Skip to main content
SpringerLink
Log in

Social Human Collective Decision-Making and Its Applications with Brain Network Models

  • Chapter
  • First Online:
Crowd Dynamics, Volume 4

Abstract

A better understanding of social human dynamics would be a powerful tool to improve nearly any computational endeavour that involves human interactions. This includes intelligent environments featuring, for instance, efficient illumination systems, smart evacuation signalling systems, intelligent transportation systems, crowd control, or disaster response. Moreover, given that the human population has significantly grown up in number and spread across the planet, the capacity to predict social human behaviours will help to demonstrate special behavioural forms observed when masses of people gather together and make crowds. Additionally, human crowd dynamics are characterized by complex psychological and sociobiological behaviour. The contributions of psychological factors need to be accounted for to obtain more reliable models. Many models have been proposed to describe the social human group dynamics in different scenarios. However, due to the complexity of such systems amplified by the above factors, social human decision-making with multiple choices has not been fully scrutinized. In this chapter, we consider probabilistic drift-diffusion models and Bayesian inference frameworks to address this issue, assisting better social human decision-making. We provide details of the models, as well as representative numerical examples, and discuss the decision-making process with a representative example of the escape route decision-making phenomena by further developing the drift-diffusion models and Bayesian inference frameworks. In the latter context, we also give a review of recent developments in human collective decision-making and its applications with brain network models. Furthermore, we provide illustrative numerical examples to discuss the role of neuromodulation, reinforcement learning in decision-making processes. Finally, we call attention to existing challenges, open problems, and promising approaches in studying social dynamics and collective human decision-making, including those arising from nonequilibrium considerations of the associated processes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. D. Fudenberg, W. Newey, P. Strack, T. Strzalecki, Testing the drift-diffusion model. Proc. Natl. Acad. Sci. 117(52), 33141–33148 (2020)

    Article  Google Scholar 

  2. S. Vellmer, B. Lindner, Decision-time statistics of nonlinear diffusion models: characterizing long sequences of subsequent trials. J. Math. Psychol. 99, 102445 (2020)

    Article  MathSciNet  Google Scholar 

  3. P.L. Smith, The poisson shot noise model of visual short-term memory and choice response time: normalized coding by neural population size. J. Math. Psychol. 66, 41–52 (2015)

    Article  MathSciNet  Google Scholar 

  4. C.J.T.P. Mavrodiev, F. Schweitzer, Quantifying the effects of social influence. Sci. Rep. 3, 1360 (2013)

    Article  Google Scholar 

  5. P. Mavrodiev, F. Schweitzer, The ambigous role of social influence on the wisdom of crowds: an analytic approach. Physica A 567, 125624 (2021)

    Article  MathSciNet  Google Scholar 

  6. M. Haghani, M. Sarvi, Imitative (herd) behaviour in direction decision-making hinders efficiency of crowd evacuation processes. Saf. Sci. 114, 49–60 (2019)

    Article  Google Scholar 

  7. J.I. Gold, M.N. Shadlen, et al., The neural basis of decision making. Ann. Rev. Neurosci. 30(1), 535–574 (2007)

    Article  Google Scholar 

  8. R. Bogacz, E.T. Brown, J. Moehlis, P. Holmes, J.D. Cohen, The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. Psychol. Rev. 113(4), 700–765 (2006)

    Article  Google Scholar 

  9. S. Bitzer, H. Park, F. Blankenburg, S.J. Kiebel, Perceptual decision making: drift-diffusion model is equivalent to a Bayesian model. Front. Hum. Neurosci. 8, 102 (2014)

    Article  Google Scholar 

  10. P.R. Fard, H. Park, A. Warkentin, S.J. Kiebel, S. Bitzer, A bayesian reformulation of the extended drift-diffusion model in perceptual decision making. Front. Comput. Neurosci. 11, 29 (2017)

    Article  Google Scholar 

  11. K. Dokka, H. Park, M. Jansen, G.C. DeAngelis, D.E. Angelaki, Causal inference accounts for heading perception in the presence of object motion. Proc. Natl. Acad. Sci. 116(18), 9060–9065 (2019)

    Article  Google Scholar 

  12. K. Dokka, G.C. DeAngelis, D.E. Angelaki, Multisensory integration of visual and vestibular signals improves heading discrimination in the presence of a moving object. J. Neurosci. 35(40), 13599–13607 (2015)

    Article  Google Scholar 

  13. J. Noel, D. Angelaki, Cognitive, systems, and computational neurosciences of the self in motion. Annu. Rev. Psychol. 73, 103–129 (2022)

    Article  Google Scholar 

  14. R. Ratcliff, A theory of memory retrieval. Psychol. Rev. 85(2), 59 (1978)

    Google Scholar 

  15. T.K.T. Thieu, A. Muntean, R. Melnik, Coupled stochastic systems of Skorokhod type: well-posedness of a mathematical model and its applications. Math. Methods Appl. Sci. 46(6), 7368–7390 (2023)

    Article  MathSciNet  Google Scholar 

  16. J. Pekkanen, O.T. Giles, Y.M. Lee, R. Madigan, T. Daimon, N. Merat, G. Markkula, Variable-drift diffusion models of pedestrian road-crossing decisions. Comput. Brain Behav. 5(1), 60–80 (2022)

    Article  Google Scholar 

  17. R. Melnik, H. He, Relaxation-time approximations of quasi-hydrodynamic type in semiconductor device modelling. Model. Simul. Mater. Sci. Eng. 8(2), 133 (2000)

