Agentbased C omplex S ystems Volker Grimm Steve Railsback Humboldt State University Christian Vincenot Kyoto University Birgit Müller and Jürgen Groeneveld UFZ and TU Dresden ID: 629709
Download Presentation The PPT/PDF document "Towards modelling resilience of" is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
Towards modelling resilience of Agent-based Complex SystemsVolker GrimmSlide2
Steve Railsback
Humboldt State UniversityChristian Vincenot Kyoto UniversityBirgit Müller and Jürgen Groeneveld UFZ and TU Dresden
SEITE
2
AcknowledgementsSlide3
SEITE
3
Complex Adaptive SystemsSlide4
SEITE
4
Complex Adaptive Systems
A
complex adaptive system is a "complex macroscopic collection" of relatively "similar and partially connected micro-structures" formed
in order to adapt to the changing environment and increase its survivability as a macro-structure. Wikipedia 20.9.2016Examples:Gobal macroeconomic network, stock market, social insect colonies, immune system, brain, ecosystem, biosphere, cells, political parties, internet, … There must be a general systems theory beyond equilibrium and negative feedbacksSlide5
SEITE
5
Complex Adaptive Systems
Initially, agent-based modelling was considered a key tool for exploring these systems
Swarm was developed in Santa Fe
But then ABMs became less integrated in CAS researchPerhaps because ABM was in its infancy in the 1990s?
Slide6
SEITE
6
1990
2005 in Ecology
The Dark Age of agent-based modellingThe Pioneering Phase of agent-based modellingSlide7
SEITE 7
We are getting there …
State of the art 2016Slide8
SEITE 8
Dream of a new systems science
Science of
Agent-based Complex Systems (ACS)
Complements and develops CAS research:
ABMs as a central tool
Focus on adaptive behaviour of agents, not of systemsResilience emerges from adaptive behaviour of agents and their interactionsSlide9
SEITE 9
ABMs and IBMs used everywhere!
Vincenot (unpubl. manuscript)Slide10
SEITE 10
Bibliometric analysis by Christian Vincenot
Vincenot (unpubl. manuscript)
Publications using the term ABM (blue), IBM (red), or both (green)Slide11
SEITE 11
Bibliometric analysis by Christian Vincenot
Vincenot (unpubl. manuscript)
Publications using the term ABM (blue), IBM (red), or both (green)Slide12
SEITE 12
Bibliometric analysis by Christian Vincenot
Vincenot (unpubl. manuscript)
What fosters the merger of ABM and IBM literature?
Six key papers:Three of them: Reviews
Two of them: ODD protocol for describing ABM/IBMsOne of them: A generic protocol for the multi-criteria design, assessment, and parameterization of ABM/IBMs (pattern-oriented modelling)Slide13
SEITE 13
Lessons from bibliometric analysis
Emergence of ACS science across disciplines is fostered by/requires:
Describing our models in a common language (currently: ODD protocol)Avoiding ad hoc design of models but use generic design principles instead (currently: POM and ODD)Reviews across disciplines to identify general questions and principles (Young folks: write more reviews!)Slide14
SEITE
14
The common language of ODDSlide15
SEITE
15
ODD looks simple, but it isn‘t upon first use
Slide from Gary PolhillSlide16
SEITE
16
Current usage of ODD
At least 60% of ABM papers in ecology are using ODD (probably rather 70)
JASSS, OpenABM recommend ODD …
ODD is not perfect, but useful. If you do it right Slide17
SEITE 17
Why a new science of ACS?
Adaptive agents everywhere
Their behavior emerges from adaptive decision making
Their decisions are based on their model of the world, which has evolved or learned
General principles of selforganization and resilience emerge from agents‘ behavioursObserve patterns at multiple scales and levels of organisationSlide18
SEITE 18
Linking scales and disciplines
Kreft et al 2013. PNAS 110
Harfoot et al 2014. PLoS Biology 12Slide19
SEITE 19
However, the real challenge
http://www.grinningplanet.com/2004/04-15/corporate-decision-making-joke.htm
Human decision makingSlide20
SEITE 20
State-of-the art in ABMs?
Review of decicion making in land use/land cover change ABMs by Groeneveld et al.
Env. Model. Software (
under review
)Slide21
SEITE 21
There is little theory (development)
Very little explicite reference to „theory“
If theory, then from economics
Very few studies, if any, compare alternative theories/models of decision making
State of the art:IncoherentAd hocNo explicit strategy for theory developmentSlide22
SEITE 22
Side remark: must read!Slide23
SEITE 23
Why bother with “theory”?Slide24
SEITE 24
Why bother with theory?
Theory predicts behaviour from first principles
Theory can be applied to new conditions, for which no data exist
Theory is re-useable
Examples from ecology:Energy budgets, stoichiometry, physiologyFitness seekingHome range behaviourForagingSlide25
SEITE 25
Generic submodels for theory
Save time
Tested submodels, known properties
No need to "defend" everything anew
Easier to communicateDifferences between agents: different parameters, not different model structuresEasier to systematically compare models of different systemsSlide26
SEITE 26
How to develop theory?
2005. Science 310Slide27
We need a “multiscopic” view
Take into account multiple patterns
Observed at different scales and/or levels of organisation
Make your model reproduce these patterns
simultaneously (visual, numerical)
Use each pattern as a „filter“ to reject unacceptable submodels or parameterizations
„Pattern-oriented modelling“(Grimm et al. 1996,2005; Grimm and Berger 2003; Wiegand et al. 2003, 2004; Grimm and Railsback 2005, Grimm and Railsback
2012, Jakoby et al 2014).
