Sociophysics

Sociophysics
Grupo Interdisciplinar de Sistemas Complejos (GISC), Departamento de Matemáticas &
Institute UC3M-BS of Financial Big Data (IﬁBiD), Universidad Carlos III de Madrid
Anxo Sánchez
Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), Universidad de Zaragoza

@anxosan
Physics / Math of
Complex Systems
Sociophysics

@anxosan
Computational
Social Science
Physics / Math of
Complex Systems
Sociophysics

@anxosan
Computational
Social Science
Physics / Math of
Complex Systems
Behavioral
Sciences
Sociophysics

@anxosan
Living on the edge
Nature (Special Issue) 525, 305
(17 September 2015)
Why scientists must
work together to save
the world PAGE305
INTERDISCIPLINARITY
THE INTERNATIONAL WEEKLY JOURNAL OF SCIENCE
To solve the grand challenges
facing society — energy, water,
climate, food, health —
scientists and social scientists
must work together.

@anxosan
Adolphe Quetelet
(1796-1874)
Astronomer,
mathematician,
statistician,
and sociologist
Frame: Adam Smith (1723-1790), David Ricardo (1772-1823), Thomas Malthus (1766-1834)
Social physics

@anxosan
Quetelet was keenly aware of the overwhelming complexity
of social phenomena, and the many variables that needed
measurement. His goal was to understand the statistical laws
underlying such phenomena as crime rates, marriage rates
or suicide rates. He wanted to explain the values of these
variables by other social factors. These ideas were rather
controversial among other scientists at the time who held
that it contradicted a concept of freedom of choice.
Social physics

@anxosan
Quetelet was keenly aware of the overwhelming complexity
of social phenomena, and the many variables that needed
measurement. His goal was to understand the statistical laws
underlying such phenomena as crime rates, marriage rates
or suicide rates. He wanted to explain the values of these
variables by other social factors. These ideas were rather
controversial among other scientists at the time who held
that it contradicted a concept of freedom of choice.
His most inﬂuential book was Sur l'homme et le développement de
ses facultés, ou Essai de physique sociale, published in 1835. In it,
he outlines the project of a social physics and describes his
concept of the "average man" (l'homme moyen) who is
characterized by the mean values of measured variables that
follow a normal distribution.
Social physics

@anxosan
To be more provocative — maybe even arrogant?— I
think that physicists are often dumfounded when they
look into economics and see the way theories get built
there. Signiﬁcantly, it is an experience they DON’T
have when they look into other ﬁelds. Neuroscientists try
to understand the brain by studying the interactions
among huge number of neurons, neurotransmitters and
so on. They’ve recently turned to very large scale
simulations as perhaps the best way to make progress,
and it is easy to see why.
Physics vs economics

@anxosan
Neuroscientists don't try to force their theories into a
form where we can think of intelligence as emerging
from the balanced interactions between one
representative neuron and one representative
neurotransmitter, because this would actually eliminate
the nonlinear feedbacks and systemic network complexity
that is the central phenomenon of study. Same goes in,
say, ecology or weather science where modern scientists
are trying to ﬁnd ways to understand complexity as it is.
To a physicist, economics looks truly weird in this
regard.
Physics vs economics

@anxosan
Physicists study collective phenomena
emerging from the interactions of
individuals as elementary units in
complex socio-technological systems
Sociophysics

@anxosan
Schelling model
Stay if at least a third of
neighbors are “similar”
Move to random location
otherwise

@anxosan
Schelling model
# Neighborhoods Happiness

@anxosan
Schelling model
Average # of regions

@anxosan
The interactions-based approach
Strategic interactions / local optimization

@anxosan
Computational Social Science
Aimed to favor and take advantage
of massive ICT data

@anxosan
Aimed to favor and take advantage
of massive ICT data
A [computer] model-based science
yielding predictive and explanatory
models

