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Autor
Bonner Richard F., Mamchych Tetyana I.
Tytuł
Can One Learn Too Much for One's Own Good? Rational Choice, Learning, and Their Interplay
Źródło
Prace Naukowe Akademii Ekonomicznej we Wrocławiu, 2005, nr 1064, s. 353-366, bibliogr. 104 poz.
Tytuł własny numeru
Pozyskiwanie wiedzy i zarządzanie wiedzą
Słowa kluczowe
Wiedza, Zarządzanie wiedzą, Proces uczenia się, Analiza piśmiennictwa ekonomicznego
Knowledge, Knowledge management, Learning process, Economic literature analysis
Uwagi
summ.
Abstrakt
W artykule rozważany jest problem uczenia, który obejmuje zmiany systemu wiedzy firmy wpływające na modyfikację działania tej firmy a zarazem jej preferencje. Podkreślono iż, podstawy tych zmian mogą zostać rozpoznane dopiero po uczeniu. Zaprezentowano krótki przegląd obecnych teorii uczenia i racjonalnego wyboru.

Authors have considered a learning problem, which occurs when changes in the knowledge system of a firm (learning) alter its business objectives (preference). Grounds for evaluating learning may become known only after the learning. The article presents a review of current learning theories and the rational choice.
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Biblioteka Główna Uniwersytetu Ekonomicznego w Poznaniu
Biblioteka Główna Uniwersytetu Ekonomicznego we Wrocławiu
Bibliografia
Pokaż
  1. Adam K., Learning While Searching for the Best Alternative, Working paper ECO 99/4, European University Institute, Department of Economics, Florence, Italy, 1999.
  2. Ahlswede R., Wegener I., Search Problems, Wiley, 1987.
  3. Angluin D., Queries and concept learning, Machine Learning, vol. 2 (1988), pp. 319-342.
  4. Angluin D., Computational Learning Theory, survey and selected bibliography. 24th Annual ACM STOC, (1992), pp. 351-359.
  5. Angluin D., A 1996 Snapshot of Computational Learning Theory, ACM Computing Surveys, vol. 28, no. 4es (1996).
  6. Angluin D., Kharitonov M., When Won't Membership Queries Help! J. Comp. Syst. Sei., vol. 50 (1995), pp. 336-355.
  7. Anthony M., Barlett P.L., Neural Network Learning, Theoretical Foundations. Cambridge University Press, 1999.
  8. Anthony M., Barlett P.L., Computational Learning, Cambridge University Press, 1992.
  9. Apesteguia J., Ballester M.A., A Theory of Reference-dependet Behavior, (February 2004). http://ssrn.com/abstract=575782
  10. Aragones E., Gilboa I., Postlewaite A., Schmeidler D., Fact-free learning, PIER Working Paper 03-023, Penn Institute for Economic Research, University of Pennsylvania, 2003.
  11. Auer P., Cesa-Bianchi N., Freund Y., Schapire R.E., The Nonstochastic Multiarmed Bandit Problem, SIAM Journal on Computing, vol. 32, no. 1 (2002), pp. 48-77.
  12. Barr J., Saraceno F., A Computational Theory of the firm, Journal of Economic Behavior and Organization, vol. 49 (2002), pp. 345-361.
  13. Bergemann D., Hege U., The financing of innovation: learning and stopping, Working Paper 16, Tilburg University, Center for Economic Research, 2000.
  14. Bertsekas D.P., Dynamic Programming and Optimal Control, Athena Scientific, 1995.
  15. Blume L.E., Easley D., Rational Expectations and Rational Learning, Economic Theory Workshop in Honor of Roy Radner, Cornell University, 1992. http://econwpa.wustl.edu/eprints/game/papers/9307/9307003.abs.
  16. Bonner R.F., Fedyszak-Koszela A., When to Stop Learning? Bounding the stopping time in the PAC model, Theory of Stochastic Processes, vol. 7(23), (2001), no.1-2, pp. 5-12.
  17. Brenner T., Modelling Learning in Economics, Edward Elgar Publishers, 1999.
  18. Brenner T., Computational Techniques for Modelling Learning in Economics. Kluwer, 1999.
  19. Cairoli R., Dalang R.C., Sequential Stochastic Optimization, Wileylnterscience, 1996.
  20. Chateauneuf A., Vergnaud J-C., Ambiguity Reduction Through new Statistical Data, 1st Int. Symp. on Imprecise Probabilities and Their Applications, Ghent, Belgium, 1999.