    Google Scholar 

  18. R. Melnik, H. He, Quasi-hydrodynamic modelling and computer simulation of coupled thermo-electrical processes in semiconductors. Math. Comput. Simul. 52(3–4), 273–287 (2000)

    Article  MathSciNet  Google Scholar 

  19. R. Melnik, H. He, Modelling nonlocal processes in semiconductor devices with exponential difference schemes. J. Eng. Math. 38, 233–263 (2000)

    Article  MathSciNet  Google Scholar 

  20. R.F. Alvarez-Estrada, New hierarchy for the Liouville equation, irreversibility and Fokker-Planck-like structures. Ann. Phys. 514, 357–385 (2002)

    Article  MathSciNet  Google Scholar 

  21. B.D. Goddard, T. Hurst, M. Wilkinson, A derivation of the Liouville equation for hard particle dynamics with non-conservative interactions. Proc. Roy. Soc. Edinb.: Sect. A Math. 151, 1040–1074 (2019)

    Google Scholar 

  22. R. Klein, L.D. Site, Derivation of Liouville-like equations for the n-state probability density of an open system with thermalized particle reservoirs and its link to molecular simulation. J. Phys. A: Math. Theor. 55, 155002 (2022)

    Article  MathSciNet  Google Scholar 

  23. P. Degond, A. Manhart, S. Merino-Aceituno, D. Peurichard, L. Sala, How environment affects active particle swarms: a case study. Roy. Soc. Open Sci. 9, 220791 (2022)

    Article  Google Scholar 

  24. Y.-Q. Jiang, Y.-G. Hu, X. Huang, Modeling pedestrian flow through a bottleneck based on a second-order continuum model. Phys. A: Stat. Mech. Appl. 608, 128272 (2022)

    Article  MathSciNet  Google Scholar 

  25. N. Bellomo, M. Esfahanian, V. Secchini, P. Terna, What is life? Active particles tools towards behavioral dynamics in social-biology and economics. Phys. Life Rev. 43, 189–207 (2022)

    Google Scholar 

  26. N. Bellomo, L. Gibelli, A. Quaini, A. Reali, Towards a mathematical theory of behavioral human crowds. Math. Models Methods Appl. Sci. 32(02), 321–358 (2022)

    Article  MathSciNet  Google Scholar 

  27. R. Ratcliff, P.L. Smith, S.D. Brown, G. McKoon, Diffusion decision model: current issues and history. Trends Cogn. Sci. 20(4), 260–281 (2016)

    Article  Google Scholar 

  28. S.I. Zhu, G.J. Goodhill, From perception to behavior: the neural circuits underlying prey hunting in larval zebrafish. Front. Neural Circuits 17, 108799 (2023)

    Article  Google Scholar 

  29. T. Saraiva, T.C. Gonçalves, The role of emotions and knowledge on preference for uncertainty: follow your heart but listen to your brain! Risks 11(1), 2 (2023)

    Google Scholar 

  30. P.J. Castagna, S. van Noordt, P.B. Sederberg, M.J. Crowley, Modeling brain dynamics and gaze behavior: starting point bias and drift rate relate to frontal midline theta oscillations. NeuroImage 268, 119871 (2023)

    Article  Google Scholar 

  31. C.E. Myers, A. Interian, A.A. Moustafa, A practical introduction to using the drift diffusion model of decision-making in cognitive psychology, neuroscience, and health sciences. Front. Psychol. 13, 1039172 (2022)

    Article  Google Scholar 

  32. T.V. Wiecki, I. Sofer, M.J. Frank, HDDM: hierarchical Bayesian estimation of the drift-diffusion model in python. Front. Neuroinform. 7, 14 (2013)

    Article  Google Scholar 

  33. M.L. Pedersen, M.J. Frank, G. Biele, The drift diffusion model as the choice rule in reinforcement learning. Psychon. Bull. Rev. 24, 1234–1251 (2017)

    Article  Google Scholar 

  34. J. Boelts, J.-M. Lueckmann, R. Gao, J.H. Macke, Flexible and efficient simulation-based inference for models of decision-making. Elife 11, e77220 (2022)

    Article  Google Scholar 

  35. S. Thapa, S. Park, Y. Kim, J.-H. Jeon, R. Metzler, M.A. Lomholt, Bayesian inference of scaled versus fractional Brownian motion. J. Phys. A: Math. Theor. 55(19), 194003 (2022)

    Google Scholar 

  36. A. Fengler, K. Bera, M.L. Pedersen, M.J. Frank, Beyond drift diffusion models: fitting a broad class of decision and reinforcement learning models with HDDM. J. Cogn. Neurosci. 34(10), 1780–1805 (2022)

    Article  Google Scholar 

  37. T.S. Manning, B.N. Naecker, I.R. McLean, B. Rokers, J.W. Pillow, E.A. Cooper, A general framework for inferring bayesian ideal observer models from psychophysical data. eNeuro 10(1), 1–17 (2023)

    Article  Google Scholar 

  38. B. Tadić, M.M. Dankulov, R. Melnik, Mechanisms of self-organized criticality in social processes of knowledge creation. Phys. Rev. E 96(3), 032307 (2017)

    Google Scholar 

  39. B. Tadić, R. Melnik, Self-organised critical dynamics as a key to fundamental features of complexity in physical, biological, and social networks. Dynamics 1(2), 181–197 (2021)

    Article  Google Scholar 

  40. M.J. Ramstead, M.D. Kirchhoff, K.J. Friston, A tale of two densities: active inference is enactive inference. Adapt. Behav. 28(4), 225–239 (2020)

