HighPasm WP3

"Some highlights about ABM modelling in quantitative geography &
the example of Lascar model"

Romain Reulier

Sébastien Rey Coyrehourcq

Rouen

09/06/2023

from definitions ...

To an observer B, an object A* is a model of an object A to the extent that B can use A* to answer questions that interest him about A

Many definitions ... static or dynamic

En construisant un modèle (model building), on crée une représentation idéalisée de la réalité afin de faire apparaître certaines de ses propriétés

[Minsky 1965]

[Hagget 1965]

"le terme de modélisation désigne à la fois l’activité pour produire un modèle et le résultat de cette activité" .

[Langlois 2005]

Le concept de modélisation est donc « [...] plus large que celui de modèle, car il recouvre l’activité humaine qui aboutit au modèle achevé, alors que le modèle est un objet (concret ou abstrait), volontairement dépouillé de l’activité qui l’a créé »

Modelling ? Simulate ?

=> Modelling is an incremental process that links peoples and tools (Socio-Technical System)

un objet médiateur qui a pour fonction de faciliter une opération cognitive dans le cadre d’un questionnement orienté, opération cognitive qui peut être de cognition pratique (manipulation, savoir-faire, apprentissage de gestes, de techniques, de conduites, etc.) ou théorique (récolte de données, formulation d’hypothèses, hypothèses de mécanismes théoriques, etc.)

"simplifying" or "facilitate" ??

[Varenne 2013]

ce n’est pas tant « le modèle » que ce qu’il y a « dans le modèle » qui nous intéresse

[Sander 2000,  Besse 2000]

Puisque l’usage du modèle est relatif (à un observateur et à un questionnement), on ne peut dire que le modèle doit être un objet simple en lui-même ou dans l’absolu. Il convient donc de regarder sous quel aspect exactement il doit apparaître simplificateur, sous quel aspect il devient un outil facilitateur, un outil de facilitation. [...] on comprend déjà qu’un modèle n’est pas ce qui est recherché en tant que tel, mais ce qui facilite la recherche d’information au sujet d’un système réel ou fictif [...] Il est le moyen plus que la fin.

[Varenne 2008]

Modelling ? Simulate ?

Exchange messages

Think ...

Look and perceive

Evolve into an environnement

Act on environnement

(more or less ...)

[Ferber 1999, Drogoul 2009]

Building and playing with Agents that Interact on Environment at different Scale : "Virtual Laboratory"

Agent-based modelling ?

City

Ants

Mosquito

People

Tree

[Drogoul Treuil 2008]

More a meta-model than a model ...

flexibility of representation & nested scaling !

Agent-based modelling ?

Simple rules can produce complex emergent phenomena with simulation

Complexity Theory and Artificial Intelligence
( auto-organization & emergence)

Complex Systems

KISS

KIDS

Stylisé

Particulier

Colodyn

Colodyn

Objectif : Explorer les processus qui conduisent un espace initialement vierge de toute présence humaine à se peupler sous l’effet d’un flux migratoire. L’objectif est d’explorer les effets de différents paramètres et de différentes situations initiales sur la configuration spatiale et le rythme du peuplement. Le processus central est celui de la migration.

Un flux de groupes migrants pénètre l’espace (grid of Cell) par le nord-ouest.

Ces groupes exploitent les ressources des cellules sur lesquelles ils sont localisés.

Face à une pénurie de ressources, le groupe se déplace.

ex : " Out of Africa"

Colodyn

moving strategy (II)

min energy

avoid competition

max energy

moving strategy (I)

Colodyn

Continuous

Front

Diffuse

R = 25

H = 30

M = 10

R = 1

H = 150

M = 10

R = 2

H = 90

M = 120

neolithic agriculture propagation

Amazonie pioneer front

Australia colonization at pleistocene

KISS

KIDS

Stylisé

Particulier

Colodyn

HUME

HUME

Objectif : Explorer les processus qui conduisent un espace initialement vierge de toute présence humaine à se peupler sous l’effet d’un flux migratoire, et cela dans un contexte de perturbations environnementales. L’objectif est d’explorer les effets de différents paramètres et de différentes situations initiales sur la configuration spatiale et le rythme du peuplement. Le processus central est celui de la migration

(HUman Migration and Environment)

Un flux de groupes migrants pénètre l’espace (représenté par une grille) par le nord-ouest. Ces groupes exploitent les ressources des cellules sur lesquelles ils sont localisés. Face à une pénurie de ressources, le groupe se déplace mais il a aussi une certaine probabilité d’innover qui lui permet de survivre tout en prélevant moins de ressources.