@anxosan

@anxosan
On modeling
“It can scarcely be denied that the supreme goal of all
theory is to make the irreducible basic elements as simple
and as few as possible without having to surrender the
adequate representation of a single datum of experience.”
Albert Einstein
The Herbert Spencer Lecture
Oxford (10 June 1933)
Also published in Philosophy of Science 1, 163-169 (1934)

@anxosan
On modeling
“This model will be a simpliﬁcation and an idealization,
and consequently a falsiﬁcation. It is to be hoped that the
features retained for discussion are those of the greatest
importance in the present state of knowledge”
Alan M. Turing
“The chemical basis of morphogenesis”
Phil. Trans. R. Soc. Lond. B 237, 37-72 (1952)

@anxosan
On modeling
Everything should be
made as simple as
possible, but not simpler

@anxosan
Behavioral Science
Systematic analysis and investigation of
human behavior through controlled and
naturalistic observation, and disciplined
scientiﬁc experimentation

@anxosan
Behavioral Science
Effects of psychological, social,
cognitive, and emotional factors on
economic decisions; bounds of
rationality of economic agents…

@anxosan
Behavioral Science
Effects of psychological, social,
cognitive, and emotional factors on
economic decisions; bounds of
rationality of economic agents…
…and back!

@anxosan
Test inferences from data
Test simulation predictions
Small vs large-scale
Emergent behavior
Challenges for new experimental work  
in integration with the modeling process:
Where disciplines meet

@anxosan
Big data
Volume. Organizations collect data from a variety of sources,
including business transactions, social media and information from
sensor or machine-to-machine data. In the past, storing it would’ve
been a problem – but new technologies (such as Hadoop) have eased
the burden.
Velocity. Data streams in at an unprecedented speed and must be
dealt with in a timely manner. RFID tags, sensors and smart
metering are driving the need to deal with torrents of data in near-
real time.
Variety. Data comes in all types of formats – from structured,
numeric data in traditional databases to unstructured text
documents, email, video, audio, stock ticker data and ﬁnancial
transactions.

@anxosan
Big data
You can take data from any source and analyze it to ﬁnd answers
that enable 1) cost reductions, 2) time reductions, 3) new product
development and optimized offerings, and 4) smart decision making.
When you combine big data with high-powered analytics, you can
accomplish business-related tasks such as:
• Determining root causes of failures, issues and defects in near- 
real time.
• Generating coupons at the point of sale based on the  
customer’s buying habits.
• Recalculating entire risk portfolios in minutes.
• Detecting fraudulent behavior before it affects your  
organization.

@anxosan
Big data (borrowed from @estebanmoro)

@anxosan
Using BigData to infer behavior or society situation
Social
Mobility
Activity
Content
Surveys
Credit card
Mobile phone
Social media
Searches
…
Demographics
Health
Economy
Unemployment
Transportation
Geography
Politics
Situation Behavior Observation
You are what you repeatedly do [Aristóteles]

