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Collective intelligence: From animal societies to human groups
Guy Theraulaz Centre de Biologie Intégrative
Centre de Recherches sur la Cognition Animale CNRS, UMR 5169, Toulouse, France
The Graduate Center City University of New-York
February 28, 2019
Collective behavior in animal societies to human groups
Camazine, S. et al., Self-organization in Biological Systems, Princeton University Press (2001); Sumpter, D.J.T., Collective Animal behavior, Princeton University Press ((2010)
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Collective behavior in human crowds
Gustave Le Bon (1841-1931)
Protesters march during a demonstration in Toulouse, France on December 29, 2018 © P. Pavani
Le Bon, G., The Crowd: A Study of the Popular Mind (French: Psychologie des Foules), Sparkling Books
edition. Sparkling Books (1896)
Collective motion in animal groups
Procaccini, A. et al., Anim. Behav. (2011); Ballerini, M. et al., PNAS (2008) Selous, E., Thought Transference ( or What? ) in Birds, (1931)
Sturnus vulgaris ( © M. Briola )
How do starlings coordinate their aerial choreography?
Edmund Selous (1857-1934)
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How do social insects coordinate the construction of their nests?
Coordination of individuals activities
Apicotermes sp. Macrotermes bellicosus
Garnier, S. et al., Swarm Intell. (2007); Perna, A. & Theraulaz, G. , J. Exp. Biol. (2017) Singh, K. et al., Sci. Adv. (2019)
Procornitermes araujoi
The « spirit of the hive »
Maurice Maeterlinck (1862-1949)
1901 The life of the bee
© Ingo Arndt
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The collective intelligence of super-organisms Complex systems with emergent properties
Sturnus vulgaris Macrotermes bellicosus
© Stéphane De Greef © Nick Dunlop
The collective intelligence of super-organisms Complex systems with emergent properties
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Mathematical model
Collective level
Individual level
Quantifying and modeling individual behavior and social interactions
How do properties emerge at the group level?
Dell, A.I. et al., Trends Ecol. Evol (2014)
© Institute of Evolutionary Genetics, Düsseldorf
Inactive bee Active bee
QR code tags
Pérez-Escudero, A. et al., Nat. Methods (2014) Lecheval, V. et al., Proc. R. Soc. B (2018)
Hemigrammus rhodostomus
Automated tracking of animal movements
How do properties emerge at the group level?
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Collective behavior in schooling fish
Analysis of social interactions between fish involved in the coordination of swimming
1 cm Hemigrammus rhodostomus
Calovi, D.S. et al., PloS Comp. Biol. (2018)
Attraction and alignment
Measurement and modeling of social interactions between pairs of fish
Calovi, D.S. et al., PloS Comp. Biol. (2018)
Distance Angular position Relative heading
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Calovi, D.S. et al., PloS Comp. Biol. (2018)
Orientation relative Distance Angular position Relative heading
Attraction and alignment
Measurement and modeling of social interactions between pairs of fish
Calovi, D.S. et al., PloS Comp. Biol. (2018)
Modeling interactions between fish Comparisons between model predictions and experimental data
Model Experiment
Distance to the wall and distance between the two fish
Relative angle to the wall
Leader Follower
Experiment Model
Experiment Model
■ The model qualitatively and quantitatively reproduces the key features of the motion and spatial distributions of fish observed in experiments
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Influence of the relative intensities of attraction and alignment on collective motion patterns
Phase 1 (« swarming »)
Calovi, D. et al., New J. Phys. (2014)
Modeling interactions in fish schools
Phase diagram
Phase 2 (« schooling »)
Calovi, D. et al., New J. Phys. (2014)
Modeling interactions in fish schools Influence of the relative intensities of attraction and alignment on collective motion patterns
Phase diagram
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Phase 3 (« milling »)
Calovi, D. et al., New J. Phys. (2014)
Modeling interactions in fish schools Influence of the relative intensities of attraction and alignment on collective motion patterns
Phase diagram
Combination of interactions driving a fish school towards a “critical” state
Phase 2 (« schooling »)
Phase 3 (« milling »)
Phase 1 (« swarming »)
Calovi, D. et al., J. R. Soc. Interface (2015)
Modeling interactions in fish schools
Phase diagram
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Phase 2 (« schooling »)
Phase 3 (« milling »)
Phase 1 (« swarming »)
Calovi, D. et al., J. R. Soc. Interface (2015)
Combination of interactions driving a fish school towards a “critical” state
Modeling interactions in fish schools
Phase diagram
Chartergus chartarius
Apis mellifera
Cubitermes fungifaber
Nasutitermes triodiae
Very elaborate nest architectures Collective nest building in social insects
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Pierre-Paul Grassé (1895-1985)
1959
“An insect does not control his own work. But its ongoing activity is guided by the by-product of its work”
Grassé, P.-P., Insectes Sociaux (1959)
The stigmergy
■ Stigmergy occurs when insect’s actions are determined or influenced by the consequences of another insect’s previous action
■ This is a form of indirect communication that makes possible the coordination and regulation of insects activities
Response!
