Modelling Learning as Modelling

References

[2] Binmore, K. and L. Samuelson, 1990. Evolutionary stability in repeated games played by finite automata. Working paper, University of Michigan, Ann Arbor, MI and University of Wisconsin, Madison, WI.

[3] Bray, M.M. and N.E. Savin, 1986. Rational expectations equilibria, learning, and model
specification. Econometrica 54, 1129-1160.

[4] Dixon, H.D., S. Moss and S. Wallis, 1995. Axelrod meets Cournot: Oligopoly and the evolutionary metaphor Part 1. Centre for Policy Modelling Report no. 006, Centre for Policy Modelling, Aytoun Bld., Aytoun St., Manchester, UK.

[5] Edmonds, B. in press. What is Complexity? The philosophy of complexity per se with applications to some example in evolution. In Heylighen F. & Aerts D. (eds.), The Evolution of Complexity. Dordrecht: Kluwer.

[6] Edmonds, B., Moss, S.J. and Wallis, S. 1996. Logic, Reasoning and a Programming Language for Simulating Economic and Business Processes with Artificial Intelligent Agents. In Ein-Dor, P. (ed.) Artificial Intelligence in Economics and Management. Boston, MA: Kluwer, 221-230.

[7] Fox, J., 1994. On the necessity of probability: Reasons to believe and grounds for doubt. In Wright G. and Ayton P. (eds.). Subjective Probability. London: John Wiley.

[8] Fox, H., Krause P.J. and Elvang Goranssan M., 1993. Argumentation as a general framework for uncertain reasoning. Uncertainty in Artificial Intelligence, 8. San Mateo: Morgan Kaufmann.

[9] Halpern, J.Y. and Fagin R. 1992. Two views of belief - belief as generalized probability and belief as evidence. Artificial Intelligence, 54(3):275-317.

[10] Koza, J. 1992. Genetic Programming. Cambridge, MA: MIT Press.

[11] Lenat, D.B. 1991. On the Thresholds of Knowledge. Artificial Intelligence, 47:185-250.

[12] Marimon, R., E. McGrattan and Sargent T.J. 1989. Money as a medium of exchange in an economy with artificially intelligent agents. Journal of Economic Dynamics and Control, 14:329-373.

[13] Masuch, M. and Huang Z. 1994. A logical deconstruction of organizational action: formalizing Thompson's `Organizations in Action' in a multi-agent action logic. CCSOM Working Paper 94-120,

[14] Moss, S., 1993. The economics of positive methodology. In Blackwell R., Chatta J. and Nell E., (eds.). Economics as Worldly Philosophy. Basingstoke: Macmillan.

[15] Moss, S., 1995. Control metaphors in the modelling of decision-making behaviour. Computational Economics, 8:283-301.

[16] Moss, S. and Edmonds B. 1994. Economic methodology and computability: implications for the evaluation of econometric forecasts. CPM Report no. 001, Centre for Policy Modelling, Aytoun Bld., Aytoun St., Manchester, UK.

[17] Moss, S. and Kuznetsova, O. 1995. Modelling the Process of Market Emergence. MODEST `95 (Modelling Economic and Social Transition), Warsaw.

[18] Pearl, J. 1978. On the Connection Between the Complexity and Credibility of Inferred Models. International Journal of General Systems, 4:255-264.

[19] Popper, K., 1965. Conjectures and Refutations: the growth of scientific Knowledge. New York: Harper Torchbooks.

[20] Quine, W.V. O. 1960. Simple Theories of a Complex World. In The Ways of Paradox. New York: Random House, 242-246.

[21] Wallis, S., Edmonds, B. and Moss, S.J. 1995. The Implementation and Logic of a Strictly Declarative Modelling Language, Expert Systems `95, Cambridge. Published as Macintosh, A. and Cooper, C. (eds.) 1995. Applications and Innovations in Expert Systems III. Oxford: SGES Publications, 351-360

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