    Article  Google Scholar 

  41. T. Isomura, Active inference leads to Bayesian neurophysiology. Neurosci. Res. 175, 38–45 (2022)

    Article  Google Scholar 

  42. L. Da Costa, P. Lanillos, N. Sajid, K. Friston, S. Khan, How active inference could help revolutionise robotics. Entropy 24(3), 361 (2022)

    Google Scholar 

  43. P.B. Badcock, M.J.D. Ramstead, Z. Sheikhbahaee, A. Constant, Applying the free energy principle to complex adaptive systems. Entropy 24, 689 (2022)

    Article  Google Scholar 

  44. J. Kiverstein, M.D. Kirchhoff, T. Froese, The problem of meaning: the free energy principle and artificial agency. Front. Neurorobot. 16, 844773 (2022)

    Article  Google Scholar 

  45. T. Xu, D. Shi, J. Chen, T. Li, P. Lin, J. Ma, Dynamics of emotional contagion in dense pedestrian crowds. Phys. Lett. A 384(3), 126080 (2020)

    Google Scholar 

  46. K. Iinuma, K. Kogiso, Emotion-involved human decision-making model. Math. Comput. Model. Dyn. Syst. 27(1), 543–561 (2021)

    Article  MathSciNet  Google Scholar 

  47. M. Fu, R. Liu, Y. Zhang, Why do people make risky decisions during a fire evacuation? Study on the effect of smoke level, individual risk preference, and neighbor behavior. Saf. Sci. 140, 105245 (2021)

    Article  Google Scholar 

  48. A.O. Hoffmann, S.F. Henry, N. Kalogeras, Aspirations as reference points: an experimental investigation of risk behavior over time. Theor. Decis. 75, 193–210 (2013)

    Article  MathSciNet  Google Scholar 

  49. J.-H. Kwon, J. Kim, S. Kim, G.-H. Cho, Pedestrians safety perception and crossing behaviors in narrow urban streets: an experimental study using immersive virtual reality technology. Accid. Anal. Prev. 174, 106757 (2022)

    Article  Google Scholar 

  50. M. Usher, J.L. McClelland, The time course of perceptual choice: the leaky, competing accumulator model. Psychol. Rev. 108(3), 550–92 (2001)

    Article  Google Scholar 

  51. N.A. Roy, J.H. Bak, A. Akrami, C.D. Brody, J.W. Pillow, Extracting the dynamics of behavior in sensory decision-making experiments. Neuron 109(4), 597–610 (2021)

    Article  Google Scholar 

  52. Y. Singer, Y. Teramoto, B.D. Willmore, J.W. Schnupp, A.J. King, N.S. Harper, Sensory cortex is optimized for prediction of future input. eLife 7, e31557 (2018)

    Article  Google Scholar 

  53. P. Miller, Decision Making, Threshold (Springer New York, New York, 2013), pp. 1–4

    Google Scholar 

  54. M. Milosavljevic, J. Malmaud, A.G. Huth, C. Koch, A. Rangel, The drift diffusion model can account for the accuracy and reaction time of value-based choices under high and low time pressure. Judgm. Decis. Mak. 5(6), 437–449 (2010)

    Article  Google Scholar 

  55. J. Masís, T. Chapman, J.Y. Rhee, D.D. Cox, A.M. Saxe, Strategically managing learning during perceptual decision making. eLife 12, e64978 (2023)

    Article  Google Scholar 

  56. S. Gerwinn, M. Bethge, J.H. Macke, M. Seeger, Bayesian inference for spiking neuron models with a sparsity prior. Adv. Neural Inf. Process. Syst. 20, 1–8 (2007)

    Google Scholar 

  57. J.H. Macke, L. Buesing, J.P. Cunningham, B.M. Yu, K.V. Shenoy, M. Sahani, Empirical models of spiking in neural populations. Adv. Neural Inf. Process. Syst. 24, 1350–1358 (2011)

    Google Scholar 

  58. D.G. Kleinbaum, K. Dietz, M. Gail, M. Klein, M. Klein, Logistic Regression (Springer, Berlin, 2002)

    Google Scholar 

  59. A.I. Weber, J.W. Pillow, Capturing the dynamical repertoire of single neurons with generalized linear models. Neural Comput. 29(12), 3260–3289 (2017)

    Article  MathSciNet  Google Scholar 

  60. L. Theis, A.M. Chagas, D. Arnstein, C. Schwarz, M. Bethge, Beyond GLMs: a generative mixture modeling approach to neural system identification. PLoS Comput. Biol. 9(11), e1003356 (2013)

    Google Scholar 

  61. L. Fortuna, A. Buscarino, Spiking neuron mathematical models: a compact overview. Bioengineering 10(2), 174 (2023)

    Google Scholar 

  62. J. Anumula, D. Neil, T. Delbruck, S.-C. Liu, Feature representations for neuromorphic audio spike streams. Front. Neurosci. 12, 23 (2018)

    Article  Google Scholar 

  63. Q. Wang, Y. Wang, P. Wang, M. Peng, M. Zhang, Y. Zhu, S. Wei, C. Chen, X. Chen, S. Luo, et al., Neural representations of the amount and the delay time of reward in intertemporal decision making. Hum. Brain Mapp. 42(11), 3450–3469 (2021)

    Article  Google Scholar 

  64. G. Cerulli, Machine learning using stata/python. Stata J. 22(4), 772–810 (2022)

    Article  Google Scholar 

  65. S. Nelli, L. Braun, T. Dumbalska, A. Saxe, C. Summerfield, Neural knowledge assembly in humans and neural networks. Neuron 111(9), 1504–1516 (2023)

