Shailendra Raj Mehta

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.

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.

Agent-based simulation approach to information warfare in the SEAS environment

Information warfare refers to actions taken by an agent to achieve information superiority in support of national military strategy by affecting an adversarys information and information systems while leveraging and protecting its own information and information systems. It employs the same basic tools as used by hackers and criminals: computer, modem, telephone, and software. The paper analyzes the behaviors of the three major types of agents: government, firms and perpetrators in an experimental setting. The experiments were designed and conducted at the Synthetic Environment for Analysis and Simulation (SEAS) Lab at Krannert Graduate School of Management, Purdue University.

The Law of One Price and a Theory of the Firm

I use a hierarchical perspective to explain the pattern of interindustry wages and capital intensities. Industries that have more productive technologies pay more than less-productive ones, and are more capital intensive as well. Results on size of the hierarchy, effort levels, and span of control are presented as well. The key insight is that supervisors both monitor and coordinate their subordinates, and time spent on one of these activities reduces the time available for the other. Several stylized facts are explained thereby.

Simulations in economics and management

References 1. Foner, L.N. A multi-agent referral system for matchmaking. In Proceedings of PAAM’96 (Apr. 22–24, London). The Practical Application Company, Lancashire, 1996, 245–261. 2. Ishida, T. Towards CommunityWare. In Proceedings of PAAM’97 (Apr. 21–23, London). The Practical Application Company, Lancashire, 1997, 7–21. 3. Kautz, H., Selman, B. and Shah, M. Referral Web: Combining social networks and collaborative filtering. Commun. ACM 40, 3 (Mar. 1997), 63–65. 4. LaLiberte, D. and Woolley, D. Presentation features of text-based conferencing systems on the WWW. Computer-Mediated Commun. 4, 5 (May 1997). 5. Matsubara, S., Ohguro, T. and Hattori, F. CommunityBoard: Social meeting system able to visualize the structure of discussions. c

Preparation for Entrepreneurship: Does it Matter?

We begin with preparation. Cooper and Mehta review the files of nearly 3,000 firms to assemble data sets to analyze a series of straightforward questions about preparing to start a business. Does it matter how much time a person spent in preparing to start her or his business? Is it better if the idea for the new business came from a prior business? Some say timing is everything. How important is timing when starting a new business? Does it matter if the founder quit their job only so they could start the new business? And how important was the role of professional advisors in the start-up? If any of these questions seem interesting to you, take a look at our lead piece.

Computational experimentations in market and supply-chain co-design: a mixed agent approach

The synthetic environment for analysis and simulations (SEAS) is a computational experimentation environment that mimics real life economies, with multiple interlinked markets, multiple goods and services, multiple firms and channels and multiple consumers, all built from the ground up. It is populated with human agents who make strategically complex decisions and artificial agents who make simple but detail intensive decisions. These agents can be calibrated with real data and allowed to make the same decisions in this synthetic economy as their real life counterparts. The resulting outcomes can be surprisingly accurate. This paper discusses the research in this area and goes on to detail the architecture of SEAS. It also presents a detailed case study of market and supply-chain co-design for business-to-business e-commerce in the PC industry.

SAMAS: Scalable Architecture for Multi-resolution Agent-Based Simulation *

Large scale agent-based simulation has become the focus of great interest from both academy and industry in resent years. It has been shown an effective tool for understanding a variety of complex systems, such as market economics, war games, and epidemic propagation models. As the systems of interest grow in complexity, it is often desirable to have different categories of artificial agents that execute tasks on different time scales. With the added complexity, the scalability of a simulation environment becomes a crucial measure of its ability in coping with the complexity of the underlying system. In this paper, we present the design of SAMAS, a highly scalable architecture for multi-resolution agent-based simulation. SAMAS is a dynamic data driven application system (DDDAS) that adopts an efficient architecture that allows large number of agents to operate on a wide range of time scales. It uses gossiping, an efficient broadcasting communication model for maintaining the overall consistency of the simulation environment. We demonstrate the effectiveness of this communication model using experimental results obtained from simulation.

Simulating sellers' behavior in a reverse auction B2B exchange

Previous research in reverse auction B2B exchanges found that in an environment where sellers collectively can cater to the total demand, with the final (i.e. the highest-priced bidding) seller catering to a residual, the sellers resort to a mixed strategy equilibrium [2]. While price randomization in industrial bids is an accepted norm, it may be argued that managers in reality do not resort to advanced game theoretic calculations to bid for an order. What is more likely is that managers learn that strategy and over time finally converge towards the theoretic equilibrium. To test this assertion, we model the two-player game in a synthetic environment, where the agents use a simple reinforcement learning algorithm to put progressively more weights on selecting price bands where they make higher profits. We find that after a sufficient number of iterations, the agents do indeed converge towards the theoretic equilibrium.