Vijay Dakshinamoorthy is currently pursuing a Ph.D. at the Ross School of Business, University of Michigan. He has a master’s in computer engineering, also from Michigan, and an undergraduate degree in electrical engineering from India. Before returning to school for his Ph.D., he worked in business and IT consulting for clients in the U.S.A. and overseas. His current studies and research includes understanding the role of information and communication technologies (ICTs) in economic development. Before attending the Summer School, he interned in rural India studying the business models of village information kiosks and their socioeconomic impact.
Project: Coevolution of Competing Players in a Region
Forecasting consumer behavior and diffusion of technology is an active area of research, with several tools available for modeling. Companies adopt different strategies based on the availability of alternatives, proximity, and customer segmentation for penetrating a market. The internet service provider (ISP) market is particularly segmented according to customer preferences, quality of service (QoS), and incentives.
The growth of service-provider networks has several implications for technology convergence and the development of infrastructure in a region. This paper attempts to capture the complexity involved in the evolution of such a market and its effect on network building efforts.
A two-dimensional cellular automaton (CA) is used to model consumer behavior in a region. Starting from a simple set of rules, complex patterns for the regions of influence of the competing service providers are obtained. Some techniques for competitive facility location are used to illustrate the spatial advantages available to the service providers. The effects of a first-mover advantage are illustrated by studying the evolution of the CA without the second provider and by introducing the provider after a specified number of time steps.
The experiments conducted on this multi-agent model reveal interesting results about the complexity that develops based on a very simple set of rules for consumer behavior.
Some fundamental concepts about carrying capacity, phase transitions, and stochastic distributions are also illustrated through this model.
Favorite Four-Color, Nearest-Neighbor, Totalistic Rule
Rule chosen: 770
I chose rule 770 as my favorite four-color totalistic cellular automaton because of the intricate complex pattern that emerges from the seemingly regular behavior seen in the beginning. It is particularly interesting to note the regularity of the cells on both the left and right extremes while there appear to be random patterns in the middle.