Stimulus! S1 S2 S3 S4 S5 Stop
time ■ This process leads to the coordination of the collective work and a
colony seems to follow a pre-defined plan
■ To understand how construction works one has to identify all indirect interactions that control the growth and shape of a nest
Theraulaz & Bonabeau, Artificial Life (1999)
The stigmergy Indirect communication
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■ Wasp nests are built with wood pulp and plant fibers
■ Colored blotting paper used as building material makes it possible the visualization of successive building steps
■ Individual construction behavior can be studied in great details such as the wasps decisions to build a new cell in particular locations on the comb
Nest construction in Polistinae wasps
Nest building in social wasps A stigmergic behavior
The first construction steps in Polistes dominulus
Nest building in social wasps
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The first construction steps in Polistes dominulus
Nest building in social wasps
The first construction steps in Polistes dominulus
Nest building in social wasps
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The first construction steps in Polistes dominulus
Nest building in social wasps
Potential building sites on a comb
The control of nest building in wasps
■ The nest structure controls the organization of building activities
■ To decide where to build a new cell, wasps make use of the information provided by the local arrangement of cells on the comb
Karsaï, I & Theraulaz, G., Sociobiology (1995)
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Construction rules in Polistes wasps Probability to build a new cell on the comb
■ Wasps have a greater probability to add new cells to a corner area (3 or 4 adjacent walls) than to initiate a new row by adding a cell on the side of an existing row (2 adjacent walls)
■ Wasps are modeled by asynchronous automata with a stimulus-response behavior
■ Virtual wasps move randomly in a 3-D discrete hexagonal lattice
■ Virtual wasps only have a local perception of their environment (the first 26 neighboring cells close the cell occupied by the wasp)
■ … and do not have any representation of the global architecture they build
Behavior of the virtual wasps
Modeling nest building in social wasps
Local neighborhood of the virtual wasp
Theraulaz, G. & Bonabeau, E., Science (1995)
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1.00
1.00
1.00
1.00
0.05
0.50
1.00
Local construction rules
■ Some configurations of cells trigger the construction of a new cell
■ Construction rules are stochastic
Modeling nest building in social wasps
Simulation results of the model
Polistes dominulus
Modeling nest building in social wasps
© Alex Wild
Theraulaz, G. & Bonabeau, E., J. Theor. Biol. (1995)
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0.05 0.5 1.0 0
Parapolybia varia ■ Encoding economy by
changing the execution probability of the building rules
1.0 1.0 1.0 0
Simulation results of the model
Modeling nest building in social wasps
Parachartergus fraternus
Vespa crabro Chartergus chartarius
Collective nest building in social wasps
Nest architectures obtained by simulation of the model
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From stigmergy to self-organization Trail recruitment in ants
■ To coordinate their movements when exploring a new territory or when foraging for food ants use chemical signals (pheromone trails)
■ The stigmergic interactions between ants lead to Amplification processes through positive feedback mechanisms
Bonabeau et al.,Trends Ecol. Evol. (1997)
■ Self-organizing processes allow social insects to develop a form of collective intelligence
Basic ingredients of self-organization
Self-organization processes
■ Positive feedbacks (amplification) promote the creation of structures
■ Negative feedbacks conterbalances positive feedbacks and helps to stabilize the collective pattern
Linepithema humile
Formation of an exploration network in the Argentine ant
Bonabeau et al.,Trends Ecol. Evol. (1997)
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Goss et al., Naturwissenchaften (1989) Linepithema humile
