Volume 9 · Number 2 ·
Pages 199–211
Subsystem Formation Driven by Double Contingency
Log in to download the full text for free
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
References
Ashby W. R. (1956) An introduction to cybernetics. Methnen, London. ▸︎ Google︎ 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) 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. ▸︎ Google︎ Scholar
Braitenberg V. (1984) Vehicles: Experiments in synthetic psychology. MIT Press, Cambridge MA. ▸︎ Google︎ 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. ▸︎ Google︎ 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. ▸︎ Google︎ 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. ▸︎ Google︎ Scholar
Di Paolo E. A. (2005) Autopoiesis, adaptivity, teleology, agency. Phenomenology and the Cognitive Sciences 4(4): 429–452. ▸︎ Google︎ 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) 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. ▸︎ Google︎ 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. ▸︎ Google︎ Scholar
Esposito E. (1996) From self-reference to autology: How to operationalize a circular approach. Social Science Information 35(2): 269–281. ▸︎ Google︎ Scholar
Foerster H. von (2003) Understanding understanding: Essays on cybernetics and cognition. Spinger, New York. ▸︎ Google︎ 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. ▸︎ Google︎ Scholar
Froese T. & Ziemke T. (2009) Enactive artificial intelligence: Investigating the systemic organization of life and mind. Artificial Intelligence 173(3–4): 466–500. ▸︎ Google︎ Scholar
Gadenne V. (2010) Why radical constructivism has not become a paradigm? Constructivist Foundations 6(1): 77–83. 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. 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. ▸︎ Google︎ 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. 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. ▸︎ Google︎ 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. ▸︎ Google︎ 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. 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. ▸︎ Google︎ 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. ▸︎ Google︎ 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) http://jasss.soc.surrey.ac.uk/12/4/9.html.
Hülse M. & Pasemann F. (2002) Dynamical neural Schmitt trigger for robot control. In: Dorronsoro J. R. (ed.) Artificial Neural Networks ICANN 2002: 783–788. ▸︎ Google︎ Scholar
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. ▸︎ Google︎ Scholar
Kaelbling L., Littman M. & Cassandra A. (1998) Planning and acting in partially observable stochastic domains. Artificial Intelligence 101: 99–134. ▸︎ Google︎ 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. 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. ▸︎ Google︎ Scholar
Klopf A. H. (1982) The hedonistic neuron: A theory of memory, learning, and intelligence. Hemisphere, Washington DC. ▸︎ Google︎ 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) 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. ▸︎ Google︎ 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. ▸︎ Google︎ Scholar
Luhmann N. (1984) Soziale Systeme. Suhrkamp, Frankfurt am Main. ▸︎ Google︎ Scholar
Luhmann N. (1991) Sistemas sociales. Lineamientos para una teoría general. Anthropos, Barcelona. German original published in 1985. ▸︎ Google︎ Scholar
Luhmann N. (1996) Die Realität der Massenmedien. Westdeutscher Verlag, Opladen. ▸︎ Google︎ Scholar
Luhmann N. (1996) La ciencia de la sociedad. Anthropos, México City. German original published in 1990. ▸︎ Google︎ Scholar
Luhmann N. (2006) La sociedad de la sociedad. Herder, México City. German original published in 1997. ▸︎ Google︎ Scholar
Matarić M. J. (1997) Reinforcement learning in the multi-robot domain. Autonomous Robots 4(1): 73–83. ▸︎ Google︎ Scholar
Maturana H. R. & Varela F. J. (1980) Autopoiesis and cognition: The realization of the living. Reidel, Dordrecht. ▸︎ Google︎ 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. ▸︎ Google︎ Scholar
Meyer B., Beekman M. & Dussutour A. (2008) Noise-induced adaptive decision-making in ant-foraging. Lecture Notes in Computer Science 5040: 415–425. ▸︎ Google︎ Scholar
Modayil J., White A. & Sutton R. S. (2014) Multi-timescale nexting in a reinforcement learning robot. Adaptive Behavior, published online 7 February 2014. ▸︎ Google︎ 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. ▸︎ Google︎ Scholar
Nakanishi J. & Schaal S. (2004) Feedback error learning and nonlinear adaptive control. Neural Networks 17(10): 1453–1465. ▸︎ Google︎ 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. ▸︎ Google︎ 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. ▸︎ Google︎ Scholar
O’Regan J. K. & Noë A. (2001) A sensorimotor account of vision and visual consciousness. Behavioral and Brain Sciences 24(5): 939–1031. ▸︎ Google︎ 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. ▸︎ Google︎ 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. ▸︎ Google︎ 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. ▸︎ Google︎ 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. ▸︎ Google︎ 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. ▸︎ Google︎ Scholar
Porr B. & Wörgötter F. (2003) Isotropic sequence order learning. Neural Computation 15: 831–864. ▸︎ Google︎ Scholar
Porr B. & Wörgötter F. (2005) What means embodiment for radical constructivists? Kybernetes 34(1/2): 105–117. ▸︎ Google︎ 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. ▸︎ Google︎ Scholar
Porr B., Egerton A. & Wörgötter F. (2006) Towards closed loop information: Predictive information. Constructivist Foundations 1(2): 83–90. 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. ▸︎ Google︎ Scholar
Reading N. C. & Sperandio V. (2006) Quorum sensing: The many languages of bacteria. FEMS Microbiology Letters 254(1): 1–11. ▸︎ Google︎ Scholar
Redgrave P., Gurney K. & Reynolds J. (2008) What is reinforced by phasic dopamine signals? Brain research reviews 58(2): 322–339. ▸︎ Google︎ 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. ▸︎ Google︎ 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. 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. 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. ▸︎ Google︎ 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. ▸︎ Google︎ Scholar
Schultz W. (1998) Predictive reward signal of dopamine neurons. Journal of Neurophysiology 80: 1–27. ▸︎ Google︎ 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. ▸︎ Google︎ 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. ▸︎ Google︎ Scholar
Sutton R. S. & Barto A. G. (1998) Reinforcement learning: An introduction. MIT Press, Cambridge MA. ▸︎ Google︎ 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. ▸︎ Google︎ 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. ▸︎ Google︎ 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. ▸︎ Google︎ 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. ▸︎ Google︎ Scholar
Varela F. J. (1989) Autonomie et connaissance: Essai sur le vivant. Edition Seuil, Paris. ▸︎ Google︎ Scholar
Varela F. J., Thompson E. & Rosch E. (1991) The embodied mind: Cognitive science and human experience. MIT Press, Cambridge MA. ▸︎ Google︎ Scholar
Weisbuch G. & Stauffer D. (2000) Hits and flops dynamics. Working Papers 00–07–036. Santa Fe Institute, Santa Fe NM. ▸︎ Google︎ Scholar
Whitehead S. D. & Ballard D. H. (1991) Learning to perceive and act by trial and error. Machine Learning 7(1): 45–83. ▸︎ Google︎ Scholar
Wischman S., Pasemann F. & Hülse M. (2004) Structure and function of evolved neuro-controllers for autonomous robots. Connections Science 16(4): 249–266. ▸︎ Google︎ 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. ▸︎ Google︎ Scholar
Comments: 0
To stay informed about comments to this publication and post comments yourself, please log in first.