Cellular automata for ecosystem simulation in a serious game: Stability analysis and user perception
Resumo
Introduction: The presence of serious games focused on environmental education has grown steadily over the years. However, relatively few initiatives incorporate simulations that explicitly model the impacts of human actions within ecosystems. Objective: This study presents an algorithm grounded in Cellular Automata for the simulation of ecosystems within GAIA, a serious game designed to support sustainability education. Methodology or Steps: The proposed system employs a simulated environment to govern the state transitions of the Cellular Automaton cells. The transition rules incorporate both intrinsic cell attributes and the states of neighboring cells, enabling context-sensitive dynamics. The system was evaluated through a combination of computational analyses and user-based assessments. Results: The results indicate that the algorithm effectively preserves the stability of cell states over time. Furthermore, user-based evaluations demonstrated positive perceptions regarding the performance of the Cellular Automaton within the simulated ecosystem.
Palavras-chave:
Cellular Automata, Serious Game, Sustainability, Ecosystem Simulation
Referências
Ahmadov, T., Karimov, A., Durst, S., Saarela, M., Gerstlberger, W., Wahl, M. F., e Karkkainen, T. (2025). A two-phase systematic literature review on the use of serious games for sustainable environmental education. Interactive Learning Environments, 33(3):1945–1966.
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CAbrAl, D. D. C. e Cesco, S. (2008). Notas para uma história da exploração madeireira na mata atlântica do sul-sudeste. Ambiente & Sociedade, 11:33–48.
Chen, J., He, M., Chen, J., e Zhang, C. (2024). Exploring the role and mechanisms of environmental serious games in promoting pro-environmental decision-making: a focused literature review and future research agenda. Frontiers in Psychology, 15:1455005.
de Brito, H. C., Rufino, I. A. A., e Djordjević, S. (2021). Cellular automata predictive model for man-made environment growth in a brazilian semi-arid watershed. Environmental Monitoring and Assessment, 193(6):323.
De Salas, K., Ashbarry, L., Seabourne, M., Lewis, I., Wells, L., Dermoudy, J., Roehrer, E., Springer, M., Sauer, J. D., e Scott, J. (2022). Improving environmental outcomes with games: an exploration of behavioural and technological design and evaluation approaches. Simulation & Gaming, 53(5):470–512.
Dieleman, H. e Huisingh, D. (2006). Games by which to learn and teach about sustainable development: exploring the relevance of games and experiential learning for sustainability. Journal of cleaner production, 14(9-11):837–847.
Dochshanov, A., Tramonti, M., e Tramonti, L. (2023). A digital learning game environment for natural resource management. In Erasmus Scientific Days 2022 (ESD 2022), pages 176–186. Atlantis Press.
Douvinet, J., Fleurant, C., Delahaye, D., e Caillault, S. (2011). Cellular automata and Agent-based models in response to different environmental problems: a review on French research over the last ten years. In European Geosciences Union General Assembly (EGU), Vienne, Austria.
Gronewold, A. e Sonnenschein, M. (1998). Event-based modelling of ecological systems with asynchronous cellular automata. Ecological Modelling, 108(1):37–52.
Hunt, R. e Colasanti, R. L. (2021). Real communities of virtual plants explain biodiversity on just three assumptions. in silico Plants, 3(1):diab015.
Madani, K., Pierce, T. W., e Mirchi, A. (2017). Serious games on environmental management. Sustainable cities and society, 29:1–11.
Mendonça, F. C. A., Ferreira, R. A., Torres, M. F. O., da Silva, A. J., Pinheiro, A. C., e Silva-Mann, R. (2026). From degradation to restoration: Soil, biotic changes, and monitoring models in the atlantic forest over 17 years. Ecological Engineering, 223:107811.
Randazzo, E. e Mordvintsev, A. (2023). Biomaker ca: a biome maker project using cellular automata.
Sarkar, P. (2000). A brief history of cellular automata. ACM Comput. Surv., 32(1):80–107.
Stehmann, J. R., Sobral, M., et al. (2017). Biodiversidade no brasil. Simões, CMO, pages 1–10.
Tabarelli, M., Pinto, L. P., Silva, J. M. C., Hirota, M. M., e Bedê, L. C. (2005). Desafios e oportunidades para a conservação da biodiversidade na mata atlântica brasileira. Megadiversidade, 1(1):132–138.
