1 / 43

THE PRINCIPLES, MODELS and applications of aNTICIPATION IN SOCIETY EVOLUTION

Alexander Makarenko, Prof., Dr. Institute for applied systems analysis National Technical University of Ukraine 'KPI', National Academy of Science of Ukraine , Prospect Pobedy 37, 03056, Kiev-56, Ukraine ; makalex@i.com.ua.

johnjjones
Télécharger la présentation

THE PRINCIPLES, MODELS and applications of aNTICIPATION IN SOCIETY EVOLUTION

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Alexander Makarenko, Prof., Dr. Institute for applied systems analysis National Technical University of Ukraine 'KPI', National Academy of Science of Ukraine , Prospect Pobedy 37, 03056, Kiev-56, Ukraine ; makalex@i.com.ua THE PRINCIPLES, MODELS andapplications of aNTICIPATIONINSOCIETY EVOLUTION ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015

  2. Historically, most of the concepts and ideas about the evolution of systems taking into account the impact of past and present on the behavior of the systems in the future. • But in recent years in many areas of science and practices associated with social systems, economics, psychology is a growing understanding that we should take into account also the effects of anticipation

  3. Anticipation, i.e. accounting for the effects of future states of the system to changes in the present moment of time (see D. Dubois, L. Leydesdorff, M. Nadin, M. Butz, R. Poli, J. Beckert, R. Miller, L. Gardini and many others).

  4. Formalization of the concept of anticipation is also the subject of many papers, especially since the publications of Robert Rosen, who considered the ratio of the system-model-environment. Note that a significant step in formalizing further made Daniel M. Dubois, who introduced the concept of strong anticipation when it is impossible to build a model to predict the future of the system.

  5. This report gives a review of the possible manifestation and understanding anticipation with the development of modern concepts. Discusses the emergence of uncertainty as the consequences of anticipation, as well as the appearance of the variety of scenarios in the behavior of social systems. Also discusses the new capabilities in understanding of the socio -economical systems, including society as a whole.

  6. We proposed and realized one new type of models of neural networks type (with associative memory), cellular automata discrete and differential equation which takes into account property of anticipation. The basic new qualities, discovered at research there is that possible multi-valued solutions of given neural networks.

  7. As examples of the application of the proposed considerations are discussed anticipation processes of local and global sustainable development, economic models, the movement of crowds of pedestrians and biological counterparts. Note that such examples can be useful when considering applications, including new approaches to risk assessment.

  8. I. TOPICS IN PRESENTATION

  9. Anticipation • The term ‘anticipation’ had been firstly attached to the systems with the intrinsic models for predicting (R. Rosen). • By Daniel M. Dubois (Belgium) the notion of ‘strong anticipation’ had been introduced and investigated. • In case of strong anticipation the system haven’t the models for predictions but are self- making with accounting presumable future states of the system. • D. Dubois introduced the notion of hyperincursive systems (with presumable multivaluedness of solutions).

  10. “Definition of an incursive discrete strong anticipatory system …: an incursive discrete system is a system which computes its current state at time, as a function of its states at past times, present time,, and even its states at future times • (1) • where the variable x at future times is computed in using the equation itself. STRONG ANTICIPATION ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015, ,

  11. Definition of an incursive discrete weak anticipatory system: an incursive discrete system is a system which computes its current state at time, as a function of its states at past times, present time, , and even its predicted states at future times • (2) • where the variable at future times are computed in using the predictive model of the system” (Dubois D., 2001). WEAK ANTICIPATION ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015, ,

  12. IIa. EXAMPLES OF SYSTEMS WITH ANTICIPATION (CELLULAR AUTOMATA) ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015, ,

  13. Game “Life”: linear dynamics Game “LifeA with anticipation ”: multiple solutions LifeA: simulations ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015, ,

  14. LifeA simulations: increasing number of presumable states • A large number of configurations coexisting in system • (additive next state function, α = 0.5, initial state is 0000) , ,

  15. IIb. EXAMPLES OF SYSTEMS WITH ANTICIPATION (NEURAL NETWORKS)

  16. In the simplest cases of discrete systems this leads to the formal dynamic equations (for the case of discrete time t=0, 1, ..., n, ... and finite number of elements M): • where R is the set of external parameters (environment, control), {si(t)} the state of the system at a moment of time t (i=1, 2, …, M), g(i) horizon of forecasting, {G} set of nonlinear functions for evolution of the elements states. ANTICIPATION ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015, ,

  17. 6IN NEURONS. CBra ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015, ,

  18. Network with 8 neurons ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015, ,

  19. IIc. DISCRETE EQUATIONS WITH ANTICIPATION ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015, ,

  20. Dbranching of the solution with timeE B ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015, ,

  21. One– dimensional discrete time equations with anticipation • Let’s consider the discrete dynamic equation with an anticipation which is the modification of well-known logistic mapping. • The proposed equation is presented as ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015, ,

  22. X 0 1 2 3 t Many solutions and single trajectory selection MAIN NOVELTY: MULTIVALUEDNESS and BRANCHING ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015, ,

  23. III NEW POSSIBILITIES FOR INVESTIGATIONS AND INTERPRETATIONS ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015

  24. Multivaluedness and self-organization • General distributed media with anticipation • ‘Chimera’ states in the systems with anticipation • Possible ambiguity in the theory of computing • Interpretation of mental reactions and the problem of consciousness. • Uncertainty and probability.

