# Agent-Based Modelling, Archaeology and
Paleoenvironment

Nicolas Bernigaud, Frédérique Bertoncello, Marie-Jeanne Ouriachi, Alberte

Bondeau, Joel Guiot

► To cite this version:

Nicolas Bernigaud, Frédérique Bertoncello, Marie-Jeanne Ouriachi, Alberte Bondeau, Joel Guiot. Agent-Based Modelling, Archaeology and Paleoenvironment. "Dynamique des peuplements, des ter- ritoires et des paysages. Bilan et perspectives en archéologie spatiale": 42e Rencontres Internationales d'Archéologie et d'Histoire de Nice Côte d'Azur, Frédérique Bertoncello; Marie-Jeanne Ouriachi; Laure Nuninger; François Favory, Oct 2022, Nice (06000), France. hal-04382120

HAL Id: hal-04382120

https://hal.science/hal-04382120v1

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Agent-Based Modelling, Archaeology and
Paleoenvironment:

*Assessment and Perspectives for the Study of Past Societies*

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

N. Bernigaud¹, F. Bertoncello², M.-J. Ouriachi²,

A. Bondeau³, J. Guiot¹

42ᵉ Rencontres Internationales d'Archéologie et d'Histoire de Nice Côte d'Azur, 12-14 octobre 2022
# Presentation outline

I. General principles of ABMs and historiographical overview

II. Case studies: ModelAnSet, ROMCLIM et FER-AGRI models

III. Research Assessment and Perspectives for ABMs development
# Agent-based modeling (ABM) or Multi-Agent System (MAS): what is this?

* Software simulating interactions between agents and their environment

* Interactions produce complex phenomena that we are trying to reproduce (or which are unexpected)

* More than fifteen computer programs are dedicated to the ABMs (NetLogo, Gama, Repast, Swarm, Mason, etc.)

* Numerous fields of application: Agriculture, Anthropology, Biomedical Research, Crime Analysis, Ecology,...

Agents Interacting in Agent Space (Grid Topology)

<figure><img src="image_6.png" /></figure>

Environment

General elements of an agent-based model

Agent Interactions with
Other Agents

<figure><img src="image_7.png" /></figure>

Agent

Attributes:

Static: name,...

Dynamic: memory, resources, neighbors,...

Methods:

Behaviors

Behaviors that modify behaviors

Update rules for dynamic attributes

...

<figure><img src="image_8.png" /></figure>

Agent Interactions with
the Environment

A typical agent

(Figures after Macal and North 2014)
# Why using ABM in archaeology ?

## ABM: a heuristic tool for testing hypothesis
(or a “tool to think”)

* Understanding the social and environmental causes of changes seen by archaeologists in archaeological records

* Test in ABM theoretical or data driven hypothesis by simulations

* Confronting outputs of the model with archaeological records for validating/invalidating hypotheses

* If all hypotheses are invalid, try to make a new hypothesis!

<figure><img src="image_9.png" /></figure>
ABM & Archaeology:
kick off from USA in the 1990'

<figure><img src="image_10.png" /><figcaption>Epstein & Axtell 1996</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Kohler & Gumerman 2000</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Kohler & van der Leeuw 2007</figcaption></figure>

1996

* Growing Artificial Societies, Social Science from the Bottom Up
  (Joshua M. Epstein, Robert Axtell)

2000

* Dynamics in Human and Primate Societies, Agent-Based Modeling of Social and Spatial Processes
  (Timothy A. Kohler & George J. Gumerman)

2002

* Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley
  (Axtell et al. 2002)

2007

* The Model-Based Archaeology of Socionatural Systems
  (Kohler & van der Leeuw 2007)

<figure><img src="image_13.png" /><figcaption>SUGARSCAPE model
(Epstein & Axtell 1996)</figcaption></figure>

<figure><img src="image_14.png" /></figure>

1 Comparison of Long House Valley simulation results with archaeological evidence.

<caption>Artificial Anasazis (AA)
(Axtell et al. 2002)</caption>
# Archaeology & ABMs: socionatural/H.E. perspective

* Complexity of human-environment dynamics (Human Ecodynamics)

* Understanding interactions between societies and environment as factor of evolutions/transformations

* Identify the internal and external causes of these changes: social, cultural, demographic and environmental factors (climate, erosion, vegetation, etc.)