Selection of the shortest route toward a food source
Collective decision-making in ants
■ Ants first use both paths in equal numbers, laying down pheromones as they move
■ Ants taking the shorter path return to the nest faster
■ The shorter path will then be doubly marked with pheromone and will thus be more attractive
■ Geometrical constraints play a key role in the collective decision-making processes that emerge at the colony level
Selection of the shortest route toward a food source
Collective decision-making in ants
Camazine, S. et al. Self-Organization in Biological Systems. Princeton University Press (2001)
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Lasius niger
Nest construction in ants Nest architecture of the black garden ant Lasius niger
10 cm
~ 5.10 3 to 10 4 ants
Architectures without architect
Lasius niger
3D structure of a Lasius niger ant nest
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1 cm Lasius niger
Construction dynamics
Real duration : 36 hours
Nest construction in ants
Rule 3 Probability of adding
a soil pellet on the side
Height of the pillar (mm)
Khuong et al., PNAS (2016)
Rule 2 Probability
of depositing a soil pellet
Nombre de boulettes déposées (n)
Probability of picking up a soil pellet
Rule 1
Number of deposited pellets (n)
Construction rules: picking up and dropping behaviors
Nest construction in ants
Number of deposited pellets (n)
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3D computational model of ant nest construction
Nest construction in ants
Topochemical landscape
3D computational model of ant nest construction
■ The pheromone is not homogeneously present onto the surface of the built structures
■ The spatial organization of pellets creates a topochemical landscape that determines the places at which ants concentrate their building activity
0 low high Pheromone density
Khuong et al., PNAS (2016)
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Comparison between experiments and simulations
10 6 Time steps 5.10 2 Ants 2.10 5 Building
particles
η = 8.10 -4
3D computational model of ant nest construction
Number of pillars
Model Experimental data
Model Experimental data
Model Experimental data
Inter-pillar distance
Distribution at 96h
Khuong et al., PNAS (2016)
Phenotypic plasticity of nest architecture
Dry environment Wet environment
Nest construction in ants
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Collective level
Individual level
Complex structures
Simple behavioral rules ■ Self-organization
processes simplify the encoding of behavioral mechanisms that make it possible the coordination of building activities
Properties
Self-organization processes
Camazine, S. et al., Self-organization in Biological Systems, Princeton University Press (2001)
Milgram, S., Bickman, L. & Berkowitz L. J. Pers. Soc. Psychol.(1969)
Social influence in human groups Effect of crowd size on Individual behavior
Size of stimulus crowd
Per
cent
of p
asse
rs b
y
Who look up
Who stop
Conformity and Independence, 1975 © Harper & Row
25
Milgram, S., Bickman, L. & Berkowitz L. J. Pers. Soc. Psychol.(1969)
Social influence in human groups Effect of crowd size on Individual behavior
■ Pedestrians assume that if lots of people are doing something, there must be a good reason why
■ A crowd becomes more influential as it becomes bigger
■ When things are uncertain, the best thing to do is just to follow along
Collective estimation tasks in human groups
■ Measuring the impact of social information on collective performance in estimation tasks
■ Groups of subjects are asked to estimate quantities about which they have low prior knowledge (186 subjects in France, 180 subjects in Japan)
■ Low demonstrability questions (number of stars in the galaxy, size of the population of Madrid, number of marbles in a jar, number of books in books does the American Congress library…)
Experimental design
Jayles, B. et al., PNAS (2017)
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Estimation without social information
Collective estimation tasks in human groups Experimental design
Jayles, B. et al., PNAS (2017)
Ep
Estimation after social influence
E
How many stars does the Milky way hold (in millions of stars)?
