Meeting Minutes - 21 July, 2008

Today Peter Berends held a presentation discussing the case “The Application of Intelligent Personalised Agents for Buyer Decision Empowerment at the Dutch Flower Auction (DFA)”.

The DFA consists of six individual auctions located throughout the Netherlands which each host a couple of clock auctions on which goods are auctioned. In total there are 39 clocks on the DFA.

When buying through a clock auction, buyers aim to buy at the lowest price. Thus, they try to show their interest at the very latest possible moment. However, caution needs to be exercised because reacting too late means forgoing the ability to buy the auctioned product because other buyers might have jumped on the opportunity. As a consequence, buying through clock auctions is not an easy job. There is also a remote application through which buyers can buy at the DFA from any location. Many buyers procure for their customers who are located at different locations, and there are six different auctions of the DFA in the Netherlands. This means that when remotely buying, buyers could optimise the transportation cost and transportation time by making sure they buy products from an auction that is in close proximity to the location where they need to ship the goods.

This means that there are four main decision parameters to be considered with every buy: (1) price; (2) quality measures; (3) transportation costs; and (4) transportation time. There was a discussion on how intelligent agent-based systems could be utilized to empower buyers in their decision making.

After the presentation a discussion was held about where best to apply the agents, their benefits, and the information the agents need to base its decision on.

Meeting Minutes - 23 June, 2008

Today Wolf gave a presentation on the ICEC '08 paper "A Semantic Web Architecture for Advocate Agents to Determine Preferences and Facilitate Decision Making". The paper deals with the architecture of a new kind of autonomous agents.

The main motivation of applying these personalized agents is that they can complement the cognitive limitations of the human mind, and therefore facilitate the decision making process to, reduce information overload (bounded rationality), increase work efficiency (i.e. speed up real-time managerial decisions), increase productivity (cost savings and ROI), increase solution (product or service) quality. Besides these tangible benefits, there are also intangible benefits, e.g. greater customer and employee satisfaction. In order to do this, these agents need to work effectively and efficiently with the human user. Meaning that the agent must learn the human user's interests, habits and preferences (as well as those of their communities). In an online retail example, recommendations can be given as to what to buy (product-brokering) and from whom to buy (merchant-brokering), based on customer criteria.

Agents and the human work in a bi-directional way through the interface called: Economic Dashboard.
"You cannot manage what you do not measure"
"What gets watched, gets done."

These statement demonstrate what the Economic Dashboard is, an "Organizational Magnifying Glass" – to focus the work of employees so everyone is going in the same direction! It business people: (1) Monitor, (2) Analyze, (3) Manage, (4) and Communicate and give feedback to the agent.

In order to work with the Economic Dashboard at all of the different organizational levels, these Economic dashboard has three types that relate to Business Intelligence:
Strategic BI: Achieve long-term organizational goals
Tactical BI: Conduct short-term analysis to achieve strategic goals
Operational BI: Provide a decision-making environment that reduces the latency between the time a significant business event happens and the business' ability to react to it.

In order to bring these personalized results, and work with the personalized results in the Economic Dashboard preferences are elicitated. Preference elicitations is the central concept of decision making and is fundamental for the analysis of human choice behavior, since people have different preferences for different roles. There are four methods or preference elicitation: (1) Questionnaire, which define roles, areas, objectives, and tasks; (2) Implicit feedback through user observation through browser extension (Piggy Bank, etc.), (3) Explicit user feedback through economic dashboard, and none intrusive sidebar in browser window, and (4) Business and Social Networks (Professional (intra company e.g. IBM, Linkedin, Plaxo, etc.) Personal (Facebook, Hi5, Hyves, etc.).

These preferences are saved in RDF stores, which allows the best abilities to apply Semantic Web agents.

In conclusion, this paper demonstrates the feasibility of Advocate Agents by presenting an architecture that integrates current technologies, such as Enterprise Service bus, XML, RDF, and machine learning techniques into a unique system and demonstrating that all the components of Advocate Agents can be built from already existing methods and elements.

After the presentation a discussion was held.

Next LARGE meeting is scheduled for 21 July.

Meeting Minutes - 16 June, 2008

Elfriede Krauth presented a paper (together with Wolf Ketter and Jacqueline Bloemhof ) on Business Intelligence: Academic vs. Industry Perspective. Dashboards are distinguishing features between the disciplines.

Meeting Minutes - 9 June 2008

Today, Ludo presented his research under the title ‘An Algorithm for Calculating the Long-Run Behavior of Genetic Algorithms in Economic Modeling’. Ludo first gave a brief overview of the research topic with which he is concerned. This is the topic of economic modeling using genetic algorithms. Ludo then discussed, at an informal level, the theoretical results that he has obtained. From these results, an algorithm can be derived that makes it possible to calculate the long-run behavior of genetic algorithms in economic modeling. Finally, Ludo discussed the application of this algorithm to a frequently cited study by Robert Axelrod (1987) on genetic algorithm modeling in iterated prisoner’s dilemmas.

Meeting Minutes - 2 June 2008

Today, 2 June, Jordan presented a summary of three presentations given at the OMPL seminar on Agent-based control of Production and Logistics: theory applications and prospects. This seminar was originally hosted on 27 March at the university of Twente. Jordan provided an overview of the presentations given by Prof. Heragu and Prof. La Poutre as well as a summary of the MAS/"OR heuristics" comparison performed by Martijn Mes.

Meeting Minutes - May 26, 2008

Today, Ludo presented the paper Robust Evolutionary Algorithm Design for Socio-Economic Simulation by Floortje Alkemade, Han La Poutré, and Hans Amman (Computational Economics, 28(4), 355–370, 2006). After a brief overview of the paper, the discussion focused on the main reasoning followed by the authors. Some objections against this reasoning were raised. We also discussed an error in the paper, focusing on what we can learn from the error and how such errors can be avoided as much as possible.

Meeting Minutes - 14 April, 2008

During this meeting Katalin has summarized a working paper on a taxonomy proposed for artificial stock markets (ASMs). The taxonomy is aimed to give a guideline for analyzing, comparing, replicating and designing ASMs. The main factors proposed are extracted from the market microstructure literature, and representation aspects are added based on the literature on ASMs. Two main groups of factors are distinguished that are used to describe real and artificial stock markets: organizational aspects and behavioral aspects. The first group is related to market organization and it contains static, well-defined aspects, such as: the type of stocks, orders, participants, trading sessions, and exchange mechanism. The second group is related to the hardly observable and varying aspects of markets such as price formation and the behavior of the various market participants. An overview of 15 various ASMs has been given using the taxonomy. The variety of various trading strategies implemented within ASMs has been illustrated. An interesting discussion point is whether and to what degree are the represented traders in these ASMs "agents", and to what degree are they "autonomous".