Alok R. Chaturvedi

Design principles for virtual worlds

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.

Matching information security vulnerabilities to organizational security profiles: a genetic algorithm approach

Organizations are making substantial investments in information security to reduce the risk presented by vulnerabilities in their information technology (IT) infrastructure. However, each security technology only addresses specific vulnerabilities and potentially creates additional vulnerabilities. The objective of this research is to present and evaluate a Genetic Algorithm (GA)-based approach enabling organizations to choose the minimal-cost security profile providing the maximal vulnerability coverage. This approach is compared to an enumerative approach for a given test set. The GA-based approach provides favorable results, eventually leading to improved tools for supporting information security investment decisions.

Economic Benefits of Renting Software

In this article, we analyze the economics of a monopoly firm selling and renting a packaged software product by employing an intertemporal monopoly pricing game to model the firms pricing strategy. The game models the software product as two versions; the first version is available in the first period and the second, a revised version, is available in the second period. The second version benefits from consumer reports of bugs and requests for additional features. This is modeled using delayed network externalities that take effect only in the second period. We observe that the introduction of the rental product in the first period leads to an increase in profits. We also find that the firms profits are monotonically increasing with the intensity of the network effect. As the intensity of the network effect becomes stronger, the firm chooses to reduce its prices in the first period to expand the size of its network and later increases prices in the second period. Because many of the customers who choose...

Competition Among Sellers in Online Exchanges

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.

System-of-Systems Modeling and Simulation of a Ship Environment With Wireless and Intelligent Maintenance Technologies

Modeling and simulation environments are needed to support decision making in Navy Warfighters, which are emergent systems that pose a challenge to operations management. Ships consist of complex interconnected systems such as the infrastructure, crew, and workflow. A system-of-systems approach using agent-based modeling is applied here to develop workflow simulations involving a ships crew conducting routine maintenance, watch duty, and reporting functions. Simple models are used to describe basic behavioral traits and intelligence in crew members; machinery including sensors for intelligent maintenance; equipment consuming power; mobile and stationary communication network access points; models for data transfer over the network; crew mobility models; power distribution and trimming models for the electrical system; and a fire model to simulate emergency scenarios. The simulation results demonstrate an increase in machine availability due to the implementation of intelligent maintenance systems. The effects of wireless-network usage on crew resource utilization and overall ship capability in normal operational scenarios are also demonstrated. A simple rescheduling algorithm is used to improve crew utilization and estimate manning requirements. The effects of emergency scenarios such as fires in different locations are also studied. Sensitivity analysis is presented to verify the developed model, and a note on validation is given.

Supporting complex real-time decision making through machine learning

Abstract This paper presents FMS-DSS, a system for supporting complex, real-time decision making in the FMS scheduling and control domain. FMS-DSS differs from traditional DSSs in that it can acquire scheduling and control knowledge from historical data comprising prior decisions. This knowledge is applied to support subsequent decision making. It manages complexity through hierarchically structuring the users objectives, and can deal with noise in the form of missing, inaccurate, or erroneous data. Results indicate that a machine learning based approach can provide effective support for repetitive real-time decision making and outperform static scheduling rules.

Context effects and models of preferential choice: implications for consumer behavior

The article summarizes extant research on context effects and choice theories in a straightforward fashion. The context effects are used as benchmarks to compare six choice theories. The context effects include similarity, attraction, compromise, and reference point effects. The considered theories include simple scalability model, random utility model, elimination by aspects model, strategy switching models, componential context model, and connectionist network model of choice. The article discusses the implications of each model for consumer behavior, and suggests that the choice of model should depend on the characteristics of products, consumers and purchase process.

Agent-based simulation for computational experimentation: Developing an artificial labor market

Abstract This paper discusses the creation of an artificial labor market (ALM) as an agent-based simulation model. We trace the development of the ALM by adapting the traditional simulation life cycle into two main parts: the model phase and the simulation phase. In the modeling phase of the life cycle, we focus upon agent representation and specification within the virtual world. In the simulation phase, we discuss the use of scenario planning as the experimentation vehicle. Throughout, we use military recruit market as an example to illustrate the methodology. The benefits of the ALM are (1) it provides a virtual world for continuous computational experimentation, (2) it supports market segmentation by allowing “drilldowns” to finer and finer levels of granularity, and (3) when connected via a common OLAP interface to a “real world” counterpart, it facilitates a tightly integrated, persistent, “sense and respond” decision support functionality.

DDDAS for fire and agent evacuation modeling of the rhode island nightclub fire

A Dynamic Data Driven Application System (DDDAS) was created to study interaction between fire and agent models during a fire evacuation. The analysis from that research can be used to validate proposed ideas in evacuation and building designs to ensure safety of buildings given various agent behaviors. Two separate models were used to simulate the components of the emergency situation: fire and agent. The independent models were able to run using data computed by the other interacting models, allowing careful examination of real-time interactions in a situation. Through study of the interactions, a better understanding is gained of how individual variables such as exit position and width affect the evacuation process and escape rate in the given scenario. Exits can be relocated and changed to quickly assess the effect on the model. The results can be used for improving building design and regulations as well as training first responders.

Privacy and pricing personal information

Abstract The issues we address here are – How should a firm (e.g. Internet service provider (ISP)) that is capable of collecting personal information (browsing information, purchase history, etc.) about consumers, price its service, given that consumers vary in their valuation for privacy, and also vary in terms of the value of their personal information to a third party (firms that need consumer information)? Should the firm have a blanket policy of never collecting, or a policy of always collecting and revealing information? Surprisingly we find that in some cases the collector of information may be no worse off in the asymmetric information case than in the full information case. The paper provides a justification for the strategy of some firms such as ISP’s which never collect information and also for the strategy of other firms, like grocery stores that do. We also find that it is non-optimal for the firm to design contracts where the consumer can choose an intermediate level of privacy.