  21. Chateauneuf A., Eichberger J., Grant S., Choice Under Uncertainty With the Best and Worst in Mind: neo-additive capacities, Working paper, 2002.
  22. Cohen M., Gilboa I., Jaffray J.Y. Schmeidler D., An Experimental Study of Updating Ambigous Beliefs, 1st Int. Symp. on Imprecise Probabilities and Their Applications, Ghent, Belgium,1999.
  23. Cover T.M., Thomas J.A., Elements of Information Theory, Wiley, 1991.
  24. Debreu G., Theory of Value: an Axiomatic Analysis of Economic Equilibrium, Wiley, 1959.
  25. DeSarbo W.S., Fong D.K.H., Liechty J., Coupland J.C., Evolutionary Preference/utility Functions, Psychometrika, in press (2004).
  26. Doyle J., Rationality and its Roles in Reasoning, Computational Intelligence, vol. 8, no. 2 (1992), pp. 376-409.
  27. Doyle J., Dean T., et al. Strategic Directions in Artificial Intelligence, ACM Computing Surveys, vol. 28, no. 4 (1996), pp. 653-670.
  28. Dubois D., et al. On the use of the Discrete Sugeno Integral in Decision-making, Int. Journal of Uncertainty, Fuzziness, and Knowledge-Based Systems, vol. 9, no. 5 (2001), pp. 539-561.
  29. Dubois D., et al. Qualitative Decision Theory: from Savage's Axioms to Nonmonotonic Reasoning, Journal of the ACM, vol. 49, no. 4 (2002), pp. 455-495.
  30. Dudley R.M., Uniform Central Limit Theorems, Cambridge University Press, 1999.
  31. Eboli M., Two Models of Information Costs Based on Computational Complexity, Computational Economics, vol. 21 (2003), pp. 87-105.
  32. Ellsberg D., Risk, Ambiguity, and the Savage Axioms, Quarterly Journal of Economics, vol. 75 (1961), pp. 645-669.
  33. FergusonT.S., Optimal Stopping and Applications, http://www.math.ucla.edu/tom/Stopping/Contents.html.
  34. Foster J.E., Mitra T., Ranking Investment Projects, Economic Theory, vol. 22 (2003), pp. 469-494.
  35. Freund Y., Schapire R.E., A Decision-theoretic Generalization of on-line Learning and an Application to Boosting, Journal of Computer and System Sciences, vol. 55, no. 1, (1997), pp. 119-139.
  36. Fudenberg D., Levine D.K., The Theory of Learning in Games, MIT Press, 1998.
  37. Fugikawa T., Oda S.H., Search and Choice Under Uncertainty, ESA International 2003. http://www.peel.pitt.edu/esa2003/participants.html.
  38. Gajdos T., Tallon J-M., Vergnaud J-C., Decision Making with Imprecise Probabilistic Information, Journal of Mathematical Economics, (2004), in press.
  39. Ganter B., Wille R., Formal Concept Analysis: Mathematical Foundations, Springer, 1999.
  40. Gigerenzer G., Selten R., Bounded Rationality: An Adaptive Toolbox, MIT Press, 2002.
  41. Gilboa I., Schmeidler D., Maxmin Expected Utility with non-unique Prior, Journal of Mathematical Economics, Vol. 18 (1993), pp. 141-153.
  42. Gilboa I., Schmeidler D., A Theory of Case-Based Decisions, Cambridge University Press, 2001.
  43. Gilboa I., Schmeidler D., Inductive Inference: an Axiomatic Approach, Cowles Foundation Discussion Paper 1339, Yale University, 2001.
  44. Gilboa I., Schmeidler D., Cognitive Foundations of Probability, Cowles Foundation Discussion Paper 1340, Yale University, 2001.
  45. Gilboa I., Schmeidler D., Subjective Distribution, Cowles Foundation Discussion Paper 1341, Yale University, 2001.
  46. Gilboa I., Schmeidler D., A Derivation of Expected Utility Maximization in the Context of a Game, Cowles Foundation Discussion Paper 1342, Yale University, 2001.
  47. Gilboa I., Postlewaite A., Schmeidler D., Rationality of Belief. Or: why Bayesianism is Neither Necessary nor sufficient for Rationality, Working Paper 04-011, Penn Institute for Economic Research, University of Pennsylvania, 2004.