@anxosan
Sources of BigData
3.3 Dynamical communication strategies 59
A
0.0
0.2
0.4
0.6
0.8
10 20 50
k
mean
g
g1
g2
g3
0.00
0.05
0.10
0.15
0.20
10 20 50
k
mean
g
g1
g2
g3
ki
pi
ci
52 105 158 211
52 105 158 211
B
C D
logn↵,i
1
2
3
4
-1 0 1 2 3 4 5
3.5e-05
7.3e-05
1.5e-04
3.2e-04
6.6e-04
1.4e-03
2.9e-03
6.0e-03
1.3e-02
2.6e-02
1
2
3
4
-1 0 1 2 3 4 5
0.00003511
0.00007296
0.00015161
0.00031503
0.00065460
0.00136021
0.00282641
0.00587305
0.01220371
0.025358322.5e-2
3.5e-5
2.8e-3
3.1e-4
A
B
log n!,ilog i
with r0
g ~5:8 km, br 51.6560.15 and k5 350km (Fig. 1d, see
Supplementary Information for statistical validation). Le´vy flights
are characterized by a high degree of intrinsic heterogeneity, raising
the possibility that equation (2) could emerge from an ensemble of
identical agents, each following a Le´vy trajectory. Therefore, we
determined P(rg) for an ensemble of agents following a random walk
(RW), Le´vy flight (LF) or truncated Le´vy flight (TLF) (Fig. 1d)8,12,13
.
We found that an ensemble of Le´vy agents display a significant degree
of heterogeneity in rg; however, this was not sufficient to explain the
truncated power-law distribution P(rg) exhibited by the mobile
phone users. Taken together, Fig. 1c and d suggest that the difference
in the range of typical mobility patterns of individuals (rg) has a
strong impact on the truncated Le´vy behaviour seen in equation
(1), ruling out hypothesis A.
If individual trajectories are described by an LF or TLF, then
the radius of gyration should increase with time as rg(t) , t3/(2 1 b)
(ref. 21), whereas, for an RW, rg(t) , t1/2
; that is, the longer we
observe a user, the higher the chance that she/he will travel to areas
not visited before. To check the validity of these predictions, we
measured the time dependence of the radius of gyration for users
whose gyration radius would be considered small (rg(T) # 3 km),
medium (20 , rg(T) # 30 km) or large (rg(T) . 100 km) at the end
of our observation period (T 5 6 months). The results indicate that
the time dependence of the average radius of gyration of mobile
phone users is better approximated by a logarithmic increase, not
only a manifestly slower dependence than the one predicted by a
power law but also one that may appear similar to a saturation
process (Fig. 2a and Supplementary Fig. 4).
In Fig. 2b, we chose users with similar asymptotic rg(T) after
T 5 6 months, and measured the jump size distribution P(Drjrg)
for each group. As the inset of Fig. 2b shows, users with small rg travel
mostly over small distances, whereas those with large rg tend to
display a combination of many small and a few larger jump sizes.
Once we rescaled the distributions with rg (Fig. 2b), we found that the
data collapsed into a single curve, suggesting that a single jump size
distribution characterizes all users, independent of their rg. This
indicates that P Dr rg

À Á
*r{a
g F Dr

rg
À Á
, where a 1.2 6 0.1 and
F(x) is an rg-independent function with asymptotic behaviour, that
is, F(x) , x2a
for x , 1 and F(x) rapidly decreases for x ? 1.
Therefore, the travel patterns of individual users may be approxi-
mated by a Le´vy flight up to a distance characterized by rg. Most
important, however, is the fact that the individual trajectories are
bounded beyond rg; thus, large displacements, which are the source
of the distinct and anomalous nature of Le´vy flights, are statistically
absent. To understand the relationship between the different expo-
nents, we note that the measured probability distributions are related
Figure 1 | Basic human mobility patterns. a, Week-long trajectory of 40
mobile phone users indicates that most individuals travel only over short
distances, but a few regularly move over hundreds of kilometres. b, The
detailed trajectory of a single user. The different phone towers are shown as
green dots, and the Voronoi lattice in grey marks the approximate reception
area of each tower. The data set studied by us records only the identity of the
closest tower to a mobile user; thus, we can not identify the position of a user
within a Voronoi cell. The trajectory of the user shown in b is constructed
from 186 two-hourly reports, during which the user visited a total of 12
different locations (tower vicinities). Among these, the user is found on 96
and 67 occasions in the two most preferred locations; the frequency of visits
for each location is shown as a vertical bar. The circle represents the radius of
gyration centred in the trajectory’s centre of mass. c, Probability density
function P(Dr) of travel distances obtained for the two studied data sets D1
and D2. The solid line indicates a truncated power law for which the
parameters are provided in the text (see equation (1)). d, The distribution
P(rg) of the radius of gyration measured for the users, where rg(T) was
measured after T 5 6 months of observation. The solid line represents a
similar truncated power-law fit (see equation (2)). The dotted, dashed and
dot-dashed curves show P(rg) obtained from the standard null models (RW,
LF and TLF, respectively), where for the TLF we used the same step size
distribution as the one measured for the mobile phone users.
LETTERS NATURE|Vol 453|5 June 2008
780
NaturePublishing Group©2008
Mobility
Development
Energy
Transport
Economy
Retail Analytics
Unemployment
Marketing
Smart Cities
Geography