Ep1
Ep2
Ep3
■ Subjects had to first provide their personal estimate Ep in a limited amount of time (25s)
■ Then, after receiving the social information (geometric mean of some previous estimates) subjects were asked to give a new estimate E
Ep1
Ep2
T
T
Ep1
T
T
T
T
Estimation without social information
Collective estimation tasks in human groups Experimental design
Jayles, B. et al., PNAS (2017)
■ Social information was controlled by introducing a controlled number of « virtual experts » (giving the right answer), without the subjects being aware of it
Ep
Estimation after social influence
E
T : true value
27
■ Precise control of the quantity and quality of the social information exchanged between subjects
■ Human groups are often composed of individuals with heterogeneous expertise
Collective estimation tasks in human groups Experimental design
Jayles, B. et al., PNAS (2017)
■ To compare the quantities that can differ by several orders of magnitude, each estimate E is normalized by the true answer T to the question and we used the log-transformed estimate X = log (E/T)
Collective estimation tasks in human groups Distribution of individual estimates
Galton, F. Nature (1907); Jayles, B. et al., PNAS (2017)
Before SI After SI
Francis Galton (1822-1911)
28
■ To compare the quantities that can differ by several orders of magnitude, each estimate E is normalized by the true answer T to the question and we used the log-transformed estimate X = log (E/T)
■ Social information allows a group to collectively improve its performance and the accuracy of its estimates
■ When there is no virtual expert the group is able to improve its collective performance after social influence
Collective estimation tasks in human groups Distribution of individual estimates
Jayles, B. et al., PNAS (2017)
Before SI After SI
Without virtual
experts Before SI After SI
■ The sensitivity to social influence Si can be uniquely defined by:
Collective estimation tasks in human groups
Jayles, B. et al., PNAS (2017)
Distribution of individual sensitivities to social influence
New estimate
Personal estimate
Social information
■ The influence of the social information on individuals’ decisions is not uniform
29
■ 5 types behavioral responses:
Collective estimation tasks in human groups
Jayles, B. et al., PNAS (2017)
Distribution of individual sensitivities to social influence
u keeping the initial estimate (S = 0)
u adopting that of others (S = 1)
u making a compromise between one’s opinion and that of the group’s (0 < S < 1)
u amplifying the social information (S > 1)
u contradicting the social information (S < 0)
■ The sensitivity of subjects to social influence increases with the difference (D) between their (log transformed) personal estimates and the social information (M) they receive
■ The subjects that are the least sensitive to social information are also those whose estimates are closest to the true values
Collective estimation tasks in human groups
Jayles, B. et al., PNAS (2017)
Impact of distance between personal’s and group’s opinion on sensitivity to social influence
Poor High
30
■ A model built and calibrated from the experimental observations quantitatively predicts the improvement in collective performance and collective accuracy as the amount of good information provided by virtual experts to the group increases
Collective estimation tasks in human groups
Jayles, B. et al., PNAS (2017)
Impact of the proportion of virtual experts on collective performance and accuracy
Collective accuracy median(⎟Xi⎟)
Collective performance ⎟median(Xi)⎟
Experience Model
Before social influence After social influence
Proportion of experts
Experience Model
Before social influence After social influence
Proportion of experts
Low performance
High performance
Low accuracy
High accuracy
■ Knowing how individuals respond to social information in animals societies and human groups allows us to understand how this information helps a group of individuals to coordinate their actions and improve their collective performance
Collective intelligence in animal and human groups Conclusions
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■ Understanding how social information influences individuals’ decisions can help us to determine the conditions under which collective opinions can come closer to the true values
■ In human groups understanding these behavioral mechanisms can help us to develop algorithms to adapt the information delivered to individuals to make optimal collective choices
■ Combining human and machine intelligence to enhance collective intelligence in crowds
Conclusions Usingsocialinforma.ontomakerecommenda.ons
Social Information
Collective intelligence in animal and human groups
Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative, CNRS,
Université Paul Sabatier, Toulouse
Service de Physique de l’Etat Condensé, CEA, Saclay
Laboratoire de Physique Théorique Université Paul Sabatier, Toulouse
Acknowledgements
D.S. Calovi V. Lecheval A. Perna J. Gautrais C. Jost A. Khuong R. Escobedo
H. Chaté C. Sire B. Jayles H.-R. Kim T. Kameda A. Blanchet
Department of Behavioral Science, Hokkaido University, Sapporo, Japan
Department of Social Psychology, The University of Tokyo, Japan
Toulouse School of Economics, France