{"id":297,"date":"2025-11-20T11:57:51","date_gmt":"2025-11-20T11:57:51","guid":{"rendered":"https:\/\/www.ieeesmc.org\/cai-2026\/?page_id=297"},"modified":"2025-12-27T11:13:07","modified_gmt":"2025-12-27T11:13:07","slug":"tutorial-8-self-organizing-ai","status":"publish","type":"page","link":"https:\/\/www.ieeesmc.org\/cai-2026\/tutorial-8-self-organizing-ai\/","title":{"rendered":"Tutorial 8: Self-Organizing AI: From Cybernetics to Multi-Stage Selection"},"content":{"rendered":"<p style=\"font-size: 1.2em;font-weight: bold\">Speakers<\/p>\n<ul>\n<li><a href=\"mailto:angel.marchev@unwe.bg\">Prof. Angel Marchev, Jr.<\/a> (University of National and World Economy (UNWE), Sofia)<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Abstract<\/p>\n<p>This tutorial introduces a principled, historically informed, and hands-on path to building AI systems that cope with real-world complexity via <strong>self-organization<\/strong> and <strong>adaptivity<\/strong>. We start from the cybernetic roots &#8211; Wiener\u2019s feedback, Ashby\u2019s law of requisite variety, and Shannon\u2019s information theory &#8211; then trace forward through neural computation and evolutionary methods to contemporary <strong>Multi-Stage Selection Procedures (MSSP)<\/strong> for autonomous model synthesis. Participants will learn how directed selection, inconclusive decision principles, and emergent modeling translate into practical workflows for AutoML-like pipelines, time-series modeling, portfolio construction, and encrypted-signal prediction (e.g., Numerai). The session blends conceptual foundations with concrete case studies and code-level guidance (Matlab library), equipping attendees to design, analyze, and explain self-organizing AI solutions in data-scarce, noisy, and highly variable settings.<\/p>\n<p>&nbsp;<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Target Audience<\/p>\n<p><strong>Who should attend:<\/strong> AI\/ML researchers and practitioners, data scientists in finance\/engineering, complexity scientists, and graduate students interested in robust modeling under uncertainty.<\/p>\n<p><strong>Prerequisites:<\/strong> Comfort with linear algebra, probability, and regression; working knowledge of ML model selection; optional Matlab\/Python familiarity for code demos.<\/p>\n<p><strong>Practical outcomes:<\/strong> Attendees will be able to (i) reason about systems via feedback and requisite variety; (ii) implement MSSP-style model synthesis; (iii) mitigate overfitting with directed selection; (iv) analyze\/visualize model genealogy and error dynamics; and (v) apply the workflow to noisy, short, or non-stationary datasets.<\/p>\n<p>&nbsp;<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Outline and Description of the Tutorial<\/p>\n<p><strong>1 Conceptual Core<\/strong><\/p>\n<ul>\n<li><strong>Complexity in AI:<\/strong> Why \u201cgood regulators are models of the system\u201d; feedback and emergent behavior; information flow. (Ashby, Wiener, Shannon; emergence \u00e0 la Huxley.)<\/li>\n<li><strong>From Early AI to Self-Organization:<\/strong> McCulloch\u2013Pitts neurons, Rosenblatt\u2019s perceptron limits, and Ivakhnenko\u2019s GMDH as adaptive structure discovery.<\/li>\n<li><strong>Directed vs. Natural Selection:<\/strong> Gabor\u2019s principle of inconclusive decisions and Marchev\u2019s MSSP\u2014growing model complexity via staged selection to avoid overfitting.<\/li>\n<\/ul>\n<p><strong>2 MSSP Methodology<\/strong><\/p>\n<ul>\n<li><strong>Population &amp; primitives:<\/strong> Feature primitives (linear, log, exponential, power, reciprocal, sine, etc.).