<figure><img src="image_15.png" /></figure>

Artificial Anasazi (AA) (Axtell et al. 2002)
https://ccl.northwestern.edu/netlogo/models/ArtificialAnasazi

<figure><img src="image_16.png" /></figure>

ENKIMDU (Wilkinson et al. 2005)
* Urbanization, Bronze Age Communities, Upper Mesopotamia

<figure><img src="image_17.png" /></figure>

The village ecodynamics project (Kohler et al. 2007)
* Modeling Pueblo Historical Ecology
* AD 600-1760

<figure><img src="image_18.png" /></figure>

MayaSim (Heckbert 2013)
* Model of the Ancient Maya Social-Ecological System
* Population growth, agricultural production, soil degradation, climate variability, etc.
* https://www.comses.net/codebases/3063/releases/1.2.0/

<figure><img src="image_19.png" /></figure>

Medland Modeling laboratory (MLL) (Barton et al. 2017)
* Agents: small-holder agropastoral households
* Simulates changes in erosion/deposition, soils & vegetation
* https://www.comses.net/codebases/4609/releases/1.1.0/

<figure><img src="image_20.png" /></figure>

THE INDUS VILLAGE (Angourakis et al. 2022)
Impact of Climate Change on Indus civilization (2500-1900 BC)
* Weather, Land and Crops in the Indus Village Model, A simulation Framework for Crop Dynamics under environmental variability and Climate Change in the Indus Civilisation
* TwoRains project
# ABM & Archaeology in Europe: awakening since 2010'

<figure><img src="image_21.png" /></figure>

**Agent-based Modeling and Simulation in Archaeology**
(Wurzer, Kowarik, Reschreiter 2015)

<figure><img src="image_22.png" /></figure>

**Finding the Limits of the Limes**
(Verhagen, Joyce, Groenhuijzen 2019)

<figure><img src="image_23.png" /></figure>

**Settling the World, from Prehistory to Metropolis area**
(Sanders 2021)

<figure><img src="image_24.png" /></figure>

**Simulating Roman Economies**
(Brughmans & Wilson 2022)

<figure><img src="image_25.png" /></figure>

**Simulating Prehistoric and Ancient Worlds**
(Barceló & Del Castilla 2016)

<figure><img src="image_26.png" /></figure>

**Integrating Qualitative and Social Science Factors in Archaeological Modelling**
(Saqalli & Vander Linden 2019)

<figure><img src="image_27.png" /></figure>

**[Agent-based modeling for Archaeology]**
(Romanowska, Wren, Crabtree 2021)

<figure><img src="image_28.png" /></figure>

**Simulating Transitions to Agriculture in Prehistory**
(Pardo-Gordo & Bergin 2022)

**Computer Applications and Quantitative Methods in Archaeology (CAA)**

• 2018 « Human History and Digital Future »

**European Association of Archaeologists (EAA)**

• 2020 # 415 « From Abacus to Calculus. Computational Approaches to Roman Economy » (I. Romanowska, Ph. Verhagen)

• 2021 # 367 « Agent-Based Modelling of socioecological Systems in Archaeology Towards a New Research Community » (D. Daems)

- **NASSA (Network for Agent-based modelling in Socio-ecological Systems in Archaeology)** (Coord. Dries Daems)
- Collect and compile ABM code elements
- Organize them as an openly accessible modelling library
# Archaeological ABMs in Europe: some examples

- **Varied periods:** Prehistory, Bronze Age, Roman period,...

- **Varied topics:** Salt Mining, Agriculture, Social phenomenon, Roman Economy

<figure><img src="image_29.png" /></figure>

► Not really a socio-natural perspective for most models

<figure><img src="image_30.png" /></figure>

## Mining with Agents (Kowarik et al. 2008)

• How mine of salt were exploited in Hallstatt in Bronze Age ?

