Exploring Climate Effects with Agent-Based Models

Daniel Vartanian

University of São Paulo

November 14, 2025

Hi there! 👋

This presentation explores how Agent-Based Models (ABMs) can integrate multiple perspectives in modeling complex systems and function as a tool for analyzing the combined impacts of climate change.

Here are the main topics:

1. Agent-Based Models
2. LogoClim: WorldClim in NetLogo
3. Logônia: An Example Application
4. Global Syndemic
5. Conclusion

Agent-Based Models

Agent-based models (ABMs) are computational models with the purpose of simulating the behavior of agents and their interactions, allowing us to study emergent phenomena.

Instead of describing a system only with variables representing the state of the whole system (a global approach/top-down), we model its individual agents (a local approach/bottom-up) (Railsback & Grimm, 2019).

Agents often represent people or other animals, but agents can also represent anything from biological cells to economic firms to political municipalities (Smaldino, 2023).

NetLogo

Pattern-Oriented Modeling

A pattern is anything beyond random variation.

We can think of patterns as regularities, signals.

Alignment: A bird tends to turn so that it is moving in the same direction that nearby birds are moving.

Separation: A bird will turn to avoid another bird which gets too close.

Cohesion: A bird will move towards other nearby birds (unless another bird is too close).

Examples

Ecology
Predator-Prey (Lotka, 1925; Volterra, 1926)

Epidemiology
Epidemic (SIR) (Kermack & McKendrick, 1927)

Examples

Physics
Chladni Figures** (Chladni, 1787)

Evolutionary Psychology
Altruism (Lehmann & Keller, 2006)

Examples

Sociology
Segregation (Schelling, 1971)

Environmental Sciences
Wildfires

Examples

Generative Agents

Applications of ABMs

Development and corroboration of theories.

Exploration of scenarios and leverage points.

Prediction of future scenarios (for empirical models only).

Given the conditions of your theory, is it possible to replicate the phenomenon and its patterns through an agent-based simulation?

[…] they think you’re done when you can’t add anything more. I think you’re done when you can’t take anything more out. (Joshua Epstein) (Rutt, 2020)

A theory which is not refutable by any conceivable event is nonscientific. Irrefutability is not a virtue of a theory (as people often think) but a vice. (Popper, 1963/2002)

Types of Models

Abstract models allow for the examination of general principles in detail (Rand & Wilensky, 2007).

Empirical models are generally more oriented towards prediction and often need to address specific questions posed by policy-makers at particular sites (Sun et al., 2016).

Full spectrum modeling combines the benefits of abstract and empirical models (Rand & Wilensky, 2007).

LogoClim

LogoClim is a NetLogo model for simulating and visualizing global climate conditions. It allows researchers to integrate high-resolution climate data into agent-based models, supporting reproducible research in ecology, agriculture, environmental sciences, and other fields that rely on climate data.

LogoClim

WorldClim 2.1 is a project by Stephen E. Fick and Robert J. Hijmans that provides high-resolution global climate data, such as temperature and precipitation, which can be used for spatial mapping and modeling (Fick & Hijmans, 2017).

The dataset includes historical series downscaled from CRU-TS-4.09, containing data interpolated from thousands of weather stations, as well as future downscaled projections derived from models of the Coupled Model Intercomparison Project Phase 6 (CMIP6).

LogoClim

Historical Climate Data

Includes 12 monthly points representing averages from 1970-2000 for temperature, precipitation, solar radiation, wind, vapor pressure, elevation, and bioclimatic variables.

Historical Monthly Weather Data

Includes 12 monthly points per year from 1951 to 2024, with minimum and maximum temperature and total precipitation, based on CRU-TS-4.09.

Future Climate Data

Includes 12 monthly points from CMIP6 projections for 2021-2100 (four periods) and four SSP scenarios (126, 245, 370, 585), covering average, maximum, and minimum temperature, precipitation, and bioclimatic variables.

LogoClim

LogoClim

Logônia

Logônia is a NetLogo model that simulates the growth response of a fictional plant, logônia, under different climatic conditions. The model uses climate data from WorldClim 2.1 and demonstrates how to integrate the LogoClim model through the LevelSpace extension.

Logônia

Logônia

Logônia

Energy and Growth Probability

Growth Phases & Reproduction

Senescence

Logônia

Logônia

Logônia

Logônia

Global Syndemic

👁️⃤

Observer

Grid Cells

⊞
🌡️
🌧️

Food

🍌🍅
🥬🌾
🥛🥩

Families

👨‍👩‍👧
👩‍👩‍👦
👨‍👨‍👧

Children

🧒
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Closing Remarks

This presentation was created with the Quarto Publishing System. The code and materials are available on GitHub.

References

In accordance with the American Psychological Association (APA) Style, 7th edition.

Brown, I., Kelter, J., & Wilensky, U. (2025). NetLogo Chladni figures model [Computer software]. Center for Connected Learning; Computer Based Modeling at Northwestern University. http://ccl.northwestern.edu/netlogo/models/ChladniFigures

Chladni, E. F. F. (1787). Entdeckungen über die Theorie des Klanges. Zentralantiquariat der Deutschen Demokratischen Republik.

Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086

Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W. M., Railsback, S. F., Thulke, H.-H., Weiner, J., Wiegand, T., & DeAngelis, D. L. (2005). Pattern-oriented modeling of agent-based complex systems: Lessons from ecology. Science, 310(5750), 987–991. https://doi.org/10.1126/science.1116681

Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 115(772), 700–721. https://doi.org/10.1098/rspa.1927.0118

Lehmann, L., & Keller, L. (2006). The evolution of cooperation and altruism – A general framework and a classification of models. Journal of Evolutionary Biology, 19(5), 1365–1376. https://doi.org/10.1111/j.1420-9101.2006.01119.x

Lotka, A. J. (1925). Elements of physical biology. Williams & Wilkins Company. http://archive.org/details/elementsofphysic017171mbp

Park, J. S., O’Brien, J., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023, October 29). Generative agents: Interactive simulacra of human behavior. Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology. https://doi.org/10.1145/3586183.3606763

Popper, K. R. (2002). Conjectures and refutations: The growth of scientific knowledge. Routledge. (Original work published in 1963)

Railsback, S. F., & Grimm, V. (2019). Agent-based and individual-based modeling: A practical introduction (2nd ed.). Princeton University Press.

Rand, W., & Wilensky, U. (2007). Full spectrum modeling: From simplicity to elaboration and realism in urban pattern formation. North American Association for Computational Social and Organizational Sciences (NAACSOS). http://ccl.northwestern.edu/2007/FullSpectrum.pdf

Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioral model. Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, 21, 25–34. https://doi.org/10.1145/37401.37406

Rutt, J. (2020, November 5). Joshua Epstein on agent-based modeling (90) [Audio recording]. https://jimruttshow.blubrry.net/joshua-epstein

Schelling, T. C. (1971). Dynamic models of segregation. The Journal of Mathematical Sociology, 1(2), 143–186. https://doi.org/10.1080/0022250X.1971.9989794

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Vartanian, D., Garcia, L., & Carvalho, A. M. (2025b). Logônia: Plant growth response model in NetLogo [Computer software]. https://doi.org/10.17605/OSF.IO/TPY7S

Volterra, V. (1926). Fluctuations in the abundance of a species considered mathematically. Nature, 118(2972), 558–560. https://doi.org/10.1038/118558a0

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Thank you!