Trading in open marketplace using trust and risk

In this paper, we propose buying and selling models for agents to trade in the open multi-agent marketplace. Unlike auctions, we take into account of the fact that agents trading in such open environments has to maximize their profits and at the same time, protect themselves from fraud and deception. We attempt to address this issue by incorporating the element of trust and risk management into our proposed buying and selling model. During buying, agents learn to select their partners based on the trustworthiness of the potential partner as well as its personal risk attitude. During selling, agents learn to increase the chances of winning a deal by adjusting their profit rate, which is a measure that considers both trust and risk. The novelty of this proposal is that it ensures agents continuing to seek maximum expected utility in a dynamic trading environment. Our experimental results confirm the feasibility of our approach. <br /

Ascertaining the financial loss from non-dependable events in business interactions by using the Monte Carlo method

Risk Assessment in business interactions is carried out to determine beforehand the occurrence ofundesirable events and their associated consequences. In the literature, various approaches have beenproposed by which an interaction initiating agent can ascertain the occurrence of undesirable event/s and determine their consequences in an interaction. But all of those approaches just consider those events that are related on the performance of the other agent, with whom the interaction initiating agent is forming an interaction with. It is possible that there are also such events that are not dependant on the other agent?s performance, but will directly or in-directly have an impact on the successful completion of the interaction. In this paper we will highlight the importance of considering such event/s during the process of risk assessment, and propose a methodology by which the interaction initiating agent can determine and quantify their effect on the successful completion of its business interaction

Probabilistic assessment of financial risk in e-business associations

Business activities are a result of carefully formed associations between different users in order to achieve certain pre-decided outcomes. Decision-making in such associations is an important step and transactional risk analysis is one of the integral processes that facilitates this step. This paper presents an approach that determines the negative consequences (termed as financial risk) of forming e-business associations. Unlike other approaches, our model captures the different types of events and their uncertainties to determine the financial risk by using the convolution operator and expressing it as a probabilistic measure rather than as a crisp financial value. Such representation makes sense as the financial risk may be determined at a point of time in future where nothing is certain. Depending upon the complexity of the problem, we explain the different ways of using the convolution operator to determine the financial risk. The simulation result shows a better representation and understanding of the financial risk that will provide important inputs to the transactional risk analysis and the decision-making process