    Article  Google Scholar 

  66. A. Reina, E. Ferrante, G. Valentini, Collective decision-making in living and artificial systems. Swarm Intell. 15(1), 1–6 (2021)

    Article  Google Scholar 

  67. T. Bose, A. Reina, J.A. Marshall, Collective decision-making. Curr. Opin. Behav. Sci. 16, 30–34 (2017)

    Article  Google Scholar 

  68. A. Teodorescu, R. Moran, M. Usher, Absolutely relative or relatively absolute: violations of value invariance in human decision making. Psychon. Bull. Rev. 23, 22–38 (2016)

    Article  Google Scholar 

  69. S. Galam, Sociophysics: a review of Galam models. Int. J. Mod. Phys. C 19(03), 409–440 (2008)

    Article  Google Scholar 

  70. P. Molavi, A. Tahbaz-Salehi, A. Jadbabaie, A theory of non-bayesian social learning. Econometrica 86(2), 445–490 (2018)

    Article  MathSciNet  Google Scholar 

  71. J.L.P. Groeber, F. Schweitzer, Dissonance minimization as a microfoundation of social influence in models of opinion formation. J. Math. Sociol. 38, 147–174 (2014)

    Article  MathSciNet  Google Scholar 

  72. H. Rauhut, J. Lorenz, The wisdom of crowds in one mind: how individuals can simulate the knowledge of diverse societies to reach better decisions. J. Math. Psychol. 55, 191–197 (2011)

    Article  MathSciNet  Google Scholar 

  73. W. Barfuss, Dynamical systems as a level of cognitive analysis of multi-agent learning: algorithmic foundations of temporal-difference learning dynamics. Neural Comput. Appl. 34(3), 1653–1671 (2022)

    Article  MathSciNet  Google Scholar 

  74. J.L. Adler, V.J. Blue, A cooperative multi-agent transportation management and route guidance system. Trans. Res. C: Emerg. Technol. 10(5–6), 433–454 (2002)

    Article  Google Scholar 

  75. R.V. Melnik, Coupling control and human factors in mathematical models of complex systems. Eng. Appl. Artif. Intell. 22(3), 351–362 (2009)

    Article  Google Scholar 

  76. N. Bulling, A survey of multi-agent decision making. KI-Künstliche Intelligenz 28, 147–158 (2014)

    Article  Google Scholar 

  77. B. Geng, S. Brahma, T. Wimalajeewa, P.K. Varshney, M. Rangaswamy, Prospect theoretic utility based human decision making in multi-agent systems. IEEE Trans. Signal Process. 68, 1091–1104 (2020)

    Article  MathSciNet  Google Scholar 

  78. B. Tadić, R. Melnik, Modeling latent infection transmissions through biosocial stochastic dynamics. PLoS ONE 15(10), e0241163 (2020)

    Google Scholar 

  79. B. Tadić, R. Melnik, Microscopic dynamics modeling unravels the role of asymptomatic virus carriers in SARS-CoV-2 epidemics at the interplay between biological and social factors. Comput. Biol. Med. 133, 104422 (2021)

    Article  Google Scholar 

  80. F. Schweitzer, T. Krivachy, D. Garcia, An agent-based model of opinion polarization driven by emotions. Complexity 2020, 1–11 (2020)

    Article  Google Scholar 

  81. T. Bose, A. Reina, J.A.R. Marshall, Inhibition and excitation shape activity selection: effect of oscillations in a decision-making circuit. Neural Comput. 31(5), 870–896 (2019)

    Article  MathSciNet  Google Scholar 

  82. S.A. Park, M. Sestito, E.D. Boorman, J.-C. Dreher, Neural computations underlying strategic social decision-making in groups. Nat. Commun. 10(1), 5287 (2019)

    Google Scholar 

  83. C. Reverberi, D. Pischedda, M. Mantovani, J.-D. Haynes, A. Rustichini, Strategic complexity and cognitive skills affect brain response in interactive decision-making. Sci. Rep. 12(1), 15896 (2022)

    Google Scholar 

  84. S. Vijayakumar, T. Hospedales, A. Haith, Generative probabilistic modeling: understanding causal sensorimotor integration, in Sensory Cue Integration (Oxford University Press, Oxford, 2011), pp. 63–81

    Google Scholar 

  85. K. Doya, S. Ishii, A. Pouget, R.P. Rao, Bayesian Brain: Probabilistic Approaches to Neural Coding (MIT Press, Cambridge, 2007)

    Google Scholar 

  86. L. Acerbi, W.J. Ma, S. Vijayakumar, A framework for testing identifiability of Bayesian models of perception. Adv. Neural Inf. Process. Syst. 27, 1–9 (2014)

    Google Scholar 

  87. W.J. Ma, K.P. Kording, D. Goldreich, Bayesian Models of Perception and Action: An Introduction (MIT Press, Cambridge, 2023)

    Google Scholar 

  88. K. Friston, The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11(2), 127–138 (2010)

    Article  Google Scholar 

  89. T. Parr, K.J. Friston, Generalised free energy and active inference. Biol. Cybern. 113, 495–513 (2019)

    Article  MathSciNet  Google Scholar 

  90. K. Khalvati, S.A. Park, S. Mirbagheri, R. Philippe, M. Sestito, J.-C. Dreher, R.P. Rao, Modeling other minds: Bayesian inference explains human choices in group decision-making. Sci. Adv. 5(11), eaax8783 (2019)

    Google Scholar 

  91. L. Zhang, J. Gläscher, A brain network supporting social influences in human decision-making. Sci. Adv. 6(34), eabb4159 (2020)

