Modelling Mobility of Hunter-Gatherer Populations: A Dynamic Simulation Approach Based on Cellular Automata.

Area of Science:

• Archaeology
• Computational modeling
• Paleoenvironmental studies

Background:

• Hunter-gatherer mobility research often lacks dynamic approaches and struggles with uncertainty.
• Traditional least-cost models assume complete traveler knowledge and known start/end points, limiting their use in data-scarce regions.
• Ethnographic data is often group- and territory-specific, hindering broader application.

Purpose of the Study:

• To address limitations in existing mobility models by developing a dynamic, agent-based approach.
• To explore hunter-gatherer movement potential in landscapes with scarce archaeological evidence.
• To test hypotheses regarding landscape barriers to past human migration.

Main Methods:

• Developed DISPERSCA, an agent-based model integrating cellular automata (CA) theory.
• Agents navigate a fitness landscape based on decisions within a defined 'decision catchment' (CA neighborhood).
• Model incorporates variable neighborhood sizes and shapes (square/hexagonal) to account for unknown decision-making areas.

Main Results:

• Applied DISPERSCA to Late Pleistocene Central Iberia, an area with limited archaeological sites.
• Findings provide support for the hypothesis that the Central Iberian and Iberian System ranges were significant barriers.
• The model's flexibility allows for incorporating prior knowledge and non-terrain fitness variables (e.g., water, game).

Conclusions:

• DISPERSCA offers a dynamic alternative to static least-cost path models for studying past mobility.
• The model successfully identifies potential landscape barriers in regions with sparse archaeological data.
• Future modifications can enhance realism by including factors beyond terrain cost, improving our understanding of hunter-gatherer dispersal.