Volume 9 · Number 2 · Pages 199–211

< Previous Paper · Next Paper >

Subsystem Formation Driven by Double Contingency

Bernd Porr & Paolo Di Prodi

Download the full text in
PDF (1650 kB)

> Citation > Similar > References > Add Comment

Abstract

Purpose: This article investigates the emergence of subsystems in societies as a solution to the double contingency problem. Context: There are two underlying paradigms: one is radical constructivism in the sense that perturbations are at the centre of the self-organising processes; the other is Luhmann’s double contingency problem, where agents learn anticipations from each other. Approach: Central to our investigation is a computer simulation where we place agents into an arena. These agents can learn to (a) collect food and/or (b) steal food from other agents. In order to analyse subsystem formation, we investigate whether agents use both behaviours or just one of these, which is equivalent to determining the number of self-referential loops. This is detected with a novel measure that we call “prediction utilisation.” Results: During the simulation, symmetry breaking is observed. The system of agents divides itself up into two subsystems: one where agents just collect food and another one where agents just steal food from other agents. The ratio between these two populations is determined by the amount of food available.

Key words: Social systems, constructivist paradigm, cybernetics, double contingency, symmetry breaking, emergence

Citation

Porr B. & Di Prodi P. (2014) Subsystem formation driven by double contingency. Constructivist Foundations 9(2): 199–211. http://constructivist.info/9/2/199

Export article citation data: Plain Text · BibTex · EndNote · Reference Manager (RIS)

Similar articles

Kauffman L. H. (2016) Cybernetics, Reflexivity and Second-Order Science

Müller H. F. J. (2007) Epistemology Returns to Its Roots

Umpleby S. A. (2016) Second-Order Cybernetics as a Fundamental Revolution in Science

Hejl P. M. (2011) The Individual in Radical Constructivism. Some Critical Remarks from an Evolutionary Perspective

Füllsack M. (2013) Constructivism and Computation: Can Computer-Based Modeling Add to the Case for Constructivism?

References

Ashby W. R. (1956) An introduction to cybernetics. Methnen, London. << Google Scholar

Barber M., Blanchard P., Buchinger E., Cessac B. & Streit L. (2006) Expectation-driven interaction: A model based on Luhmann’s contingency approach. Journal of Artificial Societies and Social Simulation 9(4) Available at http://jasss.soc.surrey.ac.uk/9/4/5.html

Beekman M., Nicolis S., Meyer B. & Dussutour A. (2009) Noise improves collective decision-making by ants in dynamic environments. Proceedings of the Royal Society London B 276: 4353–4361. << Google Scholar

Braitenberg V. (1984) Vehicles: Experiments in synthetic psychology. MIT Press, Cambridge MA. << Google Scholar

Camazine S., Franks N. R., Sneyd J., Bonabeau E., Deneubourg J.-L. & Theraula G. (2001) Self-organization in biological systems. Princeton University Press, Princeton NJ. << Google Scholar

Cangelosi A., Tikhanoff V., Fontanari J. & Hourdakis E. (2007) Integrating language and cognition: A cognitive robotics approach. IEEE Computational Intelligence Magazine 2(3): 65–70. << Google Scholar

Degris T., Pilarski P. M. & Sutton R. S. (2012) Model-free reinforcement learning with continuous action in practice. In: Proceedings of the 2012 American Control Conference, 27–29 June 2012, Montreal, Canada. IEEE Press, Piscataway NJ: 2177–2182. << Google Scholar

Di Paolo E. A. (2005) Autopoiesis, adaptivity, teleology, agency. Phenomenology and the Cognitive Sciences 4(4): 429–452. << Google Scholar

Dittrich P., Kron T. & Banzhaf W. (2003) On the scalability of social order. Modeling the problem of double and multi contingency following Luhmann. Journal of Artificial Societies and Social Simulation 6(1) Available at http://jasss.soc.surrey.ac.uk/6/1/3.html

Drescher G. L. (1991) Made-up minds: A constructivist approach to artificial intelligence. MIT Press, Cambridge MA. << Google Scholar

Emmeche C., Koppe S. & Stjernfelt F. (2000) Levels, emergence, and three versions of downward causation. In: Andersen P. B., Emmeche C., Finnemann N. O. & Christiansen P. V. (eds.) Downward causation. University of Aarhus Press, Århus DK: 13–34. << Google Scholar

Esposito E. (1996) From self-reference to autology: How to operationalize a circular approach. Social Science Information 35(2): 269–281. << Google Scholar

Foerster H. von (2003) Understanding understanding: Essays on cybernetics and cognition. Spinger, New York. << Google Scholar

