Repast: An Agent Simulation Toolkit, March 17, 2010

In this meeting Milan gave an introductory presentation about Repast Simphony (Repast S) agent-based modeling toolkit. During the meeting, Milan showcased the official Repast S Predator Prey tutorial and provided links for software download, documentation and tutorials. A brief introduction to agent-based modeling (ABM) paradigm and potential applications domains have also been given.

The advantages of a specialized toolkit for ABM, instead of using a general (usually object-oriented) programming language, are firstly in the collection of libraries for agent-based design, which covers features such as scheduling, communication mechanisms, interaction topologies (networks, grids, GIS), facilities for storing and displaying agent-states etc. Another advantage of an agent-based toolkit such as Repast S is the runtime environment, which allows us to set up simulation parameters, to execute and visualize simulations, to probe agent states, to generate graphical displays of the outputs etc. Repast S has particularly rich support for linking the results to various external programs, as well as libraries for advanced computational techniques (genetic algorithms, neural networks, regression models, Monte Carlo method etc.).

One of the most interesting features of Repast S, however, is the graphical development environment which allows us to create agents by drawing (drag-and-drop) flowcharts on top of which groovy code is then automatically generated. Also, by setting properties of various model elements and by using wizards it is possible to make further adjustments to the model without or with little actual coding, which seams an interesting features for those who would like to develop agent-based models but still possess limited programming skills. Of course, an experienced Java programmer can also write Java code from scratch, without using this graphical user interface.

Modeling Competitive Bidding: a Critical Essay, March 3rd 2010

Today I (Meditya Wasesa) presented a paper review in LARGE group. The paper is from Management Science (1994) entitled “Modeling Competitive Bidding: a Critical Essay” written by Michel Rothkopf (RIP) and Ron Harstad. These 2 big economists in auction area write an essay which explains that there is big discrepancy between the existing (though its 1994 paper, I think it’s still relevant to the current research context) auction theory and the real practice, so that the developed theory is having limited application to the real live condition. My main interpretation of the whole paper is bundled by the figure shown below:



From this paper, we learn that the theories which try to model the competitive bidding behavior in auctions (both decision theory and game theoretical model) are mostly written in a single and isolated auction context. Moreover the assumptions which frame the theories (e.g. single isolated auctions, fixed number of bidders, symmetric Nash equilibrium, risk neutral bidders, etc) also limit the real world applicability. They suggest the researchers to adjust and enrich the elegant but simplified existing models so that the bidding model could be utilized in the real world situations.

There were a was discussion on how should we develop an analytic model which will fit to our research interest, which is the Dutch Flower Auction. Our research context is unique, since we are dealing with multi unit, multi bidders, multi suppliers, multi attributes, and highly interdependent auctions. We agreed that this paper would be considered as our main reference since it brings a lot of insights on how to make a realistic an applicable model, not just an elegant model which is good for theoretical sake but could not be applied in reality.

Why and When Preferences Convex? Threshold Effects and Uncertain Quality: February 3rd, 2010

In the LARGE meeting, Yixin presented the paper by Trenton G. Smith and Attila Tasnadi.

The authors discuss the circumstances under which convexity of preferences are beneficial. Particularly, they investigate a setting in which goods possess some hidden quality with known distribution, and the consumer chooses a bundle of goods that maximizes the probability that he receives some threshold level of this quality. It is shown that if the threshold is small relative to consumption levels, preferences will tend to be convex; whereas the opposite holds if the threshold is large.

The proposed theory helps explain a broad spectrum of economic behavior (including, in particular, certain common commercial advertising strategies), to some extent. However, some of the assumptions used in developing this theory are often violated in real-world problems. Further, the cases discussed in this paper are extremely simple: the consumer only need to make a decision about his/her consumption of two products with respect to a single attribute quality measure. Therefore, the generalizability of the theory to more complicated real-world cases is not clear.

Nevertheless, this paper does add some insights to the existing work on preferences and suggests that we need to rethink when taking the convexity assumption of preferences for granted.