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Computational social science: interactions
among people and complex
socio-technological systems
Grupo Interdisciplinar de ...
@anxosan
Computational
Social Science
Physics / Math of
Complex Systems
Behavioral
Sciences
The interactions-based approach
@anxosan
Living on the edge
Nature (Special Issue) 525, 305
(17 September 2015)
Why scientists must
work together to save
...
@anxosan
Adolphe Quetelet
(1796-1874)
Astronomer,
mathematician,
statistician,
and sociologist
Frame: Adam Smith (1723-179...
@anxosan
Quetelet was keenly aware of the overwhelming complexity
of social phenomena, and the many variables that needed
...
@anxosan
Physicists and interdisciplinarity
Physicists, it turns out, are almost perfectly
suited to invading other people...
@anxosan
As irritating as this attitude can be to everybody
else, the arrival of the physicists into a
previously non-phys...
@anxosan
Physicists study collective phenomena
emerging from the interactions of
individuals as elementary units in
comple...
@anxosan
The interactions-based approach
Strategic interactions / local optimization
@anxosan
Computational Social Science
Aimed to favor and take advantage
of massive ICT data
A [computer] model-based scien...
@anxosan
Computational Social Science
@anxosan
Behavioral Science
Systematic analysis and investigation of
human behavior through controlled and
naturalistic ob...
@anxosan
Test inferences from data
Test simulation predictions
Small vs large-scale
Emergent behavior
Challenges for new e...
@anxosan
SMALL
DATA
So we look for
@anxosan
SMALL
DATA
So we look for
@anxosan
SMALL
controlled
DATA
So we look for
@anxosan
Data Science vs Behavioral Science
@anxosan
Data Science vs Behavioral Science
@anxosan
Data Science vs Behavioral Science
@anxosan
By way of llustration: Case studies
Networks, cooperation and reputation
Cooperation in hierarchical systems
Beha...
@anxosan
Work with
José A. Cuesta Carlos Gracia-Lázaro Yamir Moreno Alfredo Ferrer
Cuesta et al. Sci. Rep. 5, 7843 (2015)
...
@anxosan
Work with
Mario Gutiérrez-Roig Julián Vicens
Gutiérrez-Roig et al., in preparation (2016)
Julia Poncela-Casasnova...
@anxosan
Nowak & May, Nature 359, 826 (1992)
C
Case study 1. Networks
@anxosan
Prisoner’s dilemma
A game theoretical paradigm of social dilemma
DC
C
D
1 S
0T
• 2 players
• 2 actions: Cooperate...
@anxosan
1229 players (625, lattice; 604, heterogeneous)
Last year high school students
44% male, 56% female
42 high schoo...
@anxosan
Cooperation on networks: setup
C. Gracia-Lázaro, A. Ferrer, G. Ruiz, A. Tarancón, J. A. Cuesta, A. S., Y. Moreno,...
@anxosan
Cooperation on networks: facts
C. Gracia-Lázaro, A. Ferrer, G. Ruiz, A. Tarancón, J. A. Cuesta, A. S., Y. Moreno,...
@anxosan
Cooperation on networks: mechanism
J. Grujić, C. Gracia-Lázaro, M. Milinski, D. Semmann, A. Traulsen, J. A. Cuest...
@anxosan
Static networks do not support cooperation in a Prisoner’s Dilemma
Kirchkamp & Nagel. Games Econ. Behav. 58, 269–...
@anxosan
Dynamic networks support cooperation in a Prisoner’s Dilemma
Rand et al. Proc. Natl. Acad. Sci. USA 108, 19193 (2...
@anxosan
Wang et al. Proc. Natl. Acad. Sci. USA 109, 14363 (2012)
Dynamic networks
@anxosan
Wang et al. Proc. Natl. Acad. Sci. USA 109, 14363 (2012)
Emergence of cooperation
@anxosan
What is the mechanism?
@anxosan
Experiment on information
Stage 1: Play Prisoner’s Dilemma with current neighbors
Cuesta et al. Sci. Rep. 5, 7843...
@anxosan
Experiment on information
Stage 2: Modify network
@anxosan
Experiment on information
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]
[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
...
@anxosan
Cheaters manage to disguise
0 1 2 3 4 5
true cooperation index
participantsproportion
0.00.10.20.30.40.5
reliable...
@anxosan
Inequality increases
0 5 10 15 20 25 30
050010001500
round
cumulatedwealth
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
...
@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
Receive 40 points for both of you
@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
Case study 3: Behavioral “phenotypes”
@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
...
@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
Predicte...
@anxosan
Agnostic individual classification
@anxosan
Phenotypes
ExperimentNumericalDifference
AggregationTrustfulEnviousOptimist Pessimist Clueless
5 7 9 11 13 15
T
0...
@anxosan
Too many phenotypes?
@anxosan
Case study 4: Climate change mitigation
@anxosan
Climate change game
@anxosan
Climate change game
@anxosan
Climate change game
@anxosan
Climate change game
@anxosan
Climate change game, heterogeneous version
@anxosan
Is collective action successful?
@anxosan
How do players behave?
@anxosan
How do players behave?
@anxosan
Summary: case study 1
The mechanism for cooperation in
dynamic networks is reputation
Reputation combines last ac...
@anxosan
Summary: case studies 2 & 3
Hierarchy is detrimental
for cooperation
People seem classifiable in
a few recognizabl...
@anxosan
Summary: case study 4
Climate change is averted
by all groups (50% in 2008)
People 3 times richer
contributed 1/3...
@anxosan
Outlook
Small vs large-scale: FET Open IBSEN (Sep 15 - Aug 18)

