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Computational social science: interactions among people and complex - - PowerPoint PPT Presentation

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Anxo Snchez Computational social science: interactions among people and complex socio-technological systems Grupo Interdisciplinar de Sistemas Complejos (GISC), Departamento de Matemticas & Institute UC3M-BS of Financial Big Data

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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

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@anxosan

Computational Social Science Physics / Math of Complex Systems Behavioral Sciences

The interactions-based approach

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@anxosan

Living on the edge

Nature (Special Issue) 525, 305 (17 September 2015)

Why scientists must work together to save the world PAGE 305

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.

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@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

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@anxosan

Quetelet was keenly aware of the overwhelming complexity

  • f 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

  • r 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

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@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.

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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
  • ccasionally, 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

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@anxosan

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

The interactions-based approach

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The interactions-based approach

Strategic interactions / local optimization

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@anxosan

Computational Social Science

Aimed to favor and take advantage

  • f massive ICT data

A [computer] model-based science yielding predictive and explanatory models

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@anxosan

Computational Social Science

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@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!

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@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

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SMALL DATA

So we look for

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SMALL DATA

So we look for

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SMALL

controlled

DATA

So we look for

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Data Science vs Behavioral Science

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Data Science vs Behavioral Science

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Data Science vs Behavioral Science

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By way of llustration: Case studies

Networks, cooperation and reputation Cooperation in hierarchical systems Behavioral phenotype classification Climate change mitigation

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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

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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)

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Nowak & May, Nature 359, 826 (1992)

C

Case study 1. Networks

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Prisoner’s dilemma

A game theoretical paradigm of social dilemma

D C C D 1 S T

  • 2 players
  • 2 actions: Cooperate or Defect

T > 1 : temptation to defect S < 0 : risk in cooperation

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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

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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)
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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)
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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)

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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

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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

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@anxosan Wang et al. Proc. Natl. Acad. Sci. USA 109, 14363 (2012)

Dynamic networks

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@anxosan Wang et al. Proc. Natl. Acad. Sci. USA 109, 14363 (2012)

Emergence of cooperation

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What is the mechanism?

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Experiment on information

Stage 1: Play Prisoner’s Dilemma with current neighbors

Cuesta et al. Sci. Rep. 5, 7843 (2015)

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Experiment on information

Stage 2: Modify network

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Experiment on information No information [A] [AAB] [ABBAA]

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Results: Cooperation

[A] [AAB] [ABBAA] No information

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Results: Network

[A] [AAB] [ABBAA] No information

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Results: Network

[ABBAA] [AAB] [A] No information

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Results: Reputation

[ABBAA]

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Results: Reputation

[ABBAA] [ABBAA] [AAB]

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Results: Reputation

[ABBAA] [AAB]

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Independent confirmation

[ABBAA]

Gallo & Yan. Proc. Natl. Acad. Sci. USA 112, 3647 (2015)

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But, what if reputation can be faked?

Antonioni, Tomassini, AS, submitted (2015)

5 10 15 20 25 30 1 2 3 4 5 round cooperation index (α)
  • ●
  • ● ● ● ● ● ● ● ● ● ● ●
  • ● ● ● ● ● ● ● ● ● ●
  • RR treatment (true)
FR treatment (true) FR treatment (observable) points purchased per round participants proportion 0.0 0.1 0.2 0.3 0.4 0.5 1 1.5 2 2.5 3 3.5 4 4.5
  • 1
2 3 4 5 0.0 0.2 0.4 0.6 0.8 1.0 points purchased per round individual cooperation frequency
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Cheaters manage to disguise

1 2 3 4 5 true cooperation index participants proportion 0.0 0.1 0.2 0.3 0.4 0.5 reliable players cheater players

(a)

1 2 3 4 5
  • bservable cooperation index
participants proportion 0.0 0.1 0.2 0.3 0.4 0.5 reliable players cheater players

(b)

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Inequality increases

5 10 15 20 25 30 500 1000 1500

round cumulated wealth

  • ●
  • RR treatment
FR treatment reliable players cheater players

Gini coefficients: 0.27 (Finland) vs 0.37 (Tanzania)