    Google Scholar 

  92. S. Steixner-Kumar, T. Rusch, P. Doshi, M. Spezio, J. Gläscher, Humans depart from optimal computational models of interactive decision-making during competition under partial information. Sci. Rep. 12, 289 (2022)

    Article  Google Scholar 

  93. N. Tarcai, C. Virágh, D. Ábel, M. Nagy, P.L. Várkonyi, G. Vásárhelyi, T. Vicsek, Patterns, transitions and the role of leaders in the collective dynamics of a simple robotic flock. J. Stat. Mech.: Theory Exp. 2011(04), P04010 (2011)

    Google Scholar 

  94. R. Wang, F. Fang, J. Cui, W. Zheng, Learning self-driven collective dynamics with graph networks. Sci. Rep. 12(1), 1–11 (2022)

    Google Scholar 

  95. G. Valentini, H. Hamann, M. Dorigo, Self-organized collective decision making: the weighted voter model, in Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014), May 5-9, 2014, Paris, France, ed. by A. Lomuscio, P. Scerri, A. Bazzan, M. Huhns (2014), pp. 45–52

    Google Scholar 

  96. J. Hesse, T. Gross, Self-organized criticality as a fundamental property of neural systems. Front. Syst. Neurosci. 8, 166 (2014)

    Article  Google Scholar 

  97. L. De Arcangelis, C. Perrone-Capano, H.J. Herrmann, Self-organized criticality model for brain plasticity. Phys. Rev. Lett. 96(2), 028107 (2006)

    Google Scholar 

  98. D. Plenz, T.L. Ribeiro, S.R. Miller, P.A. Kells, A. Vakili, E.L. Capek, Self-organized criticality in the brain. Front. Phys. 9, 639389 (2021)

    Article  Google Scholar 

  99. Z. Zhang, L. Jia, Optimal guidance strategy for crowd evacuation with multiple exits: a hybrid multiscale modeling approach. Appl. Math. Modell. 90, 488–504 (2021)

    Article  MathSciNet  Google Scholar 

  100. U. Boehm, S. Cox, G. Gantner, R. Stevenson, Fast solutions for the first-passage distribution of diffusion models with space-time-dependent drift functions and time-dependent boundaries. J. Math. Psychol. 105, 102613 (2021)

    Article  MathSciNet  Google Scholar 

  101. U. Boehm, S. Cox, G. Gantner, R. Stevenson, Efficient numerical approximation of a non-regular Fokker–Planck equation associated with first-passage time distributions. BIT Numer. Math. 62, 1355–1382 (2022)

    Article  MathSciNet  Google Scholar 

  102. D. Sander, Comment: collective epistemic emotions and individualized learning: a relational account. Emot. Rev. 12(4), 230–232 (2020)

    Article  Google Scholar 

  103. C.L. Brown, K.-H. Chen, J.L. Wells, M.C. Otero, D.E. Connelly, R.W. Levenson, B.L. Fredrickson, Shared emotions in shared lives: moments of co-experienced affect, more than individually experienced affect, linked to relationship quality. Emotion 22(6), 1387–1393 (2022)

    Article  Google Scholar 

  104. A. Goldenberg, D. Garcia, E. Halperin, J.J. Gross, Collective emotions. Curr. Dir. Psychol. Sci. 29(2), 154–160 (2020)

    Article  Google Scholar 

  105. H. Metzler, B. Rimé, M. Pellert, T. Niederkrotenthaler, A. Di Natale, D. Garcia, Collective emotions during the covid-19 outbreak. Emotion, 23(3), 844–858 (2022)

    Article  Google Scholar 

  106. R. Bogacz, K.N. Gurney, The basal ganglia and cortex implement optimal decision making between alternative actions. Neural Comput. 19, 442–477 (2007)

    Article  MathSciNet  Google Scholar 

  107. D. Bang, K.T. Kishida, T. Lohrenz, J.P. White, A.W. Laxton, S.B. Tatter, S.M. Fleming, P.R. Montague, Sub-second dopamine and serotonin signaling in human striatum during perceptual decision-making. Neuron 108(5), 999–1010 (2020)

    Article  Google Scholar 

  108. A. Marzecová, L.F. Kaiser, A. Maddah, Neuromodulation of foraging decisions: the role of dopamine. Front. Behav. Neurosci. 15, 660667 (2021)

    Article  Google Scholar 

  109. C.D. Grossman, J.Y. Cohen, Neuromodulation and neurophysiology on the timescale of learning and decision-making. Annu. Rev. Neurosci. 45(1), 317–337 (2022)

    Article  Google Scholar 

  110. P.R. Montague, S.E. Hyman, J.D. Cohen, Computational roles for dopamine in behavioural control. Nature 431(7010), 760–767 (2004)

    Article  Google Scholar 

  111. S. Iglesias, L. Kasper, S.J. Harrison, R. Manka, C. Mathys, K.E. Stephan, Cholinergic and dopaminergic effects on prediction error and uncertainty responses during sensory associative learning. NeuroImage 226, 117590 (2021)

    Article  Google Scholar 

  112. R.B. Sojitra, I. Lerner, J.R. Petok, M.A. Gluck, Age affects reinforcement learning through dopamine-based learning imbalance and high decision noise—not through parkinsonian mechanisms. Neurobiol. Aging 68, 102–113 (2018)

