Hostname: page-component-74656f9884-8r8nv Total loading time: 0 Render date: 2026-11-10T06:27:31.392Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling higher order adaptivity of a network by multilevel network reification

Published online by Cambridge University Press:  04 March 2020

Jan Treur Open the ORCID record for Jan Treur [Opens in a new window]
Jan Treur*
Affiliation:
Social AI Group, Vrije Universiteit Amsterdam, Amsterdam, Netherlands (email: j.treur@vu.nl)
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

In network models for real-world domains, often network adaptation has to be addressed by incorporating certain network adaptation principles. In some cases, also higher order adaptation occurs: the adaptation principles themselves also change over time. To model such multilevel adaptation processes, it is useful to have some generic architecture. Such an architecture should describe and distinguish the dynamics within the network (base level), but also the dynamics of the network itself by certain adaptation principles (first-order adaptation level), and also the adaptation of these adaptation principles (second-order adaptation level), and may be still more levels of higher order adaptation. This paper introduces a multilevel network architecture for this, based on the notion network reification. Reification of a network occurs when a base network is extended by adding explicit states representing the characteristics of the structure of the base network. It will be shown how this construction can be used to explicitly represent network adaptation principles within a network. When the reified network is itself also reified, also second-order adaptation principles can be explicitly represented. The multilevel network reification construction introduced here is illustrated for an adaptive adaptation principle from social science for bonding based on homophily and one for metaplasticity in Cognitive Neuroscience.

Keywords

Hocine Cherinetwork reificationhigher order network adaptationadaptive social networksplasticity and metaplasticity

Information

Type
Research Article
Information
Network Science , Volume 8 , Issue S1: Complex Networks 2018 , July 2020 , pp. S110 - S144
Copyright
© Cambridge University Press 2020

