Adaptive multi-criteria ecotopes: a memory-embedded cellular automata framework

Abstract

Cellular automata (CA) are common instruments for simulating spatial dynamics in geography, ecology, and urban science. Most implementations, however, rely on fixed transition rules or static suitability surfaces that remain invariant across simulation time. This paper argues that such assumptions are inadequate for systems in which local decision metrics are themselves products of spatial interaction, competitive selection, and historical accumulation. I propose the Adaptive Multi-Criteria Ecotope Cellular Automaton (AME-CA), in which each cell on a spatial lattice evaluates its viability through a weighted multi-criteria fitness function whose criterion weights evolve endogenously. Weight adaptation is governed by local outcome history, neighbourhood diffusion of behavioural tendencies, and competitive pressure from adjacent cells. The framework introduces the multi-criteria ecotope: a spatially localized behavioural niche defined not only by environmental conditions but by historically stabilized adaptive decision rules. I implement the model on a synthetic 100 × 100 lattice and compare three variants: (1) a classic CA with fixed rules, (2) a CA with static multi-criteria evaluation, and (3) the full AME-CA, across metrics of spatial pattern formation, path dependence, competitive dynamics, and equilibrium convergence. Results show the adaptive model generates richer heterogeneity, distinct ecotopes, stable local regimes, and emergent clustering absent in fixed-rule alternatives.