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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

  1. 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. 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. 3. @anxosan Physics / Math of Complex Systems Sociophysics
  4. 4. @anxosan Computational Social Science Physics / Math of Complex Systems Sociophysics
  5. 5. @anxosan Computational Social Science Physics / Math of Complex Systems Behavioral Sciences Sociophysics
  6. 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. 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. 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. 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
  10. 10. @anxosan Sociophysics
  11. 11. @anxosan Sociophysics
  12. 12. @anxosan Sociophysics
  13. 13. @anxosan Physics vs economics
  14. 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. 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. 16. @anxosan Physicists study collective phenomena emerging from the interactions of individuals as elementary units in complex socio-technological systems Sociophysics
  17. 17. @anxosan Sociophysics
  18. 18. @anxosan Ethnic neighborhoods
  19. 19. @anxosan Schelling model Stay if at least a third of neighbors are “similar” Move to random location otherwise
  20. 20. @anxosan Schelling model # Neighborhoods Happiness
  21. 21. @anxosan Schelling model Average # of regions
  22. 22. @anxosan Schelling model
  23. 23. @anxosan Ising model
  24. 24. @anxosan The interactions-based approach Strategic interactions / local optimization
  25. 25. @anxosan Computational Social Science Aimed to favor and take advantage of massive ICT data
  26. 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
  27. 27. @anxosan Computational Social Science
  28. 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. 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. 30. @anxosan On modeling Everything should be made as simple as possible, but not simpler
  31. 31. @anxosan Behavioral Science Systematic analysis and investigation of human behavior through controlled and naturalistic observation, and disciplined scientific experimentation
  32. 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. 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. 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. 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. 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. 37. @anxosan Big data (borrowed from @estebanmoro)
  38. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
  48. 48. @anxosan Big data
  49. 49. @anxosan SMALL DATA Understanding interaction
  50. 50. @anxosan SMALL DATA Understanding interaction
  51. 51. @anxosan SMALL controlled DATA Understanding interaction
  52. 52. @anxosan Data Science vs Behavioral Science
  53. 53. @anxosan Data Science vs Behavioral Science
  54. 54. @anxosan Data Science vs Behavioral Science
  55. 55. @anxosan Sociophysics topics: a sample
  56. 56. @anxosan Sociophysics topics: a sample
  57. 57. @anxosan By way of llustration: Case studies Networks, cooperation and reputation Cooperation in hierarchical systems Behavioral phenotype classification Climate change mitigation
  58. 58. @anxosan Work with José A. Cuesta Carlos Gracia-Lázaro Yamir Moreno Alfredo Ferrer Cuesta et al. Sci. Rep. 5, 7843 (2015)
  59. 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. 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. 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. 62. @anxosan Nowak May, Nature 359, 826 (1992) Case study 1. Networks
  63. 63. @anxosan Nowak May, Nature 359, 826 (1992) C Case study 1. Networks
  64. 64. @anxosan The evolution of cooperation
  65. 65. @anxosan The evolution of cooperation M. A. Nowak, Science 314, 1560 (2006)
  66. 66. @anxosan Nowak May, Nature 359, 826 (1992) C Case study 1. Networks
  67. 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. 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. 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. 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. 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. 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. 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
  74. 74. @anxosan Cooperation level OK Modeling: inhomogeneous agents
  75. 75. @anxosan Histogram of earnings: OK Strategies can coexist: heterogeneity stable Modeling: inhomogeneous agents
  76. 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. 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. 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. 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. 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
  81. 81. @anxosan Comparison with simulations Analytical modeling
  82. 82. @anxosan Analytical modeling Comparison with simulations
  83. 83. @anxosan Explanation of lack of network reciprocity Analytical modeling Comparison with simulations
  84. 84. @anxosan Dynamic networks support cooperation in a Prisoner’s Dilemma Dynamic networks
  85. 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. 86. @anxosan Wang et al. Proc. Natl. Acad. Sci. USA 109, 14363 (2012) Dynamic networks
  87. 87. @anxosan Wang et al. Proc. Natl. Acad. Sci. USA 109, 14363 (2012) Dynamic networks
  88. 88. @anxosan Wang et al. Proc. Natl. Acad. Sci. USA 109, 14363 (2012) Emergence of cooperation
  89. 89. @anxosan What is the mechanism?