Bridging the gap: from Individual Behaviour
to th...
@anxosan
Outlook: example
Small vs large-scale: FET Open IBSEN (Sep 15 - Aug 18)
Trading in networks
@anxosan
Computational social science: interactions among people
and complex socio-technological systems
Refs available fr...
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Ciencia social computacional: Interacción entre personas y sistemas complejos socio-tecnológicos - Prof. Anxo Sánchez

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Se presenta el planteamiento de la ciencia social computacional actual como algo que va más allá del análisis de big data para enfocarse en desarrollar modelos del individuo y a través de ellos de la sociedad, en el espíritu de la física de sistemas complejos. Esta investigación involucra una combinación de experimentos, análisis de datos (machine learning) y modelos y simulaciones basadas en agentes. La presentación se centra en la clave del proyecto que es entender como interaccionamos unas personas con otras. Para ello se introducen ejemplos, desarrollados en nuestro grupo, de experimentos que tienen que ver con reputación en interacciones sociales en red, con clasificación de las personas, o con nuestro comportamiento comparado con los primates no humanos, incluyendo algún modelo construido a partir de las reglas deducidas de los resultados experimentales. Finalmente, se discuten perspectivas relevantes, como por ejemplo la diferencia entre experimentos y sistemas de pequeña escala con la gran escala característica de nuestras interacciones hoy en día, que es objeto de investigación del proyecto FET Open de H2020 IBSEN. Esta charla también se enmarca dentro de las actividades de difusión del proyecto MOSI- AGIL (S2013/ICE-3019), financiado por la CAM y fondos FEDER.

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Ciencia social computacional: Interacción entre personas y sistemas complejos socio-tecnológicos - Prof. Anxo Sánchez

