Simulations for Evaluating Electronic Markets - An Agent-based Environment
Clemens Czernohous University of Karlsruhe
Information Management and Systems Englerstr. 14, D-76 131 Karlsruhe Clemens.Czernohous@iw.uni-karlsruhe.de
Abstract
The development of electronic markets is a challenging task. The Market Engineering (ME) approach suggests a structured development process. One critical point within electronic market development is the test and verification of the market regarding its design objectives. This paper introduces simulations as an appropriate method for the evaluation of electronic markets. It describes the Agent- based MArket Simulation Environment (AMASE) as one tool for market simulations and presents an example for agent-based market simulation.
1. Introduction
Markets are well known among economists as suit- able coordination mechanism. Especially electronic mar- kets are claimed to advance efficiency of trading, not only in respect to reduction of communication, but also regard- ing resource allocation. But reality is manifold.
Markets are defined as the combination of a market institu- tion and the economic environment. The market institution contains the rules and regulations of trading (microstruc- ture), the infrastructure, and the business structure, whereas the environment describes the characteristics of the mar- ket participants and the transaction object. The market in- stitution’s microstructure has a vast impact on the market outcome. A small change in the rules can significally in- fluence the participants’ behaviour (see for example [10]).
Consequently, the design of electronic markets is a diffi- cult task. It has been more and more called for scientific support in designing and developing electronic mar- kets (see [13], [18]).
One approach for the design of markets has been intro- duced by [20] as Market Engineering (ME). Different aspects are to be analyzed, e.g. whether the market mi- crostructure meets the defined goal, or advantageous bidding strategies can be identified. Both, the market mi-
crostructure and the economic environment, have an im- pact on the market’s outcome. Notice, that a designer just is in control of the market microstructure. The eco- nomic environment and the market’s outcome can only be influenced indirectly1. Consequently, it is an interest- ing task to study the impact of specific combinations of market microstructure and economic environment (the mar- ket’s design space) on the market’s outcome. Economics has breed out three approaches to study the market’s be- haviour, (1) axiomatic approach, (2) experiments, and (3) simulation.
In an axiomatic approach economists deduce theo- rems from certain axioms, whereas experimental eco- nomics studies particular behaviour during experiments with human agents. This paper focus on the third ap- proach, using simulation methods for evaluation of elec- tronic markets.
The next section provides an overview on work in sim- ulations for economic evaluation and analysis. Section 3 presents the architecture of the Agent-based MArket Simu- lation Environment (AMASE), including a brief summary of a current AMASE-application. The present paper con- cludes the results in Section 4.
2. Simulations for economic analysis
In recent years, the use of computational methods has more and more enhanced economists’ way of study- ing the economy. Research by means of computational methods has become known as Computational Eco- nomics (CE) [1].
Intelligent software agents offers economists the opportu- nity to model individual behaviour. [21] characterizes in- telligent software agents as ”computational entity [..] that can be viewed as perceiving and acting upon its environ- ment and that is autonomous in that its behavior at least
1 [15] differentiate endogenous and exogenous parameters to determine specific settings of market institutions and economic environment
partially depends on its own experience.” [7] points out that the capability of flexible autonomous acting in differ- ent environments constitutes the intelligence of software agents. Many characteristics of intelligent agents are de- scribed in literature mentioning reactivity and proactivity [22] and rationality [12] as other main features. Adap- tive behavior comprehends the ability of learning within the agents environment [4]. Two or more intelligent agents act- ing in one system constitute a Multi Agent System (MAS) and necessitate communication and interaction amongst the agents [6]. To ensure reasonable interaction and com- munication in a MAS agents need to consider not only their own actions but also need to anticipate future ac- tions of other agents to coordinate their actions [8].
The use of autonomous interacting software-agents for rep- resenting economic agents has brought up the research area of Agent-based Computational Economics (ACE) [16] building economies as independent evolving sys- tems. Modelling individual strategies enables economists to study markets, market behaviour and its develop- ment over time and market microstructure under certain in- stitutional and environmental rules. Agents applied in simulations normally use simple decision rules, learning al- gorithms, or statistical analysis to adapt their strategies.
