Adaptive interactive profit expectations using small world networks and runtime weighted model averaging

Bell, William Paul (2008) Adaptive interactive profit expectations using small world networks and runtime weighted model averaging. Proceedings of SPIE (The International Society for Optical Engineering), 7270 727011-1-727011-12. doi:10.1117/12.813941

Author Bell, William Paul
Title Adaptive interactive profit expectations using small world networks and runtime weighted model averaging
Journal name Proceedings of SPIE (The International Society for Optical Engineering)
ISSN 1605-7422
ISBN 9780819475220
Publication date 2008-12-30
Sub-type Article (original research)
DOI 10.1117/12.813941
Open Access Status Not yet assessed
Volume 7270
Start page 727011-1
End page 727011-12
Total pages 12
Editor Dan V. Nicolau
Guy Metcalfe
Place of publication Bellingham, Washington, U.S.A.
Publisher S P I E - International Society for Optical Engineering
Language eng
Formatted abstract
The aim of this paper is to simulate profit expectations as an emergent property using an agent based model. The paper builds upon adaptive expectations, interactive expectations and small world networks, combining them into a single adaptive interactive profit expectations model (AIE). Understanding the diffusion of interactive expectations is aided by using a network to simulate the flow of information between firms. The AIE model is tested against a profit expectations survey.
The paper introduces “optimal calibration model averaging” and the “pressure to change profit expectations index” (px). Optimal calibration model averaging is an adaptation of “model averaging” to enhance the prediction performance of multiple equilibria models. The px is a subjective measure representing decision making in the face of uncertainty.
The paper benchmarks the AIE model against the adaptive expectations model and the rational expectations hypothesis, finding the firms may have adequate memory although the interactive component of AIE model needs improvement. Additionally the paper investigates the efficacy of a tuneable network and equilibrium averaging. Finding the tuneable network produces widely spaced multiple equilibria and the optimal calibration model averaging enhances calibration but not prediction. Further research includes disaggregating the AIE model, using an input–output table to reflect the intensity of interaction between firms of different divisions, and supplementing optimal calibration model averaging with runtime weighted model averaging.

Keyword Expectations
Business Cycle
Agent based model
Equilibria Averaging
References ABS 2002, Cat. No. 5250.0 Australian Business Expectations Mar and Dec 2003, Canberra. ---- 2006, Cat. No. 1292.0 Australian and New Zealand Standard Industrial Classification (ANZSIC). Amarala, LAN & Ottino, JM 2004, 'Complex networks: Augmenting the framework for the study of complex systems', The European Physical Journal B, vol. 38, pp. 147-62. Baggio, R 2008, 'Network analysis of a tourism destination', The School of Tourism, The University of Queensland, Brisbane. Bak, P, Paczuski, M & Shubik, M 1997, 'Price variations in a stock market with many agents', Physica A: Statistical and Theoretical Physics, vol. 246, no. 3-4, pp. 430-53. Bates, JM & Granger, CWJ 1969, 'The Combination of Forecasts', Operational Research Quarterly, vol. 20, no. 4, pp. 451-68. Beinhocker, ED 2006, Origin of Wealth: Evolution, Complexity, and the Radical Remaking of Economics, Harvard Business School Press, Cambridge, Mass. Bell, PW 2008a, 'Interactive Adaptive Profit Expectations and Small World Networks (2)', Forthcoming. ---- 2008b, 'Interactive Adaptive Profit Expectations and Small World Networks (3)', Forthcoming. Bowden, MP & McDonald, S 2006, 'The Effect of Social Interaction and Herd Behaviour on the Formation of Agent Expectations', Computing in Economics and Finance 2006, no. 178. Clemen, RT 1989, 'Combining forecasts: A review and annotated bibliography', International Journal of Forecasting, vol. 5, pp. 559-83. D&B 2006, Standard Industrial Classification Australia, . ---- 2008, 'National Business Expectations Survey of business executives across Australia', viewed 20 February 2008, . Farmer, JD, Patelli, P & Zovko, II 2005, 'The predictive power of zero intelligence in financial markets', Proceeding of the National Academy of Science of the USA, vol. 102, no. 6, pp. 2254-9. Flieth, B & Foster, J 2002, 'Interactive expectations', Journal of Evolutionary Economics, vol. 12, no. 4, pp. 375-95. Goldstein, J 1999, 'Emergence as a Construct: History and Issues', Emergence, vol. 1, no. 1, pp. 49-73. Gonzaleza, C, Danaa, J, Koshinob, H & Justc, M 2004, 'The framing effect and risky decisions: Examining cognitive functions with fMRI', Journal of Economic Psychology, vol. 26, no. 1. Grimm, V, Revilla, E, Berger, U, Jeltsch, F, Mooij, WM, Railsback, SF, Thulke, H-H, Weiner, J, Wiegand, T & DeAngelis, DL 2005, 'Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology', Science, vol. 310, pp. 987-91. Hanneman, RA & Riddle, M 2005, Introduction to social network methods, University of California, Riverside, California, . Hicks, JR 1939, Value and capital, Oxford University Press, London. Holt, CC, Modigliani, F, Muth, JF & Simon, HA 1960, Planning production, inventories, and work force, Prentice-Hall international series in management., Prentice-Hall, Englewood Cliffs, N.J. Kahneman, D 2002, 'Maps of Bounded Rationality: A Perspective on Intuitive Judgement and Choice', paper presented to Les Prix Nobel 2002, Stockholm, . Lovell, MC 1986, 'Tests of the Rational Expectations Hypothesis', The American Economic Review, vol. 76, no. 1, pp. 110-24. Miller, JH & Page, SE 2007, Complex adaptive systems : an introduction to computational models of social life, Princeton studies in complexity., Princeton University Press, Princeton, New Jersey. Muth, JF 1960, 'Optimal Properties of Exponentially Weighted Forecasts', Journal of the American Statistical Association, vol. 55, no. 290, pp. 299-306. ---- 1961, 'Rational Expectations and the Theory of Price Movements', Econometrica, vol. 29, no. 3, pp. 315-35. Nerlove, M 1964, 'On the Optimality of Adaptive Forecasting', Management Science, vol. 10, pp. 207-24. Ormerod, P 2005, 'On system-wide failures in complex, evolving systems', paper presented to The International Workshop on Evolutionary Macroeconomics, University of Queensland, Brisbane, 14-17 July 2005, . Shiller, RJ 2005, Irrational exuberance, 2nd edn, Princeton University Press, Princeton, N.J. Sterman, JD 2000, Business dynamics : systems thinking and modeling for a complex world, Irwin/McGraw-Hill, Boston. Tesfation, L 2008, Agent-Based Computational Economics, . Tukey, J 1962, 'The Future of Data Analysis', The Annals of Mathematical Statistics, vol. 33, no. 1, pp. 1-67. Watts, DJ & Strogatz, SH 1998, 'Collective dynamics of 'small-world' networks', Nature, vol. 393, pp. 440-2. Wilensky, U 1999, NetLogo, Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL, 30 May 2007, . ---- 2005, NetLogo Small Worlds model, Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL, . Yu, A 2008, 'Seeing patterns in randomness: Irrational superstition or adaptive behaviour?' paper presented to The Queensland Brain Institute Neuroscience Seminar Series, The University of Queensland, Brisbane, 11 February 2008.
Q-Index Code CX
Q-Index Status Provisional Code

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Economics Publications
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Sat, 27 Sep 2008, 08:42:36 EST by Mr Paul Bell on behalf of School of Communication and Arts