Policy Scenario Cellular Automata — Grid-based simulation for comparing policy impacts

Two to four scenarios are typical. Too few scenarios provide limited contrast; too many make comparative interpretation unwieldy. Each scenario should represent a meaningfully different policy stance (e.g., business-as-usual, moderate intervention, strict regulation).

Use historical spatial data (e.g., land-use maps from two time points) to fit transition probabilities, often with logistic regression or random forests linking cell-state changes to neighborhood and contextual covariates. Validate by withholding a later time period and comparing simulated to observed patterns using metrics such as figure-of-merit.

Either. Deterministic variants apply rules without randomness, producing one outcome per scenario. Stochastic variants add a random component to transition probabilities and require multiple runs per scenario to characterize outcome distributions. The stochastic approach is generally preferred for realistic policy analysis.

Input grids should be raster data (GeoTIFF, ASCII grid) with a consistent coordinate reference system and cell size. Categorical land-use or state classes are standard; continuous covariates (elevation, distance to roads) are used to weight transition rules.

Standard CA runs a single rule set to simulate a system's evolution. PSCA runs the same CA engine under multiple, explicitly contrasted rule sets (scenarios), making the policy question the organizing frame rather than an afterthought, and the comparison of scenario outputs the primary analytical product.