Warianty tytułu
Języki publikacji
Abstrakty
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.
Rocznik
Strony
353-366
Opis fizyczny
Twórcy
autor
autor
Bibliografia
- Adam K., Learning While Searching for the Best Alternative, Working paper ECO 99/4, European University Institute, Department of Economics, Florence, Italy, 1999.
- Ahlswede R., Wegener I., Search Problems, Wiley, 1987.
- Angluin D., Queries and concept learning, Machine Learning, vol. 2 (1988), pp. 319-342.
- Angluin D., Computational Learning Theory, survey and selected bibliography. 24th Annual ACM STOC, (1992), pp. 351-359.
- Angluin D., A 1996 Snapshot of Computational Learning Theory, ACM Computing Surveys, vol. 28, no. 4es (1996).
- Angluin D., Kharitonov M., When Won't Membership Queries Help! J. Comp. Syst. Sei., vol. 50 (1995), pp. 336-355.
- Anthony M., Barlett P.L., Neural Network Learning, Theoretical Foundations. Cambridge University Press, 1999.
- Anthony M., Barlett P.L., Computational Learning, Cambridge University Press, 1992.
- Apesteguia J., Ballester M.A., A Theory of Reference-dependet Behavior, (February 2004). http://ssrn.com/abstract=575782
- 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.
- 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.
- Barr J., Saraceno F., A Computational Theory of the firm, Journal of Economic Behavior and Organization, vol. 49 (2002), pp. 345-361.
- Bergemann D., Hege U., The financing of innovation: learning and stopping, Working Paper 16, Tilburg University, Center for Economic Research, 2000.
- Bertsekas D.P., Dynamic Programming and Optimal Control, Athena Scientific, 1995.
- 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.
- 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.
- Brenner T., Modelling Learning in Economics, Edward Elgar Publishers, 1999.
- Brenner T., Computational Techniques for Modelling Learning in Economics. Kluwer, 1999.
- Cairoli R., Dalang R.C., Sequential Stochastic Optimization, Wileylnterscience, 1996.
- Chateauneuf A., Vergnaud J-C., Ambiguity Reduction Through new Statistical Data, 1st Int. Symp. on Imprecise Probabilities and Their Applications, Ghent, Belgium, 1999.
- Chateauneuf A., Eichberger J., Grant S., Choice Under Uncertainty With the Best and Worst in Mind: neo-additive capacities, Working paper, 2002.
- 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.
- Cover T.M., Thomas J.A., Elements of Information Theory, Wiley, 1991.
- Debreu G., Theory of Value: an Axiomatic Analysis of Economic Equilibrium, Wiley, 1959.
- DeSarbo W.S., Fong D.K.H., Liechty J., Coupland J.C., Evolutionary Preference/utility Functions, Psychometrika, in press (2004).
- Doyle J., Rationality and its Roles in Reasoning, Computational Intelligence, vol. 8, no. 2 (1992), pp. 376-409.
- Doyle J., Dean T., et al. Strategic Directions in Artificial Intelligence, ACM Computing Surveys, vol. 28, no. 4 (1996), pp. 653-670.
- 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.
- 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.
- Dudley R.M., Uniform Central Limit Theorems, Cambridge University Press, 1999.
- Eboli M., Two Models of Information Costs Based on Computational Complexity, Computational Economics, vol. 21 (2003), pp. 87-105.
- Ellsberg D., Risk, Ambiguity, and the Savage Axioms, Quarterly Journal of Economics, vol. 75 (1961), pp. 645-669.
- FergusonT.S., Optimal Stopping and Applications, http://www.math.ucla.edu/tom/Stopping/Contents.html.
- Foster J.E., Mitra T., Ranking Investment Projects, Economic Theory, vol. 22 (2003), pp. 469-494.
- 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.
- Fudenberg D., Levine D.K., The Theory of Learning in Games, MIT Press, 1998.