@anxosan
¡
Timescale
Nodes appear/
disappear
Tie activity is bursty
Ties form/decay
Ties activity is
correlated
Communities form/
change/decay
Networks grow/
change/decay
NodesTiesCommunitiesNetwork
t t+ t
t2
t1 t2 t3
t1 t2 t3
t1 t2 t3
t1 t2 t3
t1 t2 t3

@anxosan
• Embeddedness / clustering / triadic closure / weak ties
• Embeddedness, clustering: 
People who spend time with a third 
are likely to encounter each other 
(triadic closure). Minimizes conﬂict,  
maximizes trusts,…
• Bridges, structural holes (Burt):  
Bridges have structural advantages 
since they have access to non- 
redundant information
• Weak ties (Granovetter): weak ties  
tend to connect different areas of  
the network (they are more likely to  
be sources of novel information)
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weak tie
structural hole
bridge
strong tie

@anxosan
Hypothesis: our activity in
social networks is correlated
with our socio-economical
status
Geo-localized tweets in Spain
• From 29th Nov 2012 to  
30th June 2013
• 19.6 million tweets
• 0.57 million unique users

@anxosan
• Our daily activity is impacted by our socio-economical situation
• At the individual level
working Unemployed
0
20
40
0 5 10 15 20 25
dos
count
0 4 8 12 16 20 24
Numberoftweets
0 4 8 12 16 20 24
0
10
20
30
40
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uno
count
10
20
40
30
20
10
Hora Hora

@anxosan
• Our daily activity is impacted by our socio-economical situation
• At group/city level
Torrijos, 26% unempl.
Sobrarbe, 7% unempl.
2
4
6
8
5 10 15 20
hour
fraction
0 4 8 12 16 20
2%
Fracctionoftweets
4%
6%
8%
Hour

@anxosan
• Simple linear regression
x
y
5 10 15 20 25
510152025
%Unemployment(predicted)
Penetration
Entropy (social)
Activity (morning)
#misspellers
unemployment
0 10 20 30 40
*R2 = 0.64
% Unemployment (real) % weight in the model

@anxosan
Model Error = Model[variables] - Ofﬁcial unemployment
15 20 25 30 35
−0.3−0.10.00.10.20.3
tt$sumergida
error
30%
20%
10%
0%
-10%
-20%
-30%
Error
% Shadow Economy *
15 20 25 30 35
(* GESTHA report 2012)
Model predicts
there is “less
unemployment” in
areas with more
shadow economy

@anxosan
SMALL
DATA
Understanding interaction

@anxosan
SMALL
controlled
DATA
Understanding interaction

@anxosan
Data Science vs Behavioral Science

@anxosan
Sociophysics topics: a sample

@anxosan
By way of llustration: Case studies
Networks, cooperation and reputation
Cooperation in hierarchical systems
Behavioral phenotype classiﬁcation
Climate change mitigation

@anxosan
Work with
José A. Cuesta Carlos Gracia-Lázaro Yamir Moreno Alfredo Ferrer
Cuesta et al. Sci. Rep. 5, 7843 (2015)

@anxosan
Work with
José A. Cuesta Carlos Gracia-Lázaro Yamir Moreno Alfredo Ferrer
Cronin et al, Sci. Rep. 5, 18 634 (2015)
Katherine A. Cronin Daniel J. Acheson Penélope Hernández

