Uses of Agent‐Based Modeling in Innovation/New Product Development Research*

Abstract

Little has been written in the new product development literature about the simulation technique agent‐based modeling, which is a by‐product of recent explorations into complex adaptive systems in other disciplines. Agent‐based models (ABM) are commonly used in other social sciences to represent individual actors (or groups) in a dynamic adaptive system. The social system may be a marketplace, an organization, or any type of system that acts as a collective of individuals. Agents represent autonomous decision‐making entities that interact with each other and/or with their environment based on a set of rules. These rules dictate the behavioral choices of the agents. In these simulation models, heterogeneous agents interact with each other in a repetitive process. It is from the interactions between agents that aggregate macroscale behaviors or trends emerge. The simulated environment can be thought of as a “virtual” society in which actions taken by one agent may have an effect on the resulting actions of another agent. This article is an introduction to the ABM methodology and its possible uses for innovation and new product development researchers. It explores the benefits and issues with modeling dynamic systems using this methodology. Benefits of ABMs found in sociology and management studies have found that as the heterogeneity of individuals increase in a system or as network effects become more important in a system, the effectiveness of ABMs as a methodology increases. Additionally, the more adaptive a system or the more the system evolves over time, the greater the opportunity to learn more about the adaptive system using ABMs. Limitations to using this methodology include some knowledge of computer‐programming techniques. Three potential areas of research are introduced: diffusion of innovations, organizational strategy, and knowledge and information flows. A common use of ABMs in the extant literature has been the modeling of the diffusion process between networked heterogeneous agents. ABMs easily allow the modeling of different types of networks and the impact of these networks on the diffusion process. A demonstrative example of an agent‐based model to address the research question of how should manufacturers allocate resources to research (exploration) and development (exploitation) projects is provided. Future courses of study using ABMs also are explored.