Forecasting new product diffusion with agent-based models

Yu Xiao and Jingti Han

Technological Forecasting and Social Change, 2016, vol. 105, issue C, 167-178

Abstract: Agent-based model (ABM) has been widely used to explore the influence of complex interactions and individual heterogeneity on the diffusion of innovation, while it is seldom used as a forecasting tool in the innovation diffusion literature. This paper introduces a novel approach of forecasting new product diffusion with ABMs. The ABM is built on the hidden influence network (HIN) over which the innovation diffuses. An efficient method is presented to estimate non-structural parameters (i.e., p, q and m) and a multinomial logistic model is formulated to identify the type of the HIN for diffusion data. The simulation study shows that the trained logistic model performs well in inferring the HINs for most simulated diffusion data sets but poorly for those generated by ABMs with similar HINs. Therefore, to reduce the possible prediction loss arising from the misspecification of the HIN, three methods, namely, the predicted HIN, the weighted averaging and simple averaging, are developed to forecast new products diffusion. Their performances are evaluated by using a data set composed of 317 time series on consumer durables penetration. The results show that most identified HINs have moderate topology, and that our methods outperform four classical differential equation based diffusion models in both short-term and long-term prediction.

Keywords: Agent-based model; Bass model; Social network; Combined prediction; Forecasting model (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162516000202
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:105:y:2016:i:c:p:167-178

DOI: 10.1016/j.techfore.2016.01.019

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().