Franks N. R., Mallon E. B., Bray H. E., Hamilton M. J. & Mischler T. C. (2003) Strategies for choosing between alternatives with different attributes: Exemplified by house-hunting ants. Animal Behaviour 65(1): 215–223. << Google Scholar

Froese T. & Ziemke T. (2009) Enactive artificial intelligence: Investigating the systemic organization of life and mind. Artificial Intelligence 173(3–4): 466–500. << Google Scholar

Gadenne V. (2010) Why radical constructivism has not become a paradigm? Constructivist Foundations 6(1): 77–83. Available at http://www.univie.ac.at/constructivism/journal/6/1/077.gadenne

Georgeon O. & Hassas S. (2013) Single agents can be constructivist too. Constructivist Foundations 9(1): 40–42. Available at http://www.univie.ac.at/constructivism/journal/9/1/040.georgeon

Georgeon O. & Marshall J. (2013) Demonstrating sensemaking emergence in artificial agents: A method and an example. International Journal of Machine Consciousness 5(2): 131–144. << Google Scholar

Georgeon O. & Sakellariou I. (2012) Designing environment-agnostic agents. In: Howley E., Vrancx P. & Knudson M. (eds.) Proceedings of the Adaptive and Learning Agents Workshop, 4–5 June 2012, Valencia, Spain: 25–32. Available at http://ai.vub.ac.be/ALA2012/

Georgeon O., Marshall J. & Manzotti R. (2013) ECA: An enactivist cognitive architecture based on sensorimotor modeling. Biologically Inspired Cognitive Architectures 6: 46–57. << Google Scholar

Georgeon O., Wolf C. & Gay S. (2013) An enactive approach to autonomous agent and robot learning. Proceedings of the IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (EPIROB2013) Osaka, Japan. 1–6. << Google Scholar

Glasersfeld E. von (1984) An introduction to radical constructivism. In: Watzlawick P. (ed.) The invented reality: How do we know what we believe we know? W. W. Norton, New York: 17–40. Available at http://www.vonglasersfeld.com/070.1

Grant C. B. (2002) Complexities of self and social communication. In: Grant C. B., (ed.) Radical communication: Rethinking interaction and dialogue. John Benjamins, Amsterdam: 101–125. << Google Scholar

Grondman I., Busoniu L., Lopes G. A. D. & Babuska R. (2012) A survey of actor-critic reinforcement learning: Standard and natural policy gradients. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 42(6): 1291–1307. << Google Scholar

Hülse M. & Pasemann F. (2002) Dynamical neural Schmitt trigger for robot control. In: Dorronsoro J. R. (ed.) Artificial Neural Networks ICANN 2002: 783–788. << Google Scholar

Heath B., Hill R. & Ciarallo F. (2009) A survey of agent-based modeling practices (January 1998 to July 2008) Journal of Artificial Societies and Social Simulation 12(4) Available at http://jasss.soc.surrey.ac.uk/12/4/9.html.

Jost J., Bertschinger N., Olbrich E., Ay N. & Fraenkel S. (2007) An information theoretic approach to system differentiation on the basis of statistical dependencies between subsystems. Physica A 378: 1–10. << Google Scholar

Kaelbling L., Littman M. & Cassandra A. (1998) Planning and acting in partially observable stochastic domains. Artificial Intelligence 101: 99–134. << Google Scholar

Kenny V. (2009) “There’s nothing like the real thing.” Revisiting the need for a third-order cybernetics. Constructivist Foundations 4(2): 100–111. Available at http://www.univie.ac.at/constructivism/journal/4/2/100.kenny

Kernbach S., Thenius R., Kornienko O. & Schmickl T. (2009) Re-embodiment of honeybee aggregation behavior in an artificial micro-robotic swarm. Adaptive Behavior 17: 237–259. << Google Scholar

Klopf A. H. (1982) The hedonistic neuron: A theory of memory, learning, and intelligence. Hemisphere, Washington DC. << Google Scholar

Leydesdorff L. (2005) Anticipatory systems and the processing of meaning: A simulation inspired by Luhmann’s theory of social systems. Journal of Artificial Societies and Social Simulation 8(2) Available at http://jasss.soc.surrey.ac.uk/8/2/7.html.