<figure><img src="image_31.png" /></figure>

## Patagonian Hunter-gatherers (Barceló et al. 2010)

• Emergence of ethnicity & cultural differentiation

<figure><img src="image_32.png" /></figure>

## "Celtic Oppida" (Cimler et al. 2012, )

• Hypotheses related to agricultural practices around Celtic oppida, Carrying capacity, population growth

<figure><img src="image_33.png" /></figure>

## MERCURY (Brughmans & Poblome 2015)

• Market Economy and Roman Ceramics Redistribution

<figure><img src="image_34.png" /></figure>

## ROMFARM (Joyce 2019, Verhagen 2022)

• Modelling Agricultural Strategies in the Dutch Roman Limes
# ABM in France: geographers open the path

## Research programs

<figure><img src="image_35.png" /></figure>

### SimPop (Guérin-Pace et al. 1993, Pumain et al. 1995)

* Settlement pattern evolution

* Genesis of urban phenomenon in southern France

<figure><img src="image_36.png" /></figure>

### OBRESOC/ BEAN (Bocquet et al. 2015, Dubouloz et al. 2017, Zanotti 2016)

* Spread of agriculture/LBK culture in Europe in early Neolithic

* [ANR OBRESOC]

<figure><img src="image_37.png" /></figure>

### AmphorABM (Crabtree 2016)

* Modeling the Trade of Wine in the Bronze to Iron Age Transition in Southern France

* [ANR TransMonDyn]

<figure><img src="image_38.png" /></figure>

### ModelAnSet (Bertoncello et al. 2018)

* Settlement pattern and dynamics during the Roman period in South-Eastern France (impact of social & environmental factors)

<figure><img src="image_39.png" /></figure>

### SIMFEODAL (Cura 2020, Tannier et al. 2020)

* Concentration of settlement in Middle Age

* [ANR TransMonDyn]

<figure><img src="image_40.png" /></figure>

### ROMCLIM/FER AGRI (Bernigaud et al. 2022)

* Impact of Climate Change on gaulish and roman societies in southern Gaul

* [RDMed project]

### ANR OBRESOC (2009-2011)

* Trajectoire du Néolithique Rubané

* Coord. J.-P. Bocquet-Appel

### ANR TransMonDyn (2011-2014)

* Modéliser les grandes transitions de l'évolution du peuplement dans l'Ancien et le Nouveau Monde

* Coord. L. Sanders

### Project A\*Midex RDMed (2018-2020)

* Resilience and Adaptation to Droughts and Extreme Climate Events in the Mediterranean

* Coord. J. Guiot

* Still few models!
Coupling ABM & GIS

- Incorporating a « realistic » geographic space & environments by importing GIS files (raster, vectors)

- Topography (DEM), hydrology, geology, soils, climate, etc.

- Agents (humans/animals) can have a cognitive knowledge of their environment

<figure><img src="image_41.png" /><figcaption>Lake 2000</figcaption></figure>

<figure><img src="image_42.png" /><figcaption>MERCURY</figcaption></figure>

<figure><img src="image_43.png" /><figcaption>Patagonia</figcaption></figure>

<figure><img src="image_44.png" /><figcaption>Hohokam</figcaption></figure>

Examples of models without realistic space

<figure><img src="image_45.png" /><figcaption>ModelAnSet</figcaption></figure>

<figure><img src="image_46.png" /><figcaption>MayaSim</figcaption></figure>

<figure><img src="image_47.png" /><figcaption>Cardial Spread</figcaption></figure>

Models importing GIS files
## Main topics for ABMs

<figure><img src="image_48.png" /></figure>

### Trajectories of ancient societies (Human Ecodynamics/socionatural approach)

* Societal & Environmental causes [e. g. Artificial Anasazi (AA), ENKIMDU, MayaSim, ModeAnSet, etc.]

* Impact of climate change [e. g. BACO, ROMCLIM, FERAGRI]

<figure><img src="image_49.png" /></figure>

### Social dynamics (Anthropological & Ethnoarchaeological approach)

* Origin and genesis of social inequality

* Hierarchization of societies

* Ethnogenesis [Prehistoric Patagonia]

* Conflicts

<figure><img src="image_50.png" /></figure>

### Subsistence strategy & Ressources management (Archaeological/Quantitative approach)

* Prehistoric Hunters-Gatherers [e. g. Central place foraging (CPF)]

* Agricultural systems [e.g. AA, Celtic Oppida, Romfarm, etc.]

* Ressources exploitation & management: salt [Mining with Agents], water [Hohokam water management], etc.

