Agent-based models of animal behaviour - Universiteit Utrecht · Agent-based Models = Agent-based Models (ABM) = Individual-based Models (IBM) = Individual-oriented Models(IOM)...

Agent-based models

of animal behaviour

From micro to macro

and back.

Ellen Evers, Behavioural Biology, University Utrecht.

Outline

... - genome – cell – organism – group – population – ecosystem - ...

� Organization, structure, patterns(spatial, temporal, social, ...)

� Behaviour of animal groups(coordination)

� Self-organization(complexity arises from simple rules)

� Tool to understand and study: Agent-based Models (ABM)

Flocking

� Examples real:

� Starlings, ants (movie), fish

Collective motion

� Bird flocks (starlings), insect swarms (flies), mammal herds (wildebeest), fish schools (herring)

Fish schooling

School level:

� Locomotion:Simultaneous change of direction

� Defense:Flash expansion, fountain effect, aggregation

No disorganized crowd, but coordinated behaviour between members of the group!

HOW to get structure, patterns, organization in a system?

Organization

Leader:

� Well informed (top-down)

� Provides everyone with instructions

� No! Fish change position within school

Blueprint, Recipe, Template:

� Plan or procedure

� Fixed, not very flexible

� No! Unlikely in big groups

Fish schooling

Individual level:

� Vision (location of neighbours)

� Lateral line (orientation of neighbours)

Coordination only with nearest neighbours, whole swarm locomotion through:

SELF-ORGANIZATION!

Self-Organization

... process in which a pattern at the global level of a system emerges solely from numerous interactions among the lower-level components of the system. (Camezine et al, 2001)

� No external, centralized control (no leader)

� Local information, simple rules (no global plan, overview)

� ORGANIZATION! (bottom-up)

FISH SCHOOLING? COLLECTIVE MOTION?

Fish schooling

Self-Organization Hypothesis:

Few simple rules in response to local information from neighbouring fish generate schooling patterns without directly coding for them. (Huth & Wissel, 1992)

Schooling rules:

1. Cohesion 2. Separation 3. Alignment

Fish schooling

Self-Organization Hypothesis:

Few simple rules in response to local information from neighbouring fish generate schooling patterns without directly coding for them. (Huth & Wissel, 1992)

To test hypothesis and understand mechanism:

Models?!

Agent-Based Models (ABM)

Models

What is a model?

Models

Models

Models

Models

Models

Definition:

Representation of a system of entities, phenomena, or processes

to simplify, visualize, simulate, manipulate and gain intuition about the entity, phenomenon or process being represented.

Simplified Realistic Understandable Predictive Explaining Precise

Simplification versus Realism

www.wikipedia.org

Collective motion

Model rules:

Craig Reynolds 1987: bird flocking (BOIDS)

Huth & Wissel 1992: fish schooling

Hooper, 1999: predator avoidance (cool school)

1. Cohesion 2. Separation 3. Alignment

Collective motion

Conclusions from the model:

� Simple rules can account for complex pattern

� No leader, no external cue nessecary

� Schools could be self-organizing

� No proof of real mechanism

� Proven: possible explanation works

2nd part

Scientific models

Spatial CA nonlineardynamical

ordinary differential equationsdeterministic stochastic IBM

monte carlo simulation IOM

partial differential equationsagent-based models

complex systemsstatic probabalistic linear

ABM

difference equation ODE PDE

cellular automata

AGENT-BASED MODELS(ABM)

ORDINARYDIFFERENTIAL EQUATIONS

(ODE)

-

Predator-prey system

sheep wolves

+

sheep sheep wolves

wolves

a) data: b) model:

-+

-

sheep wolves+

+ -

ODE:

� Population densities

ABM:

� Discrete indiviuals

sheep wolves

-

sheep wolves+

+ -

ODE:

� Equations

ABM:

� Rules (if-then, loop, scheduling)

dS/dt = bS – pSWdW/dt = pSW - dW

Sheep = + birth*Sheep– pred.*Sheep*Wolves

Wolves = + pred.*Sheep*Wolves– death*Wolves

SHEEP:

If I meet wolve: die!With chance = birthrate: Reproduce!

WOLVES:

If I meet sheep: energy +1If energy (from sheep) = 0: die!With chance = birthrate: Reproduce!

-

sheep wolves+

+ -

ODE:

� Deterministic

ABM:

� Stochasticity(pseudo-random numbers)

... to represent

variability of processes

that cannot or need notto be modelled deterministically

sheep wolves

After Grimm 2005

-

sheep wolves+

+ -

ODE:

� Non-spatial

� Homogenity

� Well mixed

ABM:

� Explicitly spatial

� Heterogenity

� Local interactions(bottom-up, adaptive)

� Patterns

-

sheep wolves+

+ -

ODE:

� Homogeneous population

ABM:

� Individual variation(age, colour, knowledge, spatial position, size, sex, ...)

