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Towards Nonlocal Field-Like Social Interactions: Oscillating Agent Based Conceptual and Simulation Framework

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Agent-Based Simulation of Organizational Behavior

Abstract

This chapter takes a multidisciplinary perspective to examine a fundamentally novel approach to the agency and field-based nonlocal organization of digitally interconnected social systems. The main theoretical cornerstone of the new modeling approach (OSIMAS—oscillation-based multi-agent system) is built on the premises of an agent as a coherent system of oscillations. In our approach, the theoretical assumptions of the oscillating agent model are backed up with experimental brain-imaging studies inspired by cognitive neuroscience (electroencephalography—EEG), which reveal people’s states of mind in terms of the specific distribution of coherent brainwaves. Based on the premises of OSIMAS and our experimental findings, in this chapter we review our two different approaches to the construction of oscillating agent models: (1) phonons as vibrating quanta, and (2) quantum mechanical wave function. Both approaches are designated for the simulation of the oscillating agent model and subsequently field-like nonlocal social interactions. Some initial work-in-progress simulation results of stylized local and nonlocal excitation propagation in the social mediums are also provided in the final section.

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Notes

  1. 1.

    Field-based or oscillations-based terms can be used interchangeably in the presented context.

  2. 2.

    In quantum physics, the property of nonlocality (“instantaneous action at a distance”) is associated with the so-called quantum entanglement when physical properties such as position, momentum, spin, polarization, etc. of entangled particles are found to be appropriately correlated at a big distance. So that the quantum state of each particle cannot be described independently. Instead, a quantum state may be given for the system of particles as a whole. Recent evidences show that in the similar manner correlated states can be found between distant molecules and even cells (Josephson & Pallikari-Viras, 1991; Thaheld, 2005).

  3. 3.

    In general, self-organization is interpreted as a global order or coordination that arises from spontaneous local interactions between components of an initially disordered system. The resulting organization is wholly distributed among all the components of the system. Hence, our proposed pervasive information field (PIF) concept can be interpreted in a similar manner, i.e. as a spontaneously evolving order or coordination of the components of a system. In the context of OSIMAS, these components of a social system are individual mind-fields. Hence, the term ‘self-organization’ implies global order or a coordination mechanism among individual mind-fields.

  4. 4.

    The OSIMAS paradigm pertains to the idea that our conscious minds are a certain type of field. Such a view goes back at least as far as the insights of the gestalt psychologists of the early twentieth century. They emphasized the holistic nature of perception, which they claimed was more akin to fields than to particles. Later, Karl Popper proposed that consciousness is a manifestation of an overarching force field in the brain that can integrate the diverse information held in distributed neurons. Only recently an understanding has emerged that this force field is actually generated by the bioelectromagnetic activity of neurons in the form of the conscious mind as an electromagnetic field. It is through this mechanism that humans acquired the capacity to become conscious agents who are able to influence the world (Malik, 2002).

  5. 5.

    For instance, contextual (implicit) information spread using social media (e.g., propaganda, political campaigns, and information wars), network models of the diffusion of innovations, models of self-excitatory wave propagation in social media, etc.

  6. 6.

    The evolving multidisciplinary OSIMAS paradigm not only spans the breadth of several research domains, but also plunges through several levels of self-organized complexity, beginning with fundamental quantum, proceeding to the biological level and ending at a social level.

  7. 7.

    Our experimental findings are based on the EEG signals recorded using the BioSemi ActiveTwo Mk2 system with 64 channels. We used 64 channels in the BioSemi ActiveTwo Mk2 EEG measuring system. This system obtains and records high-quality (resolution LSB = 31.25 nV) and low-noise (total input noise for Z e < 10 kΩ is 0.8 μVRMS) electric local field potentials from the surface of the skull (Nunez & Srinivasan, 2005).

  8. 8.

    We have included the γ frequency range in order to embrace whole brainwaves region reported in the literature. However, in some of our analyses we have used only Δ, Θ, α, and β brainwaves in order to eliminate the influence of Fourier boundary transformation conditions, where a high noise-to-signal ratio prevails.

  9. 9.

    A phonon is a quantum mechanical description of an elementary vibrational motion in which a lattice of atoms or molecules uniformly oscillates at a single (natural) frequency. A phonon represents an excited state in the quantum mechanical quantization of the modes of vibrations of elastic structures of interacting particles. The approach through phonons is appealing, because it is used to describe a collective excitation in a periodic, elastic arrangement of atoms (or molecules) or in our case—agents as coherent sets of oscillations.