<figure><img src="image_51.png" /></figure>

### Settlement Pattern genesis (geographical/archaeological approach)

* Genesis of Settlement Pattern [e. g. TravellerSim]

* Genesis of urban phenomenon [e.g. SimPop]

* Concentration of habitat [e. g. SIMFEODAL]

<figure><img src="image_52.png" /></figure>

### Spreading, circulation & exchanges phenomena (geographical/archaeological/historical approach)

* Populations/archaeological cultures [e. g. OBRESOC/BEAN, CARDIAL SPREAD MODEL]

* Artifacts

* Plagues [e. g. The Antonine Plague]

* Commercial exchanges [e.g. MERCURY]

## Modelling approaches

<figure><img src="image_53.png" /><figcaption>Classification (Schmitt & Pumain 2013)</figcaption></figure>

<figure><img src="image_54.png" /></figure>
<figure><img src="image_55.png" /></figure>

International Workshop on Multi-Agent Systems and Agent-Based Simulation

MABS 2004: **Multi-Agent and Multi-Agent-Based Simulation** pp 130–144 | Cite as

## From KISS to KIDS – An ‘Anti-simplistic’ Modelling Approach

Bruce Edmonds & Scott Moss

<figure><img src="image_56.png" /></figure>

<figure><img src="image_57.png" /></figure>

## From KISS to KIDS – ‘An Anti-simplistic’ Modelling Approach
(Edmonds & Moss **2004**)

The Complexities of Agent-Based Modeling Output Analysis
(Lee et al. **2015**)

https://www.jasss.org/18/4/4.html

Uncertainty and Sensitive Analysis in Computational
Modeling
(Brouwer Burg, Peeters, Lovis **2016**)

Rethinking the Role of Agent-Based modeling in Archaeology
( Cegielski & Rogers **2016**)

# ABM & Archaeology:
main issues & progress

* Building a simple or a complex model? (KISS vs KIDS approach)

* Opacity of models (« Black boxes »)

* How evaluate the performance and the outputs of a model?

* Protocol « Overview, Design, Details » (ODD)

* Open Source Repositories:
  https://github.com/
  https://www.comses.net/

* Sensitive Analysis (SA)
# ModelAnSet: a socio-environmental Model

* **ModelAnSet** simulates the processes of creation, growth, abandonment or maintenance of the rural settlements

* **Agents:**

* 3 types of landowners (**FARMERS**) (**BIG LANDOWNERS**), (**ARISTOCRATS**) with different economic power, social status and behavior

* 2 types of agricultural settlement (**FARM**) (**VILLA**)

* **Model dynamics:**

* The profit made by each settlement depends on its environmental context, which is influenced by the climatic conditions, and on the transportation cost to the nearest marketplace

* According to their economic power and to the profit they derive from each of their settlements, landowners decide to maintain, enlarge, improve or abandon it, or to create a new farm/villa

Explore the respective roles of social & environmental factors in the settlement pattern and dynamics during the Roman period in the roman city of **Forum Julii** (Fréjus, Var)

<figure><img src="image_58.png" /><figcaption>Instanciation of the model: the territory of the Roman colony of *Forum Julii* (Fréjus, Var, France)
(Bertoncello et al. 2018)</figcaption></figure>

**Settlement pattern**

<figure><img src="image_59.png" /></figure>

Forum Julii
(Capital of city)

1: Aristocratic villa

2: Aristocratic farm

3: Big Landowner villa

4: Big Landowner farm

5: Farm

<figure><img src="image_60.png" /></figure>
# ModelAnSet: a socio-environmental Model

* According to their social status (farmer, big landowner, aristocrat), **landowners have different behaviour (spatial, symbolic) and perceptions** (of land fertility, of the macro-economic context)

⇒ The repeated landowners' decision-making produces a changing macro-level settlement pattern, in terms of number, type and location of the settlements

* Needed improvements:

* Introduce “cognitive agents” (vs “reactive agents” acting according to fixed rules) who can diversify their actions according to their beliefs (about themselves and about their environment)

* Refine the calculation of the settlement's profit

* More accurate simulation of the climate impacts on the agricultural yield

<figure><img src="image_61.png" /></figure>

Settlement pattern at the beginning of the simulation (T = 0)

<figure><img src="image_62.png" /></figure>

<figure><img src="image_63.png" /></figure>

Settlement pattern after 231 years of simulation (T=231)