-

sheep wolves+

+ -

Agent-based Models

= Agent-based Models (ABM)

= Individual-based Models (IBM)

= Individual-oriented Models (IOM)

... describe behaviour, variability and

interactions of autonomous individuals.

After Grimm 2005

Collective foraging

� Ants: Trail formation, transporter recruitment, efficient path finding, PHEROMONES!

Collective foraging

Collective decision of colony:

� Selection of richest of two foods

� Selection of shortest path

� Hyp 1: directed by leader (queen?)

� Hyp 2: Self-organization

� Global pattern emergesfrom simple rules and local information

Collective foraging

Foraging rules:

� Execute random walkor follow strongest pheromone trail

� Food found: walk home & leave pheromone trace

Resnick, M. (1994)

Collective foraging

Self-organization:

� Simple rules

� No global knowledge, local information

� Self-reinforcing phermone trails

� Feedback via environment

Emergence of accurate (not perfect!) collective decision:efficient path selection.

Self-organization

Mechanism:

� Information from neighbours

� Information from local environment (stigmergy)

� Feedback from emerging structure(control, signal)

Emergent properties

... arise out of interactions between lower-level entities, yet are novel and irreducible to them.

(Stanford Encyclopedia of Philosophy)

Emergent property:

Order, organization,

pattern on group level

Entities:

Individuals without

global knowledge,

plan or intent

Emergence

� Self-organization

� Complex Systems� physical and chemical systems (liquid)

� biological systems (cell, brain)

� social systems and organizations (stock market, social structures)

Complex system patterns often

emerge through self-organization.

Complex Systems

Multiple levels of organization!

� Individual < subgroup < population

� Interconnected, interdependent

� Higher level affects lower one

� Higher level has “behaviour” on its own

� Emergent property: Substrate to evolution

3rd part

ABM in science?

Criticism:

� Too complex to be understood

� Impossible to include everything in a model

� Too many parameters unknown

� Hard to test

� Parameters can tweak everything

� “Gaming”

After Grimm 2005

Modelling cycle

After Grimm 2005

Modelling cycle

After Grimm 2005

Formulate the question:

� Filter for essential processes

� Not model per se,but for specific purpose

Modelling cycle

After Grimm 2005

Assemble hypotheses:

� Starting point: conceptual model (drawing)

� Based on empirical data,experience, theory, gut feeling

� What do we expect to see?PATTERNS (at multiple levels)

Modelling cycle

After Grimm 2005

Chose model structure:

� Which entities?

� Which processes?

� How to model/represent processes?

Modelling cycle

After Grimm 2005

Implement model:

� Write program code(if-then rules, loops)

Modelling cycle

After Grimm 2005

Analyze the model I:

� Controlled simulation experiments(visual debugging, process understanding,extreme tests)

� Design and analysis just as with “real” experiments(statistics on program generated data)

Modelling cycle

After Grimm 2005

Analyze the model II:

� Much freedom and control in experimental setup(upscale numbers and time, hypothetical thought experiments)

� Can the model reproduce PATTERNS?(at multiple levels!!!)

Group

Individual

Group

Individual

Group

Individual

Group

Individual

Group

Individual

Group

Individual

Group

Individual

Group

Individual

Same parametersetting:

Changing parameter

setting:

Modelling cycle

After Grimm 2005

Analyze the model IIIa:

� Change parameter/rule: Effect on system behaviour?(knockout, sufficient & neccessary)

Modelling cycle

Analyze the model IIIb:

� Multiple runs, same parameter set(account for stochasticity)

After Grimm 2005

Group

Individual

Group

Individual

Group

Individual

Group

Individual

Same parametersetting:

Modelling cycle

After Grimm 2005

Communicate the model:

� Materials & Methods

� Reproducability of results

ABM – a scientific method

Model validation:

� New hypotheses from the model

� Not built into the model beforehand

� Can be tested in “real” system

After Grimm 2005

Primate social cognition

� Social structure: dominance hierachy

� Spatial structure: centrality of dominants


� DOMWORLD (Hemelrijk 1999, Hogeweg1988)

� Simple rules > complex behaviour

1. Grouping rules:

Max view

Near view

Personal space


� Dom-value: rank(initially all the same)

2. Interaction rules:

� Prediction(mental simulation)

� Dominance contest:� Win chance ~ rank

� Loser flees, winner chases

� Dom-value update according to expected outcome(winner-loser effect)


� Dominance hierarchy (differentiation from initially equal individuals)(individual variation!!!)