  10. 10.

    We chose these basic mind states (BMS)—sleeping, wakefulness, thinking, and resting. We make use of the fact that each BMS has characteristic brainwave pattern, which can be identified using power spectral density (PSD) distribution analyses (Müller et al., 2008; Plikynas, Basinskas, Kumar, et al., 2014).

  11. 11.

    Despite a century of clinical use, the underlying origins of EEG rhythms have remained a mystery. However, microtubule quantum vibrations (e.g., in the megahertz frequency range) appear to interfere and produce much slower EEG “beat frequencies” in the range 4–70 Hz. Clinical trials of brief brain stimulation—aimed at microtubule resonances with megahertz mechanical vibrations using transcranial ultrasound—have shown reported improvements in people mood (Hameroff & Penrose, 2014).

References

  • Aguilar, M., Congedo, M., & Minguez, J. (2011). A data-driven process for the development of an eyes-closed EEG normative database. In Conference Proceedings: … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2011 (pp. 7306–7309). doi:10.1109/IEMBS.2011.6091704.

  • Bandini, S., Manzoni, S., & Vizzari, G. (2004). Multi-agent approach to localization problems: The case of multilayered multi-agent situated system. Web Intelligence and Agent Systems, 2(3), 155–166.

    Google Scholar 

  • Bandini, S., Manzoni, S., & Vizzari, G. (2006). Toward a platform for multi-layered multi-agent situated system (MMASS)-based simulations: Focusing on field diffusion. Applied Artificial Intelligence, 20(2-4), 327–351. doi:10.1080/08839510500484272.

    Article  Google Scholar 

  • Buzsaki, G. (2011). Rhythms of the brain (1st ed.). New York: Oxford University Press.

    Google Scholar 

  • Cacioppo, J. T., Berntson, G. G., & Decety, J. (2010). Social neuroscience and its relationship to social psychology. Social Cognition, 28(6), 675–685.

    Article  Google Scholar 

  • Cacioppo, J. T., & Decety, J. (2011). Social neuroscience: Challenges and opportunities in the study of complex behavior. Annals of the New York Academy of Sciences, 1224(1), 162–173. doi:10.1111/j.1749-6632.2010.05858.x.

    Article  Google Scholar 

  • Camurri, M., Mamei, M., & Zambonelli, F. (2007). Urban traffic control with co-fields. In D. Weyns, H. V. D. Parunak, & F. Michel (Eds.), Environments for multi-agent systems III (pp. 239–253). Berlin, Heidelberg: Springer.

    Google Scholar 

  • Centola, D. (2010). The spread of behavior in an online social network experiment. Science, 329(5996), 1194–1197. doi:10.1126/science.1185231.

    Article  Google Scholar 

  • David, O. (2007). Dynamic causal models and autopoietic systems. Biological Research, 40(4), 487–502. doi:10.4067/S0716-97602007000500010.

    Article  Google Scholar 

  • Do, A. H., Wang, P. T., King, C. E., Chun, S. N., & Nenadic, Z. (2013). Brain–computer interface controlled robotic gait orthosis. Journal of NeuroEngineering and Rehabilitation, 10(1), 111. doi:10.1186/1743-0003-10-111.

    Article  Google Scholar 

  • Engel, G. S., Calhoun, T. R., Read, E. L., Ahn, T.-K., Mančal, T., Cheng, Y.-C., et al. (2007). Evidence for wavelike energy transfer through quantum coherence in photosynthetic systems. Nature, 446(7137), 782–786. doi:10.1038/nature05678.

    Google Scholar 

  • Fingelkurts, A. A., Fingelkurts, A. A., Ermolaev, V. A., & Kaplan, A. Y. (2006). Stability, reliability and consistency of the compositions of brain oscillations. International Journal of Psychophysiology, 59(2), 116–126. doi:10.1016/j.ijpsycho.2005.03.014.

    Article  Google Scholar 

  • Georgiev, D. D., & Glazebrook, J. F. (2006). Dissipationless waves for information transfer in neurobiology—Some implications. Informatica, 30, 221–232.

    Google Scholar 

  • Gudmundsson, S., Runarsson, T. P., Sigurdsson, S., Eiriksdottir, G., & Johnsen, K. (2007). Reliability of quantitative EEG features. Clinical Neurophysiology, 118(10), 2162–2171. doi:10.1016/j.clinph.2007.06.018.