@anxosan
Work with
Mario Gutiérrez-Roig Julián Vicens
Gutiérrez-Roig et al., in preparation (2016)
Julia Poncela-Casasnovas Jesús Gómez-Gardeñes
Josep Perelló Jordi Duch Nereida Bueno
Poncela-Casasnovas et al., submitted (2016)

@anxosan
Work with
Mario Gutiérrez-Roig Julián Vicens
Gutiérrez-Roig et al., in preparation (2016)
Julia Poncela-Casasnovas Jesús Gómez-Gardeñes
Josep Perelló Jordi Duch
Antonioni et al., submitted (2016)
Alberto Antonioni Marco Tomassini
Nereida Bueno
Poncela-Casasnovas et al., submitted (2016)

@anxosan
Nowak May, Nature 359, 826 (1992)
Case study 1. Networks

@anxosan
Nowak May, Nature 359, 826 (1992)
C
Case study 1. Networks

@anxosan
The evolution of cooperation

@anxosan
The evolution of cooperation
M. A. Nowak, Science 314, 1560 (2006)

@anxosan
Prisoner’s dilemma
A game theoretical paradigm of social dilemma
DC
C
D
1 S
0T
• 2 players
• 2 actions: Cooperate or Defect

@anxosan
Prisoner’s dilemma
A game theoretical paradigm of social dilemma
DC
C
D
1 S
0T
• 2 players
• 2 actions: Cooperate or Defect
T 1 : temptation to defect
S 0 : risk in cooperation

@anxosan
1229 players (625, lattice; 604, heterogeneous)
Last year high school students
44% male, 56% female
42 high schools in Aragón
From 10 AM till noon
10 000 €, on December 20, 2011; largest size ever
C. Gracia-Lázaro, A. Ferrer, G. Ruiz, A. Tarancón, J. A. Cuesta, A. S., Y. Moreno,
Proc. Natl. Acad. Sci USA 109, 12922-12926 (2012)
Cooperation on networks: setup

@anxosan
Cooperation on networks: setup

@anxosan
Cooperation on networks: facts

@anxosan
Cooperation on networks: mechanism
J. Grujić, C. Gracia-Lázaro, M. Milinski, D. Semmann, A. Traulsen, J. A. Cuesta, A. S., Y. Moreno, Sci. Rep. 4, 4615 (2014)

@anxosan
Static networks do not support cooperation in a Prisoner’s Dilemma
Kirchkamp Nagel. Games Econ. Behav. 58, 269–292 (2007)
Traulsen et al. Proc. Natl. Acad. Sci. USA 107, 2962 (2010)
Grujić et al. PLOS ONE 5, e13749 (2010)
Gracia-Lázaro et al. Proc. Natl. Acad. Sci. USA 109, 12922 (2012)
Grujić et al. Sci. Rep. 4, 4615 (2014)
No network reciprocity

@anxosan
Cooperation level OK
Modeling: inhomogeneous agents

@anxosan
Histogram of earnings: OK
Strategies can coexist: heterogeneity stable
Modeling: inhomogeneous agents

@anxosan
Mean ﬁeld approach for the stationary state (different from previous
analysis, no dynamics)
P(A) is the cooperation probability for strategy A=C, D, X
Analytical modeling

@anxosan
Mean ﬁeld approach for the stationary state (different from previous
analysis, no dynamics)
P(A) is the cooperation probability for strategy A=C, D, X
C. Gracia-Lázaro, J. A. Cuesta, A. S., Y. Moreno, Sci. Rep. 2, 325 (2012)
Analytical modeling

@anxosan
Comparison with simulations
Analytical modeling

@anxosan
Analytical modeling

@anxosan
Explanation of lack of network reciprocity
Analytical modeling

@anxosan
Dynamic networks support cooperation in a Prisoner’s Dilemma
Dynamic networks

@anxosan
Dynamic networks support cooperation in a Prisoner’s Dilemma
Rand et al. Proc. Natl. Acad. Sci. USA 108, 19193 (2011)
Wang et al. Proc. Natl. Acad. Sci. USA 109, 14363 (2012)
Dynamic networks

@anxosan
Dynamic networks

@anxosan
Emergence of cooperation

@anxosan
What is the mechanism?