  90. 90. @anxosan Experiment on information Stage 1: Play Prisoner’s Dilemma with current neighbors Cuesta et al. Sci. Rep. 5, 7843 (2015)
  91. 91. @anxosan Experiment on information Stage 2: Modify network
  92. 92. @anxosan Experiment on information Stage 2: Modify network
  93. 93. @anxosan Experiment on information No information [A] [AAB] [ABBAA]
  94. 94. @anxosan Results: Cooperation [A] [AAB] [ABBAA] No information
  95. 95. @anxosan Results: Network [A] [AAB] [ABBAA] No information
  96. 96. @anxosan Results: Network [ABBAA] [AAB] [A] No information
  97. 97. @anxosan Results: Reputation [ABBAA]
  98. 98. @anxosan Results: Reputation [ABBAA]
  99. 99. @anxosan Results: Reputation [ABBAA] [AAB]
  100. 100. @anxosan Results: Reputation [ABBAA] [ABBAA][AAB]
  101. 101. @anxosan Results: Reputation [ABBAA][AAB]
  102. 102. @anxosan Independent confirmation [ABBAA] Gallo Yan. Proc. Natl. Acad. Sci. USA 112, 3647 (2015)
  103. 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. 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. 105. @anxosan Inequality increases 0 5 10 15 20 25 30 050010001500 round cumulatedwealth ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● RR treatment FR treatment reliable players cheater players
  106. 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. 107. @anxosan Case study 2: Bridging experiments and reality
  108. 108. @anxosan Case study 2: Bridging experiments and reality
  109. 109. @anxosan Non-non-human primate project Cottontop tamarin (Saguinus oedipus)
  110. 110. @anxosan Non-non-human primate project Chimpanzee (Pan troglodytes)
  111. 111. @anxosan Experimentally induced hierarchy Cronin et al., Sci Rep. 5, 18 634 (2015)
  112. 112. @anxosan Experimentally induced hierarchy Cronin et al., Sci Rep. 5, 18 634 (2015)
  113. 113. @anxosan Experimentally induced hierarchy Cronin et al., Sci Rep. 5, 18 634 (2015)
  114. 114. @anxosan Experimentally induced hierarchy Cronin et al., Sci Rep. 5, 18 634 (2015)
  115. 115. @anxosan Collaborative task Contribute to a pot totalling 20 points or more
  116. 116. @anxosan Collaborative task Contribute to a pot totalling 20 points or more Receive 40 points for both of you
  117. 117. @anxosan Splitting task Higher ranked guy proposes a splitting (ultimatum-like)
  118. 118. @anxosan Splitting task Higher ranked guy proposes a splitting (ultimatum-like) Lower-ranked guy accepts or “fights”
  119. 119. @anxosan Hierarchy decreases cooperation
  120. 120. @anxosan Role of the lower ranked subject
  121. 121. @anxosan Role of the lower ranked subject
  122. 122. @anxosan Rank difference predicts contributions
  123. 123. @anxosan Offers and expectations
  124. 124. @anxosan Case study 3: Behavioral “phenotypes”
  125. 125. @anxosan Case study 3: Behavioral “phenotypes”
  126. 126. @anxosan Case study 3: Behavioral “phenotypes”
  127. 127. @anxosan Case study 3: Behavioral “phenotypes”
  128. 128. @anxosan Case study 3: Behavioral “phenotypes”
  129. 129. @anxosan Case study 3: Behavioral “phenotypes”
  130. 130. @anxosan Case study 3: Behavioral “phenotypes”
  131. 131. @anxosan Social dilemmas DC C D 1 S 0T
  132. 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. 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. 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
  135. 135. @anxosan Agnostic individual classification
  136. 136. @anxosan Agnostic individual classification
  137. 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
  138. 138. @anxosan Too many phenotypes?
  139. 139. @anxosan Too many phenotypes?
  140. 140. @anxosan Case study 4: Climate change mitigation
  141. 141. @anxosan Climate change game
  142. 142. @anxosan Climate change game
  143. 143. @anxosan Climate change game
  144. 144. @anxosan Climate change game
  145. 145. @anxosan Climate change game, heterogeneous version
  146. 146. @anxosan Is collective action successful?
  147. 147. @anxosan How do players behave?
  148. 148. @anxosan How do players behave?
  149. 149. @anxosan Summary: case study 1 The mechanism for cooperation in dynamic networks is reputation
  150. 150. @anxosan Summary: case study 1 The mechanism for cooperation in dynamic networks is reputation
  151. 151. @anxosan Summary: case study 1 The mechanism for cooperation in dynamic networks is reputation Reputation combines last action with average action
  152. 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. 153. @anxosan Summary: case studies 2 3 Hierarchy is detrimental for cooperation
  154. 154. @anxosan Summary: case studies 2 3 Hierarchy is detrimental for cooperation
  155. 155. @anxosan Summary: case studies 2 3 Hierarchy is detrimental for cooperation People seem classifiable in a few recognizable phenotypes
  156. 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. 157. @anxosan Summary: case study 4 Climate change is averted by all groups (50% in 2008)
  158. 158. @anxosan Summary: case study 4 Climate change is averted by all groups (50% in 2008)
  159. 159. @anxosan Summary: case study 4 Climate change is averted by all groups (50% in 2008) People 3 times richer contributed 1/3 less
  160. 160. @anxosan Sociophysics Human Interaction
  161. 161. @anxosan Sociophysics Human Interaction Socio- technological context
  162. 162. @anxosan Sociophysics Human Interaction Socio- technological context Information
  163. 163. @anxosan Sociophysics Human Interaction Socio- technological context Information Consistent behavior
  164. 164. @anxosan Sociophysics Human Interaction Socio- technological context Information Consistent behavior Modeling
  165. 165. @anxosan Sociophysics

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