Collective Behaviour and Swarm Intelligence

Jens Krause Humboldt University & IGB

Talk Structure

1. Part

Models of collective behaviour: predictions

Empirical tests: fish and humans

2. Part

Swarm intelligence

Applications

Mechanisms: How do animals group?

Iain Couzin

zx

y

zoa

zor

zoo

αº

(360 - α)º

Couzin, Krause, James, Ruxton & Franks 2002. JTB 218: 1-11

Metric modelsCouzin, Krause, James, Ruxton & Franks 2002.

JTB 218: 1-11Huth & Wissel 1992. JTB 156: 365-385

Topological modelsBallerini et al. 2008 PNAS 105: 1232-1237

Neurobiological modelsLemasson et al. 2009 JTB 261: 501-510

Predictive modelsMoussaïd et al. 2011

Split effect

CouzinCouzin & Krause 2003 Advances in the Study of Behavior 32: 1& Krause 2003 Advances in the Study of Behavior 32: 1--6767

Vacuolation

Advances in the Study of Behavior (2003) 32: 1Advances in the Study of Behavior (2003) 32: 1--6767

■■ 5-10 Individuals

How many leaders to guide a group?

00.10.20.30.40.50.60.70.80.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1ti f i f d i di id l (%)

00.10.20.30.40.50.60.70.80.9

1

0 5 10 15 20 25 30 35 40 45 50number of informed individuals

accu

racy

00.10.20.30.40.50.60.70.80.9

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1ti f i f d i di id l (%)

00.10.20.30.40.50.60.70.80.9

1

0 5 10 15 20 25 30 35 40 45 50number of informed individuals

accu

racy

aac

cura

cy

Couzin, Krause, Franks& Levin 2005 Nature 433: 513-516

proportion of informed individuals p

group size = 10

group size = 30

group size = 50

group size = 100

group size = 200

N = 10

N = 30

N = 50

N = 100

N = 200

Group sizeGroup size

1122

33

1616

44

101099

88

77

66

55

1414

1515

1313

1212

1111

11

AA

DDEE

CCBB

FFGG

HH

Experimental Tests: Human Groups

When experiment commences…….

Start walking at a normal speed

Remain within arm’s length of others and

Do not talk or gesture with each other.

Dyer et al. 2008, Animal Behaviour 75: 461-470

John Dyer

‘Stay within arm’s length of others’

‘Go to number X, without leaving the group’

Two private behavioural rules:

1122

33

1616

44

101099

88

77

66

55

1414

1515

1313

1212

1111

11

AA

DDEE

CC

BB

FF

GG

HH

Experimental Setup

Time and deviation from target are recorded.

Dyer et al. 2008, Animal Behaviour 75: 461-470

Human Groups

CologneCologne

■■ 200 People200 PeopleFreiburgFreiburg

■■ 100 People100 People

Group size = 20010 Leaders

Dyer et al. 2009 Phil Trans Roy Soc 364: 781-789

No Leaders → Torus

Dyer et al. 2009 Phil Trans Roy Soc 364: 781-789

Torus

Diameter fluctuates

Location keeps changing

Dyer et al. 2009 Phil Trans Roy Soc 364: 781-789

Conflict 10 vs 20

Dyer et al. 2009 Phil Trans Roy Soc 364: 781-789

Conclusions from experiments

Study of human crowd behaviour:

People being led without knowing that they were being led

Anyone with information can be a leader (self-organisation)

Only small number of leaders required

ApplicationsEvacuation of buildings and public squaresOrganisational principles for large crowd events

Robofish

Faria, Dyer, Holt, Waters, Ward, Clement, Goldthorpe, Couzin & Krause 2010. BES 64: 1211-1218

Research with a computer-controlled fish robot

Robotic fish (London/Exeter)

Robotic fish (MIT)

Robofish - Design

x

y

Faria et al. BES 64: 1211-1218

Decision-making in fish

Ashley WardAshley Ward

Decision-making in fish

8815193

9815192

125181

12110robofish

8421

Group sizePredation

91015192

6711181

11120robofish

8421

Group sizeFood

N=20

Ward, Sumpter, Couzin, Hart & Krause 2008. PNAS 105: 6948-6953

Fast & accurate decisions in groups

1, 2, 4, 8, 16 fish

Ward, Herbert-Read, Sumpter & Krause 2011. PNAS 108: 2312-2315

Circles: all fishCrosses: focal fish

Fast & accurate decisionsdecision accuracy

Ward, Herbert-Read, Sumpter & Krause 2011. PNAS 108: 2312-2315

Approach zoneDecision zone

Fast & accurate decisionsdecision time

Ward, Herbert-Read, Sumpter & Krause 2011. PNAS 108: 2312-2315

Fast & accurate decisionsshared vigilance

Approach zoneDecision zone

Ward, Herbert-Read, Sumpter & Krause 2011. PNAS 108: 2312-2315

Swarm Intelligence (SI)(animal and humans)

Krause, Ruxton & Krause 2010. Trends in Ecology and Evolution 25: 28-34

Main characteristics:1. Individuals independently acquire information

2. information is combined and processed through social interaction

3. a cognitive problem is solved in a way that cannot be implemented by isolated individuals.