<\/li>\n<li><strong>Generation:<\/strong> Exhaustive pair crossing; linear\/power mating functions; tensor-friendly breeder representation.<\/li>\n<li><strong>Selection:<\/strong> Thresholding by mean relative error; complexity-by-layers rule; terminal conditions (generations\/time\/error progression).<\/li>\n<li><strong>Explainability:<\/strong> Genealogical trees, progression plots, pruned graphs, residual diagnostics, and symbolic forms.<\/li>\n<\/ul>\n<p><strong>3 Applications &amp; Demos<\/strong><\/p>\n<ul>\n<li><strong>Portfolio Modeling:<\/strong> Treat feature weights as portfolios; evolve weight structures under constraints; risk-weighted performance analysis.<\/li>\n<li><strong>Encrypted-Signal Prediction (Numerai):<\/strong> Handling low-signal, era-based generalization with MSSP; cross-validation by eras; performance visualization.<\/li>\n<li><strong>Matlab Library (v0.9 \u2192 roadmap):<\/strong> Data prep, lagging\/normalization, solver stack (QR, Cholesky, banded, etc.), logging table, and analysis UI; roadmap to 1.0+.<\/li>\n<\/ul>\n<p><strong>4 Hands-On Segment<\/strong><\/p>\n<ul>\n<li>Recreate a small MSSP pipeline from primitives to selected breeders; interpret error dynamics; export a symbolic model; discuss deployment considerations.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Reading List<\/p>\n<p><strong>Foundations \/ Classics<\/strong><\/p>\n<ul>\n<li>Shannon, C.E. (1940) An Algebra for Theoretical Genetics. PhD thesis. Massachusetts Institute of Technology. (Also: Shannon, C.E. (1993) \u2018An Algebra for Theoretical Genetics\u2019, in Sloane, N.J.A. and Wyner, A.D. (eds.) Claude Elwood Shannon: Collected Papers. New York: IEEE Press, pp. 891\u2013896.)<\/li>\n<li>Huxley, T.H. and Huxley, J. (1947) Evolution and Ethics 1893-1943. London: Pilot Press.<\/li>\n<li>Wiener, N. (1948) Cybernetics: or Control and Communication in the Animal and the Machine. New York: John Wiley &amp; Sons. (Also: Wiener, N. (1961) Cybernetics: or Control and Communication in the Animal and the Machine. 2nd edn. Cambridge, MA: MIT Press.)<\/li>\n<li>Ashby, W.R. (1958) \u2018Requisite variety and its implications for the control of complex systems\u2019, Cybernetica, 1(2), pp. 83\u201399.<\/li>\n<\/ul>\n<p><strong>Self-Organization &amp; Early AI<\/strong><\/p>\n<ul>\n<li>Turing, A.M. (1950) \u2018Computing machinery and intelligence\u2019, Mind, 59(236), pp. 433\u2013460.<\/li>\n<li>von Neumann, J. (1966) Theory of Self-Reproducing Automata. Edited by A.W. Burks. Urbana: University of Illinois Press. (Based on manuscripts from 1948\u20131953.)<\/li>\n<li>Ivakhnenko, A.G. (1970) \u2018Heuristic self-organization in problems of engineering cybernetics\u2019, Automatica, 6, pp. 207\u2013219.<\/li>\n<li>Ivakhnenko, A.G. (1971) \u2018Polynomial theory of complex systems\u2019, IEEE Transactions on Systems, Man, and Cybernetics, SMC-1(4), pp. 364\u2013378.<\/li>\n<\/ul>\n<p><strong>Decision &amp; Evolutionary Perspectives<\/strong><\/p>\n<ul>\n<li>Gabor, D. (1969) \u2018Open-Ended Planning\u2019, in Jantsch, E. (ed.) Perspectives of Planning. Paris: OECD, pp. 329\u2013350. (Based on ideas from Gabor, D. (1959) Electronic Inventions and Their Impact on Civilization. Inaugural Lecture, 3 March. London: Imperial College of Science and Technology.)<\/li>\n<li>Fogel, L.J., Owens, A.J. and Walsh, M.J. (1966) Artificial Intelligence through Simulated Evolution. New York: John Wiley &amp; Sons.