  1. 1. Computational social science: interactions among people and complex socio-technological systems 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
  2. 2. @anxosan Computational Social Science Physics / Math of Complex Systems Behavioral Sciences The interactions-based approach
  3. 3. @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.
  4. 4. @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
  5. 5. @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
  6. 6. @anxosan Physicists and interdisciplinarity Physicists, it turns out, are almost perfectly suited to invading other people’s disciplines, being not only extremely clever but also generally much less fussy than most about the problems they choose to study. Physicists tend to see themselves as the lords of the academic jungle, loftily regarding their own methods as above the ken of anybody else and jealously guarding their own terrain. But their alter egos are closer to scavengers, happy to borrow ideas and technologies from anywhere if they seem like they might be useful, and delighted to stomp all over someone else’s problem.
  7. 7. @anxosan As irritating as this attitude can be to everybody else, the arrival of the physicists into a previously non-physics area of research often presages a period of great discovery and excitement. Mathematicians do the same thing occasionally, but no one descends with such fury and in so great a number as a pack of hungry physicists, adrenalized by the scent of a new problem. Physicists and interdisciplinarity
  8. 8. @anxosan Physicists study collective phenomena emerging from the interactions of individuals as elementary units in complex socio-technological systems The interactions-based approach
  9. 9. @anxosan The interactions-based approach Strategic interactions / local optimization
  10. 10. @anxosan Computational Social Science Aimed to favor and take advantage of massive ICT data A [computer] model-based science yielding predictive and explanatory models
  11. 11. @anxosan Computational Social Science
  12. 12. @anxosan Behavioral Science Systematic analysis and investigation of human behavior through controlled and naturalistic observation, and disciplined scientific experimentation Effects of psychological, social, cognitive, and emotional factors on economic decisions; bounds of rationality of economic agents… …and back!
  13. 13. @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
  14. 14. @anxosan SMALL DATA So we look for
  15. 15. @anxosan SMALL DATA So we look for
  16. 16. @anxosan SMALL controlled DATA So we look for
  17. 17. @anxosan Data Science vs Behavioral Science
  18. 18. @anxosan Data Science vs Behavioral Science
  19. 19. @anxosan Data Science vs Behavioral Science
  20. 20. @anxosan By way of llustration: Case studies Networks, cooperation and reputation Cooperation in hierarchical systems Behavioral phenotype classification Climate change mitigation
  21. 21. @anxosan Work with José A. Cuesta Carlos Gracia-Lázaro Yamir Moreno Alfredo Ferrer Cuesta et al. Sci. Rep. 5, 7843 (2015) Cronin et al, Sci. Rep. 5, 18 634 (2015) Katherine A. Cronin Daniel J. Acheson Penélope Hernández
  22. 22. @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)
  23. 23. @anxosan Nowak & May, Nature 359, 826 (1992) C Case study 1. Networks
  24. 24. @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
  25. 25. @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
  26. 26. @anxosan Cooperation on networks: setup 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)
  27. 27. @anxosan Cooperation on networks: facts 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)
  28. 28. @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)
  29. 29. @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
  30. 30. @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
  31. 31. @anxosan Wang et al. Proc. Natl. Acad. Sci. USA 109, 14363 (2012) Dynamic networks
  32. 32. @anxosan Wang et al. Proc. Natl. Acad. Sci. USA 109, 14363 (2012) Emergence of cooperation
  33. 33. @anxosan What is the mechanism?
  34. 34. @anxosan Experiment on information Stage 1: Play Prisoner’s Dilemma with current neighbors Cuesta et al. Sci. Rep. 5, 7843 (2015)
  35. 35. @anxosan Experiment on information Stage 2: Modify network
  36. 36. @anxosan Experiment on information No information [A] [AAB] [ABBAA]
  37. 37. @anxosan Results: Cooperation [A] [AAB] [ABBAA] No information
  38. 38. @anxosan Results: Network [A] [AAB] [ABBAA] No information
  39. 39. @anxosan Results: Network [ABBAA] [AAB] [A] No information
  40. 40. @anxosan Results: Reputation [ABBAA]
  41. 41. @anxosan Results: Reputation [ABBAA] [ABBAA][AAB]
  42. 42. @anxosan Results: Reputation [ABBAA][AAB]
  43. 43. @anxosan Independent confirmation [ABBAA] Gallo & Yan. Proc. Natl. Acad. Sci. USA 112, 3647 (2015)
  44. 44. @anxosan But, what if reputation can be faked? Antonioni, Tomassini, AS, submitted (2015) 0 5 10 15 20 25 30 012345 round cooperationindex(α) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 1 2 3 4 5 0.00.20.40.60.81.0 points purchased per round individualcooperationfrequency
  45. 45. @anxosan Cheaters manage to disguise 0 1 2 3 4 5 true cooperation index participantsproportion 0.00.10.20.30.40.5 reliable players cheater players (a) 0 1 2 3 4 5 observable cooperation index participantsproportion 0.00.10.20.30.40.5 reliable players cheater players (b)
  46. 46. @anxosan Inequality increases 0 5 10 15 20 25 30 050010001500 round cumulatedwealth ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● RR treatment FR treatment reliable players cheater players Gini coefficients: 0.27 (Finland) vs 0.37 (Tanzania)
  47. 47. @anxosan Case study 2: Bridging experiments and reality
  48. 48. @anxosan Non-non-human primate project Cottontop tamarin (Saguinus oedipus)
  49. 49. @anxosan Non-non-human primate project Chimpanzee (Pan troglodytes)
  50. 50. @anxosan Experimentally induced hierarchy Cronin et al., Sci Rep. 5, 18 634 (2015)
  51. 51. @anxosan Collaborative task Contribute to a pot totalling 20 points or more Receive 40 points for both of you
  52. 52. @anxosan Splitting task Higher ranked guy proposes a splitting (ultimatum-like) Lower-ranked guy accepts or “fights”
  53. 53. @anxosan Hierarchy decreases cooperation
  54. 54. @anxosan Role of the lower ranked subject
  55. 55. @anxosan Rank difference predicts contributions
  56. 56. @anxosan Offers and expectations
  57. 57. @anxosan Case study 3: Behavioral “phenotypes”
  58. 58. @anxosan Case study 3: Behavioral “phenotypes”
  59. 59. @anxosan Case study 3: Behavioral “phenotypes”
  60. 60. @anxosan Social dilemmas DC C D 1 S 0T
  61. 61. @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
  62. 62. @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
  63. 63. @anxosan Agnostic individual classification
  64. 64. @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
  65. 65. @anxosan Too many phenotypes?
  66. 66. @anxosan Case study 4: Climate change mitigation
  67. 67. @anxosan Climate change game
  68. 68. @anxosan Climate change game
  69. 69. @anxosan Climate change game
  70. 70. @anxosan Climate change game
  71. 71. @anxosan Climate change game, heterogeneous version
  72. 72. @anxosan Is collective action successful?
  73. 73. @anxosan How do players behave?
  74. 74. @anxosan How do players behave?
  75. 75. @anxosan Summary: case study 1 The mechanism for cooperation in dynamic networks is reputation Reputation combines last action with average action Faking reputation does not affect cooperation but increases inequality
  76. 76. @anxosan Summary: case studies 2 & 3 Hierarchy is detrimental for cooperation People seem classifiable in a few recognizable phenotypes No (self-regarding) rationality
  77. 77. @anxosan Summary: case study 4 Climate change is averted by all groups (50% in 2008) People 3 times richer contributed 1/3 less
  78. 78. @anxosan Outlook Small vs large-scale: FET Open IBSEN (Sep 15 - Aug 18)
 Bridging the gap: from Individual Behaviour to the Socio-tEchnical maN http://www.ibsen-h2020.eu @IBSEN_H2020
  79. 79. @anxosan Outlook: example Small vs large-scale: FET Open IBSEN (Sep 15 - Aug 18) Trading in networks
  80. 80. @anxosan Computational social science: interactions among people and complex socio-technological systems Refs available from http://www.anxosanchez.eu

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