• Naturally evolved SI (often used without realising it)

• Purposefully applied SI (biomimetics)− Evaluation of information contained in set of independent

individual estimates (“SI potential”)

− Utilisation of both independent individual estimates and social interaction

Swarm Intelligencein humans

Krause et al. 2010. Trends in Ecology and Evolution 25: 28-34

... ?... ?

Experiment (2008) at biomimetics-exhibition in Liebermannhaus, Brandenburg Gate Foundation, Berlin

Two Questions:

1.Estimate the number of marbles in a large glass jar

2.Estimate how many times a coin needs to be tossed for the probability that the coin will show heads each time to be as small as that of winning the German lotto

Swarm Intelligence Potential

Krause, Faria, James, Ruxton & Krause 2011 Animal Behaviour 81: 941-948

Swarm Intelligence Potential

Correctvalue = 562

Mean = 553.57Median = 516 Mean = 498.30

Median = 100Correctvalue = 24

Error of mean = 1.5% Error of mean = 1976%

Krause et al. 2011 Animal Behaviour 81: 941-948

Swarm Intelligence PotentialHow many estimates does it need to achieve a

prediction of a certain quality with high probability?

Swarm Intelligence PotentialHow many estimates does it need to achieve a

prediction of a certain quality with high probability?

Swarm intellgence potentialProbability of obtaining a good answer depends on number of estimates

Number of estimates

Prob.

• Electronic media− Access to knowledge, memory and creativity of huge

collections of people

− Interaction of large groups possible regardless of distances

• Expert knowledge can be insufficient in an increasingly complex and rapidly changing world

Companies and organisations that know how to harness SI are likely to have a competitive advantage

Why has SI becomesuch a hot topic?

Krause et al. 2010. Trends in Ecology and Evolution 25: 28-34

DemocratsDemocrats RepublicansRepublicans

2008 U.S. Presidential Election real2008 U.S. Presidential Election real--money vote share market money vote share market ~~~~ ~~~~

2006 2007 2008

Applications of SI

Source: http://iemweb.biz.uiowa.edu/graphs/graph_Pres08_VS_jpg.cfm

“Prediction markets”: Predicting future events by trading virtual shares.

Example: Iowa Electronic Markets (University of Iowa) http://www.biz.uiowa.edu/iem/index.cfm

Performance of Iowa Electronic Markets IEM:2008 U.S. Presidential Election Forecast

Source: http://tippie.uiowa.edu/iem/media/08pres.html

Applications of SI

User driven content

tapping intocollective creativity

Example: LEGO®

On an internet platform users can talk about design ideas, showcase their work and vote on each other’s designs.

Creative new designs that have the potential to reach the mass-market.

Goal

Applications of SI

Applications of SI

Collective management

Example: Ebbsfleet United football club owned by the members of MyFootballClubhttp://www.myfootballclub.co.uk/

Swarm Intelligence ?

"September 2008 – MyFC members voted 82% in favour of EUFC selling John Akinde to Bristol City for £150,000 (plus add-ons). This was a world's first, where the fans had the final say in a transfer deal. Over 7,000 members voted in 48 hours."

Swarm Intelligence

Does SI spell out the end of leadership in favour of socially self-organised systems?

Probably not...

• Many problems (including utilisation of SI itself) require expert knowledge

• Someone needs to take responsibility for decisions

SI will mainly be a tool that aids decision-making and the organisation of processes

Krause, Ruxton & Krause 2010. Trends in Ecology and Evolution 25: 28-34

Acknowledgements

Jolyon Faria

Darren CroftBen Chapman

Colin Tosh

Iain Couzin

Dean Waters

Ashley Ward

David Sumpter

Dick James

Graeme Ruxton

Lesley Morrell

ChristosIoannou

Stefan Krause

The Money:

The Brains:

The Media: Claudia Ruby, Jacob Kneser,Ranga Yogishwar, Ismeni Walter, Markus Peter

Stiftung Brandenburger Tor

John Dyer

Chantima Piyapong