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

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Abstract

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.

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Notes

  1. Details of the exact functional forms are available upon request. What follows is a highly condensed description.

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Acknowledgements

This research was funded by National Science Foundation grants # EIA-0075506 and DMI-0122214. As required by the Memorandum of Understanding between the authors and Purdue University, it is disclosed that some or all of the intellectual property described herein may be commercialized.

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Authors and Affiliations

  1. Krannert Graduate School of Management, Purdue University, West Lafayette, IN, 47907, USA

    Alok R. Chaturvedi, Shailendra Raj Mehta, Daniel Dolk & Mukul Gupta

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  1. Alok R. Chaturvedi
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  2. Shailendra Raj Mehta
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  3. Daniel Dolk
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  4. Mukul Gupta
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Corresponding author

Correspondence to Alok R. Chaturvedi.

Appendix

Appendix

1.1 SEAS virtual execution environment

An Internet-based SEAS virtual execution environment (VEE) is depicted in Fig. 8. In SEAS’ VEE, participants from anywhere can take part in an exercise. The VEE consists of three classes of servers: application servers, distributed database servers, and proxy servers. Proxy servers ensure restricted access for the subscribers. There are three different classes of application servers. The agent-processing servers are capable of running hundreds of thousands of different kinds of agents. Economic processing servers are capable of representing different types of markets. Finally, the visualization servers generate advanced 3-D displays of the data generated during the exercise.

Fig. 8
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The SEAS virtual execution environment

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There are individual database servers that support each of these application servers. These database servers may run at one or more locations. Thus, the distributed design enables any numbers of participants to take part in an exercise, and many exercises may be available at any given time.

The SEAS software environment is highly reconfigurable. It consists of three classes of active objects called “bots” as depicted in Fig. 9. The ServBots are autonomous agents that perform the business tasks. Examples of ServBots are BuyBot (agents that perform buying function), SellBot, AcquireBot, ProduceBot, etc. These bots also function as application programming interface (API) for third party development.

Fig. 9
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Anatomy of the SEAS virtual execution environment

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The second class of bots is SysBots. This class of autonomous agents performs system level tasks such as read, write, update, open, and close connections.

The third class of bots is the data bots. These bots are being developed to interface with enterprise systems so that firms can seamlessly integrate with SEAS to run dynamic exercise to explore new strategy spaces or may use it as a wind tunnel of corporate strategies. These bots interact with the data manager for their data needs using the SEAS middleware.

The VEE interacts with reconfigurable interface classes. There are eight types of interface classes—ticker, display, system control, local control, global control, user, role, and feedback. ServBots, SysBots, and DataBots are dynamically assembled for each of these interface classes based on the participants profiles or demands by an application synthesizer. This architecture provides the necessary flexibility to adapt SEAS to a wide variety of problem domains.

1.2 SEAS agent architecture

SEAS use intelligent agents (IA) to represent economic realities of electronic markets and hierarchies in a decentralized manner. Intelligent agents in SEAS are autonomous processes that are adaptive and behave like human agents in a narrow domain. In their respective domains, each agent has a well-defined set of responsibilities and authorities so that it can execute its tasks effectively. Examples of SEAS’ IAs are: economic agents—consumer IAs, producer IAs, and regulator IAs and political agents—government IAs, special interest IAs, etc. An agent in SEAS is equipped with reasoning, action, and communication skills required for performing their respective tasks. A SEAS IA is characterized by the knowledge it possesses.

Each agent has a set of seven behavior primitives that enable him or her to perform actions autonomously as shown in Fig. 10. These primitive SysBots are used to initiate, search, decide, execute, update, communicate, and to terminate. Different algorithms for SysBots are used to differentiate between the agents. For example, an agent representing a smart individual will have a more sophisticated search and evaluation algorithm than that of a not so smart agent. There can be several instantiations of each SysBot. SysBots that constitute an agent are described below:

  • Initiate: these bots are the sensors for a given ServBot. When certain conditions are met then they trigger action from the ServBot.

  • Search: these bots provide the ability to search the space for best price, quantity, etc.

  • Evaluate: these bots enable the servbot to evaluate different alternatives and select the most appropriate course of action.

  • Execute: these bots execute the course of actions needed by the ServBot.

  • Communicate: these bots have the knowledge of the workflow and the chain of command. After the action is taken, these bots communicate the appropriate message/information to the appropriate parties.

  • Update: these bots update the relevant information/data at the appropriate times. These bots are critical for the system performance.

  • Terminate: These are quality assurance bots that make sure that the tasks are completed satisfactorily.

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SysBots are coalesced together to form a ServBot

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1.3 SEAS market architecture

SEAS markets are implemented in JavaSpace. Javaspace is a descendant of Linda system developed at Yale University (Carriero and Gelertner 1989). Java Space defines the market structure as shown in Fig. 11. The four different types of markets structures supported by SEAS are: posted price, double auction, single auction, and reverse auction. The rules for all the markets are implemented through JavaSpace, which in turn synchronizes the thread between the agents. Agents maintained in the space are updated through their working status. JavaSpace also forms the connectivity between the Enterprise Java Beans (EJB) and the Database, and therefore, after completing the transactions, it updates the database.

Fig. 11
figure 11

An example of a posted price implemented in JavaSpace

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JavaSpace supports simultaneous running of multiple games. Every agent created in the EJB has an AgentSpace object. AgentSpace gives every agent partial access to the JavaSpace. This way the agent does not have to worry about the implementation of the space. It gives a clean abstract and encapsulated cover to the implementation of the space.

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Chaturvedi, A.R., Mehta, S.R., Dolk, D. et al. Computational experimentations in market and supply-chain co-design: a mixed agent approach. ISeB 4, 25–48 (2006). https://doi.org/10.1007/s10257-005-0021-6

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