    Article  Google Scholar 

  • Haan, M. de, & Gunnar, M. R. (2011). Handbook of developmental social neuroscience. New York: Guilford Press.

    Google Scholar 

  • Hameroff, S., & Penrose, R. (2014). Consciousness in the universe: A review of the “Orch OR” theory. Physics of Life Reviews, 11(1), 39–78. doi:10.1016/j.plrev.2013.08.002.

    Article  Google Scholar 

  • Haven, E., & Khrennikov, A. (2013). Quantum social science. New York: Cambridge University Press.

    Book  Google Scholar 

  • Haykin, S. (1998). Neural networks: A comprehensive foundation (2nd ed.). Upper Saddle River, NJ: Prentice Hall.

    Google Scholar 

  • Jackson, M. O. (2010). Social and economic networks. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Josephson, B. D., & Pallikari-Viras, F. (1991). Biological utilization of quantum nonlocality. Foundations of Physics, 21(2), 197–207. doi:10.1007/BF01889532.

    Article  Google Scholar 

  • Kezys, D., & Plikynas, D. (2014). Prognostication of human brain EEG signal dynamics using a refined coupled oscillator energy exchange model. Neuroquantology, 12(4), 337–349. doi: 10.14704/nq.2014.12.4.779.

  • Lebedev, M. A., & Nicolelis, M. A. L. (2006). Brain–machine interfaces: Past, present and future. Trends in Neurosciences, 29(9), 536–546. doi:10.1016/j.tins.2006.07.004.

    Article  Google Scholar 

  • Likens, A. D., Amazeen, P. G., Stevens, R., Galloway, T., & Gorman, J. C. (2014). Neural signatures of team coordination are revealed by multifractal analysis. Social Neuroscience, 9(3), 219–234. doi:10.1080/17470919.2014.882861.

    Article  Google Scholar 

  • Lindenberger, U., Li, S.-C., Gruber, W., & Müller, V. (2009). Brains swinging in concert: Cortical phase synchronization while playing guitar. BMC Neuroscience, 10(1), 22. doi:10.1186/1471-2202-10-22.

    Article  Google Scholar 

  • Lutz, A., Greischar, L. L., Rawlings, N. B., Ricard, M., & Davidson, R. J. (2004). Long-term meditators self-induce high-amplitude gamma synchrony during mental practice. Proceedings of the National Academy of Sciences of the United States of America, 101(46), 16369–16373. doi:10.1073/pnas.0407401101.

    Article  Google Scholar 

  • Malik, K. (2002). Man, beast, and zombie: What science can and cannot tell us about human nature (1st ed.). New Brunswick, NJ: Rutgers University Press.

    Google Scholar 

  • Mamei, M., & Zambonelli, F. (2006). Field-based coordination for pervasive multiagent systems. Berlin: Springer Science & Business Media.

    Google Scholar 

  • Martin, M., & McIntyre, L. C. (Eds.). (1994). Readings in the philosophy of social science. Cambridge, MA: A Bradford Book.

    Google Scholar 

  • Maturana, H. R. (1980). Autopoiesis and cognition: The realization of the living. Berlin: Springer Science & Business Media.

    Book  Google Scholar 

  • McFadden, J. (2002). The conscious electromagnetic information (CEMI) field theory: The hard problem made easy? Journal of Consciousness Studies, 9(8), 45–60.

    Google Scholar 

  • Müller, K.-R., Tangermann, M., Dornhege, G., Krauledat, M., Curio, G., & Blankertz, B. (2008). Machine learning for real-time single-trial EEG-analysis: From brain–computer interfacing to mental state monitoring. Journal of Neuroscience Methods, 167(1), 82–90. doi:10.1016/j.jneumeth.2007.09.022.

    Article  Google Scholar 

  • Newandee, D. A., & Reisman, S. S. (1996). Measurement of the electroencephalogram (EEG) coherence in group meditation. In Bioengineering Conference, 1996., Proceedings of the 1996 IEEE Twenty-Second Annual Northeast (pp. 95–96). doi:10.1109/NEBC.1996.503234.

  • Nummenmaa, L., Glerean, E., Viinikainen, M., Jääskeläinen, I. P., Hari, R., & Sams, M. (2012). Emotions promote social interaction by synchronizing brain activity across individuals. Proceedings of the National Academy of Sciences of the United States of America, 109(24), 9599–9604. doi:10.1073/pnas.1206095109.