HUME

(HUman Migration and Environment)

src :

Agent-based approaches are also employed in physical geography !!

Observe the interactions between a phenomenon and space (spatial relations)

Modification of the phenomenon according to geographical objects

Modification of geographical objects according to the phenomenon

Management of spatial and temporal scales

Help in understanding spatial structures

Helping to understand and contextualize the simulation for decision-makers in a prospective approach

Agent-based approaches are also employed in physical geography !!

• Agents are designed to mimic physical components of geographical systems (ie. geomorphology or hydrology)

• In these cases, agents often represent elementary particles in a physical system. Their ‘agency’ may be described in terms of the physical factors that determine their state, as in phase transition (ie: ice, vapor and water)

Not a state of the art, just a few examples

Paul M. Torrens, 2010

Physical processes with CA & ABM

Physical processes with Netlogo

Physical process model with NetLogo

An Agent-Based Model of Mediterranean Agricultural Land-Use/Cover Change for Examining Wildfire Risk

James Millington et al. 2008

S. Caillault

Local fire spread with 3 constraints:

Functioning and data at initialization :

Fire spread

S. Caillault

real

simulated

Simulation par Automate Cellulaire et Système Multi-Agents des conséquences du changement climatique sur l’aire de distribution de Pinus Cembra dans le Mercantour / Simulation by Cellular Automata and Multi-Agent System of the consequences of climate change on the distribution area of Pinus Cembra in Mercantour
Matthieu Vignal, Julien Andrieu, 2016

Simulation par Automate Cellulaire et Système Multi-Agents des conséquences du changement climatique sur l’aire de distribution de Pinus Cembra dans le Mercantour / Simulation by Cellular Automata and Multi-Agent System of the consequences of climate change on the distribution area of Pinus Cembra in Mercantour
Matthieu Vignal, Julien Andrieu, 2016

Reulier, 2019

• Software agents that follow hydrological rules
• Agents are able to move over a surface
• The agent can uses probability theory to decide its movement
• The life history of each agent is stored and can be analysed using GIS

• Separation of the environment generator and the agent population
• The environment for the agent can be generated by a hydrological model

Lascar

LandscApe StruCture And Runoff

Treuil, Servat, Reaney

Physical processes with CA & ABM

Random                                                   structured

Snow avalanche with cellular automata model

Discretization of water flows by ABM

Servat, 1998

Geomorphological process by CA

Chase 1992

Kronholm and Birkeland, 2005

Lascar

[Reulier, 2015 ; Reulier et al. 2019]

Complexity

Diachronic

NETLOGO / GIS complementarity

NetLogo can read geographic data in different format

Vectoriel (point, ligne, polygone) de type shapefiles (shp)

Raster (.asc et .grd)

“Geographers’ work with ABMs has helped to strengthen existing ties with related disciplines such as computer science and informatics, ecology, sustainability science, economics, anthropology, political science and the earth sciences.

Primarily because of the value placed on spatial science and behavioral geography in agent-based modeling, work of this kind is helping to infuse geographical perspectives and ‘spatial thinking’ into these fields. »

Paul M. Torrens, 2010

• is old in geography and SHS
• is multi-paradigm (non linear SD, CA, ABM)
• is linked to complex-system theory
• works perfectly with human and physical objects / process
• is an incremental and dynamic process which ...
• ... organise the "facilitation" of reality in order to ...
• ... answer needed researchs questions

In Brief ...

create simulation models

in a nutshell

specific, complementary,  and/or ambiguous vision of reality

Un contexte inter-disciplinaire

Ex : SimFéodal

[Cura et al. 2017]

Conceptual models

Entity

Interaction

SPACE

+ and +

... to build "Systems" answering questionS

... focus on

Sub-Systems to answer one question

Using simple words ...