  48. Gilli M., A General Approach to Rational Learning in Games, Bulletin of Economic Research, vol. 53, no. 4 (2001), pp. 275-303.
  49. Giraud R., Reference-dependent Preferences: Rationality, Mechanism and Welfare Implications, RUD (Risk, Uncertainty and Decisions) 2004 Conference, Zell Center for Risk Research, Kellog School of Management, Northwestern University, Illinois, 2004 http://www.kellogg.northwestern.edu/research/risk/rud/papers/giraud.pdf
  50. Gold E.M., Language Identification in the Limit, Information and Control, v. 10 (1967), pp. 447-474.
  51. Grant S., Kajii A., Polak B., Preference for Information and Dynamic Consistency, Cowles Foundation Discussion Paper 1208, Yale Universityle University, 1999.
  52. Grant S., Kajii A., Polak B., Decomposable Choice Under Uncertainty, Cowles Foundation Discussion Paper 1207, Yale Universityle University, 1999.
  53. Halpern J.Y., Conditional Plausibility Measures and Bayesian Networks, Journal of Artificial Intelligence Research, vol. 14 (2001), pp. 359-389.
  54. Haussler D., Decision Theoretic Generalizations of the PAC Model for Neural Nets and Other Learning Applications, Information and Computation, vol. 100 (1992), pp. 78-150.
  55. Haussler D., Kearns M.J., Littlestone N., Warmuth M.K., Equivalence of Models for Polynomial Learnability, Information and Computation, vol. 95 (1991), pp. 129-161.
  56. Haussler D., Littlestone N., Warmuth M.K., Predicting (0; 1) Functions on Randomly Drawn Points, Information and Computation, vol. 115 (1994), pp. 284-293.
  57. Heinemann M., Efficiency of Rational Learning Under Asymmetric Information, Económica, vol. 70 (2003), pp. 1-15.
  58. Jain S., Osherson D., Royer J.S., Sharma A., Systems that learn, 2nd edition. MIT Press, 1999.
  59. Jerrey R.C., The Logic of Decision, 2nd edition, University of Chicago Press, 1983.
  60. Kahneman D., Tversky A., Choices, Values, and Frames, Cambridge University Press, 2000.
  61. Kalai G., Lehrer E., Weak and Strong Merging of Opinions, Journal of Mathematical Economics, vol. 23, (1994), pp. 73-100.
  62. Kalai G., Learnability and Rationality of Choice, Journal of Economic Theory, vol. 113, no. 1 (2003), pp. 104-117.
  63. Kalai G., Rubinstein A., Spiegler R., Rationalizing Choice Functions by Multiple Rationals, Econometrica, vol. 70 (2002), pp. 2481-2488.
  64. Kalai E., Solan E., Randomization and Simplification in Dynamic Decisionmaking, Journal of Economic Theory, vol. 111 (2003), pp. 251-264.
  65. Kearns M.J., Vazirani U.V., An Introduction to Computational Learning Theory, MIT Press, 1994.
  66. Kearns M.J., Valiant L., Cryptographic Limitations on Learning Boolean Formulae and finite Automata, Journal of the ACM, vol. 41, no. 1 (1994), pp. 67-95.
  67. Khardon R., Roth D., Learning to Reason, Journal of the ACM, vol. 44, no. 5 (1997), pp. 697-725.
  68. Kiefer J., Sequential Minimax Search for a Maximum, Proc. American Mathematical Society, vol. 4, no. 2 (1953), pp. 502-506.
  69. Kohn M., Shavell S., The Theory of Search, Journal of Economic Theory, vol. 4, no. 2 (1974), pp. 593-123.
  70. Koszegi B., Rabin M., A Model of Reference-dependent Preferences, Working Paper E04-337,Department of Economics, University of California, Berkley, 2004. http://repositories.cdlib.org/iber/econ/E04-337
  71. Marchand M., Shawe-Taylor J., The Set Covering Machine, Journal of Machine Learning Research, vol. 3, (2002), pp. 723-746.
  72. McAllester D.A., Some PAC-Bayesian Theorems, COLT 98, ACM (1998), pp. 230-234.
  73. McClennen E.F., Rationality and Dynamic Choice: Foundational Explorations, Cambridge University Press, 1990.
  74. Mukerji S., Talion J-M., Ellsbergs two-color Experiment, Portfolio Inertia and Ambiguity, Journal of Mathematical Economics, Vol. 39, Issues 3-4, June (2003), pp. 299-316.
