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Sociophysics

Presentación utilizada por por Anxo Sanchez (@anxosan) en la segunda sesión del Curso de Introducción a los Sistemas organizado por la Fundacion Sicomoro y Complejimad

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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
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.

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Sociophysics

  • 1. 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
  • 2. 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
  • 3. @anxosan Physics / Math of Complex Systems Sociophysics
  • 4. @anxosan Computational Social Science Physics / Math of Complex Systems Sociophysics
  • 5. @anxosan Computational Social Science Physics / Math of Complex Systems Behavioral Sciences Sociophysics
  • 6. @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.
  • 7. @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
  • 8. @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
  • 9. @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
  • 14. @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
  • 15. @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
  • 16. @anxosan Physicists study collective phenomena emerging from the interactions of individuals as elementary units in complex socio-technological systems Sociophysics
  • 19. @anxosan Schelling model Stay if at least a third of neighbors are “similar” Move to random location otherwise
  • 24. @anxosan The interactions-based approach Strategic interactions / local optimization
  • 25. @anxosan Computational Social Science Aimed to favor and take advantage of massive ICT data
  • 26. @anxosan Computational Social Science Aimed to favor and take advantage of massive ICT data A [computer] model-based science yielding predictive and explanatory models
  • 28. @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)
  • 29. @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)
  • 30. @anxosan On modeling Everything should be made as simple as possible, but not simpler
  • 31. @anxosan Behavioral Science Systematic analysis and investigation of human behavior through controlled and naturalistic observation, and disciplined scientific experimentation
  • 32. @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…
  • 33. @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!
  • 34. @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
  • 35. @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.
  • 36. @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.
  • 37. @anxosan Big data (borrowed from @estebanmoro)
  • 38. @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] Big data (borrowed from @estebanmoro)
  • 39. @anxosan Big data (borrowed from @estebanmoro) 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
  • 40. @anxosan Big data (borrowed from @estebanmoro) ¡ 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
  • 41. @anxosan Big data (borrowed from @estebanmoro) • 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) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● weak tie structural hole bridge strong tie
  • 42. @anxosan Big data (borrowed from @estebanmoro) 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
  • 43. @anxosan Big data (borrowed from @estebanmoro) 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
  • 44. @anxosan Big data (borrowed from @estebanmoro) • 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 0 5 10 15 20 25 uno count 10 20 40 30 20 10 Hora Hora
  • 45. @anxosan Big data (borrowed from @estebanmoro) • 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
  • 46. @anxosan Big data (borrowed from @estebanmoro) • 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
  • 47. @anxosan Big data (borrowed from @estebanmoro) 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
  • 52. @anxosan Data Science vs Behavioral Science
  • 53. @anxosan Data Science vs Behavioral Science
  • 54. @anxosan Data Science vs Behavioral Science
  • 57. @anxosan By way of llustration: Case studies Networks, cooperation and reputation Cooperation in hierarchical systems Behavioral phenotype classification Climate change mitigation
  • 58. @anxosan Work with José A. Cuesta Carlos Gracia-Lázaro Yamir Moreno Alfredo Ferrer Cuesta et al. Sci. Rep. 5, 7843 (2015)
  • 59. @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
  • 60. @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)
  • 61. @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)
  • 62. @anxosan Nowak May, Nature 359, 826 (1992) Case study 1. Networks
  • 63. @anxosan Nowak May, Nature 359, 826 (1992) C Case study 1. Networks
  • 65. @anxosan The evolution of cooperation M. A. Nowak, Science 314, 1560 (2006)
  • 66. @anxosan Nowak May, Nature 359, 826 (1992) C Case study 1. Networks
  • 67. @anxosan Prisoner’s dilemma A game theoretical paradigm of social dilemma DC C D 1 S 0T • 2 players • 2 actions: Cooperate or Defect
  • 68. @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
