APossible

APossible is a software framework for modeling, simulating, and analyzing possible futures under uncertainty. It provides a domain-specific language to describe states, actions, and probabilistic outcomes, and a modular simulation engine to explore scenario trees and Monte Carlo runs. The project emphasizes decision support under incomplete information by enabling users to define constraints, goals, and risk metrics.

Core components include a modeling layer, a simulation core, an analytics module, and a visualization layer. The modeling language allows definitions of discrete states, actions with preconditions and effects, and probabilistic transitions. The engine can perform forward simulation, generate alternative futures, and compute metrics such as expected value, risk exposure, and robustness. It supports scenario generation, counterfactual analysis, and integration with data sources and external planners through an API.

APossible is designed as an open-source project with a permissive license and an emphasis on interoperability. It features a plugin architecture for additional planners, probability distributions, and visualization backends. The governance model relies on community contributions and documentation-driven development.

Use cases include robotics and autonomous systems planning, supply chain risk management, disaster response, and urban-planning simulations. In practice, users cite its approachable modeling language and the ability to compare multiple futures side-by-side, though some workflows can be computationally intensive for large-scale models, requiring approximations or sampling.

APossible has been discussed in academic and industrial circles as a tool for formalizing decision problems under uncertainty and for teaching concepts from decision theory and planning. See also modal logic and possible worlds, AI planning, and risk analysis.