Scenario Analysis of Technology Products With an Agent-Based Simulation and Data Mining Framework

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.

Simulation for Promotion of Solar Energy Diffusion in Residential Consumer Market with Agent-Based Modeling and Random Forest

“…2) Calculation of value proposition for energies: For value proposition of energies features, utility theory [5,13] is used to describe it. Eq.2 is used when the agents target a maximum value of features (to calculate desirability function of saner and con).…”

“…Agent based model has been used on study of technology products diffusion [5]. In energy field, T. Zhang et.…”