Page 27Slide28
SEITE
28
Pattern-oriented Modelling: Three elements
Provide state
variables (and processes)
so that patterns observed in reality in principle also can emerge in the model
Contrast alternative theories (=models) of certain adaptive
behaviours (pattern-oriented theory development), aka „strong inference“, similar to „model selection“
Use multiple patterns to determine entire sets of unknown parameters („inverse modelling“)Slide29
SEITE 29
P
attern-oriented theory development
Theory in ACS science is across-levels
Theory=models of what individuals do that explain
system dynamics (Capture enough essence of individual behavior to model the system)Slide30
SEITE 30
THEORY DEVELOPMENT CYCLE
Characteristic patterns of emergent behaviorSlide31
SEITE 31
EXAMPLE: VULTURES AND CARCASSES
Pattern: # of feeders at a carcass
Jackson et al. 2008. Biology Letters 4
Cortes-Avizanda A, Jovani R, Donázar JA, Grimm V. Ecology (2014)Slide32
SEITE 32
EXAMPLE: VULTURES AND CARCASSES
Cortes-Avizanda, Jovani, Donázar & Grimm. 2014. Ecology.Slide33
SEITE 33
EXAMPLE: VULTURES AND CARCASSES
Cortes-Avizanda, Jovani, Donázar & Grimm. 2014. Ecology.Slide34
SEITE
34
EXAMPLE: VULTURES AND CARCASSES
Cortes-Avizanda, Jovani, Donázar & Grimm. 2014. Ecology.Slide35
SEITE 35
Multiple patterns as filters
Grimm et al. 2005. Science 310
Huth and Wissel 1994. Ecol. Model. 135Slide36
SEITE 36
Why a new science of ACS?
Adaptive agents everywhere
Their behavior emerges from adaptive decision making
Their decisions are based on their model of the world, which has evolved or learned
Identify general principles of selforganization and resilienceObserve patterns at multiple scales and levels of organisationSlide37
Page
37
Resilience
“Are
you a tennis ball or an egg? Do you bounce back or do you crack? Learn to bounce back when the stressors of everyday life start getting you down by attending the ACS Resilience Training.“http://www.blissmwr.com/resilience/Resilience_WebBanner.png Slide38
Page
38
Resilience
We are talking about:
Agent-based Complex Systems (ACS), self-organized, self-similar over time
Resilience is one of myriads of stability concepts in ecology – but it has become the dominant oneIt has replaced the old paradigm of „balance of nature“Slide39
Page
39
STABILITY CONCEPTS IN ECOLOGY
A terminological morass:Slide40
Three stability propertiesResistance: Fluctuations in state variables are buffered, i.e. they change not as much as assumed based on the fluctuations of drivers.
Constancy/Variability are related properties.Recovery: State variables
return
back to their initial range of values after temporary changes based on disturbances.Persistence: The analysed system is defined by a set of characteristic spatial-temporal patterns of state variables on explicitly defined scales. It exists as identifiable unit over long time periods.Slide41
Three stability propertiesResistance: Fluctuations in state variables are buffered, i.e. they change not as much as assumed based on the fluctuations of drivers
. Constancy/Variability are related properties.
Recovery
: State variables return back to their initial range of values after temporary changes based on disturbances.Persistence: The analysed system is defined by a set of characteristic spatial-temporal patterns of state variables on explicitly defined scales. It exists as identifiable unit over long time periods.Slide42
Page
42
HOLLING’s RESILIENCE
"Resilience determines the
persistence of relationships within a system
and is a measure of the ability of these systems to absorb changes
of state variables, driving variables, and parameters, and still persist. In this definition resilience is the property of the system and persistence or probability of extinction is the result.„Holling combines resistance, recovery, and persistence into one concept: resilienceConcept used A LOT in socio-ecological literatureHowever: This mixes two different kinds of stability conceptsSlide43
Page
43
ANALYTIC VS. SYNTHETIC STABILITY CONCEPTS
Distinguish between synthetic and analytic stability concept
Synthetic
:PersistenceHolistic: applies to the entire systemAnalytic:
Recovery, resistanceReductionistic: applies to specific ecological situations, defined by state variables, scales, disturbance (Grimm and Wissel 1997)Slide44
Page
44
DILEMMA
Synthetic: persistence – Holling‘s resilience
Promise: Exploring and speaking about resilience will lead us to comprehensive understanding of ecologies
Analytic: resistance, recoveryReality: We can only achieve myriads of more or less unrelated stability assessments of very limited explanatory power (state variables, scales, disturbances)Slide45
Page
45
From recovery and resistance to persistence
Synthetic: Clearly define the system!
Nobody wants to hear this, because it is very difficult, and depends on our questions or purpose
Analytic: Explore recovery and resistance for a wide range of ecological situations (variables, scales, disturbances) to understand where and how persistence emergesLimited in individual studiesRequires integrated research program
Agent-based Complex Systems science Slide46
Page
46
Research program for ACS science
Detect patterns at all levels and scales (big data, machine learning, whatever is there and cool)
Use ABMs to reproduce these patterns and constrast alternative theories of behaviours, in particular decision making
Explore recovery and resistance for differentLevels of organization
State variablesTemporal and spatial scalesTypes of disturbances and changes in driversReference states or dynamicsIntegrate findings into lessons about persistence and resilience (which defines „the system“)Slide47
Summary
Agent-based Complex System science!!
In the last decade, potential to unify started to unfold
Generic language and protocols for model development are needed (and exist)
Pattern-oriented theory development: develop theories of human decision making in ACS models
Resilience: integrate reductionistic and holistic perspectives… this will keep us busy for quite some time! Mille grazie per la vostra attenzione!
Page 47