� Emergence of spatial structure(centrality of dominant individuals as unintentional side-effect)

� Stabilizing feedback from spatial structure on social structure:� neighbors of similar rank

� no unexpected outcomes

� no big updates of dom-value

Hemelrijk, 1998


INSIGHTS:

� No centripetal instinct necessary for spatial structure� From side effect to evolutionary substrate� Emerging spatial structure feeds back on social structure

ABM internship: http://bio.uu.nl/behaviour/people/Evers/index.html

Evolution in silico

Evolution ingredients:

� Mutations:� Inheritence of traits (to offspring)

� Chance of changing traits

� Selection:� Define fitness measure

� Explicit selection(once in a while: take out 100 best and reproduce)

� Reproduction/death(dependent on fitness measure)

Conclusions

Biological systems:

� Organisation / patterns

Several possibilities:

� Central, fixed control (leader, blueprint, ...)

� Self-organisation� Interactions between individuals

� Emergence of pattern on group level

Conclusions

ABM: useful tool to study / understand

� Multiple connected levels (individual, group)

� Behavioural rules

� Explicit spatiality:

� Locality

� Heterogenous space

� Individual variation (hierarchy, evolution)

Heraklit: You never enter the same river twice.

Conclusions

Models are useful:

� Development: Explicit formulation of processes

� Simplification: Identify essential (sufficient & nessecary) processes

� Scientific research tool:

� Controlled experiments (knockout, change parameter/rule)

� Series of runs (variation)

� Statistics on generated data

� Material & methods

� Reproduced results: validation

Group

Individual

Group

Individual

Group

Individual

Conclusions

Models advantages:

� Freedom in experimental setup

� Little time, money, animals, facilities needed

� No ethical objections

� Generate new hypotheses

� Reveal general and unexpected properties of a system

� Help understanding a system

� Change way of thinking about a system

Proust: The real voyage of discovery lies not in

finding new landscapes, but in having new eyes.

Sources

Modelling, generalModelling, generalModelling, generalModelling, general

J. J. BrysonJ. J. BrysonJ. J. BrysonJ. J. Bryson, Y. Ando, and H. Lehmann, “Agent-based modelling as scientific method: a case study analysing primate social behaviour,”Philosophical Transactions of the Royal Society B: Biological Sciences 362, no. 1485 (2007): 1685-1698.

D. L. DeAngelisD. L. DeAngelisD. L. DeAngelisD. L. DeAngelis and W. M. Mooij, Individual-based modeling of ecological and evolutionary processes. Annual Reviews in Ecology, Evolution and Systematics 36: 147-168.

V. GrimmV. GrimmV. GrimmV. Grimm, “Ten years of individual-based modelling in ecology: what have we learned and what could we learn in the future?,” Ecological Modelling 115, no. 2 (1999): 129-148.

V. GrimmV. GrimmV. GrimmV. Grimm and S. F. Railsback, Individual-based Modeling and Ecology (Princeton University Press, 2005). V. GrimmV. GrimmV. GrimmV. Grimm et al., “Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology,” Science 310, no. 5750 (November 11,

2005): 987-99. J. W. HaefnerJ. W. HaefnerJ. W. HaefnerJ. W. Haefner, Modeling Biological Systems: Principles and Applications, 2nd ed. (Springer, 2005). C. K. HemelrijkC. K. HemelrijkC. K. HemelrijkC. K. Hemelrijk, “Understanding Social Behaviour with the Help of Complexity Science (Invited Article),” Ethology 108, no. 8 (2002): 655-671. P. HogewegP. HogewegP. HogewegP. Hogeweg, “MIRROR beyond MIRROR, Puddles of LIFE,” Artificial Life (1989): 297–316. T. J. PrescottT. J. PrescottT. J. PrescottT. J. Prescott, J. J. Bryson, and A. K. Seth, “Introduction. Modelling natural action selection,” Philosophical Transactions of the Royal Society

B: Biological Sciences 362, no. 1485 (2007): 1521-1529.

http://www.swarm.org/index.php/Main_Pagehttp://ccl.northwestern.edu/netlogo/http://www.abmsystemsbiology.info

Complexity, Emergence and SelfComplexity, Emergence and SelfComplexity, Emergence and SelfComplexity, Emergence and Self----organization, generalorganization, generalorganization, generalorganization, general

http://www.santafe.edu/http://www.vs.uni-kassel.de/systems/index.php/Emergencehttp://www.pbs.org/wgbh/nova/sciencenow/3410/03.htmlhttp://www.cna.org/isaac/Glossb.htm