@anxosan
Experiment on information
Stage 1: Play Prisoner’s Dilemma with current neighbors

@anxosan
Stage 2: Modify network

@anxosan
No information
[A]
[AAB]
[ABBAA]

@anxosan
Results: Cooperation
[A]
[AAB]
[ABBAA]
No information

@anxosan
Results: Network
[A]
[AAB]
[ABBAA]
No information

@anxosan
Results: Network
[ABBAA] [AAB] [A] No information

@anxosan
Results: Reputation
[ABBAA]

@anxosan
Results: Reputation
[ABBAA]
[AAB]

@anxosan
Results: Reputation
[ABBAA]
[ABBAA][AAB]

@anxosan
Results: Reputation
[ABBAA][AAB]

@anxosan
Independent conﬁrmation
[ABBAA]
Gallo Yan. Proc. Natl. Acad. Sci. USA 112, 3647 (2015)

@anxosan
But, what if reputation can be faked?
Antonioni, Tomassini, AS, submitted (2015)
0 5 10 15 20 25 30
012345
round
cooperationindex(α)
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● RR treatment (true)
FR treatment (true)
FR treatment (observable)
points purchased per round
participantsproportion
0.00.10.20.30.4
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
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points purchased per round
individualcooperationfrequency

@anxosan
Cheaters manage to disguise
0 1 2 3 4 5
true cooperation index
0.00.10.20.30.40.5
reliable players
cheater players
(a)
0 1 2 3 4 5
observable cooperation index
0.00.10.20.30.40.5
reliable players
cheater players
(b)

@anxosan
Inequality increases
0 5 10 15 20 25 30
050010001500
round
cumulatedwealth
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● RR treatment
FR treatment
reliable players
cheater players

@anxosan
Inequality increases
0 5 10 15 20 25 30
050010001500
round
cumulatedwealth
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● RR treatment
FR treatment
reliable players
cheater players
Gini coefﬁcients: 0.27 (Finland) vs 0.37 (Tanzania)

@anxosan
Case study 2: Bridging experiments and reality

@anxosan
Non-non-human primate project
Cottontop tamarin (Saguinus oedipus)

@anxosan
Non-non-human primate project
Chimpanzee (Pan troglodytes)

@anxosan
Experimentally induced hierarchy
Cronin et al., Sci Rep. 5, 18 634 (2015)

@anxosan
Collaborative task
Contribute to a pot totalling 20 points or more

@anxosan
Collaborative task
Contribute to a pot totalling 20 points or more
Receive 40 points for both of you

@anxosan
Splitting task
Higher ranked guy proposes a splitting (ultimatum-like)

@anxosan
Splitting task
Higher ranked guy proposes a splitting (ultimatum-like)
Lower-ranked guy accepts or “ﬁghts”

@anxosan
Hierarchy decreases cooperation

@anxosan
Role of the lower ranked subject

@anxosan
Rank difference predicts contributions

@anxosan
Offers and expectations

@anxosan
Case study 3: Behavioral “phenotypes”

@anxosan
Social dilemmas
DC
C
D
1 S
0T

@anxosan
Behavior across different situations
5 7 9 11 13 15
T
0
2
4
6
8
10
S
5 7 9 11 13 15
T
0
2
4
6
8
10
S
5 7
0
2
4
6
8
10
S
PD
SH
SG
HG

@anxosan
Aggregate results
15 5 7 9 11 13 15
T
0
2
4
6
8
10
S
5 7 9 11 13 15
T
0
2
4
6
8
10
S
0
0.2
0.4
0.6
0.8
1

@anxosan
Aggregate results
15 5 7 9 11 13 15
T
0
2
4
6
8
10
S
5 7 9 11 13 15
T
0
2
4
6
8
10
S
0
0.2
0.4
0.6
0.8
1
Predicted Observed

