing a unique benchmark for evaluating AI's adaptable decision-making capabilities. By simulating complex amusement park dynamics, MAPs offers a new benchmark for AI applications in complex business decisions.
Methodology
- �� Benchmarked using ReAct and PPO algorithms. • Evaluated AI performance in easy and medium modes. • Provided expert human performance as a comparison. • Assessed AI's long-term planning and uncertainty modeling capabilities across various settings.
Experiments
The experimental design includes easy and medium difficulty modes, using ReAct and PPO algorithms for benchmarking. AI performance in long-term planning and uncertainty modeling is evaluated by comparing with human experts.
Results
Results show AI achieves only 11.4% performance in easy mode and 6.5% in medium mode compared to human experts. These results highlight AI's deficiencies in integrating multiple capabilities, particularly in long-term planning and uncertainty modeling.
Applications
MAPs provides a new benchmark for AI applications in complex business decisions, particularly in long-horizon planning and uncertain environments. The platform can be used to evaluate and improve AI performance in business management.
Limitations & Outlook
AI still struggles with long-term planning and uncertainty, especially in complex environments. Current models show significant gaps in spatial reasoning and sample-efficient learning.
Plain Language Accessible to non-experts
Imagine you're running an amusement park. You need to decide where to build new rides, how to allocate staff, and how to attract more visitors. It's like playing a complex strategy game where you need to consider the long-term impact of each decision. AI struggles in this simulated environment because it finds it difficult to plan long-term and handle uncertainty like humans do.
ELI14 Explained like you're 14
Imagine you're playing a simulation game where you manage an amusement park. You have to decide where to build new rides, how to allocate staff, and how to attract more visitors. The game is complex because you need to think about the long-term impact of each decision. AI struggles in this game because it finds it hard to plan long-term and handle uncertainty like humans do.
Glossary
Long-Horizon Planning
The process of making long-term decisions in uncertain environments.
Evaluating AI's long-horizon planning ability in MAPs.
Sample-Efficient Learning
The ability to learn quickly from limited samples.
MAPs tests AI's learning ability with limited data.
Spatial Reasoning
The ability to understand and reason about spatial relationships.
AI needs effective spatial layout in MAPs.
Uncertainty Modeling
The ability to make decisions in uncertain environments.
MAPs tests AI's performance under uncertainty.
ReAct Algorithm
An algorithm for decision-making that combines historical actions and observations.
Used for benchmarking AI in MAPs.
Open Questions Unanswered questions from this research
- 1 AI still struggles with long-term planning, especially in complex environments.
- 2 Current models show significant gaps in sample-efficient learning and spatial reasoning.
Applications
Immediate Applications
Business Management
Used to evaluate and improve AI performance in business decision-making, especially in complex environments.
Long-term Vision
Intelligent Decision Systems
Develop more adaptable AI systems for decision-making in complex business environments.
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