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Artificial Intelligence and Knowledge Management Methodological Questions

Abstract

The article considers the knowledge management as a modern management concept, where a variety of both classical and non-classical areas of scientific knowledge: philosophy, computer science, artificial intelligence, political science, Economics, organizational behavior. The article highlights the main methodological problems of knowledge management: the effectiveness and limits of applicability of the concept of knowledge in organization management, interdisciplinarity of knowledge management, collective implicit knowledge, structure of knowledge in the organization. The article analyzes the new technology of working with Big Data knowledge, the possibility of its use in political forecasting.

About the Author

Yury Petrunin
Lomonosov Moscow State University
Russian Federation


References

1. Encyclopedia of knowledge management / ed. by D. Schwartz. - Hershey; L.; Melbourne; Singapore: Idea Group Reference, 2006. P. 27.

2. Wiig K. Knowledge Management Foundations: How People and Organizations Create, Represent and Use Knowledge. - Arlington (TX): Schema Press, 1993

3. Wiig K. Knowledge Management: The Central Management Focus for Intelligent-Acting Organizations. - Arlington (TX), Schema Press: 1994

4. Wiig K. Knowledge Management Methods: Practical Approaches to Managing Knowledge. - Arlington (TX): Schema Press, 1995.

5. Сенге П. Пятая дисциплина. Искусство и практика самообучающейся организации. - Харьков, 2006.

6. Марьясин Д.С. Административная реформа как процесс самообучения государства // Вестник Моск. ун-та. Серия 21. Управление (государство и общество). 2006. № 4.

7. Сенге П. Пятая дисциплина. Искусство и практика самообучающейся организации. - Харьков, 2006. С. 32.

8. Петрунин Ю.Ю., Петрунина Е.Ю. Модели и методы искусственного интеллекта в управлении знаниями. 2010. Вып. 24. Сентябрь.

9. Дрейфус Х. Чего не могут вычислительные машины. Критика искусственного разума. - М., 1978 (англ. изд. 1972 г.).

10. Power B. Artificial Intelligence Is Almost Ready for Business // Harvard Business Review. March 19, 2015.

11. Monroe B.L., Pan J., Roberts M.E., Sen M., Sinclair B. No! Formal Theory, Causal Inference, and Big Data Are Not Contradictory Trends in Political Science // PS: Political Science and Politics. 2015. № 48 (1). P. 71-74

12. Nagler J., Tucker J. Drawing Inferences and Testing Theories with Big Data // PS: Political Science and Politics. 2015. № 48 (1). P. 84-88

13. Grimmer J. We Are All Social Scientists Now: How Big Data, Machine Learning, and Causal Inference Work Together // PS: Political Science and Politics. 2015. № 48 (1). P. 80-83

14. Ansolabehere S., Hersh E. Validation: What Big Data Reveal about Survey Misreporting and the Real Electorate // Political Analysis. 2012. № 20 (4). P. 437459

15. Berinsky A.J., Huber G.A., Lenz G.S. Evaluating Online Labor Markets for Experimental Research: Amazon.com’s Mechanical Turk // Political Analysis. 2012. № 20 (3). P. 351-368

16. Monroe B.L., Colaresi M.P., Quinn K.M. Fightin’ Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict // Political Analysis. 2008. № 16 (4). P. 372-403

17. Ratkovic M.T., Eng K.H. Finding Jumps in Otherwise Smooth Curves: Identifying Critical Events in Political Processes // Political Analysis. 2010. № 18 (1). P. 55-77.


Review

For citations:


Petrunin Yu. Artificial Intelligence and Knowledge Management Methodological Questions. Russian Journal of Philosophical Sciences. 2016;(8):67-74. (In Russ.)



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ISSN 0235-1188 (Print)
ISSN 2618-8961 (Online)