Studying the Ecology of Globalized Societies

René te Boekhorst

Adaptive Systems Research GroupDept. of Computer ScienceUniversity of Hertfordshire

U.K

r.teboekhorst@herts.ac.uk

Theme: Modelwork on the effect of the

Local environment on social interactions/ social structure

Primates,Other Animals,Robots,

Behaviour OfIndividual Agents

Structure Of The Environment

Micro-levelinteractions

Macro Patterns

“Self” - Organization

Social Interactions

Broader Scope:

Pattern Formation&

Connectivity of Participating Entities

Models to study:

ECOLOGY*Diversity*Stability of EcoSystems*

*Meta systems : “entities” can be systems (communities) themselves

The Information Revolution

The IT Designers Dream …

All People Happily Connected&

Absorbing Information

Knowledge To All!

No More Drudge

Fraternité

Equalité

Liberté

… Is the Environmentalists’ Fear

• Domination of Multi-Nationals (e-mperialism)

Global WarmingBiotech

Degradation of Biodiversity

Destruction of Environment

• Slavish attitude of governments towards business and e-capitalism

Social Issue

Opposing Views

Scientific Approach to a Social/Political Issue

   

“Connectedness”

Diversification

of• Society• Culture• Behaviour

ORImpoverishment

Superficialisation

ExploitationDigital gap

This Talk:

Suggestions

for applying those ecological models to

Pattern Formation

&Connectedness of

Participating Human Communities

*Diversity*Stability of Social Systems*

*Meta systems

Integrated Project forFrameWork VI

Theoretical Ecology

*Czárán, T. (1998): Spatiotemporal Models of Population and Community Dynamics. Chapman & Hall (London)

“ Mass –Interaction ” Models

Type of Models*

Object – oriented(neighbourhood -)Models

Patch – AbundanceModels

• What are their merits/flaws ?

• Can they be used to study social structure ?

in particular networks of human communities

Examples fromPrimate studies

Proposals

Beyond modelling:Experiments with robots

“Mass-Interaction” Models

Basic object is the population: homogeneous mass instead of individuals

Analogous to reaction kinetics (“mass – action”) of a well – mixed chemical system

Model formalism: set of coupled (non – linear ) linear, (partial) ODEs

Dynamical Systems Approach

• (limited) Population Growth Logistic Equation(continuous)

Asymptotic density

0

0.05

0.1

0.15

0.2

t

N

tNK

r)t(Nr

dt

tdN 2

K

Carrying Capacity

Instantaneous rate of increase = + Net birth rate Net migration rate

• Interactions between Populations

x2

r/s

r/S k

K

x1

r/s

x2

K

R/Skx1

Coexistence:r/k > SR/K > s

Lotka – VolterraEquations

(Un-)stable equilibriaCompetition

212

22

211

11

xSx)K

x1(Rxx

xsx)k

x1(rxx

0x1

0x2 isoclines

Exclusion

s

rx

ks

rx

0x

12

1

KxR

KSx

0x

12

2

Multi-Species (Community) Models: Stability and Complexity

Coexistence of 2 competing species (communities):

parameters must satisfy 2 inequalities (r/k > S AND R/K > s)

Strobeck (1973): n competing species (communities):

2(n-1) inequalities

Chance of coexistence for large n is small (when parameters have randomly chosen values)

If number of species (m) OR connectance C increases:

average strength of interactions S ≈ Stability decreases

when interactions are random

May, R.M (1973): Stability and Complexity in Model Ecosystems. Princeton Univ. Press

Systematic Connectivity

X1X2

X3 X4

C

D

X5 X6

E

Resources

Prey

Interm. Predators

Top Predators(generalists)

destabilizing

inconsistent

stabilizing

Same conclusions

Maynard Smith, J. (1974): Models` in Ecology. Cambridge Univ. Press

From Communities Down To Groups

Within-Group Competition (WGC)

212

22

211

11

xSx)K

x1(x r x

xsx)k

x1(rx x

Instantaneous rate of increase = +

Between-Group Competition (BGC)