References

Abraham, W. C., & Bear, M. F. (1996). Metaplasticity: The plasticity of synaptic plasticity. Trends in Neuroscience, 19(4), 126–130.CrossRefGoogle ScholarPubMed
Andina, D., Jevtic, A., Marcano, A., & Adame, J. M. B. (2007). Error weighting in artificial neural networks learning interpreted as a metaplasticity model. In Proceedings of IWINAC’07, Part I (pp. 244–252), Lecture notes in computer science. Springer.Google Scholar
Andina, D., Alvarez-Vellisco, A., Jevtic, A., & Fombellida, J. (2009). Artificial metaplasticity can improve artificial neural network learning. Intelligent Automation and Soft Computing, 15(4), 681–694.Google Scholar
Arnold, S., Suzuki, R.,& Arita, T. (2015). Selection for representation in higher-order adaptation. Minds and Machines, 25(1), 73–95.CrossRefGoogle Scholar
Ashby, W. R. (1960). Design for a brain (2nd ed.). London: Chapman and Hall.CrossRefGoogle Scholar
Beukel, S. V. D., Goos, S. H., & Treur, J. (2019). An adaptive temporal-causal network model for social networks based on the homophily and more- becomes-more principle. Neurocomputing, 338, 361–371.CrossRefGoogle Scholar
Blankendaal, R., Parinussa, S., & Treur, J. (2016). A temporal-causal modelling approach to integrated contagion and network change in social networks. In Proceedings of the 22nd European Conference on Artificial Intelligence, ECAI’16 (pp. 1388–1396), Frontiers in artificial intelligence and applications, vol. 285. IOS Press.Google Scholar
Boomgaard, G., Lavitt, F., & Treur, J. (2018). Computational analysis of social contagion and homophily based on an adaptive social network model. In Koltsova, O., Ignatov, D. I., & Staab, S. (Eds.), Proceedings of the 10th international conference on social informatics, SocInfo’18, vol. 1 (pp. 86–101), Lecture notes in computer science, vol. 11185. Springer.Google Scholar
Bowen, K. A., & Kowalski, R. (1982). Amalgamating language and meta-language in logic programming. In Clark, K. & Tarnlund, S. (Eds.), Logic programming (pp. 153–172). New York: Academic Press.Google Scholar
Byrne, D. (1986). The attraction hypothesis: Do similar attitudes affect anything? Journal ofPersonality and Social Psychology, 51(6), 1167–1170.CrossRefGoogle Scholar
Carley, K. M. (2002). Inhibiting adaptation. In Proceedings of the 2002 command and control research and technology symposium (pp. 1–10). Monterey, CA: Naval Postgraduate School.Google Scholar
Carley, K. M. (2006). Destabilization of covert networks. Computational & MathematicalOrganization Theory, 12, 51–66.Google Scholar
Chandra, N., & Barkai, E. (2018). A non-synaptic mechanism of complex learning: Modulation of intrinsic neuronal excitability. Neurobiology of Learning and Memory, 154, 30–36.CrossRefGoogle ScholarPubMed
Daimon, K., Arnold, S., Suzuki, R., & Arita, T. (2017). The emergence of executive functions by the evolution of second–order learning. Artificial Lifeand Robotics, 22, 483–489.CrossRefGoogle Scholar
Demers, F. N., & Malenfant, J. (1995). Reflection in logic, functional and objectoriented programming: a Short Comparative Study. In Workshop on reflection and meta-level architecture and their application in ai,IJCAI’95 (pp. 29–38).Google Scholar
Fessler, D. M. T., Clark, J. A., & Clint, E. K. (2015). Evolutionary psychology and evolutionary anthropology. In Buss, D. M. (Eds.), The handbook of evolutionary psychology (pp. 1029–1046). New York: Wiley and Sons.Google Scholar
Fessler, D. M. T., Eng, S. J., & Navarrete, C. D. (2005). Elevated disgust sensitivity in the first trimester of pregnancy: Evidence supporting the compensatory prophylaxis hypothesis. Evolution & Human Behavior, 26(4), 344–351.CrossRefGoogle Scholar
Fleischman, D. S., & Fessler, D. M. T. (2011). Progesterone’s effects on the psychology of disease avoidance: Support for the compensatory behavioral prophylaxis hypothesis. Hormones and Behavior, 59(2), 271–275.CrossRefGoogle ScholarPubMed
Fombellida, J., Ropero-Pelaez, F.J., & Andina, D. (2017). Koniocortex-like network unsupervised learning surpasses supervised results on wbcd breast cancer database. In Proceedings of IWINAC’17, Part II (pp. 32–41), Lecture notes in computer springer science, vol. 10338. Springer.Google Scholar
Galton, A. (2006). Operators vs. Arguments: The ins and outs of reification. Synthese, 150, 415–441.CrossRefGoogle Scholar
Hebb, D. O. (1949). The organization of behavior: A neuropsychological theory. New York: Wiley and Sons.Google Scholar
Helbing, D., Brockmann, D., Chadefaux, T., Donnay, K., Blanke, U., Woolley-Meza, O., … Perc, M. (2015). Saving human lives: What complexity science and information systems can contribute. Journal of Statistical Physics, 158, 735–781.CrossRefGoogle ScholarPubMed
Hofstadter, D. R. (1979). Gödel, Escher, Bach. New York: Basic Books.Google Scholar