[16] provides a detailed overview on ACE research and de- scribes studies of market simulations in electricity and fi- nancial markets (for examples see also [2], [9], [11], [14], [19]).
Thus, ACE is a promising research area to study out- come of certain environmental and institutional settings.
A few agent-based simulation tools are available so far, as SWARM2or RePast3. These toolkits are widely used for so- cial group scenarios, but disregard support for inter agent communication and distribution of agents on multiple com- puters. Distribution of agents enable simulations with large agent populations (e.g. market platform performance tests). Distributed agents require a standard for informa- tion exchange and highly flexible message structure (e.g.
in market simulation scenarios, where the influence of cer- tain information is studied). The FIPA4 has developed standards for inter agent communication and for con- struction of an agent platform that are realized in JADE5. This paper presents the Agent-based Market Simula- tion Environment (AMASE) based on JADE providing functionality for agents on multiple platforms and stan- dardized message exchange as a helpful tool supporting ME. The next section describes AMASE in more de- tail.
2 http://www.swarm.org/
3 http://repast.sourceforge.net
4 Foundation for Intelligent Physical Agents http://www.fipa.org 5 Java Agent DEvelopment Framework: http://jade.tilab.com
3. The Agent-based MArket Simulation Envi- ronment (AMASE)
The generic market platform meet2trade enables the user to individually define market rules, facilitates the con- figuration of markets, and allows to run various markets at the same time (see for example [5]). Therefore, it pro- vides helpful support in ME. The test and evaluation of certain market rules is a crucial step within ME. The cur- rent contribution of agent-based simulation to economic studies appears to be a promising approach for evaluat- ing electronic markets. To benefit from the functional- ity of meet2trade and ACE, agents have to be able (i) to trade on meet2trade (send orders), (ii) to receive informa- tion about meet2trade markets, and (iii) to easily exchange messages amongst each other. Another aim is to (iv) dis- tribute agents over different platforms to not limit the amount of agents due to computing ressources. Cur- rent simulation tools as RePast do not yet support dis- tributed agents and are limited in communication fea- tures. Therefore, AMASE was developed to enable discrete event market simulations with respect to the named re- quirements.
AMASE uses the JADE Agent libraries that real-
AMASE
Message Transport System
Simulation Control Agent Simulation
Agents
E-FIT Market Server Core
Configurator Client
Domain Facilitator
Agent Management
System
Agent JADE
e-FIT-System
Figure 1. The basic AMASE architecture
izes the FIPA specifications providing an agent platform (AP) with Agent Management System (AMS), Mes- sage Transport System (MTS), Agents and additional services. Figure 1 displays the FIPA AP, the AMASE ex- tentions and the communication to the meet2trade platform.
The central agent of the JADE AP is the AMS keeping su- pervisory control over AP access and AP use. The AMS Agents consists only once even on a distributed plat- form. The Domain Facilitator provides information (yellow pages) service within the platform. All JADE Agents im- plement an agent life cycle and inherite functionality to add/remove certain behaviours, and to exchange mes- sages over the MTS. JADE allows to distribute the AP on
several computers, enabling agents to move between the computers. JADE does not provide any simulation spe- cific functionality.
AMASE adds the Simulation Control Agent (SCA) provid- ing simulation management functions described in more detail below. Additionally, specific behaviours are imple- mented to equip simple JADE agents with basic simulation control. The SCA Graphical User Interface (GUI) facili- tates simulation set up and control.