*Larger group displaces smaller group *Members defect smaller group

Net birth rate

Net migration rate

2

121 x

xlncx,x

*Dominants should relax WGC“Egalitarian” instead of “Despotic” societies

Dynamics of Group Size

*van Hooff, J. A. R. A. M & C. P. van Schaik (1992). In: Coalitions and Alliances in Humans and other Animals. Oxford University Press.

k

K

x2

x1

x 2 =

x 1. e

/c

x2 = x1

.e -/c

x 2 = x 1 Isoclines of x1

Isoclines of x2

0x1

0x2

2

121 x

xlncx,xr

te Boekhorst, I.J.A. & Hemelrijk, C. K. (2000). In: “Dynamics of Human and Primate Societies: Agent-Based Modelling of Social and Spatial Processes”. Santa Fe Institute Studies in the Sciences of Complexity. Oxford University Press.

Claim of van Hooff & van SchaikDOES NOT hold! Smaller groups can come into equilibrium and hence coexist with larger ones

AND MUCH MORE POSSIBILITIES

• Dynamical systems model for sub-group formation (in prep) (following a charismatic leader)

• Homeomorphism: Same equations model dynamics of a general motivational system based on allocation of (physiological) energy

* Can possibly be realized in hardware* Brings together concepts from various ethological models* Rich behaviour: dynamical threshold hysteresis excitable (viz. neuronal dynamics)

Problem Mass – Interaction Models:

• Not based on what individuals actually do

• Quickly analytically intractable

• Strategic models: deep(er) theoretic insight hard to back up by empirical data

Object – oriented (neighbourhood -) Models

• Explicitly spatial and local sites: Cellular Automata entities: Individual Oriented Models

• Simulation instead of Analysis

Two individuals cannot be present on the same place simultaneously

and that matters!

Agents

EXAMPLE: Socio-spatial structuring in primates te Boekhorst & Hogeweg: CHIMPs and ORANGs Hemelrijk: Dominance structures in Macaques

Select Random Partner

DOMINANCEINTERACTION

Ego Wins Ego Looses

Flee FromOpponent

GotoOpponent

OTHERS < PERSSPACE?

YES

OTHERS < NEARVIEW?

NO

OTHERS < MAXVIEW?

YES

Move On

NO

MoveTo Other

Turn SEARCH ANGLEAt Random To Left or

Right

YESNO

Behavioural rules of the entities in Hemelrijk’s models. Execution of acts depends on whether threshold values of threeparameters are surpassed that symbolize critical distances: “Personal Space” (PERSSPACE) < NEARVIEW < MAXVIEW

3.033 4.067 5.1 6.134 7.167 8.2 above

X-POSITION

Y-P

OS

ITO

N

-40

-30

-20

-10

0

10

20

30

40

-30 -20 -10 0 10 20 30 40 50

Ranks:

n0

2

4

6

8

10

12D

omV

alue

time

Dominance Hierarchy

Socio-Spatial Distribution

te Boekhorst, I.J.A. & Hemelrijk, C. K. (2000). In: “Dynamics of Human and Primate Societies: Agent-Based Modelling of Social and Spatial Processes”. Santa Fe Institute Studies in the Sciences of Complexity. Oxford University Press.

Problem Individual-based Models

Its all in silica

How Real are the Rules?

No real-world constraints

Classical A.I.Computer/Programming metaphors:

• Cognition as Computation

• Behavior as Problem Solving

Cognitive Science

Rational Solutions(Knowledge, Reasoning & Planning)

AlgorithmicDisembodiedNon-situated

I exist( … I think ..)

Cognitive Ethology/Psychology “Intelligent” Behaviour

Only One Problem:

IT DOESN’T WORK

Or just marginally and thenonly in a very artificial environment

• Static• Decomposable• Linear• Deterministic

UNFORTUNATELY, the real world is Dynamic, Convoluted, Nonlinear, Messy and Noisy

Confronts us with:

• important real world conditions and

• physical constraints

that are hard to program or would go otherwise unnoticed

Artificial Autonomous Agents

Why Robots?