<\/li>\n<li>Ivakhnenko, N.A. and Marchev, A.A. (1978) \u2018Self-organization of a mathematical model for long-range planning of construction and installation activities\u2019, Soviet Automatic Control, 11(3), pp. 9\u201314.<\/li>\n<li>Ivakhnenko, A.G., Ivakhnenko, G.A. and M\u00fcller, J.-A. (1994) \u2018Self-organization of neural networks with active neurons\u2019, Pattern Recognition and Image Analysis, 4(2), pp. 185\u2013196.<\/li>\n<\/ul>\n<p><strong>MSSP &amp; Applications<\/strong><\/p>\n<ul>\n<li>Marchev, A.A. and Motzev, M.R. (1989) \u2018Principles of Multi-Stage Selection in Software Development in Decision Support Systems\u2019, in Lewandowski, A. and Stanchev, I. (eds.) Methodology and Software for Interactive Decision Support. Berlin: Springer-Verlag (Lecture Notes in Economics and Mathematical Systems, Vol. 337), pp. 181\u2013189.<\/li>\n<li>Marchev A. (2012) Multi-stage selection procedure for investment portfolio management, Proceedings of the IEEE International Conference on Control Applications, art. no. 6402732, pp. 593 \u2013 598, DOI: 10.1109\/CCA.2012.6402732<\/li>\n<li>Marchev A., Jr., Marchev A. (2014) Autonomous portfolio investment by multi-stage selection procedure, AIP Conference Proceedings, 1631, pp. 313 \u2013 322, DOI: 10.1063\/1.4902492<\/li>\n<li>Marchev A., (2016) Self-organization types for autonomous investment portfolio, 2016 IEEE 8th International Conference on Intelligent Systems, IS 2016 \u2013 Proceedings, art. no. 7737498, pp. 658 \u2013 663, DOI: 10.1109\/IS.2016.7737498<\/li>\n<li>Marchev A., Jr., Piryankova M. (2022) Evolution of the Concept of Self-Organization by the Founding Fathers of A.I., 10th International Scientific Conference on Computer Science, COMSCI 2022 \u2013 Proceedings, DOI: 10.1109\/COMSCI55378.2022.9912577<\/li>\n<li>Marchev A. (2023) An Implementation of Self-Organizing Multi-Stage Selection Procedure, 2023 11th International Scientific Conference on Computer Science, COMSCI 2023 \u2013 Proceedings, DOI: 10.1109\/COMSCI59259.2023.10315898<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Vertical<\/p>\n<p>Cutting-edge AI Research<\/p>\n<p style=\"font-size: 1.2em;font-weight: bold\">Timeline<\/p>\n<p>4 hours<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Speakers Prof. Angel Marchev, Jr. (University of National and World Economy (UNWE), Sofia) &nbsp; Abstract This tutorial introduces a principled, historically informed, and hands-on path to building AI systems that cope with real-world complexity via self-organization and adaptivity. We start from the cybernetic roots &#8211; Wiener\u2019s feedback, Ashby\u2019s law of requisite variety, and Shannon\u2019s information&#8230;<\/p>\n","protected":false},"author":2627,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-297","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Self-Organizing AI: Cybernetics to Multi-Stage Selection<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ieeesmc.org\/cai-2026\/tutorial-8-self-organizing-ai\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Self-Organizing AI: Cybernetics to Multi-Stage Selection\" \/>\n<meta property=\"og:description\" content=\"Speakers Prof. Angel Marchev, Jr. (University of National and World Economy (UNWE), Sofia) &nbsp; Abstract This tutorial introduces a principled, historically informed, and hands-on path to building AI systems that cope with real-world complexity via self-organization and adaptivity. 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