Title :
Agent-Based Modeling of Ambidextrous Organizations: Virtualizing Competitive Strategy
Author :
Tay, Nicholas S P ; Lusch, Robert F.
Author_Institution :
Univ. of San Francisco, San Francisco
Abstract :
Turbulence, uncertainty, dynamic processes, and networks increasingly characterize competitive markets and business strategies. Consequently, there´s a need to model such markets and strategies as dynamic, evolutionary processes that is, as complex adaptive systems. Agent-based modeling, a rich platform for studying complex evolving systems, is used to model a market where ambidextrous and nonambidextrous organizations compete for buyers. Viewing competitive market and business processes as interactions among agents who mutually influence each other reduces economics to its most microscopic level. Social networks such as the Internet have attracted much research attention because of the rise in stock fraud on the Internet.
Keywords :
Internet; learning by example; multi-agent systems; social sciences computing; virtual enterprises; Internet; adaptive system; agent-based modeling; ambidextrous organization; business strategy; evolutionary process; social network; virtual competitive market; Adaptive systems; Analytical models; Data analysis; Environmental economics; Game theory; Marketing and sales; Microscopy; Technological innovation; Time series analysis; Uncertainty; agent-based model; ambidextrous; competitive strategy; fuzzy logic; genetic algorithms;
Journal_Title :
Intelligent Systems, IEEE
DOI :
10.1109/MIS.2007.4338494