  25. IV APPLICATIONS TO SOCIO-ECONOMIC SYSTEMS

  26. IVa. Sustainable development ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015

  27. Basic description of SD • “Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs. • -following G.H.Bruntland Commission (1987) • FORMAL DEFINITION OF SUSTAINABLE DEVELOPMENT • (A. Makarenko, 2003, 2009, 2011) ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015

  28. The role of strong and weak anticipation in SD processes • Note here some important issues of SD applications and considerations concerning local and global aspects of SD. • Frequently the ‘next generation requirements’ is omitted. Also usually the local management doesn’t means considering of far future. • Common tools in advanced practice in regional management is using of mathematical modeling for forecasting of processes flows in different fields: ecology, economy, societal. • The mathematical modeling in regional planning is used for prediction of possible scenarios for evolution of real natural, technical, social systems. Usually the peoples from real regional management consider the SD as specific ‘best’ regime in given system operating. • Note that ‘long term’ prediction is impossible first of all because of intrinsic complexity of such systems. At second it is recognized the principal impossibility of forecasting of single way of development for large socio-technical-environmental systems. ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015

  29. Multi-valued solutions in anticipatory systems with known restrictions ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015

  30. Multi-valued solutions in anticipatory systems with anticipatory restrictions ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015

  31. Multivaluednes • The main essential new property is the possibility of multi-valued solution (that is many values of solution for some moments of time and initial conditions). This may be interpreted as the possibility of many scenarios of development for real social, natural, biological, psychological systems. • The second key issue is connected to property that the real social system has single realization of historical way (trajectory). So the social system as the whole makes the choice of the own trajectory at any moment of time. Global SD processes are strongly anticipative ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015

  32. IVb BAKGROUND FOR SOCIAL SYSTEMS MODELING Associative memory approach to large socio- economico-technical systems (Makarenko, 1992, 1998, 2001, 2003) ‘Patterns’ and networks structures • The ‘pattern’ is the collection of elements and bonds between them at any moment of time. • Such description is useful as for environment as for the mental structures of individuals (or agents in the models).

  33. Pattern of system in given time moment

  34. Internal representation of external world and mental properties

  35. Next steps: • Introduction the agents with strong anticipation into models • Development of ‘Artificial Life’ Models with anticipation for society

  36. EXAMPLE OF MODEL’S APPLICATIONS: Modeling of future geopolitical world situations1994 Result

  37. Acknowledge for collaboration: S. Levkov, V. Solia, D. Krushinskiy, B. Goldengorin, S. Lazarenko, A. Stashenko, V. Biliuga, N. Smilianec, N. Yatciuk, A. Popov, V. Zavertaniy, E. Terpil ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015

  38. CONCLUSIONS • Hyperincursion => Possible multivaluednes in model’s solutions • New multivalued solutions => New possibilities in Interpretations 3. The WORLD may be really multivalued but we may see (feel, measure) only one branch just in ‘classical’ situations

  39. Multivaluedness in physics • Multivaluedness by strong anticipation • Some examples • Computation theory • Uncertainty • Neuroscience • Models of large social systems • Further research fields ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015

  40. Also it may be considered the future directions of reforms in Ukraine, taking into account possible uncertainty scenarios of global world.

  41. REFERENCES • 1. DUBOIS D., Introduction to computing Anticipatory Systems. International Journal of Computing Anticipatory Systems, (Liege), 1998, Vol. 2, pp.3-14. • 2. DUBOIS D. Introduction to the Natural and the Artificial Anticipator. AIP Conf. Proceedings, (Ed. Daniel M. Dubois), 2010. vol.1303. pp. 17-24. • 3. LEYDORFF L., DUBOIS D., Anticipation in Social Systems: the Incursion and Communication of Meaning, Int. Journal of Computing Anticipatory Systems, 15, 2004. pp. 203 – 216. • 4. MAKARENKO A., New Neuronet Models of Global Socio- Economical Processes. In 'Gaming /Simulation for Policy Development and Organizational Change' (J.Geurts, C.Joldersma, E.Roelofs eds) , Tillburg University Press. 1998. p. 133- 138, • 5. MAKARENKO A. Anticipation in modeling of large social systems – neuronets with internal structure and multivaluedness. Int. J. Comput. Anticipatory Systems, 2002. Vol.11, 16 p. • 6. MAKARENKO A., Sustainable Development and Risk Evaluation: Challenges and Possible new Methodologies, In. Risk Science and Sustainability: Science for Reduction of Risk and Sustainable Development of Society, eds. T.Beer, A.Izmail- Zade, Kluwer AP, Dordrecht, 2003. pp. 87- 100. ANTICIPATION, 1st Conf., Trento, Italy, 07 November 2015

  42. 7. Makarenko A.(2008); Cellular Automata with anticipation: Some new Research Problems; Int. Journal of Computing Anticipatory Systems (Belgium). Vol. 20 (pp. 230 – 242) 8. Makarenko A. (2011); Neural networks with anticipation and some problems of complexity theory; Proc. Of Int. Conf. on Complex Systems: Synergy of Control, Communications and Computing COSY 2011, Ohrid, Macedonia, 2011. (pp.257-262) 9. Makarenko A. (2012); On possible role of anticipatory effects in neurophysiology and consciousness theory (short abstracts); Proceed. Of XVI Int. Conf. On Neurocybernetics (ICNC-12), 24-28 Sept. 2012. Rostov-on-Don, Russia, (3 p.) [in Russian]. 10. Makarenko A. (2013); On some model construction with ambiguity and simulation of probability laws. 1. Elementary examples and posing of problems; Analysis, models and control. Vol.1. Book of papers. ESC “IASA” NTUU “KPI”, 2013. (pp. 54-72) [In Russian]. 11. Makarenko A. (2013); System analysis, formalization and modeling of sustainable development; Analysis, models and control. Vol.1. Book of papers. ESC “IASA” NTUU “KPI”, (pp. 73-111) [In Russian].

  43. Thanks for attention makalex@i.com.ua makalex51@gmail.com http://isgeo.com.ua/en http:/consens1.org/en

More Related