- Fugikawa T., Oda S.H., Search and Choice Under Uncertainty, ESA International 2003. http://www.peel.pitt.edu/esa2003/participants.html.
- Gajdos T., Tallon J-M., Vergnaud J-C., Decision Making with Imprecise Probabilistic Information, Journal of Mathematical Economics, (2004), in press.
- Ganter B., Wille R., Formal Concept Analysis: Mathematical Foundations, Springer, 1999.
- Gigerenzer G., Selten R., Bounded Rationality: An Adaptive Toolbox, MIT Press, 2002.
- Gilboa I., Schmeidler D., Maxmin Expected Utility with non-unique Prior, Journal of Mathematical Economics, Vol. 18 (1993), pp. 141-153.
- Gilboa I., Schmeidler D., A Theory of Case-Based Decisions, Cambridge University Press, 2001.
- Gilboa I., Schmeidler D., Inductive Inference: an Axiomatic Approach, Cowles Foundation Discussion Paper 1339, Yale University, 2001.
- Gilboa I., Schmeidler D., Cognitive Foundations of Probability, Cowles Foundation Discussion Paper 1340, Yale University, 2001.
- Gilboa I., Schmeidler D., Subjective Distribution, Cowles Foundation Discussion Paper 1341, Yale University, 2001.
- Gilboa I., Schmeidler D., A Derivation of Expected Utility Maximization in the Context of a Game, Cowles Foundation Discussion Paper 1342, Yale University, 2001.
- 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.
- Gilli M., A General Approach to Rational Learning in Games, Bulletin of Economic Research, vol. 53, no. 4 (2001), pp. 275-303.
- 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
- Gold E.M., Language Identification in the Limit, Information and Control, v. 10 (1967), pp. 447-474.
- Grant S., Kajii A., Polak B., Preference for Information and Dynamic Consistency, Cowles Foundation Discussion Paper 1208, Yale Universityle University, 1999.
- Grant S., Kajii A., Polak B., Decomposable Choice Under Uncertainty, Cowles Foundation Discussion Paper 1207, Yale Universityle University, 1999.
- Halpern J.Y., Conditional Plausibility Measures and Bayesian Networks, Journal of Artificial Intelligence Research, vol. 14 (2001), pp. 359-389.
- 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.
- 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.
- Haussler D., Littlestone N., Warmuth M.K., Predicting (0; 1) Functions on Randomly Drawn Points, Information and Computation, vol. 115 (1994), pp. 284-293.
- Heinemann M., Efficiency of Rational Learning Under Asymmetric Information, Económica, vol. 70 (2003), pp. 1-15.
- Jain S., Osherson D., Royer J.S., Sharma A., Systems that learn, 2nd edition. MIT Press, 1999.
- Jerrey R.C., The Logic of Decision, 2nd edition, University of Chicago Press, 1983.
- Kahneman D., Tversky A., Choices, Values, and Frames, Cambridge University Press, 2000.
- Kalai G., Lehrer E., Weak and Strong Merging of Opinions, Journal of Mathematical Economics, vol. 23, (1994), pp. 73-100.
- Kalai G., Learnability and Rationality of Choice, Journal of Economic Theory, vol. 113, no. 1 (2003), pp. 104-117.
- Kalai G., Rubinstein A., Spiegler R., Rationalizing Choice Functions by Multiple Rationals, Econometrica, vol. 70 (2002), pp. 2481-2488.
- Kalai E., Solan E., Randomization and Simplification in Dynamic Decisionmaking, Journal of Economic Theory, vol. 111 (2003), pp. 251-264.
- Kearns M.J., Vazirani U.V., An Introduction to Computational Learning Theory, MIT Press, 1994.
- 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.
- Khardon R., Roth D., Learning to Reason, Journal of the ACM, vol. 44, no. 5 (1997), pp. 697-725.
- Kiefer J., Sequential Minimax Search for a Maximum, Proc. American Mathematical Society, vol. 4, no. 2 (1953), pp. 502-506.