3.1. Simulation Control Agent (SCA)
The SCA is the central management entity and enables discrete event simulation. AMASE Agents are coordinated by control messages to synchronize their activity. Since the focus is on round based simulations, start and end of rounds are managed by the SCA. Control Messages are exchanged using the JADE MTS and a specified protocol. The simula- tion modelling is facilitated by the Simulation Management Behaviour (SMB). Users can either use the default SMB with certain control message sequence, or can easily adapt the SMB for individual purpose. Additional data log func- tionality is provided to write agent specific data in a text file using comma separated values for easy export to sta- tistical standard software. Due to the fact, that agent con- trol takes place within JADE, AMASE is also able to sup- port not only meet2trade simulations but also enables sim- ulations on a stand alone basis.
Since JADE supports distributed agents, simulations can be performed using one SCA and serveral simulation agents on remote AP containers.
3.2. Graphical User Interface (GUI)
The SCA Graphical User Interface (GUI) enables users to easily define simulation settings such as duration, types of agents or parameter settings. The GUI enables the man- agement of user defined agent classes, and it generates a specified number of agents of one type. The GUI is dev- ided in three main windows, Agent Window (AW), Settings Window (SW), and Data Window (DW). Agent types and agents are displayed in the AW, settings can be configured in the SW, and data will be shown in the DW. The SW supports data base management of the meet2trade system including user management, parameter initialization, basic endowment determination, and agent population manage- ment. Figure 2 shows a screenshot of the SCA GUI.
3.3. AMASE agents
Simulation agents can easily be generated using the JADE agent class and adding the Agent Control Behaviour (ACB). The ACB manages the control message exchange
Figure 2. SCA GUI Screenshot
with the SCA. ACB can be adapted for individual need by overwriting the methods start(), stop(), and initialize(). Al- ternatively, specific behaviours for start, stop and intialize can be implemented and registered at ACB to handle the particular occuring event. Beside the AMASE specific be- haviour, user can define behaviours for individual needs.
AMASE agents are capable to send orders to meet2trade and can receive market specific information for individual reaction. Particular strategies for different agent types are to be determined and implemented simulation specifically.
Communication between agents is supported by JADE. On- tologies and FIPA negotiation protocols facilitate the con- versation between agents. Thus, AMASE remains highly flexible for various simulations and can be used for ME evaluation.
3.4. Applying Agent-based Market Simulation
Our actual work shows promising results in using simu- lations for market microstructure analysis. We study the in- fluence of the buy price mechanism and compare the mar- ket results to a single sided ascending bid auction with hard end as for example provided by eBay. The buy price is spec- ified by the auctioneer and offers the opportunity to buy the product immediately at any time during the auction. Con- sequently, the buy price is an upper limit for the auction, and bidding the buy price immediately terminates the auc- tion regardless the determined ending time. This work is de- scribed in more detail in [19] and adapts a model of [17].
First results of this work support the theoretical expecta- tions of the studied scenarios. Simulations appear to be a powerful tool for Market Engineering in supporting the test and evaluation phase of new electronic markets.
4. Conclusion
Agent-based simulations are an evolving research field with promising results for the analysis of market de- sign. [3] for example, successfully analyse the electric- ity trading mechanism of England and Wales with an agent-based approach. [17] studies the influence of end- ing rules in online auctions on the bidder behavior and the market outcome. [19] analyses buy price and fixed end- ing rules in online auctions by applying an agent-based ap- proach.
The Agent-based MArket Simulation Environment (AMASE) provides basic functionality to set up and con- duct simulations. In combination with the generic market platform meet2trade AMASE provides a powerful tool for design test bedding of electronic markets. This is an impor- tant step within the Market Engineering approach. In order to design a market for a certain situation, the market engi- neer has to find out interdependencies and behaviours of a market. AMASE supports the market engineer to anal- yse certain market microstructures within determined envi- ronments.
Thus, the combination of AMASE and meet2trade facil- itates the study of various simulation scenarios in the fu- ture. This includes simulations on both, single sided and double sided markets. Simulations are a promis- ing method for testing electronic markets, but simulations are not feasable for every situation or problem. Experi- mental studies need to supplement computational studies and open the opportunity for benchmarking experimen- tal and computational results.
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