### Settlement pattern

<figure><img src="image_64.png" /></figure>

Capital of city

<figure><img src="image_65.png" /></figure>

1: Aristocratic villa

<figure><img src="image_66.png" /></figure>

2: Aristocratic farm

<figure><img src="image_67.png" /></figure>

3: Big Landowner villa

<figure><img src="image_68.png" /></figure>

4: Big Landowner farm

<figure><img src="image_69.png" /></figure>

5: Farm

<figure><img src="image_70.png" /></figure>
RDMed
Project

# ROMCLIM:
a socio-climate Model

* **ROMCLIM** simulates the evolution of potential profits made by virtual agricultural exploitations (wine, olive and wheat production) in southern Gaul (Gallia Narbonensis), according Climate Change

* **Agents: [WINERIES] [OLIVE GROVES] [CEREAL FARMS] [MAJOR TOWNS]** (programmed according to agronomic treatises)

* **Potential yields** (wine, olive, cereals) are calculated by an emulation the agro-ecosystemic model **LUND-POTSDAM-JENA-MANAGED-LAND (LPJML)**

* **Paleoclimatic data** needed by LPJmL are processed for each century between the VIth c BC and the VIIth PC from an actual climate reconstruction (ALADIN) modified with paleoclimatic anomalies (from Guiot & Kaniewski 2015)

## Did the Roman Climate Optimum (RCO) boost the economy of the Roman Empire?

<figure><img src="image_71.png" /></figure>

<figure><img src="image_72.png" /></figure>

Evolution of temperatures and precipitations in the last three millennia (data extracted from Guiot & Kaniewski 2015)

Precipitations (mm) 2500BP

<figure><img src="image_73.png" /></figure>

Precipitations (mm) 2000BP

<figure><img src="image_74.png" /></figure>

Temperatures (C°) 2500BP

<figure><img src="image_75.png" /></figure>

Temperatures (C°) 2000BP

<figure><img src="image_76.png" /></figure>

Spatial reconstruction of temperatures and precipitations in southern Gaul in VIth c. BC (2500 BP) and Ist c. BC(2000 BP)

<figure><img src="image_77.png" /></figure>

<figure><img src="image_78.png" /></figure>

<figure><img src="image_79.png" /></figure>

Snapshot of the interface of the ROMCLIM Model
Main characteristics of the agents:

<table><thead><tr><th>Agents :</th><th>Wheat farm</th><th>Olive grove</th><th>Winerie</th></tr></thead><tbody><tr><th>Number of farmers</th><td>6</td><td>13</td><td>14</td></tr><tr><th>Cultivated area</th><td>0-2 ha</td><td>240 jugera (60 ha) (Caton)</td><td>100 jugera (25 ha) (Columelle)</td></tr><tr><th>Yields</th><td colspan="3">Returned by LPJmL emulator calculation/climate</td></tr><tr><th>Harvest</th><td colspan="3">Cultivated area * LPJmL yield</td></tr><tr><th>Price of harvest</th><td>0-100 denarius/modius*</td><td>0-40 denarius/sextarius**</td><td>0-30 denarius/sextarius**</td></tr><tr><th>Costs of exploitations</th><td>Quantity of grain necessary for feed the family + necessary seed for next year</td><td>Number of persons * 140 sesterces/year</td><td>140 sesterces/year/slave</td></tr><tr><th>Distance to the nearest capital of city</th><td colspan="3">Calculated by the model</td></tr><tr><th>Cost of transports</th><td colspan="3">0-1 sesterce/kg/km * Distance to the nearest capital</td></tr><tr><th>Benefits</th><td>Surplus price – costs</td><td>Harvest price – costs</td><td></td></tr><tr><th>Action</th><td> disappear if annual harvest can't feed the family</td><td> disappear if benefits < 0</td><td></td></tr></tbody></table>
## ROMCLIM: main (counterfactuals) results

* Strong positive impact of RCO on the potential agricultural profits (peak in the II<sup>nd</sup> c. BC), slight decrease during Late Antiquity (IV<sup>th</sup>-VII<sup>th</sup> c. AD)

* Maps of potential profits very different according climate characteristics

<figure><img src="image_80.png" /></figure>

Evolution of potential profits made by agricultural exploitations (according Climate Change only)

