Bell, William Paul (2008): Adaptive interactive profit expectations using small world networks and runtime weighted model averaging. Published in: Biomedical Applications of Micro- and Nanoengineering IV and Complex Systems (Proceedings Volume) , Vol. 7270, (30. December 2008)
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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 “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.
|Item Type:||MPRA Paper|
|Original Title:||Adaptive interactive profit expectations using small world networks and runtime weighted model averaging|
|Keywords:||Small World Networks; Agent Based Model; Adaptive; Interactive; Profits; Expectations; Model Averaging; Survey; Australia; Business Cycle|
|Subjects:||C - Mathematical and Quantitative Methods > C6 - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D81 - Criteria for Decision-Making under Risk and Uncertainty
C - Mathematical and Quantitative Methods > C6 - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling > C63 - Computational Techniques; Simulation Modeling
D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D82 - Asymmetric and Private Information; Mechanism Design
D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D85 - Network Formation and Analysis: Theory
B - History of Economic Thought, Methodology, and Heterodox Approaches > B2 - History of Economic Thought since 1925 > B25 - Historical; Institutional; Evolutionary; Austrian
D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D84 - Expectations; Speculations
E - Macroeconomics and Monetary Economics > E2 - Macroeconomics: Consumption, Saving, Production, Employment, and Investment > E27 - Forecasting and Simulation: Models and Applications
C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods
C - Mathematical and Quantitative Methods > C6 - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling > C61 - Optimization Techniques; Programming Models; Dynamic Analysis
D - Microeconomics > D8 - Information, Knowledge, and Uncertainty
E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications
|Depositing User:||Dr William Paul Bell|
|Date Deposited:||13. Apr 2012 01:20|
|Last Modified:||23. Feb 2013 07:09|
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