    Article  Google Scholar 

  113. L.-L. Oettl, M. Scheller, C. Filosa, S. Wieland, F. Haag, C. Loeb, D. Durstewitz, R. Shusterman, E. Russo, W. Kelsch, Phasic dopamine reinforces distinct striatal stimulus encoding in the olfactory tubercle driving dopaminergic reward prediction. Nat. Commun. 11(1), 3460 (2020)

    Google Scholar 

  114. M. Belkaid, J.L. Krichmar, Modeling uncertainty-seeking behavior mediated by cholinergic influence on dopamine. Neural Netw. 125, 10–18 (2020)

    Article  Google Scholar 

  115. M.M. Botvinick, Hierarchical reinforcement learning and decision making. Curr. Opin. Neurobiol. 22(6), 956–962 (2012)

    Article  Google Scholar 

  116. W. Dabney, Z. Kurth-Nelson, N. Uchida, C.K. Starkweather, D. Hassabis, R. Munos, M.M. Botvinick, A distributional code for value in dopamine-based reinforcement learning. Nature 577, 671–675 (2020)

    Article  Google Scholar 

  117. M. Rmus, S.D. McDougle, A.G. Collins, The role of executive function in shaping reinforcement learning. Curr. Opin. Behav. Sci. 38, 66–73 (2021)

    Article  Google Scholar 

  118. M.K. Eckstein, L. Wilbrecht, A.G. Collins, What do reinforcement learning models measure? Interpreting model parameters in cognition and neuroscience. Curr. Opin. Behav. Sci. 41, 128–137 (2021)

    Google Scholar 

  119. B. Lin, D. Bouneffouf, G. Cecchi, Predicting human decision making in psychological tasks with recurrent neural networks. PLOS ONE 17, 1–18 (2022)

    Google Scholar 

  120. D. Hassabis, D. Kumaran, C. Summerfield, M. Botvinick, Neuroscience-inspired artificial intelligence. Neuron 95(2), 245–258 (2017)

    Article  Google Scholar 

  121. Y. Chen, Z. Wei, H. Gou, H. Liu, L. Gao, X. He, X. Zhang, How far is brain-inspired artificial intelligence away from brain? Front. Neurosci. 16, 1096737 (2022)

    Article  Google Scholar 

  122. R.S. Sutton, A.G. Barto, Reinforcement Learning: An Introduction (MIT Press, Cambridge, 2018)

    Google Scholar 

  123. A.A. Faisal, L.P. Selen, D.M. Wolpert, Noise in the nervous system. Nat. Rev. Neurosci. 9(4), 292–303 (2008)

    Article  Google Scholar 

  124. A.C. Gallup, J.J. Hale, D.J.T. Sumpter, S. Garnier, A. Kacelnik, J.R. Krebs, I.D. Couzin, Visual attention and the acquisition of information in human crowds. Proc. Natl. Acad. Sci. 109(19), 7245–7250 (2012)

    Article  Google Scholar 

  125. J. Bragge, H. Kallio, T. Seppälä, T. Lainema, P. Malo, Decision-making in a real-time business simulation game: cultural and demographic aspects in small group dynamics. Int. J. Inf. Technol. Decis. Making 16(03), 779–815 (2017)

    Article  Google Scholar 

  126. A.N. Tump, M. Wolf, P. Romanczuk, R.H.J.M. Kurvers, Avoiding costly mistakes in groups: the evolution of error management in collective decision making. PLOS Comput. Biol. 18(8), 1–21 (2022)

    Article  Google Scholar 

  127. S. Hamblin, On the practical usage of genetic algorithms in ecology and evolution. Methods Ecol. Evol. 4(2), 184–194 (2013)

    Article  Google Scholar 

  128. M. Delgado, F. Herrera, E. Herrera-Viedma, L. Martinez, Combining numerical and linguistic information in group decision making. Inf. Sci. 107(1–4), 177–194 (1998)

    Article  MathSciNet  Google Scholar 

  129. R.M. Rodriguez, L. Martinez, F. Herrera, Hesitant fuzzy linguistic term sets for decision making. IEEE Trans. Fuzzy Syst. 20(1), 109–119 (2011)

    Article  Google Scholar 

  130. F. Herrera, L. Martínez-López, V. Torra, Z. Xu, Hesitant fuzzy sets: an emerging tool in decision making. Int. J. Intell. Syst. 29(6), 493–494 (2014)

    Article  Google Scholar 

  131. S. Yao, J. Hu, Combining comparative linguistic expressions and numerical information in multi-attribute group decision making—a simulation-based approach. J. Intell. Fuzzy Syst. 33(6), 3835–3852 (2017)

    Article  Google Scholar 

  132. M.M. Dankulov, R. Melnik, B. Tadić, The dynamics of meaningful social interactions and the emergence of collective knowledge. Sci. Rep. 5(1), 12197 (2015)

    Google Scholar 

  133. M. Mitrović Dankulov, B. Tadić, R. Melnik, Analysis of worldwide time-series data reveals some universal patterns of evolution of the sars-cov-2 pandemic. Front. Phys. 10, 936618 (2022)

    Article  Google Scholar 

  134. J.A. Scales, R. Snieder, The anatomy of inverse problems. Geophysics 65(6), 1708–1710 (2000)

    Article  Google Scholar 

  135. M. Touqeer, R. Umer, A. Ahmadian, S. Salahshour, M. Salimi, Signed distance-based closeness coefficients approach for solving inverse non-linear programming models for multiple criteria group decision-making using interval type-2 Pythagorean fuzzy numbers. Granular Comput. 7, 881–901 (2021)

    Article  Google Scholar 

  136. I. Poulakakis, G.F. Young, L. Scardovi, N.E. Leonard, Information centrality and ordering of nodes for accuracy in noisy decision-making networks. IEEE Trans. Autom. Control 61(4), 1040–1045 (2016)

