Simulation and Agent-based modelling

An introduction to simulation in archaeology

Andreas Angourakis | @AndrosSpica

https://andros-spica.github.io/URV-computational-archaeology-2024/simulation

What is simulation?
1. Definition
2. Place as mathematical modelling
3. Agent-based modelling

Uses in archaeology
1. Methodological considerations
2. Application domains
3. Examples (case studies)

Limits and expectations

Conclusion

1. What is simulation?

1.1. Definition

"a situation in which a particular set of conditions is created artificially in order to study or experience something that could exist in reality." (Oxford Advanced American Dictionary)

"a: the imitative representation of the functioning of one system or process by means of the functioning of another a computer simulation of an industrial process
b: examination of a problem often not subject to direct experimentation by means of a simulating device"
(simulation, Merrian-Webster)

"A simulation is an imitative representation of a process or system that could exist in the real world. In this broad sense, simulation can often be used interchangeably with model. Sometimes a clear distinction between the two terms is made, in which simulations require the use of models; the model represents the key characteristics or behaviors of the selected system or process, whereas the simulation represents the evolution of the model over time. Another way to distinguish between the terms is to define simulation as experimentation with the help of a model."
(Simulation, Wikipedia)

reality model

simulation

1. What is simulation?

1.2. Place as mathematical modelling

Models and mathematical models

Diagrams available at https://github.com/Andros-Spica/modelling-simulation-graphs
Icons by "The Noun Project", various authors (thenounproject.com)

observations

Picture maze solving - descriptive model

descriptive model
a model that return the output given the input...

Picture maze solving - explanatory model

explanatory model
... and the definition of a mechanism

Mathematical models and simulation models

Diagrams available at https://github.com/Andros-Spica/modelling-simulation-graphs
Icons by "The Noun Project", various authors (thenounproject.com)

	Input	Model	Output
Machine learning	known	learned after implementation	known+ predicted after training
Simulation	known+ assumed	known before implementation	learned after iterations +searched

modelling steps

1. What is simulation?

1.3. Agent-based modelling

The prism of complexity science

Complex system
complexity: number and diversity of causal relationships
nonlinearity, self-organisation and self-similarity
Unifying frameworks:
- theoretical (e.g., generative social science, socio-ecological systems)
- methodological (e.g., open databases, GIS, ABM)

Breeder pattern in Conway's Game of Life
derivative work: George (talk)Conways_game_of_life_breeder.png: Hyperdeath [CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons

Agent-based modelling (ABM )

dynamics
formalisation
rules
population
bottom-up
stochasticity

→ simulation

→ definitions

→ algorithms

→ distributed processes

→ emergent properties

→ probabilistic results

Flocking behaviour in 'Behavioral systems' by Danil Nagy in 'Generative Design', medium.com

Background: pseudo-code for the Gale–Shapley algorithm
to solve the Stable Marriage Problem

Schelling's segregation model

Schelling's segregation model

2. Uses in archaeology

2.1. Methodological considerations

Simulation in archaeological methodology

Diagrams available at https://github.com/Andros-Spica/modelling-simulation-graphs

2. Uses in archaeology

2.2. Application domains

Physical-chemical dynamics

Artifact production: operational chain (chaîne opératóire), authorship and style, material transformations during manufacturing (Sorensen y Scherjon 2018), use and deposition (Gravel-Miguel et al. 2021)
Site formation: distribution of artifacts and structures (Gravel-Miguel et al. 2021), preservation, strata formation and taphonomy (Davies et al. 2016), sample bias

Ecological dynamics

Climate: seasonality (Angourakis et al. 2022), regional variations, climate change (Bocquet-Appel et al. 2014)
Soils: erosion and sediment accumulation (Kabora et al. 2020; Robinson et al. 2018; Ullah et al. 2019)
Hydrology: water availability, runoff, irrigation (Altaweel y Watanabe 2012)
Plants: plant domestication (Angourakis et al. 2022); crop dynamics (Angourakis et al. 2022; Baum et al. 2016); biomass or productivity estimation (Boogers y Daems 2022; Christiansen y Altaweel 2006; Joyce 2019; Robinson et al. 2018); deforestation and wildfires (Boogers y Daems 2022; Snitker 2021)
Animals: population dynamics under human influence (Morrison y Allen 2017); domestic animal population dynamics (Günther et al. 2021); herd behavior, foraging, and transhumance (Günther et al. 2021; Rogers 2013)