EMBODIEDinstead of algorithmic

SITUATEDNESSLocal Interactions instead of Global Knowledge

PHYSICAL AND INFORMATIONAL CONSTRAINTS

EMERGENCEthrough

SELF-STRUCTURINGSELF-STRUCTURINGDISTRIBUTED ORGANIZATION

instead of Central Control

Robot Architecture following “New A.I.”

Artificial Autonomous Agents

Architecture & Operation

Obstacle Detector (IR sensor)

Excitatory Connection

Inhibitory Connection

OBSTACLE

MOTORS

Object Avoidance by a Braitenberg Vehicle(Braitenberg, 1984)

Based on R/C car (Tyco Scorcher) •Very fast •Differential 4WD (4 propulsed out of 6) •Intel 16-bit 196KD microcontrollers (20 MHz) •IR, and ambient light sensors •Programmable in C and assembler

Realization: DIDABOT(Marinus Maris

www.ifi.unizh.ch/groups/ailab/robots)

Experimental Set Up

260 cm

230 cm

initial configuration

ad.d adjust in presence of didabotad.o adjust in presence of objectav.d avoid didabotfo.d follow didabotnb.o nudge object with backsideta..d turn around (> 180o) in presence of didabotta.o turn around (> 180o) in presence of objectta.ow turn around (> 180o) before an object against the wallto.o touch objectwi wigglewi.d wiggle in front of didabotta.o ad.d

ta.ow

ta.d

ad.o

av.d

to.o

.800.

76

0.70

0.780.76

0.79

0.86

0.84

0.77

0.72

F5 = nb.o(0.12)

0.78

0.77

F4 = av.o(0.10)

0.64

wi

wi.d

0.77

fo.d0.76

F3 = ObjectAversiveDidaOriented

(0.23)

F1 = ObjectOriented, DidaAversive

(0.27)

F6(0.19)

F3=ObjectAversive

0

1

2

3

4

5

6

7

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97

F1=DidAv/ObjOr

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

F4=ObjectAvoid

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

F 2 =WiF o

0

0.5

1

1 .5

2

2 .5

3

3 .5

NudgeObject

0

0.5

1

1.5

2

2.5

Factor Analysis of Didabot Behaviour

Behaviour of situatedrobots can be very complicated!

Typical end configuration for experimentwith three robots

12 3 4

5

How Didabots bring objects together

HOW ARE LARGE HEAPS FORMED ?

Why not just PAIRS of blocks ?

PAIRS: Still many configurations that escape detection

TRIO’S/QUARTETS: Prob(Contact) much lower!

CLUSTERS (n > 4) GROW BY ADDITION OF SINGLE BLOCKS

Chains of lined-up blocksare rarer the more blocksmake up a constellation

Instead, better detectable “globular” configurations are more common

Object right ahead Against Wall

Other Dida approaches

Leave Object

Avoid Dida

Heap

Destroy Pair/Trio

Single object

Push object

Against other object(s)=

Pair (Trio)

SOME SUGGESTIONS

!!

Motivational dynamics(to signal “willingness”)

mdm)mEnergy (cm

mm

DynSysModels

“Hebbian social learning”? If individuals simultaneously and mutually signal “willingness” their bond strengthens

ANN’s

In Robots?

*encounter frequency*group size *time budget of individuals

Indiv.BasedModelsWhat type of network

(topology) developsdepending on:

What happens whenestablished topologiesbecome connected?

?

Can we study stability/diversity of these (linked)webs as in ecology?

Forgetting-rate!

WHAT IS LACKING

• Dynamical Systems Models*

• Network Models

• Individual Oriented Models*

• Multi-agent (hardware) Experiments**

for“Mixed Societies”

* Proposed IP by J.L. Deneubourg in FETCurrent Project

“ROBOT CULTURES”