- Kohn M., Shavell S., The Theory of Search, Journal of Economic Theory, vol. 4, no. 2 (1974), pp. 593-123.
- 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
- Marchand M., Shawe-Taylor J., The Set Covering Machine, Journal of Machine Learning Research, vol. 3, (2002), pp. 723-746.
- McAllester D.A., Some PAC-Bayesian Theorems, COLT 98, ACM (1998), pp. 230-234.
- McClennen E.F., Rationality and Dynamic Choice: Foundational Explorations, Cambridge University Press, 1990.
- 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.
- 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.
- Nakhaeizadeh G., Taylor C.C., Machine Learning and Statistics: the Interface, Wiley, 1997.
- Orbay H., Information Processing Hierarchies, Journal of Economic Theory, vol. 105 (2002),pp. 370-407.
- Ribeiro C., Reinforcement Learning Agents, Artificial Intelligence Review, vol. 17 (2002), pp. 223-250.
- Pollard D., Empirical Processes: Theory and Applications, NSF-CBMS Regional Conference Series in Probability and Statistics, vol. 2, Institute of Mathematical Statistics, 1990.
- Poznyak A.S., Najim K., Learning Automata and Stochastic Optimization, Springer, 1997.
- Sandroni A., Smorodinsky R., The Speed of Rational Learning, International Journal of Game Theory, vol. 28 (1999), pp. 199-210.
- Savage L.J., Bounded Rationality in Macroeconomics: The Arne Ryde Memorial Lectures, Oxford University Press, 1994.
- Savage L.J., The Foundations of Statistics, Wiley, 1954.
- Schmeidler D., Subjective Probability and Expected Utility Without Additivity, Econometrica, vol. 57, No. 3, (1989), pp. 571-587.
- Scholkopf B., Burges C.J.C., Smola A.J., Advances in Kernel Methods: Support Vector Learning, MIT Press, 1998.
- Shoham Y., The Open Scientific Borders of AI, and the case of Economics, ACM Computing Surveys, vol. 28, no. 4es (1996).
- 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.
- Sobel J., Economists' Models of Learning, Journal of Economic Theory, vol. 94, (2000), pp. 241-261.
- Sutton R., Barto A., Reinforcement Learning, MIT Press, 1999.
- Szepesvari C., Littman M.L., A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms, Neural Computation, vol. 8, (1999), pp. 217-259.
- 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
- Tallon J-M., Vergnaud J-C., Beliefs and Dynamic Consistency, Working Paper, 2003. http://eurequa.univ-paris 1 .fr/membres/tallon/tallon.htm.
- Taylor A., Mathematics and Politics: Strategy, Voting, Power and Proof, Springer-Verlag, 1995.
- Valiant L.G., A Theory of the Learnable,Comm.ACM,vol.21,no. 11, (1984), pp. 1134-114.
- Vapnik V.N., Statistical Learning Theory, Wiley Interscience, 1998.
- Vapnik V.N., The Nature of Statistical Learning Theory, Springer, 1995.
- Vidyasagar M., A Theory of Learning and Generalization, Springer, 1997.
- Wakai K., Aggregation of Agents with Multiple Priors and Homogeneous Equilibrium Behavior, Working Paper, Yale University, 2001.
- Wang T., A Class of Multi-prior Preferences, Working Paper, University of British Columbia, 2003.
- Weitzman M., Optimal Search for the Best Alternative, Econometrica, vol. 47, no. 3, (1979), pp. 641-654.
- Wellman M, The Economic Approach to Artificial Intelligence, ACM Computing Surveys, vol. 27, no. 3 (1995), pp. 360-362.
- Wellman M., Rationality in Decision Machines, AAAI Fall Symposium on Rational Agency, November 1995.
- Whittle P., Optimization Over Time, Wiley, 1982.
- Zilberstein S., Resource-bounded Reasoning in Intelligent Systems, ACM Computing Surveys,vol. 28, no. 4es, 1996.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000093074799