eprintid: 13583 rev_number: 8 eprint_status: archive userid: 353 dir: disk0/00/01/35/83 datestamp: 2016-08-04 12:01:28 lastmod: 2022-02-02 14:10:33 status_changed: 2016-08-04 12:01:28 type: article metadata_visibility: show item_issues_count: 1 creators_name: Ermoliev, Y. creators_id: 1445 title: Learning and adoption versus optimization ispublished: pub abstract: The aim of the talk is to discuss the role of stochastic optimization techniques in designing learning and adaptive processes. Neural and Bayesian networks, path-dependent adaptive urn's processes, automation learning problems and agent based models are considered. A unified general framework of stochastic optimization is proposed enabling us to derive various known and many other adaptive procedures for such different classes of models. We emphasize similarities with natural evolutionary processes, but at the same time we show that this similarity may be misleading when we deal with man-made systems. The "particles" (economic agents, enterprises, countries) of such systems do not follow strong laws like the laws in mechanics and physics (for instance, gravity law). Economic "particles" have flexibility to choose different behavioral patterns (policies, decisions). The uncertainty is a key issue in the modeling of anthropogenic systems and the main methodological challenge is to address related uncertainties explicitly within overall risk-based decision making processes. Purely myopic, trial and error approaches may be expensive, time consuming and even dangerous because of the irreversible nature of decisions. The decisive feature of man-made systems is their ability to anticipate and affect possible future outcomes. Approaches facilitating our ability for making decisions in the presence of uncertainties and related risks are discussed. date: 2005-06-03 date_type: published id_number: 10.1007/3-540-63077-5_20 creators_browse_id: 338 full_text_status: none publication: Lecture Notes in Computer Science volume: 1237 pagerange: 2-2 refereed: TRUE issn: 0302-9743 book_title: Multi-Agent Rationality coversheets_dirty: FALSE fp7_project: no fp7_type: info:eu-repo/semantics/article citation: Ermoliev, Y. (2005). Learning and adoption versus optimization. Lecture Notes in Computer Science 1237 p. 2. 10.1007/3-540-63077-5_20 .