SCENARIO ANALYSIS OF TECHNOLOGY PRODUCTS WITH AN AGENT-BASED SIMULATION AND DATA MINING FRAMEWORK

Amit Shinde (), Moeed Haghnevis (), Marco A. Janssen (), George C. Runger () and Mani Janakiram ()
Additional contact information
Amit Shinde: School of Computing, Informatics and Decision Systems Engineering, Arizona State University, P. O. Box 878809, Tempe, Arizona 85287, USA
Moeed Haghnevis: School of Computing, Informatics and Decision Systems Engineering, Arizona State University, P. O. Box 878809, Tempe, Arizona 85287, USA
Marco A. Janssen: School of Human Evolution and Social Change, Arizona State University, P. O. Box 872402, Arizona 85287, USA
George C. Runger: School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, P. O. Box 878809, Tempe, Arizona 85287, USA
Mani Janakiram: Intel Corporation, 5000 West Chandler Boulevard, Chandler AZ 85226, USA

International Journal of Innovation and Technology Management (IJITM), 2013, vol. 10, issue 05, 1-22

Abstract: A framework is presented to simulate and analyze the effect of multiple business scenarios on the adoption behavior of a group of technology products. Diffusion is viewed as an emergent phenomenon that results from the interaction of consumers. An agent-based model is used in which potential adopters of technology product are allowed to be influenced by their local interactions within the social network. Along with social influence, the effect of product features is important and we ascribe feature sensing attributes to the consumer agents along with sensitivities to social influence. The model encompasses utility theory and discrete choice models in the decision-making process for the consumers. We use expressive machine learning algorithms that can handle complex, nonlinear, and interactive effects to identify important inputs that contribute to the model and to graphically summarize their effects. We present a realistic case study that demonstrates the ability of this framework to model changes in market shares for a group of products in response to business scenarios such as new product introduction and product discontinuation under different pricing strategies. The models and other tools developed here are envisioned to be a part of a recommender system that provides insights into the effects of various business scenarios on shaping market shares of different product groups.

Keywords: Scenario analysis; technology substitution; dependency plots (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1142/S0219877013400191

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