Tan, C. K. W. e Nurul-Asna, H. (2023). Serious games for environmental education. Integrative Conservation, 2(1):19–42.
Aurenhammer, F. (1991). Voronoi diagrams—a survey of a fundamental geometric data structure. ACM computing surveys (CSUR), 23(3):345–405.
CAbrAl, D. D. C. e Cesco, S. (2008). Notas para uma história da exploração madeireira na mata atlântica do sul-sudeste. Ambiente & Sociedade, 11:33–48.
Chen, J., He, M., Chen, J., e Zhang, C. (2024). Exploring the role and mechanisms of environmental serious games in promoting pro-environmental decision-making: a focused literature review and future research agenda. Frontiers in Psychology, 15:1455005.
de Brito, H. C., Rufino, I. A. A., e Djordjević, S. (2021). Cellular automata predictive model for man-made environment growth in a brazilian semi-arid watershed. Environmental Monitoring and Assessment, 193(6):323.
De Salas, K., Ashbarry, L., Seabourne, M., Lewis, I., Wells, L., Dermoudy, J., Roehrer, E., Springer, M., Sauer, J. D., e Scott, J. (2022). Improving environmental outcomes with games: an exploration of behavioural and technological design and evaluation approaches. Simulation & Gaming, 53(5):470–512.
Dieleman, H. e Huisingh, D. (2006). Games by which to learn and teach about sustainable development: exploring the relevance of games and experiential learning for sustainability. Journal of cleaner production, 14(9-11):837–847.
Dochshanov, A., Tramonti, M., e Tramonti, L. (2023). A digital learning game environment for natural resource management. In Erasmus Scientific Days 2022 (ESD 2022), pages 176–186. Atlantis Press.
Douvinet, J., Fleurant, C., Delahaye, D., e Caillault, S. (2011). Cellular automata and Agent-based models in response to different environmental problems: a review on French research over the last ten years. In European Geosciences Union General Assembly (EGU), Vienne, Austria.
Gronewold, A. e Sonnenschein, M. (1998). Event-based modelling of ecological systems with asynchronous cellular automata. Ecological Modelling, 108(1):37–52.
Hunt, R. e Colasanti, R. L. (2021). Real communities of virtual plants explain biodiversity on just three assumptions. in silico Plants, 3(1):diab015.
Madani, K., Pierce, T. W., e Mirchi, A. (2017). Serious games on environmental management. Sustainable cities and society, 29:1–11.
Mendonça, F. C. A., Ferreira, R. A., Torres, M. F. O., da Silva, A. J., Pinheiro, A. C., e Silva-Mann, R. (2026). From degradation to restoration: Soil, biotic changes, and monitoring models in the atlantic forest over 17 years. Ecological Engineering, 223:107811.
Randazzo, E. e Mordvintsev, A. (2023). Biomaker ca: a biome maker project using cellular automata.
Sarkar, P. (2000). A brief history of cellular automata. ACM Comput. Surv., 32(1):80–107.
Stehmann, J. R., Sobral, M., et al. (2017). Biodiversidade no brasil. Simões, CMO, pages 1–10.
Tabarelli, M., Pinto, L. P., Silva, J. M. C., Hirota, M. M., e Bedê, L. C. (2005). Desafios e oportunidades para a conservação da biodiversidade na mata atlântica brasileira. Megadiversidade, 1(1):132–138.
Tan, C. K. W. e Nurul-Asna, H. (2023). Serious games for environmental education. Integrative Conservation, 2(1):19–42.
Publicado
29/09/2026
Como Citar
THICHAKI, Lucas Evangelista; LEITE, André Castilho; KIEL, Kaio Henrique Gava; GARCIA, Maria Caroline Alves; RIBEIRO, Milton Cezar; PEREIRA, Leonardo Tórtoro.
Cellular automata for ecosystem simulation in a serious game: Stability analysis and user perception
. In: SIMPÓSIO BRASILEIRO DE JOGOS E ENTRETENIMENTO DIGITAL (SBGAMES), 25. , 2026, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 1092-1103.
DOI: https://doi.org/10.5753/sbgames.2026.26412.