    Article  Google Scholar 

  • Nunez, P. L., & Srinivasan, R. (2005). Electric fields of the brain: The neurophysics of EEG (2nd ed.). New York: Oxford University Press.

    Google Scholar 

  • O’Reilly, E. J., & Olaya-Castro, A. (2014). Non-classicality of the molecular vibrations assisting exciton energy transfer at room temperature. Nature Communications, 5. doi:10.1038/ncomms4012.

  • Oppenheim, J., & Wehner, S. (2010). The uncertainty principle determines the nonlocality of quantum mechanics. Science, 330(6007), 1072–1074. doi:10.1126/science.1192065.

    Article  Google Scholar 

  • Orme-Johnson, D. W., & Oates, R. M. (2009). A field-theoretic view of consciousness: Reply to critics. Journal Of Scientific Exploration, 23(2), 139–166.

    Google Scholar 

  • Perry, R. B. (1996). The thought and character of William James. Nashville: Vanderbilt University Press.

    Google Scholar 

  • Pessa, E., & Vitiello, G. (2004). Quantum noise induced entanglement and chaos in the dissipative quantum model of brain. International Journal of Modern Physics B, 18(06), 841–858. doi:10.1142/S0217979204024045.

    Article  Google Scholar 

  • Pizzi, R., Fantasia, A., Gelain, F., & Rossetti, D. (2004). Non-local correlation between human neural networks on printed circuit board. In Toward a science of consciousness conference. Tucson, Arizona.

    Google Scholar 

  • Plikynas, D. (2015). Oscillating agent model: Quantum approach. NeuroQuantology, 13(1), 20–34. doi:10.14704/nq.2015.13.1.796.

  • Plikynas, D. (2010). A virtual field-based conceptual framework for the simulation of complex social systems. Journal of Systems Science and Complexity, 23(2), 232–248. doi:10.1007/s11424-010-7239-1.

    Article  Google Scholar 

  • Plikynas, D., Basinskas, G., Kumar, P., Masteika, S., Kezys, D., & Laukaitis, A. (2014). Social systems in terms of coherent individual neurodynamics: Conceptual premises, experimental and simulation scope. International Journal of General Systems, 43(5), 434–469. doi:10.1080/03081079.2014.888552.

    Article  Google Scholar 

  • Plikynas, D., Basinskas, G., & Laukaitis, A. (2014). Towards oscillations-based simulation of social systems: A neurodynamic approach. Connection Science, 0(0), 1–24. doi:10.1080/09540091.2014.956293.

    Google Scholar 

  • Plikynas, D., Raudys, A., & Raudys, S. (2014). Agent-based modelling of excitation propagation in social media groups. Journal of Experimental & Theoretical Artificial Intelligence, 0(0), 1–16. doi:10.1080/0952813X.2014.954631.

    Google Scholar 

  • Popescu, S., & Rohrlich, D. (1994). Quantum nonlocality as an axiom. Foundations of Physics, 24(3), 379–385. doi:10.1007/BF02058098.

    Article  Google Scholar 

  • Poslad, S. (2009). Ubiquitous computing: Smart devices, environments and interactions (1st ed.). Chichester, UK: Wiley.

    Google Scholar 

  • Pribram, K. H. (1999). Quantum holography: Is it relevant to brain function? Information Sciences, 115(1–4), 97–102. doi:10.1016/S0020-0255(98)10082-8.

    Google Scholar 

  • Raudys, A., Plikynas, D., & Raudys, Š. (2014). Novel automated multi-agent investment system based on simulation of self-excitatory oscillations. Transformations in Business & Economics, 13(2), 42–59.

    Google Scholar 

  • Raudys, S. (2001). Statistical and neural classifiers: An integrated approach to design. Berlin: Springer Science & Business Media.

    Book  Google Scholar 

  • Schrödinger, E. (1955). WHAT IS LIFE? – The physical aspect of the living cell (Fifth Printing ed.). Cambridge: Cambridge University Press.

    Google Scholar 

  • Secchi, D. (2011). The “Docile” organization. In Extendable rationality (pp. 113–133). New York: Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-1-4419-7542-3_9

    Google Scholar 

  • Servat, D., & Drogoul, A. (2002). Combining amorphous computing and reactive agent-based systems: A paradigm for pervasive intelligence? In Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems: Part 1 (pp. 441–448). New York, NY, USA: ACM. doi:10.1145/544741.544842.

