Marek Spišák

Transaction costs influence on the stability of financial market: agent-based simulation

We implement an agent-based simulation of financial market model. Agent-based simulations are used nowadays as an alternative to the traditional models, based on predetermined equilibrium state theory. Agent technology brings some kind of local intelligence and rational expectations to the decision support system of financial market participants. Agents follow technical and fundamental trading rules to determine their speculative investment positions. We consider direct interactions between speculators and they may decide to change their trading behaviour. If a technical trader meets a fundamental trader and they realize that fundamental trading has been more profitable than technical trading in recent past, the probability that the technical trader switches to the fundamental trading rules is relatively high. In particular the influence of transaction costs is studied in this paper. Transaction costs can be increased by the off-market regulation (for example in the form of taxes) on financial market stability, by overall volume of trade and other market characteristics. The paper shows a positive impact of suitable transaction costs on the financial market stability in the long run.

Control Loop Model of Virtual Company in BPM Simulation

The motivation of the paper is to introduce agent-based technology in the business process simulation. As in other cases, such simulation needs sufficient input data. However, in the case of business systems, real business data are not always available. Therefore, multi-agent systems often operate with randomly (resp. pseudo randomly) generated parameters. This method can also represent unpredictable phenomena. The core of the paper is to introduce the control loop model methodology in JADE business process simulation implementation. At the end of this paper the analysis of agent-based simulation outputs through process mining methods and methods for analysis of agents’ behavior in order to verify the correctness of used methodology is presented. The business process simulation inputs are randomly generated using the normal distribution. The results obtained show that using random number generation function with normal distribution can lead to the correct output data and therefore can be used to simulate real business processes.

An influence of random number generation function to multiagent systems

The paper deals with modeling and simulation of business processes. A multiagent system was implemented as a tool to manage the simulation. Multiagent systems often operate with random (respectively pseudorandom) generated parameters in order to represent unpredictable phenomena. The aiml of the paper is to show the influence of different random number generation functions to the real multiagent system outputs. It is obvious, that outputs of the multiagent system simulation differs from turn to turn, but the motivation was to find, if the differences are significant. An accurate number of agents with the same parameters were used for each case, with different kinds of randomness while generating agents internal state attributes. The results obtained show that using inappropriate random number generation function leads to significant output data distortion, so the generation function selection must be done very carefully.

Microeconomic Demand Functions Implementation in Java Experiments

The aim of this paper is to introduce microeconomic demand functions (Marshallian demand function and Cobb-Douglas utility function) in Java simulation experiments. The motivation is to use these function as a core element in a seller-to-customer price negotiation in an agent-based simulations. Furthermore, multi-agent model is proposed and implemented in Java to serve as a simulation framework to support the virtual company trading processes. The main background of this framework is to be integrated in management information systems as a decision support module. The paper firstly presents some of the existing principles about consumer behavior, agent-based modeling and simulation in the same area and demand function theory. Secondly, presents multi-agent model and demand functions negotiations. Lastly, depicts some of the simulation results in a trading processes throughout one year of selling commodities to consumers. The results obtained show that in some metrics the demand functions could be used to predict the trading results of a company.

Trading Agents’ Negotiation in Business Management Using Demand Functions: Simulation Experiments with Binomial Distribution☆

Abstract The aim of this paper is to propose an experimental business management approach to cover a seller-to-customer price negotiation in an agent-based simulations. The core element in this approach is the price negotiation. We used Marshallian demand function and a Cobb-Douglas utility function in the negotiation process. Moreover, multi-agent model is proposed and implemented in Jade development platform. Its task is to serve as a simulation framework for the trading processes execution. The main background of this framework is to be integrated in management information systems as a decision support module for a prediction of key performance indicators of a virtual company. A binomial distribution was used in presented experiments to simulate the quantity of negotiated commodities. The paper firstly presents some of the existing principles about consumer behavior, agent-based modeling and simulation in the same area and demand function theory. Secondly, presents multi-agent model and demand functions negotiations more formally. Finally, shows some of the simulation results in a trading processes throughout one year of selling commodities to consumers. The results obtained show that the demand functions could be properly used to simulate trading processes.

Towards JAVA Simulation Experiment With Agent-Based Trading Processes.

The motivation of the paper is to introduce an agentbased business process model in the Java simulation experiment. A virtual company trading processes are simulated in order to use proposed methods as a part of a decision support tool. As in other cases, such simulation needs sufficient input parameters. However, in the case of business systems, real business parameters are not always available. Therefore, multi-agent system as a simulation framework, often operates with randomly (resp. pseudo randomly) generated data. Some of the business process simulation inputs are randomly generated in this paper. This method can also represent unpredictable phenomena. The core of the paper is to introduce the business process simulation implementation (simulation framework) and the simulation results. Finally, obtained results after the validation show that using business process model and the implementation proposed can lead to the correct output data and therefore can be used to simulate real business processes.