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

•! 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

Dresden

Tharandt

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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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

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)

12th Summer School in IBM/ABM at the TU Dresden

agent-based models (social sciences)

stand simulators (forest sciences)

indiv.-based models (ecology)

1st Example Behavioural Ecology

Basic  principles  of  ABM:  interac.ons

10 (65)

Basic principles: interactions of organisms

a starling flocks at the sky of Termini (Italy) 11 (65)

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screenshots of an ABM developed by Hanno Hildenbrandt

(Uni Groningen, Netherlands)

http://news.nationalgeographic.com/news/2009/01/photogalleries/locust-swarm-theory-serotonin/photo5.html

Locust Swarms - Subsahara

DeAngelis D and Zhang B: F1000Prime Recommendation of [Dkhili J et al., Ecol Modell 2017, 361:26-40].

2nd Example Antibiotica Resistance

(Plasmid  Biology)  

Basic  principles  of  ABM:  interac5on  &  variability

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

Theory: replication and plasmid burden lead to a loss without positive selection.

Reality

Concept for the persistence of plasmids without positive selection.

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Genetic Inspiration: Human Genome Project (MIT, early 2000) Computer Science: Bioinformatics, Data Mining, Simulation Experiments (last decade) Material Science: currently

3rd Example Plant Ecology

ABM  meets  Machine  Learning

Competition

Symmetric

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Lin et al. 2013

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stress gradient

Schwinning & Weiner 1998, Lin et al. 2012, 2014

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dm/dt = fpfqcA[ 1 – ( m / M)1/4 ] m .. Current biomass M .. Maximum biomass C .. Initial growth rate A .. Area within the plant exploits resources fp .. competition among neighbouring plants fq … facilitation among neighbouring plants (in case of stress)

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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 ..)

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.

Peters & Berger (2016)

Machine-Learning

Peters & Berger (2016)

SOM training SOM layer after training

Peters & Berger (2016)

Peters & Berger (2016)

Peters & Berger (2016)

IBM / ABM

towards  agent-­‐based  science?

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

agent-based models (social sciences)

stand simulators (forest sciences)

indiv.-based models (ecology)

Vinc

enot

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8) P

roc.

R. S

oc. B

285

: 201

7236

0

Vinc

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(201

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roc.

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oc. B

285

: 201

7236

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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

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)

https://tu-dresden.de/bu/umwelt/forst/ww/bsa/forschung/projekte?set_language=en