    Article  MathSciNet  Google Scholar 

  137. Y.-P. Gunji, T. Kawai, H. Murakami, T. Tomaru, M. Minoura, S. Shinohara, Lévy walk in swarm models based on bayesian and inverse bayesian inference. Comput. Struct. Biotechnol. J. 19, 247–260 (2021)

    Article  Google Scholar 

  138. A. Šošić, W.R. KhudaBukhsh, A.M. Zoubir, H. Koeppl, Inverse reinforcement learning in swarm systems, in Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, pp. 1413–1421. International Foundation for Autonomous Agents and Multiagent Systems (2017)

    Google Scholar 

  139. Z. Wang, T. Zhang, X. Wu, X. Huang, Predicting transient building fire based on external smoke images and deep learning. J. Build. Eng. 47, 103823 (2022)

    Article  Google Scholar 

  140. T. Zhang, Z. Wang, H.Y. Wong, W.C. Tam, X. Huang, F. Xiao, Real-time forecast of compartment fire and flashover based on deep learning. Fire Saf. J. 130, 103579 (2022)

    Article  Google Scholar 

  141. A. Whiten, D. Biro, N. Bredeche, E. Garland, S. Kirby, The emergence of collective knowledge and cumulative culture in animals, humans and machines. Philos. Trans. R. Soc. Lond. B Biol. Sci. 377, 20200306 (2022)

    Article  Google Scholar 

  142. Y.K. Dwivedi, L. Hughes, E. Ismagilova, G. Aarts, C. Coombs, T. Crick, Y. Duan, R. Dwivedi, J. Edwards, A. Eirug, V. Galanos, P.V. Ilavarasan, M. Janssen, P. Jones, A.K. Kar, H. Kizgin, B. Kronemann, B. Lal, B. Lucini, R. Medaglia, K. Le Meunier-FitzHugh, L.C. Le Meunier-FitzHugh, S. Misra, E. Mogaji, S.K. Sharma, J.B. Singh, V. Raghavan, R. Raman, N.P. Rana, S. Samothrakis, J. Spencer, K. Tamilmani, A. Tubadji, P. Walton, M.D. Williams, Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manage. 57, 101994 (2021)

    Google Scholar 

  143. Y.-X. Kong, R.-J. Wu, Y.-C. Zhang, G.-Y. Shi, Utilizing statistical physics and machine learning to discover collective behavior on temporal social networks. Inf. Process. Manage. 60(2), 103190 (2023)

    Google Scholar 

  144. H. Hornischer, P.J. Pritz, J. Pritz, M.G. Mazza, M. Boos, Modeling of human group coordination. Phys. Rev. Res. 4(2), 023037 (2022)

    Google Scholar 

  145. D. Sytnyk, R. Melnik, Mathematical models with nonlocal initial conditions: an exemplification from quantum mechanics. Math. Comput. Appl. 26(4), 73 (2021)

    Google Scholar 

  146. S.-N. Chow, W. Huang, Y. Li, H. Zhou, Fokker–Planck equations for a free energy functional or Markov process on a graph. Arch. Ration. Mech. Anal. 203, 969–1008 (2012)

    Article  MathSciNet  Google Scholar 

  147. C. Jarzynski, Nonequilibrium work relations: foundations and applications. Eur. Phys. J. B 64, 331–340 (2008)

    Article  MathSciNet  Google Scholar 

  148. D. Conti, T. Mora, Nonequilibrium dynamics of adaptation in sensory systems. Phys. Rev. E 106(5), 054404 (2022)

    Google Scholar 

  149. E. Roldán, I. Neri, M. Dörpinghaus, H. Meyr, F. Jülicher, Decision making in the arrow of time. Phys. Rev. Lett. 115, 250602 (2015)

    Article  Google Scholar 

  150. W. Wu, M. Black, Y. Gao, M. Serruya, A. Shaikhouni, J. Donoghue, E. Bienenstock, Neural decoding of cursor motion using a Kalman filter. Adv. Neural Inf. Process. Syst. 15, 1–8 (2002)

    Google Scholar 

  151. H. Vroylandt, L. Goudenège, P. Monmarché, F. Pietrucci, B. Rotenberg, Likelihood-based non-Markovian models from molecular dynamics. Proc. Natl. Acad. Sci. 119(13), e2117586119 (2022)

    Google Scholar 

  152. A.M. Stefan, F.D. Schönbrodt, N.J. Evans, E.-J. Wagenmakers, Efficiency in sequential testing: comparing the sequential probability ratio test and the sequential bayes factor test. Behav. Res. Methods 54(6), 3100–3117 (2022)

    Article  Google Scholar 

  153. S. Roweis, Z. Ghahramani, A unifying review of linear gaussian models. Neural Comput. 11(2), 305–345 (1999)

    Article  Google Scholar 

  154. C.M. Bishop, N.M. Nasrabadi, Pattern Recognition and Machine Learning, vol. 4 (Springer, Berlin, 2007)

    Google Scholar 

  155. F.S. Gnesotto, G. Gradziuk, P. Ronceray, C.P. Broedersz, Learning the non-equilibrium dynamics of Brownian movies. Nat. Commun. 11(1), 5378 (2020)