  • Shen, W.-M., Salemi, B., & Will, P. (2002). Hormone-inspired adaptive communication and distributed control for CONRO self-reconfigurable robots. IEEE Transactions on Robotics and Automation, 18(5), 700–712. doi:10.1109/TRA.2002.804502.

    Article  Google Scholar 

  • Spach, M. S. (1997). Discontinuous cardiac conduction: Its origin in cellular connectivity with long-term adaptive changes that cause arrhythmias. In Discontinuous conduction in the heart (pp. 5–51). Armonk, NY: Futura Publ. Company, Inc.

    Google Scholar 

  • Standish, L. J., Kozak, L., Johnson, L. C., & Richards, T. (2004). Electroencephalographic evidence of correlated event-related signals between the brains of spatially and sensory isolated human subjects. Journal of Alternative and Complementary Medicine (New York, NY), 10(2), 307–314. doi:10.1089/107555304323062293.

    Google Scholar 

  • Stevens, R. H., & Galloway, T. L. (2014). Toward a quantitative description of the neurodynamic organizations of teams. Social Neuroscience, 9(2), 160–173. doi:10.1080/17470919.2014.883324.

    Article  Google Scholar 

  • Stevens, R., Galloway, T., Wang, P., Berka, C., Tan, V., Wohlgemuth, T., et al. (2012). Modeling the neurodynamic complexity of submarine navigation teams. Computational and Mathematical Organization Theory, 19(3), 346–369. doi:10.1007/s10588-012-9135-9.

    Google Scholar 

  • Tesfatsion, L., & Judd, K. L. (Eds.). (2006). Handbook of computational economics, volume 2: Agent-based computational economics (1st ed.). Amsterdam; New York: North Holland.

    Google Scholar 

  • Thaheld, F. H. (2005). An interdisciplinary approach to certain fundamental issues in the fields of physics and biology: Towards a unified theory. Biosystems, 80(1), 41–56. doi:10.1016/j.biosystems.2004.10.001.

    Article  Google Scholar 

  • Thatcher, R. W. (2010). Validity and reliability of quantitative electroencephalography. Journal of Neurotherapy, 14(2), 122–152. doi:10.1080/10874201003773500.

    Article  Google Scholar 

  • Travis, F. T., & Orme-Johnson, D. W. (1989). Field model of consciousness: EEG coherence changes as indicators of field effects. International Journal of Neuroscience, 49(3-4), 203–211. doi:10.3109/00207458909084826.

    Article  Google Scholar 

  • Travis, F., & Arenander, A. (2006). Cross-sectional and longitudinal study of effects of transcendental meditation practice on interhemispheric frontal asymmetry and frontal coherence. The International Journal of Neuroscience, 116(12), 1519–1538. doi:10.1080/00207450600575482.

    Article  Google Scholar 

  • Valente, T. W. (1996). Network models of the diffusion of innovations. Computational & Mathematical Organization Theory, 2(2), 163–164. doi:10.1007/BF00240425.

    Article  Google Scholar 

  • Vitiello, P. D. G. (2001). My double unveiled: The dissipative quantum model of brain. Amsterdam; Philadelphia, PA: John Benjamins Publishing Company.

    Google Scholar 

  • Wang, X., Tao, H., Xie, Z., & Yi, D. (2012). Mining social networks using wave propagation. Computational and Mathematical Organization Theory, 19(4), 569–579. doi:10.1007/s10588-012-9142-x.

    Article  Google Scholar 

  • Young, H. P. (2006). The diffusion of innovations in social networks. In The economy as an evolving complex system III: Current perspectives and future directions (p. 267).

    Google Scholar 

  • Zhang, Y., & Wu, Y. (2011). How behaviors spread in dynamic social networks. Computational and Mathematical Organization Theory, 18(4), 419–444. doi:10.1007/s10588-011-9105-7.

    Article  Google Scholar 

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Acknowledgments

This research project is funded by the European Social Fund under the Global Grant measure (project No. VP1-3.1-SMM-07-K-01-137). We are grateful to the anonymous reviewers for their insightful comments, which have helped to improve this manuscript.

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Plikynas, D., Raudys, S. (2016). Towards Nonlocal Field-Like Social Interactions: Oscillating Agent Based Conceptual and Simulation Framework. In: Secchi, D., Neumann, M. (eds) Agent-Based Simulation of Organizational Behavior. Springer, Cham. https://doi.org/10.1007/978-3-319-18153-0_12

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