Adaptive interactive profit expectations using small world networks and runtime weighted model averaging
AbstractThe 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 “runtime weighted model averaging” and the “pressure to change profit expectations index” (px). Runtime weighted model averaging combines the Bayesian Information Criteria and Kolmogorov’s Complexity to enhance the prediction performance of models with varying complexity but a fixed number of parameters. The px is a subjective measure representing decision making in the face of uncertainty. The paper benchmarks the AIE model against 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. The tuneable network produces widely spaced multiple equilibria and runtime weighted model averaging improves prediction but there are issues with calibration.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 38027.
Date of creation: 10 Dec 2008
Date of revision:
Small World Networks; Agent Based Model; Adaptive; Interactive; Profits; Expectations; Model Averaging; Survey; Australia; Business Cycle;
Find related papers by JEL classification:
- C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
- D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
- B25 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Historical; Institutional; Evolutionary; Austrian
- D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
- E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
- C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- D8 - Microeconomics - - Information, Knowledge, and Uncertainty
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Mark Bowden & Stuart McDonald, 2006. "Social interaction, herd behaviour and the formation of agent expectations," Computing in Economics and Finance 2006 178, Society for Computational Economics.
- John Foster & Burkhard Flieth, 2002. "Interactive expectations," Journal of Evolutionary Economics, Springer, vol. 12(4), pages 375-395.
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