In this research note, we examine the design, development, validation, and use of virtual worlds. Our purpose in doing so is to extend the design science paradigm by developing a set of design principles applicable to the context of virtual environments, particularly those using agent-based simulation as their underlying technology. Our central argument is that virtual worlds comprise a new class of information system, one that combines the structural aspects of traditional modeling and simulation systems in concert with emergent user dynamics of systems supporting emergent knowledge processes. Our approach involves two components. First, we review the characteristics of agent-based virtual worlds (ABVWs) to discern design requirements that may challenge current design theory. From this review, we derive a set of design principles based on deep versus emergent structures where deep structures reflect conventional modeling and simulation system architectures and emergent structures capture the unpredictable user-system dynamics inherent in emergent knowledge processes, which increasingly characterize virtual worlds. We illustrate how these design challenges are addressed with an exemplar of a complex mirror world, a large-scale ABVW we developed called Sentient World. Our contribution is the insight of partitioning ABVW architectures into deep and emergent structures that mirror modeling systems and emergent knowledge processes respectively, while developing extended design principles to facilitate their integration. We conclude with a discussion of the implications of our design principles for informing and guiding future research and practice.
With the advent of the Internet, and the minimal information technology requirements of a trading partner to join an exchange, the number of sellers who can qualify and participate in online exchanges is greatly increased. We model the competition between two sellers with different unit costs and production capacities responding to a buyer demand. The resulting mixed-strategy equilibrium shows that one of the sellers has a normal high price with random sales, while the other seller continuously randomizes its prices. It also brings out the inherent advantages that sellers with lower marginal costs or higher capacities have in joining these exchanges, and provides a theoretical basis for understanding the relative advantages of various types of sellers in such exchanges.
Con siderable attention in the information systems and management science literature has focused on computer-based modeling environments, sometimes called integrated modeling environments or model management systems. This research has been primarily concerned with suggesting features/components of modeling environments such as improved executable modeling languages for model creation, integration, and data representation; specialized database systems for managing model data; and customized model-solver software. However, there has been little (if any) empirical guidance offered in the literature about the specific needs of business and industry for computer-based integrated modeling environments. Using a data set compiled from a national survey of modelers (analysts) and model users (decision makers), we empirically investigate the validity of several of the key assumptions of modeling environment research reported in the literature, and examine the relationships between the modeling factors: data complexity, model complexity, modeling intensity, modeler/user requirements, and need for computer-based integrated modeling environments in organizations. Our empirical analysis of the data set shows that practitioners rank automated access to model data and automated error checking (e.g., model syntax and semantics checking) high as desirable components in modeling environments. We find that users prefer to have modeling environments linked to their current modeling and modeling-support software systems.Our findings further suggest that a high percentage of modelers and users are dissatisfied with the software systems they are currently using to support their modeling activities. Finally, a covariance structure analysis of the modeling environment factors clearly shows that: (a) model complexity has a direct positive effect on modeling intensity; (b) data complexity has an insignificant direct effect on modeling intensity, but has a negative effect on modeler/user req...