<figure><img src="image_81.png" /></figure>

Roman capital of city

<figure><img src="image_82.png" /></figure>

Benefits of wineries

<figure><img src="image_83.png" /></figure>

Benefits of olive groves

<figure><img src="image_84.png" /></figure>

Benefits of wheat farms

<figure><img src="image_85.png" /></figure>

<figure><img src="image_86.png" /></figure>

Maps of potential agricultural profit with climate characteristics of the VI<sup>th</sup> c. BC and the I<sup>st</sup> c. BC
# ROMCLIM: predictive model outputs vs archaeological data

* For winemaking archaeological data for wine roughly match with the area of highest potential profits (Antibes-Fréjus area, middle et lower Rhone valley, languedocian plains)

<figure><img src="image_87.png" /></figure>

* For olive oil, main archaeological data are concentrated in Languedoc and mainly in the Var region (thanks to J.-P. Brun works), where the potential appears medium. The area with highest potential profits in the languedocian hinterland doesn't match with archaeological data, but researches are not very intensive in this area.

Difficulty to validate the outputs of the model with archaeological data due to their spatial heterogeneity -> but model have a stimulating predictive value for archaeological research

<figure><img src="image_88.png" /></figure>

<figure><img src="image_89.png" /></figure>
Olive growing: model vs archaeological data

<figure><img src="image_90.png" /><figcaption>• 28: Carte de localisation des installations de pressurages et des ateliers d'amphores vinaires.</figcaption></figure>

<figure><img src="image_91.png" /></figure>

After Garcia 1992

ROMCLIM model
# FER-AGRI Model: vulnerability and resilience of Iron Age societies to Climate Change

*What was the impact of Climate Change on the Iron Age demography in Gaul between the VIth c. BC and the Ist c. BC ?*

* Impact of Climate Change on potential cereal production and demographical growth are simulated

* Cereals yields vary according to paleoclimatic data (LPJmL emulation)

* Population grows each year according to a demographic rate

* Agricultural workers (workforce) make a harvest per year for 6 centuries

* When the harvest and stocks are insufficient in one cell to feed everybody, population disappears

<figure><img src="image_92.png" /><figcaption>Density of settlement in Northern Gaul in Iron Age and climatic fluctuations (Malrain et al. 2013)</figcaption></figure>

Precipitations

Temperatures

Numbers of sites per regions

<figure><img src="image_93.png" /><figcaption>Interface of FER-AGRI Model (Bernigaud et al. in press)</figcaption></figure>
FER-AGRI Model:
outputs, results and interpretation

* Noticeable RCO effect on cereals surplus, but not really on demographical growth

* Cooling of more than -2° C have a negative impact on cereal yields and cause a demographical collapse (system resilient under this threshold)

<figure><img src="image_94.png" /></figure>

<figure><img src="image_95.png" /></figure>

<figure><img src="image_96.png" /></figure>

<figure><img src="image_97.png" /></figure>

<figure><img src="image_98.png" /></figure>

<figure><img src="image_99.png" /></figure>

<figure><img src="image_100.png" /></figure>

<figure><img src="image_101.png" /></figure>

Simulation

Number of
archaeological sites

<figure><img src="image_102.png" /></figure>

Archaeodemographical modelling
(Isoardi 2008)
-> Assessment of our models

**Strengths:**

- social modeling for gallo-roman society
  [ModelAnset]

- climate-agricultural yields reconstructions
  [ROMCLIM/FER-AGRI]

<figure><img src="image_103.png" /></figure>

**Perspectives:**

- Hybridizing the strengths of ModelAnSet & ROMCLIM/FER-AGRI in a socio-environmental perspective
Assessments and Conclusions:
ABM: The End of the Beginning?

Global assessment

* Powerful tool for explore hypothesis & rethinking historical narratives

* Use of ABMs grows in Europe, but probably still in a learning & experimental phase in France

* Development of ABMs in France still shy

* Various difficulties entertain methods improvements

* **Heuristic virtues:** test hypotheses & models

* **Integrative power:** stimulates collaborations between communities of archaeologists, environmentalists and computer scientists (cf OBRESOC/RDMed projects)

* Environmental change still few integrated in most models

Global perspectives

<figure><img src="image_104.png" /></figure>

* Development for research in landscape archeology (test hypothesis for the causes of fluctuations between cultivated/wooded areas)

* Development for research in the socionatural perspective in the long term & transitions between chronoculturals periods

* But necessity of strengthen collaboration with geoscientifics & paleoenvironmentalists (archaeobotany, archaeozoology, geoarcheology,...)