    Google Scholar 

  156. A. Seif, M. Hafezi, C. Jarzynski, Machine learning the thermodynamic arrow of time. Nat. Phys. 17, 105–113 (2021)

    Article  Google Scholar 

  157. Y.S. Perl, H. Bocaccio, C. Pallavicini, I. Pérez-Ipiña, S. Laureys, H. Laufs, M.L. Kringelbach, G. Deco, E. Tagliazucchi, Nonequilibrium brain dynamics as a signature of consciousness. Phys. Rev. E 104(1–1), 014411 (2021)

    Google Scholar 

  158. S. Pal, R. Melnik, Nonlocal models in the analysis of brain neurodegenerative protein dynamics with application to Alzheimer’s disease. Sci. Rep. 12(1), 7328 (2022)

    Google Scholar 

  159. H. Shaheen, S. Pal, R. Melnik, Multiscale co-simulation of deep brain stimulation with brain networks in neurodegenerative disorders. Brain Multiphys. 3, 100058 (2022)

    Article  Google Scholar 

  160. S. Pal, R. Melnik, Non-Markovian behaviour and the dual role of astrocytes in alzheimer’s disease development and propagation. arXiv preprint arXiv:2208.03540 (2023)

    Google Scholar 

  161. E.A. Kensinger, D.K. Shearer, J.J. Locascio, J.H. Growdon, S. Corkin, Working memory in mild alzheimer’s disease and early Parkinson’s disease. Neuropsychology 17(2), 230 (2003)

    Google Scholar 

  162. L.G. Gagnon, S. Belleville, Working memory in mild cognitive impairment and alzheimer’s disease: contribution of forgetting and predictive value of complex span tasks. Neuropsychology 25(2), 226–236 (2011)

    Article  Google Scholar 

  163. C.L. Stopford, J.C. Thompson, D. Neary, A.M. Richardson, J.S. Snowden, Working memory, attention, and executive function in alzheimer’s disease and frontotemporal dementia. Cortex 48(4), 429–446 (2012)

    Article  Google Scholar 

  164. H. Yan, J. Wang, Non-equilibrium landscape and flux reveal the stability-flexibility-energy tradeoff in working memory. PLoS Comput. Biol. 16, e1008209 (2020)

    Article  Google Scholar 

  165. J.D. Murray, J. Jaramillo, X.-J. Wang, Working memory and decision-making in a frontoparietal circuit model. J. Neurosci. 37(50), 12167–12186 (2017)

    Article  Google Scholar 

  166. S.B. Brent, Prigogine’s model for self-organization in nonequilibrium systems: its relevance for developmental psychology. Hum. Dev. 21(5/6), 374–387 (1978)

    Article  Google Scholar 

  167. M. Chapman, Self-organization as developmental process: beyond the organismic and mechanistic models? Ann. Theor. Psychol. 335–348 (1991)

    Google Scholar 

  168. B.J. West, P. Grigolini, Networking of psychophysics, psychology, and neurophysiology. Front. Physiol. 3, 423 (2012)

    Article  Google Scholar 

  169. S. Boettcher, C. Brunson, Renormalization group for critical phenomena in complex networks. Front. Physiol. 2, 102 (2011)

    Article  Google Scholar 

  170. J. Kaupužs, R.V.N. Melnik, Functional truncations for the solution of the nonperturbative RG equations. J. Phys. A: Math. Theor. 55(46), 465002 (2022)

    Google Scholar 

  171. B. Tadić, M.M. Dankulov, R. Melnik, Evolving cycles and self-organised criticality in social dynamics. Chaos Solitons Fractals 171, 113459 (2023)

    Article  MathSciNet  Google Scholar 

  172. B.D. Shizgal, Kappa and other nonequilibrium distributions from the Fokker-Planck equation and the relationship to Tsallis entropy. Phys. Rev. E 97, 052144 (2018)

    Article  MathSciNet  Google Scholar 

  173. S. Tanaka, T. Umegaki, A. Nishiyama, H. Kitoh-Nishioka, Dynamical free energy based model for quantum decision making. Phys. A: Stat. Mech. Appl. 605, 127979 (2022)

    Article  MathSciNet  Google Scholar 

  174. J. Johnson, A. Nowak, P. Ormerod, B. Rosewell, Y.-C. Zhang, Non-equilibrium Social Science and Policy. Understanding Complex Systems Book Series (Springer, Berlin, 2017)

    Book  Google Scholar 

Download references

Acknowledgements

Authors are grateful to the NSERC and the CRC Program for their support. RM is also acknowledging support of the BERC 2022–2025 program and Spanish Ministry of Science, Innovation and Universities through the Agencia Estatal de Investigacion (AEI) BCAM Severo Ochoa excellence accreditation SEV-2017-0718.

Author information

Authors and Affiliations

  1. MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, ON, Canada

    Thoa Thieu

  2. Department of Mathematics, Indiana University, Bloomington, Indiana, USA

    Thoa Thieu

  3. MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, ON, Canada

    Roderick Melnik

  4. BCAM - Basque Center for Applied Mathematics, Bilbao, Spain

    Roderick Melnik

Authors
  1. Thoa Thieu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roderick Melnik
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roderick Melnik .

Editor information

Editors and Affiliations

  1. Department of Applied Mathematics, University of Granada, Granada, Spain

    Nicola Bellomo

  2. Institute for Multiscale Thermofluids, University of Edinburgh, Edinburgh, UK

    Livio Gibelli

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Thieu, T., Melnik, R. (2023). Social Human Collective Decision-Making and Its Applications with Brain Network Models. In: Bellomo, N., Gibelli, L. (eds) Crowd Dynamics, Volume 4. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser, Cham. https://doi.org/10.1007/978-3-031-46359-4_5

Download citation

Publish with us

Policies and ethics

Search

Navigation