@anxosan
Agnostic individual classiﬁcation

@anxosan
Phenotypes
ExperimentNumericalDifference
AggregationTrustfulEnviousOptimist Pessimist Clueless
5 7 9 11 13 15
T
0
2
4
6
8
10
S
0
0.2
0.4
0.6
0.8
1
5 7 9 11 13 15
T
0
2
4
6
8
10
S
0
0.2
0.4
0.6
0.8
1
5 7 9 11 13 15
T
0
2
4
6
8
10
S
0
1
2
3
4
5 7 9 11 13 15
T
0
2
4
6
8
10
S
0
0.2
0.4
0.6
0.8
1
5 7 9 11 13 15
T
0
2
4
6
8
10
S
0
0.2
0.4
0.6
0.8
1
5 7 9 11 13 15
T
0
2
4
6
8
10
S
0
1
2
3
4
5 7 9 11 13 15
T
0
2
4
6
8
10
S
0
0.2
0.4
0.6
0.8
1
5 7 9 11 13 15
T
0
2
4
6
8
10
S
0
0.2
0.4
0.6
0.8
1
5 7 9 11 13 15
T
0
2
4
6
8
10
S
0
1
2
3
4
5 7 9 11 13 15
T
0
2
4
6
8
10
S
0
0.2
0.4
0.6
0.8
1
5 7 9 11 13 15
T
0
2
4
6
8
10
S
0
0.2
0.4
0.6
0.8
1
5 7 9 11 13 15
T
0
2
4
6
8
10
S
0
1
2
3
4
5 7 9 11 13 15
T
0
2
4
6
8
10
S
0
0.2
0.4
0.6
0.8
1
5 7 9 11 13 15
T
0
2
4
6
8
10
S
0
0.2
0.4
0.6
0.8
1
5 7 9 11 13 15
T
0
2
4
6
8
10
S
0
1
2
3
4
5 7 9 11 13 15
T
0
2
4
6
8
10
S
0
0.2
0.4
0.6
0.8
1
5 7 9 11 13 15
T
0
2
4
6
8
10
S
0
0.2
0.4
0.6
0.8
1
5 7 9 11 13 15
T
0
2
4
6
8
10
S
Risk-aversion
-----
-----Defeats
opponent
Maximizes
max-payoff
Maximizes
min-payoff
Cooperates
always
Decides
randomly
S - T ≥ 0T R S P p(C) = 1 p(C) = 0.5
0
0.2
0.4
0.6
0.8
1

@anxosan
Case study 4: Climate change mitigation

@anxosan
Climate change game, heterogeneous version

@anxosan
Is collective action successful?

@anxosan
How do players behave?

@anxosan
Summary: case study 1
The mechanism for cooperation in
dynamic networks is reputation

@anxosan
Reputation combines last action
with average action

@anxosan
Reputation combines last action
with average action
Faking reputation does not affect
cooperation but increases inequality

@anxosan
Summary: case studies 2 3
Hierarchy is detrimental
for cooperation

@anxosan
for cooperation
People seem classiﬁable in
a few recognizable phenotypes

@anxosan
for cooperation
People seem classiﬁable in
a few recognizable phenotypes
No (self-regarding) rationality

@anxosan
Climate change is averted
by all groups (50% in 2008)

@anxosan
Climate change is averted
by all groups (50% in 2008)
People 3 times richer
contributed 1/3 less

@anxosan
Sociophysics
Human
Interaction

@anxosan
Sociophysics
Human
Interaction
Socio-
technological
context

@anxosan
Sociophysics
Human
Interaction
Socio-
technological
context
Information

@anxosan
Sociophysics
Human
Interaction
Socio-
technological
context
Information
Consistent
behavior

@anxosan
Sociophysics
Human
Interaction
Socio-
technological
context
Information
Consistent
behavior
Modeling

Sociophysics

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