Biological examples of emergence and selfBiological examples of emergence and selfBiological examples of emergence and selfBiological examples of emergence and self----organizationorganizationorganizationorganization

J. BuckJ. BuckJ. BuckJ. Buck, “Synchronous Rhythmic Flashing of Fireflies. II.,” The Quarterly Review of Biology 63, no. 3 (1988): 265. S. CamazineS. CamazineS. CamazineS. Camazine et al., Self-Organization in Biological Systems: (Princeton University Press, 2003). http://mute-net.sourceforge.net/howAnts.shtml

Sources

AgentAgentAgentAgent----based models of animal behaviourbased models of animal behaviourbased models of animal behaviourbased models of animal behaviour

R. AxelrodR. AxelrodR. AxelrodR. Axelrod, The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration (Princeton University Press, 1997). R. AxelrodR. AxelrodR. AxelrodR. Axelrod and W. D. Hamilton, “The evolution of cooperation,” Science 211, no. 4489 (1981): 1390-1396. H. de VriesH. de VriesH. de VriesH. de Vries and J. C. Biesmeijer, “Modelling collective foraging by means of individual behaviour rules in honey-bees,” Behavioral Ecology and

Sociobiology 44, no. 2 (1998): 109-124. H. de VriesH. de VriesH. de VriesH. de Vries and J. C. Biesmeijer, “Self-organization in collective honeybee foraging: emergence of symmetry breaking, cross inhibition and

equal harvest-rate distribution,” Behavioral Ecology and Sociobiology 51, no. 6 (2002): 557-569. C. K. HemelrijkC. K. HemelrijkC. K. HemelrijkC. K. Hemelrijk, “Towards the integration of social dominance and spatial structure,” Animal Behaviour 59, no. 5 (May 2000): 1035-1048,

doi:10.1006/anbe.2000.1400. C. K. HemelrijkC. K. HemelrijkC. K. HemelrijkC. K. Hemelrijk, “Social phenomena emerging by self-organisation in a competitive, virtual world (‘DomWorld’),” 1st International Conference

of the European Society for Social Simulation (ESSA 2003), Groningen, September (2003). P. HogewegP. HogewegP. HogewegP. Hogeweg, “MIRROR beyond MIRROR, Puddles of LIFE,” Artificial Life (1989): 297–316. P. HogewegP. HogewegP. HogewegP. Hogeweg and B. Hesper, “The ontogeny of the interaction structure in bumble bee colonies: A MIRROR model,” Behavioral Ecology and

Sociobiology 12, no. 4 (1983): 271-283. C. HonkC. HonkC. HonkC. Honk and P. Hogeweg, “The ontogeny of the social structure in a captive Bombus terrestris colony,” Behavioral Ecology and Sociobiology

9, no. 2 (1981): 111-119. H. LehmannH. LehmannH. LehmannH. Lehmann, J. Wang, and J. J. Bryson, “Tolerance and Sexual Attraction in Despotic Societies: A Replication and Analysis of Hemelrijk

(2002),” Modelling Natural Action Selecton: Proceedings of an International Workshop, Edinburgh. The Society for the Study of ArtificialIntelligence and the Simulation of Behaviour (AISB) (2005).

S. C. PrattS. C. PrattS. C. PrattS. C. Pratt et al., “An agent-based model of collective nest choice by the ant Temnothorax albipennis,” Animal Behaviour 70, no. 5 (2005): 1023-1036.

M. ResnickM. ResnickM. ResnickM. Resnick, Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds (MIT Press, 1994). C. W. ReynoldsC. W. ReynoldsC. W. ReynoldsC. W. Reynolds, “Flocks, Herds, and Schools: A Distributed Behavioral Model,” Computer Graphics 21, no. 4 (1987): 25-34. W. I. SellersW. I. SellersW. I. SellersW. I. Sellers, R. A. Hill, and B. S. Logan, “An agent-based model of group decision making in baboons,” Philosophical Transactions of the

Royal Society B: Biological Sciences 362, no. 1485 (2007): 1699-1710. D. J. van der PostD. J. van der PostD. J. van der PostD. J. van der Post and P. Hogeweg, “Resource distributions and diet development by trial-and-error learning,” Behavioral Ecology and

Sociobiology 61, no. 1 (2006): 65-80. D. J. van der PostD. J. van der PostD. J. van der PostD. J. van der Post and P. Hogeweg, “Diet traditions and cumulative cultural processes as side-effects of grouping,” Animal Behaviour 75, no. 1

(2008): 133-144.

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Agent-based models of animal behaviour - Universiteit Utrecht · Agent-based Models = Agent-based Models (ABM) = Individual-based Models (IBM) = Individual-oriented Models(IOM)...

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