  • 69. @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
  • 70. @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)
  • 71. @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)
  • 72. @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)
  • 73. @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
  • 75. @anxosan Histogram of earnings: OK Strategies can coexist: heterogeneity stable Modeling: inhomogeneous agents
  • 76. @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
  • 77. @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
  • 78. @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
  • 79. @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
  • 80. @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
  • 83. @anxosan Explanation of lack of network reciprocity Analytical modeling Comparison with simulations
  • 84. @anxosan Dynamic networks support cooperation in a Prisoner’s Dilemma Dynamic networks
  • 85. @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
  • 86. @anxosan Wang et al. Proc. Natl. Acad. Sci. USA 109, 14363 (2012) Dynamic networks
  • 87. @anxosan Wang et al. Proc. Natl. Acad. Sci. USA 109, 14363 (2012) Dynamic networks
  • 88. @anxosan Wang et al. Proc. Natl. Acad. Sci. USA 109, 14363 (2012) Emergence of cooperation
  • 89. @anxosan What is the mechanism?
  • 90. @anxosan Experiment on information Stage 1: Play Prisoner’s Dilemma with current neighbors Cuesta et al. Sci. Rep. 5, 7843 (2015)
  • 93. @anxosan Experiment on information No information [A] [AAB] [ABBAA]
  • 102. @anxosan Independent confirmation [ABBAA] Gallo Yan. Proc. Natl. Acad. Sci. USA 112, 3647 (2015)
  • 103. @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
  • 104. @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)
  • 105. @anxosan Inequality increases 0 5 10 15 20 25 30 050010001500 round cumulatedwealth ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● RR treatment FR treatment reliable players cheater players
  • 106. @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)
  • 107. @anxosan Case study 2: Bridging experiments and reality
  • 108. @anxosan Case study 2: Bridging experiments and reality
  • 111. @anxosan Experimentally induced hierarchy Cronin et al., Sci Rep. 5, 18 634 (2015)
  • 112. @anxosan Experimentally induced hierarchy Cronin et al., Sci Rep. 5, 18 634 (2015)
  • 113. @anxosan Experimentally induced hierarchy Cronin et al., Sci Rep. 5, 18 634 (2015)
  • 114. @anxosan Experimentally induced hierarchy Cronin et al., Sci Rep. 5, 18 634 (2015)
  • 115. @anxosan Collaborative task Contribute to a pot totalling 20 points or more
  • 116. @anxosan Collaborative task Contribute to a pot totalling 20 points or more Receive 40 points for both of you
  • 117. @anxosan Splitting task Higher ranked guy proposes a splitting (ultimatum-like)
  • 118. @anxosan Splitting task Higher ranked guy proposes a splitting (ultimatum-like) Lower-ranked guy accepts or “fights”
  • 120. @anxosan Role of the lower ranked subject
  • 121. @anxosan Role of the lower ranked subject
  • 124. @anxosan Case study 3: Behavioral “phenotypes”
  • 125. @anxosan Case study 3: Behavioral “phenotypes”
  • 126. @anxosan Case study 3: Behavioral “phenotypes”
  • 127. @anxosan Case study 3: Behavioral “phenotypes”
  • 128. @anxosan Case study 3: Behavioral “phenotypes”
  • 129. @anxosan Case study 3: Behavioral “phenotypes”
  • 130. @anxosan Case study 3: Behavioral “phenotypes”
  • 132. @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
  • 133. @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
  • 134. @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
  • 137. @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
  • 140. @anxosan Case study 4: Climate change mitigation
  • 145. @anxosan Climate change game, heterogeneous version
  • 149. @anxosan Summary: case study 1 The mechanism for cooperation in dynamic networks is reputation
  • 150. @anxosan Summary: case study 1 The mechanism for cooperation in dynamic networks is reputation
  • 151. @anxosan Summary: case study 1 The mechanism for cooperation in dynamic networks is reputation Reputation combines last action with average action
  • 152. @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
  • 153. @anxosan Summary: case studies 2 3 Hierarchy is detrimental for cooperation
  • 154. @anxosan Summary: case studies 2 3 Hierarchy is detrimental for cooperation
  • 155. @anxosan Summary: case studies 2 3 Hierarchy is detrimental for cooperation People seem classifiable in a few recognizable phenotypes
  • 156. @anxosan Summary: case studies 2 3 Hierarchy is detrimental for cooperation People seem classifiable in a few recognizable phenotypes No (self-regarding) rationality
  • 157. @anxosan Summary: case study 4 Climate change is averted by all groups (50% in 2008)
  • 158. @anxosan Summary: case study 4 Climate change is averted by all groups (50% in 2008)
  • 159. @anxosan Summary: case study 4 Climate change is averted by all groups (50% in 2008) People 3 times richer contributed 1/3 less