  75. Mukerji S., Tallon J-M., An Overview of Economic Applications of David Schmeidler's Models of Decision Making Under Uncertainty, Chapter 13 in: I. Gilboa (ed.). Uncertainty in Economic Theory: A collection of essays in honor of David Schmeidler's 65th birthday. Routledge Publishers, 2004.
  76. Nakhaeizadeh G., Taylor C.C., Machine Learning and Statistics: the Interface, Wiley, 1997.
  77. Orbay H., Information Processing Hierarchies, Journal of Economic Theory, vol. 105 (2002),pp. 370-407.
  78. Ribeiro C., Reinforcement Learning Agents, Artificial Intelligence Review, vol. 17 (2002), pp. 223-250.
  79. Pollard D., Empirical Processes: Theory and Applications, NSF-CBMS Regional Conference Series in Probability and Statistics, vol. 2, Institute of Mathematical Statistics, 1990.
  80. Poznyak A.S., Najim K., Learning Automata and Stochastic Optimization, Springer, 1997.
  81. Sandroni A., Smorodinsky R., The Speed of Rational Learning, International Journal of Game Theory, vol. 28 (1999), pp. 199-210.
  82. Savage L.J., Bounded Rationality in Macroeconomics: The Arne Ryde Memorial Lectures, Oxford University Press, 1994.
  83. Savage L.J., The Foundations of Statistics, Wiley, 1954.
  84. Schmeidler D., Subjective Probability and Expected Utility Without Additivity, Econometrica, vol. 57, No. 3, (1989), pp. 571-587.
  85. Scholkopf B., Burges C.J.C., Smola A.J., Advances in Kernel Methods: Support Vector Learning, MIT Press, 1998.
  86. Shoham Y., The Open Scientific Borders of AI, and the case of Economics, ACM Computing Surveys, vol. 28, no. 4es (1996).
  87. Cucker F., Smale S., On the Mathematical Foundations of Learning, Bulletin (New Series) of the American Mathematical Society, Vol. 39, No. 1 (2001), pp. 1-49.
  88. Sobel J., Economists' Models of Learning, Journal of Economic Theory, vol. 94, (2000), pp. 241-261.
  89. Sutton R., Barto A., Reinforcement Learning, MIT Press, 1999.
  90. Szepesvari C., Littman M.L., A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms, Neural Computation, vol. 8, (1999), pp. 217-259.
  91. Tallon J-M., Vergnaud J-C., Choice Axioms for Positive Value of Information, Working Paper, 2002. http://eurequa.univ-parisl .fr/membres/tallon/tallon.htm
  92. Tallon J-M., Vergnaud J-C., Beliefs and Dynamic Consistency, Working Paper, 2003. http://eurequa.univ-paris 1 .fr/membres/tallon/tallon.htm.
  93. Taylor A., Mathematics and Politics: Strategy, Voting, Power and Proof, Springer-Verlag, 1995.
  94. Valiant L.G., A Theory of the Learnable,Comm.ACM,vol.21,no. 11, (1984), pp. 1134-114.
  95. Vapnik V.N., Statistical Learning Theory, Wiley Interscience, 1998.
  96. Vapnik V.N., The Nature of Statistical Learning Theory, Springer, 1995.
  97. Vidyasagar M., A Theory of Learning and Generalization, Springer, 1997.
  98. Wakai K., Aggregation of Agents with Multiple Priors and Homogeneous Equilibrium Behavior, Working Paper, Yale University, 2001.
  99. Wang T., A Class of Multi-prior Preferences, Working Paper, University of British Columbia, 2003.
  100. Weitzman M., Optimal Search for the Best Alternative, Econometrica, vol. 47, no. 3, (1979), pp. 641-654.
  101. Wellman M, The Economic Approach to Artificial Intelligence, ACM Computing Surveys, vol. 27, no. 3 (1995), pp. 360-362.
  102. Wellman M., Rationality in Decision Machines, AAAI Fall Symposium on Rational Agency, November 1995.
  103. Whittle P., Optimization Over Time, Wiley, 1982.
  104. Zilberstein S., Resource-bounded Reasoning in Intelligent Systems, ACM Computing Surveys,vol. 28, no. 4es, 1996.
Cytowane przez
Pokaż
ISSN
0324-8445
Język
eng
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