Jones, B.C., Perrett, D.I., Little, A.C., Boothroyd, L., Cornwell, R.E., Feinberg, D.R., … Moore, F. R. (2005). Menstrual cycle, pregnancy and oral contraceptive use alter attraction to apparent health in faces. Proceedings of the Royal Society B, 272, 347–354.CrossRefGoogle ScholarPubMed
Kuipers, B. J. (1984). Commonsense reasoning about causality: Deriving behavior from structure. Artificial Intelligence, 24, 169–203.CrossRefGoogle Scholar
Kuipers, B. J., & Kassirer, J. P. (1983). How to discover a knowledge representation for causal reasoning by studying an expert physician. In Proceedings of the eighth international joint conference on artificial intelligence, IJCAI’83 (pp. 49–56). Los Altos, CA: William Kaufman.Google Scholar
Lovejoy, C. O. (2005). The natural history of human gait and posture. Part 2: Hip and thigh. Gait & Posture, 21(1), 113–124.CrossRefGoogle Scholar
Magerl, W., Hansen, N., Treede, R. D., & Klein, T. (2018). The human pain system exhibits higher-order plasticity (metaplasticity). Neurobiology of Learning and Memory, 154, 112–120.CrossRefGoogle Scholar
Marcano-Cedeno, A., Marin-De-La-Barcena, A.,Jimenez-Trillo, J., Pinuela, J. A., & Andina, D.Artificial metaplasticity neural network applied to credit scoring. International Journal Of Neural Systems, 21(4), 311–317.CrossRefGoogle Scholar
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review Sociology, 27, 415–444.CrossRefGoogle Scholar
Oh, M. M., Kuo, A. G., Wu, W. W., Sametsky, E. A., & Disterhoft, J. F. (2003). Watermaze learning enhances excitability of CA1 pyramidal neurons. Journal of Neurophysiology, 90(4), 2171–2179.CrossRefGoogle ScholarPubMed
Parsons, R. G. (2018). Behavioral and neural mechanisms by which prior experience impacts subsequent learning. Neurobiology of Learning and Memory, 154, 22–29.CrossRefGoogle ScholarPubMed
Pearl, J. (2000). Causality. NewYork: Cambridge University Press.Google Scholar
Pearson, M., Steglich, C., & Snijders, T. (2006). Homophily and assimilation among sport-active adolescent substance users. Connections, 27(1), 47–63Google Scholar
Perc, M., & Szolnoki, A. (2010). Coevolutionary games - A mini review. BioSystems, 99, 109–125.CrossRefGoogle Scholar
Robinson, B. L., Harper, N. S.,& McAlpine, D. (2016). Meta-adaptation in the auditory midbrain under cortical influence. Nature Communications, 7, 13442.CrossRefGoogle ScholarPubMed
Sehgal, M., Song, C., Ehlers, V. L., & Moyer., J. R. Jr (2013). Learning to learn – Intrinsic plasticity as a metaplasticity mechanism for memory formation. Neurobiology of Learning andMemory, 105, 186–199.CrossRefGoogle ScholarPubMed
Schmidt, M. V., Abraham, W. C., Maroun, M., Stork, O., & Richter-Levin, G. (2013). Stress-Induced Metaplasticity: From Synapses To Behavior. Neuroscience, 250, 112–120.CrossRefGoogle Scholar
Sharpanskykh, A., & Treur, J. (2014). Modelling and analysis of social contagion in dynamic networks. Neurocomputing, 146, 140–150.CrossRefGoogle Scholar
Sjöström, P. J., Rancz, E. A., Roth, A., & Hausser, M. (2008). Dendritic excitability and synaptic plasticity. Physiological Reviews, 88, 769–840.CrossRefGoogle ScholarPubMed
Sterling, L., & Shapiro, E. (1996). The art of prolog. Chapter 17 (pp. 319–356). Cambridge, London: MITPress.Google Scholar
Sterling, L., & Beer, R. (1989). Metainterpreters for expert system construction. Journal of Logic Programming, 6, 163–178.CrossRefGoogle Scholar
Treur, J. (2016). Network-oriented modeling: addressing complexity of cognitive, affective and social interactions. Cham: Springer.CrossRefGoogle Scholar
Treur, J. (2018). Network reification as a unified approach to represent network adaptation principles within a network. In Proceedings of the 7th International Conference on Natural Computing (pp. 344–358), Lecture notes in computer science, vol 11324. Springer.Google Scholar
Treur, J. (2018). Multilevel network reification: Representing higher order adaptivity in a network. In Proceedings of the 7th International Conference on Complex Networks and their Applications, ComplexNetworks’18, vol. 1 (pp.635–651), Studies in computational intelligence, vol. 812. Cham: Springer.Google Scholar
Treur, J. (2019a). The ins and outs of network-oriented modeling: From biological networks and mental networks to social networks and beyond. In Nguyen, N. T. (Ed.), Transactions on computational collective Intelligence 32 (pp. 120–139). Heidelberg: Springer. Contents of Keynote Lecture at ICCCI’18.Google Scholar
Treur, J. (2019b). Design of a software architecture for multilevel reified temporal-causal networks. doi:10.13140/RG.2.2.23492.07045. Retrieved from https://www.researchgate.net/publication/333662169.Google Scholar
Weyhrauch, R. W. (1980). Prolegomena to a theory of mechanized formal reasoning. Artificial Intelligence, 13, 133–170.CrossRefGoogle Scholar
Zelcer, I., Cohen, H., Richter-Levin, G., Lebiosn, T., Grossberger, T., & Barkai, E. (2006). A cellular correlate of learning-induced metaplasticity in the hippocampus. Cerebral Cortex, 16, 460–468.CrossRefGoogle ScholarPubMed