... in space, time and point of view using a "virtual laboratory"

Discuss

Choosing rules, interaction between rules, observables, stylized facts, etc.

Organizing and discuting hypothesis

integrate

SIMULATEUR

substrat silicium

"reconstruction"

... in space, time and point of view using a "virtual laboratory"

Organizing and discuting hypothesis

... in space, time and point of view using a "virtual laboratory"

Organizing and discuting hypothesis

research question

... in space, time and point of view using a "virtual laboratory"

Organizing and discuting hypothesis

"The general idea of POM (Pattern Oriented Modelling) is to use multiple observed patterns as indicators of a system’s internal organization. Trying to reproduce such patterns with our ABMs makes us decode the internal organization of the real system.  The point of POM is to consciously think in terms of pattern, regularities or stylized facts right from the beginning and to select model structure accordingly."

Railsback et Al. 2012

internal mechanisms

patterns multiples

... a good way to think ABM building and evaluation

round trip

German School in Ecology

[ see all Volker Grimm works ]

1-  START with a null functional model

2 - Test the benefice of each new hypothesis to progress

"null"

"medawar Zone"

SURPRISE !

German School in Ecology

... a good way to think ABM building and evaluation

[ see all Volker Grimm works ]

Choices

generate paths / models ...

which could lead to the same pattern(s)

1

2

(    )

(    ,    )

[Grimm2016, Cottineau2016]

Equifinality

p1 cluster

Path Dependency ?

a) Organize the confrontation between points of view

c) Organize "surprise" by observing gap between patterns simulated vs patterns observed

d) Find bugs and limit case

b) Explain the path / the choice you take during construction, for you, your team, and all others !

Building models absolutly need a systemic exploration to ...

So What ?

Ok, i see. But how i organize all of this in order to reproduce, to discuss with others ?

Applying this methodology to Cyprus ...

... a proposal to discuss :D :D

Different axis of complexification

Climate

Land-use

C0

C1

C2

LU0

LU1

LU2

Different axis of complexification

Places & Networks

Agriculture

A0

A1

A2

P0

P1

P2

Different axis of complexification

Epidemy emergence dynamics

Fighting diseases

F0

F1

F2

E0

E1

E2

Familly of models based on

Epidemics

Fighting

F0

F1

F2

E0

E1

E2

Places & Networks

Agriculture

A0

A1

A2

P0

P1

P2

Climate

C0

C1

C2

LU0

LU1

LU2

... a wide range of combinations !

x

x

x

x

x

729 models

possible

impossible

interesting

not interesting

Familly of models

... that imply a complex chain of dependencies

Modules needs to work : Parameter (P), Empirical Data (ED) to activate new Mechanism (M)

F1

F0

>= LU1

>= A2

>= E1

LU1.ED: land-use raster & scale ?

LU1.ED: amenities location

F0.M: draining how-to ?

E0.ED: density threshold ?

E1.ED: water infection threshold ?

LU1.ED: amenities water capacity

F1 need F0, because we start fighting with draining and after that we use biological way.
F0 and F1 need management of land-use, starting in our roadmap at LU1 model.
F1 need E1 because we need amenities in the model to fight using fish
F1 need A2 because well are justified by the existance of amenities for agriculture

A2 need LU1 because we need complex land use to justify agriculture with amenities

F1 works with any Climate or Places modules.

LU1.M : land-use location how-to ?

LU1.ED: land-use switching strategy

LU1.M : land-use evolution how-to ?

A2.ED: impacting water quality / quantity by land use ?

E1.ED: number of decease by event ?

Validity of these assumptions over different period of time ?

+amenities

+ water

+ complex land use

need

need

need

need

Complexification roadmap

model

observable(s)

empirical data

null model

observable

empirical data

Ox_sim

EDx

generate

extracted

needed

extracted from

parameters + values

Px

Ox_ed

vs

?

 ?

Generic Template for each step of complexification ...

STEP 0 : Building the Null Model !!