uta berger - institut de recherche pour le développement
TRANSCRIPT
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Is our world agent-based and can we model it?
Uta Berger
Faculty of Environmental Sciences, Department of Forest Sciences, Institute of Forest Growth and Computer Sciences
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•! Faculty of Environmental Sciences
•! Department of Forest Sciences (Forest Faculty Tharandt)
•! Institute of Forest Growth and Computer Sciences
•! Professorship of Forest Biometry and Systems Analysis
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Dresden
Tharandt
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Forest Academy Tharandt • 2nd oldest in the world (est. by Heinrich Cotta 2011) • Term “sustainability” established
• ~ 700 students (~20.000 TU Dresden) • Forest Growth And Computer Sciences, Silviculture and
Forest Protection, Forest Botany and Forest Zoology, Soil Sciences, Forest Technics, Forest Economy, Plant Chemistry, ..
1763-184
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Professorship of Forest Biometry and Systems Analysis !"#$#%&'(!)*+#&#$+!•! !"#$%&'()''#%$,#')#$!%-%,+%./*$*%/&0%*)$1)'!)*+#&%'./*$%+#,%!-#.%('/0%
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Is our world agent-based and can we model it?
1.! Basic principles of IBM / ABM (example models: Behavioural Ecology, Plasmid Biology)
2.! IBM / ABM meets Machine Learning (example model: plant ecology) 3.! IBM merges ABM towards agent-based sciences
Faculty of Environmental Sciences, Department of Forest Sciences, Institute of Forest Growth and Computer Sciences
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Same objectives •! understanding the functioning of complex
systems •! multiple linkages of social-economic-
ecological processes •! quantitative forecasts of effects of a
changing environment (climate, extreme events, !)
while considering •! variability of agents, organisms (plants
animals), microbes, .. (entities) •! interactions •! behaviour •! adaptation •! decision making
agent-based models (social sciences)
stand simulators (forest sciences)
individual-based models (ecology)
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12th Summer School in IBM/ABM at the TU Dresden
agent-based models (social sciences)
stand simulators (forest sciences)
indiv.-based models (ecology)
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1st Example Behavioural Ecology
Basic principles of ABM: interac.ons
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10 (65)
Basic principles: interactions of organisms
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a starling flocks at the sky of Termini (Italy) 11 (65)
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pictures: Hanno Hildenbrandt
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pictures: Hanno Hildenbrandt
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screenshots of an ABM developed by Hanno Hildenbrandt
(Uni Groningen, Netherlands)
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http://news.nationalgeographic.com/news/2009/01/photogalleries/locust-swarm-theory-serotonin/photo5.html
Locust Swarms - Subsahara
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DeAngelis D and Zhang B: F1000Prime Recommendation of [Dkhili J et al., Ecol Modell 2017, 361:26-40].
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2nd Example Antibiotica Resistance
(Plasmid Biology)
Basic principles of ABM: interac5on & variability
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Vertical component (cell fission) persistence from one generation to the next
all figures: Wikimedia commons
Horizontal component (conjugation) persistence from one generation to the next
non transmissible transmissible •! repressed •! derepressed
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Theory: replication and plasmid burden lead to a loss without positive selection.
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Reality
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Concept for the persistence of plasmids without positive selection.
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Werisch et al. (2017)
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Zwanzig et al. submitted
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Genetic Inspiration: Human Genome Project (MIT, early 2000) Computer Science: Bioinformatics, Data Mining, Simulation Experiments (last decade) Material Science: currently
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3rd Example Plant Ecology
ABM meets Machine Learning
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Competition
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Asymmetric
Lin et al. 2013
Symmetric
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stress gradient
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Schwinning & Weiner 1998, Lin et al. 2012, 2014
Plant-Interaction (PI) Modell
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The two-layer version of the PI model including both above- and belowground compartments connected via metabolic constraints.
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Lin
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Plant-Interaction (PI) Modell
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”I have a dream …”
Observations above-ground Dynamic (Mortality, Spatial distribution …)
.. can be used to reveal mechanisms below-ground (competition mode, resource limitation ..)
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Dynamic observations at stand level: •! Mortality •! Spatial distribution (Clark Evans Index)
Mechanisms to be detected: •! Below-ground competition mode •! Resource limitation
The self-organizing map (SOM = Kohonen layer) links the factors without defining dependencies.
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Peters & Berger (2016)
Machine-Learning
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Peters & Berger (2016)
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SOM training SOM layer after training
Peters & Berger (2016)
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Peters & Berger (2016)
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Peters & Berger (2016)
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IBM / ABM
towards agent-‐based science?
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Full-fledged
Discrete Individuals
Local Interactions Finite
numbers of individuals
Variation among Individuals
Moderate life history structure and behaviors
Complex life histories including memory and other cumulatively changing internal variables
Adaptive behavior
Age , size, stage structure models; pde and matrix
Spatial Models; e.g., pdes, metapopulation models
Classical Progenitors
Level 1 Level 2 Level 3 Level 4 Level 5
Evolutionary change
Physiology
See also: DeAngelis & Mooij (2005). Ann. Rev. Ecol. Syst. 36 42 (30) IBM
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agent-based models (social sciences)
stand simulators (forest sciences)
indiv.-based models (ecology)
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Vinc
enot
(201
8) P
roc.
R. S
oc. B
285
: 201
7236
0
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Vinc
enot
(201
8) P
roc.
R. S
oc. B
285
: 201
7236
0
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Conclusions • tendency to full-fledged models (mechanistic, physiology
based) • IBM / ABM toolbox (standardization, ODD, TRACE) • merging with formerly separated modelling techniques • ready to go towards agent-based sciences
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Acknowledgements • Cyril Piou (CIRAD, UMR CBGP Montpellier, France) • Vanlerberghe Flavie (CIRAD, UMR CBGP Montpellier, France) • Jamila Dhkili (Université Ibn Zohr, Agadir, Morocco) • Martin Zwanzig (alias Werisch, TU Dresden, Germany) • Yue Lin (Scion, Rotura, New Zealand) • Ronny Peters (TU Dresden, Germany) • Hanno Hildenbrandt (University of Groningen, The Netherlands) • Christian Vincenot (Kyoto University, Japan)
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https://tu-dresden.de/bu/umwelt/forst/ww/bsa/forschung/projekte?set_language=en