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Case study 2: Bridging experiments and reality

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Non-non-human primate project

Cottontop tamarin (Saguinus oedipus)

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Non-non-human primate project

Chimpanzee (Pan troglodytes)

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Experimentally induced hierarchy

Cronin et al., Sci Rep. 5, 18 634 (2015)

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Collaborative task

Contribute to a pot totalling 20 points or more Receive 40 points for both of you

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Splitting task

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

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Hierarchy decreases cooperation

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Role of the lower ranked subject

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Rank difference predicts contributions

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Offers and expectations

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Case study 3: Behavioral “phenotypes”

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Case study 3: Behavioral “phenotypes”

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Case study 3: Behavioral “phenotypes”

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Social dilemmas

D C C D 1 S T

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Behavior across different situations

5 7 9 11 13 15 T 2 4 6 8 10 S 5 7 9 11 13 15 T 2 4 6 8 10 S 2 4 6 8 10 S

PD SH SG HG

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Aggregate results

15 5 7 9 11 13 15 T 2 4 6 8 10 S 5 7 9 11 13 15 T 2 4 6 8 10 S 0.2 0.4 0.6 0.8 1

PD SH

Predicted Observed

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Agnostic individual classification

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Phenotypes

Experiment Numerical Difference Aggregation Trustful Envious Optimist Pessimist Clueless 5 7 9 11 13 15 T 2 4 6 8 1 S 0.2 0.4 0.6 0.8 1 5 7 9 11 13 15 T 2 4 6 8 1 S 0.2 0.4 0.6 0.8 1 5 7 9 11 13 15 T 2 4 6 8 1 S 1 2 3 4 5 7 9 11 13 15 T 2 4 6 8 1 S 0.2 0.4 0.6 0.8 1 5 7 9 11 13 15 T 2 4 6 8 1 S 0.2 0.4 0.6 0.8 1 5 7 9 11 13 15 T 2 4 6 8 1 S 1 2 3 4 5 7 9 11 13 15 T 2 4 6 8 1 S 0.2 0.4 0.6 0.8 1 5 7 9 11 13 15 T 2 4 6 8 1 S 0.2 0.4 0.6 0.8 1 5 7 9 11 13 15 T 2 4 6 8 1 S 1 2 3 4 5 7 9 11 13 15 T 2 4 6 8 1 S 0.2 0.4 0.6 0.8 1 5 7 9 11 13 15 T 2 4 6 8 1 S 0.2 0.4 0.6 0.8 1 5 7 9 11 13 15 T 2 4 6 8 1 S 1 2 3 4 5 7 9 11 13 15 T 2 4 6 8 1 S 0.2 0.4 0.6 0.8 1 5 7 9 11 13 15 T 2 4 6 8 1 S 0.2 0.4 0.6 0.8 1 5 7 9 11 13 15 T 2 4 6 8 1 S 1 2 3 4 5 7 9 11 13 15 T 2 4 6 8 1 S 0.2 0.4 0.6 0.8 1 5 7 9 11 13 15 T 2 4 6 8 1 S 0.2 0.4 0.6 0.8 1 5 7 9 11 13 15 T 2 4 6 8 1 S Risk-aversion
  • Defeats
  • pponent
Maximizes max-payoff Maximizes min-payoff Cooperates always Decides randomly

S - T ≥ 0 T < R S > P p(C) = 1 p(C) = 0.5

0.2 0.4 0.6 0.8 1
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Too many phenotypes?

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Case study 4: Climate change mitigation

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Climate change game

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Climate change game

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Climate change game

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Climate change game

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Climate change game, heterogeneous version

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Is collective action successful?

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How do players behave?

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How do players behave?

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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

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Summary: case studies 2 & 3

Hierarchy is detrimental for cooperation People seem classifiable in a few recognizable phenotypes No (self-regarding) rationality

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Summary: case study 4

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

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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

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Outlook: example

Small vs large-scale: FET Open IBSEN (Sep 15 - Aug 18) Trading in networks

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Computational social science: interactions among people and complex socio-technological systems

Refs available from http://www.anxosanchez.eu