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An adaptive regression for agent-based modeling
 
PIIS042473880028256-0-1
DOI10.31857/S042473880028256-0
Publication type Article
Status Published
Authors
Alexander Tsyplakov 
Affiliation:
Institute of Economics and Industrial Engineering, Siberian Branch of the Russian Academy of Sciences
Department of Economics, Novosibirsk State University
Address: Novosibirsk, Russia
Journal nameEkonomika i matematicheskie metody
EditionVolume 59 no. 4
Pages111-125
Abstract

The article discusses an algorithm, which that can be used to implement adaptive behavior of agents in agent-based models (ABM). It is assumed that an agent has some internal parametric model of the surrounding world, which motivates a likelihood function for the information about the world received by the agent. The process of adaptive learning of an agent via changing parameters is presented as filtering in a general state space model. By using a linear Gaussian transition density and a quadratic approximation for the log-likelihood function, an algorithm is obtained, which is called SQ filter in the article. This algorithm is a modification of the classical Kalman filter. It is applied to the linear regression with time-varying parameters. When an agent receives new information, the parameter estimates, which include both the regression coefficients and the error variance, are adjusted adaptively by taking into account possible outliers. The performance of the proposed adaptive regression was tested on two economic ABM. The algorithm showed good results both in an artificial stock market model where trader agents predict the market price and in a model of the Russian economy where firms predict demand for their output. With its help, it is possible to endow agents with plausible behavior without using overly complex calculations.

Keywordsadaptive learning, Kalman filter, agent-based models
AcknowledgmentThe paper was prepared according to the Research plan of the IEIE SB RAS “Tools, technologies and results of analysis, modeling and forecasting of the spatial development of the Russia’s socioeconomic system and its individual territories” (project no. 121040100262-7).
Received13.11.2023
Publication date28.12.2023
Number of characters40586
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