See references in Angourakis 2023 Vegeta

Anthropological dynamics I

Individuals: pedestrian dynamics (Lake 2001); foraging (Brantingham 2006; Oestmo et al. 2016); kinship (Rogers 2013); mating, marriage, and reproduction (Verhagen 2019); health and mortality (Verhagen 2019); cognition (memory, rationality and learning) (Mokom 2015; Premo y Tostevin 2016; Sousa et al. 2019); cooperation and competition between individuals (Graham 2009; Sousa et al. 2019; White 2013); learning and norm emergence (Gower-Winter 2022; Mokom 2015)

Groups: household organization and microeconomics (Christiansen y Altaweel 2006; Joyce 2019); emergence of alliances and organizations (Cioffi-Revilla et al. 2015; White 2013); cooperation and competition between groups (Angourakis et al. 2014, 2015, 2017; Cioffi-Revilla et al. 2015; Rogers 2013); group mobility (Rogers 2013; Santos et al. 2015); logistics and military tactics (Rubio-Campillo et al. 2014, 2015; Verhagen 2019)

See references in Angourakis 2023 Vegeta

Anthropological dynamics II

Settlements: demographic dynamics (natural growth and migration) (Verhagen 2019); resource exploitation (Boogers y Daems 2022); trade (Carrignon et al. 2020; Chliaoutakis y Chalkiadakis 2020; Lawall y Graham 2018; Ortega et al. 2014; Sakahira et al. 2020); cultural evolution (Carrignon et al. 2020; Gower-Winter 2022; Lake y Crema 2012; Mokom 2015; Sakahira et al. 2020); settlement patterns (Altaweel 2015; Chliaoutakis y Chalkiadakis 2016); land use (Angourakis et al. 2014, 2017; Boogers y Daems 2022; Joyce 2019; Robinson et al. 2018; Rogers 2013; Snitker 2021; Ullah et al. 2019); polytogenesis (Cioffi-Revilla et al. 2015; Rogers 2013; Turchin et al. 2013); catastrophic collapse or abandonment (Kohler y Varien 2012; McAnany et al. 2015)

At regional to global scales: cooperation and competition between territorial states (Turchin et al. 2013); trade routes (Chliaoutakis y Chalkiadakis 2020; Lawall y Graham 2018; Ortega et al. 2014); human species dispersal (Callegari et al. 2013) and genetic and cultural diffusions (Bocquet-Appel et al. 2014; Kovacevic et al. 2015; Mokom 2015)

See references in Angourakis 2023 Vegeta

2. Uses in archaeology

2.3. Examples

Artificial Anasazi

Axtell, R. L., Epstein, J. M., Dean, J. S., Gumerman, G. J., Swedlund, A. C., Harburger, J., … Parker, M. (2002). Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley. Proceedings of the National Academy of Sciences, 99(Supplement 3), 7275–7279. https://doi.org/10.1073/pnas.092080799

Janssen, M. A. (2009). Understanding Artificial Anasazi. Journal of Artificial Societies and Social Simulation, 12(4). http://jasss.soc.surrey.ac.uk/12/4/13.html

HOMINIDS

Griffith, C. S., Long, B. L., and Sept, J. M. (2010). HOMINIDS: An agent-based spatial simulation model to evaluate behavioral patterns of early Pleistocene hominids. Ecological Modelling, 221(5), 738–760. https://doi.org/10.1016/j.ecolmodel.2009.11.009

MedLanD

Barton, C. M., Ullah, I. I. T., Bergin, S. M., Mitasova, H., and Sarjoughian, H. (2012). Looking for the future in the past: Long-term change in socioecological systems. Ecological Modelling, 241, 42–53. https://doi.org/10.1016/J.ECOLMODEL.2012.02.010

HouseholdsWorld

Rogers, J. D., Nichols, T., Emmerich, T., Latek, M., and Cioffi-Revilla, C. (2012). Modeling scale and variability in human–environmental interactions in Inner Asia. Ecological Modelling, 241, 5–14. https://doi.org/10.1016/J.ECOLMODEL.2011.11.025

MayaSim

Heckbert, S. (2013). MayaSim. Journal of Artificial Societies & Social Simulation, 16(4), 11. http://jasss.soc.surrey.ac.uk/16/4/11.html

Indus Village model

Angourakis et al. 2022, graphical abstract

Andros-Spica/diagrams
/RoadMapSoFar_2022-06.png

Angourakis et al. 2022, Quaternary | repositorio: https://github.com/Andros-Spica/indus-village-model

Carrignon, S., Brughmans, T., & Romanowska, I. (2020). Tableware trade in the Roman East: Exploring cultural and economic transmission with agent-based modelling and approximate Bayesian computation. PLOS ONE, 15(11), e0240414. 10.1371/journal.pone.0240414