Linden D. J. (2003) From molecules to memory in the cerebellum. Science 301: 1682–1685. << Google Scholar

Littman M. L., Sutton R. S. & Singh S. (2002) Predictive representations of state. In: Dietterich T. G., Becker S. & Ghahramani Z. (eds.) Advances in Neural Information Processing Systems 14. Proceedings of the 2001 Conference. MIT Press, Cambridge MA: 1555–1561. << Google Scholar

Luhmann N. (1984) Soziale Systeme. Suhrkamp, Frankfurt am Main. << Google Scholar

Luhmann N. (1991) Sistemas sociales. Lineamientos para una teoría general. Anthropos, Barcelona. German original published in 1985. << Google Scholar

Luhmann N. (1996) Die Realität der Massenmedien. Westdeutscher Verlag, Opladen. << Google Scholar

Luhmann N. (1996) La ciencia de la sociedad. Anthropos, México City. German original published in 1990. << Google Scholar

Luhmann N. (2006) La sociedad de la sociedad. Herder, México City. German original published in 1997. << Google Scholar

Matarić M. J. (1997) Reinforcement learning in the multi-robot domain. Autonomous Robots 4(1): 73–83. << Google Scholar

Maturana H. R. & Varela F. J. (1980) Autopoiesis and cognition: The realization of the living. Reidel, Dordrecht. << Google Scholar

McCallum A. (1996) Learning to use selective attention and short-term memory in sequential tasks. In: Maes P., Mataric M. J., Meyer J.-A., Pollack J. & Wilson S. W. (eds.) From animals to animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior. MIT Press, Cambridge MA: 315–324. << Google Scholar

Meyer B., Beekman M. & Dussutour A. (2008) Noise-induced adaptive decision-making in ant-foraging. Lecture Notes in Computer Science 5040: 415–425. << Google Scholar

Modayil J., White A. & Sutton R. S. (2014) Multi-timescale nexting in a reinforcement learning robot. Adaptive Behavior, published online 7 February 2014. << Google Scholar

Nakamura K. & Ono T. (1986) Lateral hypothalamus neuron involvement in integration of natural and artificial rewards and cue signals. Journal of Neurophysiology 55(1): 163–181. << Google Scholar

Nakanishi J. & Schaal S. (2004) Feedback error learning and nonlinear adaptive control. Neural Networks 17(10): 1453–1465. << Google Scholar

Niv Y. (2007) Cost, benefit, tonic, phasic: What do response rates tell us about dopamine and motivation? Annals of the New York Academy of Sciences 1104: 357–376. << Google Scholar

O’Regan J. K. & Noë A. (2001) A sensorimotor account of vision and visual consciousness. Behavioral and Brain Sciences 24(5): 939–1031. << Google Scholar

O’Regan J. K., Myin E. & Noë A. (2005) Phenomenal consciousness explained (better) in terms of bodiliness and grabbiness. Phenomenology and the Cognitive Sciences 4(4): 369–387. << Google Scholar

Oudeyer P.-Y., Kaplan F. & Hafner V. (2007) Intrinsic motivation systems for autonomous mental development. IEEE Transactions on Evolutionary Computation 11(2): 265–286. << Google Scholar

Parsons T. (1968) Social interaction. In: Sills D. L. (ed.) International encyclopedia of the social sciences. Volume 12. Macmillan/Free Press, New York: 429–440. << Google Scholar

Pfeifer R. & Scheier C. (1994) From perception to action: The right direction? In: Gaussier P. & Nicoud J.-D. (eds.) From perception to action. IEEE Computer Society Press, Los Alamitos CA: 1–11. << Google Scholar

Pilarski P. M., Dick T. B. & Sutton R. S. (2013) Real-time prediction learning for the simultaneous actuation of multiple prosthetic joints. In: Proceedings of the 2013 IEEE International Conference on Rehabilitation Robotics, Seattle, USA, 24–26 June 2013. IEEE Press, Piscataway NJ: 1–8. << Google Scholar

Porr B. & Wörgötter F. (2002) Isotropic sequence order learning using a novel linear algorithm in a closed loop behavioural system. Biosystems 67(1–3):195–202. << Google Scholar

Porr B. & Wörgötter F. (2003) Isotropic sequence order learning. Neural Computation 15: 831–864. << Google Scholar

Porr B. & Wörgötter F. (2005) What means embodiment for radical constructivists? Kybernetes 34(1/2): 105–117. << Google Scholar

Porr B. & Wörgötter F. (2006) Strongly improved stability and faster convergence of temporal sequence learning by utilising input correlations only. Neural Computation 18(6): 1380–1412. << Google Scholar

Porr B., Egerton A. & Wörgötter F. (2006) Towards closed loop information: Predictive information. Constructivist Foundations 1(2): 83–90. Available at http://www.univie.ac.at/constructivism/journal/1/2/083.porr