Eastern Mediterranean tableware trade (Hellenistic and Roman periods)
8730 fragments, 5 types, 178 sites (presence/absence by site-type)
A problem of cultural evolution
Simulation of cultural transmission algorithms (three hypotheses) together with a market economy model, producing spatial distributions of cultural traits of the merchants of each settlement
Stochastic exploration of the parameter space for each algorithm, evaluated in light of empirical data, using Approximate Bayesian Computing (ABC) + Population Monte Carlo (ABCPMC)

Combined use of ABM and machine learning

Carrignon et al. 2020 - Fig. 2 — Carrignon et al. 2020, Fig. 2 and 5

Carrignon et al. 2020 - Fig. 5 — Carrignon et al. 2020, Fig. 2 and 5

See also:
Carrignon, S., Bentley, R. A., & Ruck, D. (2019). Modelling rapid online cultural transmission:
Evaluating neutral models on Twitter data with approximate Bayesian computation.
Palgrave Communications, 5(1), Article 1. https://doi.org/10.1057/s41599-019-0295-9

2. Uses in archaeology

2.4. Learning resources

Manual

Romanowska, Iza, Colin D. Wren, and Stefani A. Crabtree. 2021. Agent-Based Modeling for Archaeology. Electronic. SFI Press. https://doi.org/10.37911/9781947864382

Theoretical and practical introduction
Does not require prior programming knowledge
Examples and exercises in NetLogo

Tutorial online

Angourakis, A. (2022). Andros-Spica/ABM-tutorial-koeln-2022: Archaeological ABM at Cologne: from concept to application [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.6668143 https://github.com/Andros-Spica/ABM-tutorial-koeln-2022/

From prototyping to the use of archaeological and environmental data
Progressive code development in NetLogo

screenshots/BlockB_PondTrade_step13_output-statistics-interface.png

screenshots/BlockC_module6_MesaraTrade-interface.png

3. Limits and expectations

Methodology

Disambiguation
Consolidation of shared models
Virtual laboratory
Interdisciplinarity

Epstein, J. M. (2008). Why Model? Journal of Artificial Societies and Social Simulation, 11(4), 12.
http://jasss.soc.surrey.ac.uk/11/4/12.html

Theory

Social emergency and socioecological systems
Resilience, adaptation, change and collapse
Settlement and mobility
Behavior and cognition

Kintigh, K. W., Altschul, J. H., Beaudry, M. C., Drennan, R. D., Kinzig, A. P., Kohler, T. A., … Zeder, M. A. (2014).
Grand challenges for archaeology. Proceedings of the National Academy of Sciences of the United States of America, 111(3), 879–880.
https://doi.org/10.1073/pnas.1324000111

Preadaptation to the archaeological perspective

Social processes associated with materiality
Any space and time scale
Data collection and standardization
Theory construction and hypothesis generation

Madella, M., Rondelli, B., Lancelotti, C., Balbo, A. L., Zurro, D., Rubio Campillo, X., & Stride, S. (2014). Introduction to Simulating the Past. Journal of Archaeological Method and Theory, 21(2), 251–257. https://doi.org/10.1007/s10816-014-9209-8

Rogers, J. D., & Cegielski, W. H. (2017). Opinion: Building a better past with the help of agent-based modeling. Proceedings of the National Academy of Sciences of the United States of America, 114(49), 12841–12844. https://doi.org/10.1073/pnas.1718277114

Cegielski, W. H., & Rogers, J. D. (2016). Rethinking the role of Agent-Based Modeling in archaeology. Journal of Anthropological Archaeology, 41, 283–298. https://doi.org/10.1016/J.JAA.2016.01.009

Angourakis, A. (2023). El lugar de la simulación social en arqueología. Vegueta: Anuario de la Facultad de Geografía e Historia, 23(1), 15–55. https://doi.org/10.51349/veg.2023.1.02

Limits

It's not a magic solution
Long way for standard
Difficult learning
Biases
Underdeveloped validation, documentation and understanding

Future?

Video game design
(e.g. Project Highrise)

screenshot of OpenAI emergent behaviour example

Deep learning (e.g. robotics)

4. Conclusion

Leonardo AI take on simulation and mathematical models in archaeology 1

Leonardo AI take on simulation and mathematical models in archaeology 2

Leonardo AI take on simulation and mathematical models in archaeology 3

Leonardo AI take on simulation and mathematical models in archaeology 4

Leonardo AI take on simulation in archaeology 1

Leonardo AI take on simulation in archaeology 2

Images created by Leonardo.AI (Diffusion XL) about
"computer simulation in archaeology" and
"computer simulation and mathematical modelling in archaeology".

Mechanism/explanation is the cornerstone of simulation models
ABM + archeology:
many avenues available
and yet to be explored
Past social-ecological systems require long-term modelling (deep-time, complex, multidisciplinary)