Porr B., Ferber C. von & Wörgötter F. (2003) ISO-learning approximates a solution to the inverse-controller problem in an unsupervised behavioural paradigm. Neural Computation 15: 865–884. << Google Scholar

Reading N. C. & Sperandio V. (2006) Quorum sensing: The many languages of bacteria. FEMS Microbiology Letters 254(1): 1–11. << Google Scholar

Redgrave P., Gurney K. & Reynolds J. (2008) What is reinforced by phasic dopamine signals? Brain research reviews 58(2): 322–339. << Google Scholar

Redish A. D. (2013) The mind within the brain: How we make decisions and how those decisions go wrong. Oxford University Press, New York. << Google Scholar

Riegler A. (2007) The radical constructivist dynamics of cognition. In: Wallace B. (ed.) The mind, the body and the world: Psychology after cognitivism? Imprint, London: 91–115. Available at http://www.univie.ac.at/constructivism/riegler/44

Roesch E., Spencer M., Nasuto S., Tanay T. & Bishop J.-M. (2013) Exploration of the functional properties of interaction: Computer models and pointers for theory. Constructivist Foundations 9(1): 26–32. Available at http://www.univie.ac.at/constructivism/journal/9/1/026.roesch

Salgado M. & Gilbert N. (2008) Emergence and communication: Overcoming some epistemological drawbacks in computational sociology. In: Proceedings of the Third Edition of Epistemological Perspectives on Simulation. Lisbon: 105–124. << Google Scholar

Schmidhuber J. (1991) Curious model-building control systems. In: Proceedings of the International Joint Conference on Neural Networks, Singapore, Volume 2. IEEE Press, Piscataway NJ: 1458–1463. << Google Scholar

Schultz W. (1998) Predictive reward signal of dopamine neurons. Journal of Neurophysiology 80: 1–27. << Google Scholar

Singh S., Barto A. & Chentanez N. (2005) Intrinsically motivated reinforcement learning. In: Saul L. K., Weiss Y. & Bottou L. (eds.) Advances in Neural Information Processing Systems. MIT Press, Cambridge MA: 1281–1288. << Google Scholar

Steels L. (2004) The autotelic principle. In: Fumiya I., Pfeifer R., Steels L. & Kunyoshi K. (eds.) Embodied artificial intelligence. Lecture Notes in AI 3139. Springer, Berlin: 231–242. << Google Scholar

Sutton R. S. & Barto A. G. (1998) Reinforcement learning: An introduction. MIT Press, Cambridge MA. << Google Scholar

Sutton R. S., Modayil J., Delp M., Degris T., Pilarski P. M., White A. & Precup D. (2011) Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction. In: Tumer K., Yolum P., Sonenberg L. & Stone P. (eds.) Proceedings of the Tenth International Conference on Autonomous Agents and Multiagent Systems, 2–6 May 2011, Taipei, Taiwan. International Foundation for Autonomous Agents and Multiagent Systems, Richland SC: 761–768. << Google Scholar

Tani J. & Nolfi S. (1999) Learning to perceive the world as articulated: An approach for hierarchical learning in sensory-motor systems. Neural Networks 12: 1131–1141. << Google Scholar

Touchette H. & Lloyd S. (2004) Information-theoretic approach to the study of control systems. Physica A Statistical Mechanics and its Applications, 331: 140–172. << Google Scholar

van Gelder T. (1997) Dynamics and cognition. In: Haugland J. (ed.) Mind design II: Philosophy, psychology, artificial intelligence. MIT Press, Cambridge MA: 421–450. << Google Scholar

Varela F. J. (1989) Autonomie et connaissance: Essai sur le vivant. Edition Seuil, Paris. << Google Scholar

Varela F. J., Thompson E. & Rosch E. (1991) The embodied mind: Cognitive science and human experience. MIT Press, Cambridge MA. << Google Scholar

Wörgötter F. & Porr B. (2005) Temporal sequence learning, prediction and control. A review of different models and their relation to biological mechanisms. Neural Computation 17(2): 245–319. << Google Scholar

Weisbuch G. & Stauffer D. (2000) Hits and flops dynamics. Working Papers 00–07–036. Santa Fe Institute, Santa Fe NM. << Google Scholar

Whitehead S. D. & Ballard D. H. (1991) Learning to perceive and act by trial and error. Machine Learning 7(1): 45–83. << Google Scholar

Wischman S., Pasemann F. & Hülse M. (2004) Structure and function of evolved neuro-controllers for autonomous robots. Connections Science 16(4): 249–266. << Google Scholar

Comments: 0

To stay informed about comments to this publication and post comments yourself, please log in first.