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on Information and Communication Technologies |
| By: | Oksana Loginova (Department of Economics, University of Missouri-Columbia) |
| Abstract: | Although the Internet reduces market frictions by making it easier for consumers to obtain information about prices and product offerings, goods sold by electronic firms are not perfect substitutes for otherwise identical goods sold by conventional stores. Online purchases, due to non-zero shipping time, are associated with waiting costs, and they do not allow consumers to inspect the product prior to purchase. Visiting a conventional store, on the other hand, involves positive travelling costs. A model extending the circular city paradigm with two types of firms, conventional and electronic, is studied. Under the benchmark setting with only conventional firms in the market, each consumer visits the nearest store and purchases the product there. When electronic firms enter the market, an intriguing type of market segmentation may arise. First, each consumer travels to the nearest conventional store to "try on" the product. Second, conventional retailers increase their prices and sell the good only to consumers who discover that they have high valuations; consumers with low valuations return "home" and order the good online. In spite of the increased competition from Internet retailers, welfare decreases. |
| Keywords: | Electronic Commerce, Oligopoly Pricing, Market Segmentation, Spatial Competition. |
| JEL: | D43 D81 L11 |
| Date: | 2007–07–31 |
| URL: | http://d.repec.org/n?u=RePEc:umc:wpaper:0715&r=ict |
| By: | Gary E Bolton; Claudia Loebbecke; Axel Ockenfels |
| Abstract: | Many Internet markets rely on ‘feedback systems’, essentially social networks of reputation, to facilitate trust and trustworthiness in anonymous transactions. Market competition creates incentives that arguably may enhance or curb the effectiveness of these systems. We investigate how different forms of market competition and social reputation networks interact in a series of laboratory online markets, where sellers face a moral hazard. We find that competition in strangers networks (where market encounters are one-shot) most frequently enhances trust and trustworthiness, and always increases total gains-from-trade. One reason is that information about reputation trumps pricing in the sense that traders usually do not conduct business with someone having a bad reputation not even for a substantial price discount. We also find that a reliable reputation network can largely reduce the advantage of partners networks (where a buyer and a seller can maintain repeated exchange with each other) in promoting trust and trustworthiness if the market is sufficiently competitive. We conclude that, overall, competitive online markets have more effective social reputation networks. |
| Date: | 2007–08–01 |
| URL: | http://d.repec.org/n?u=RePEc:kls:series:0032&r=ict |
| By: | Rubin, Raphael |
| Abstract: | The DARPA Grand Challenge in which the Cornell Racing Team participates requires the completion of a Simulator, which purports all errors in the artificial intelligence path planning down below and back up. The simulator comes as the last layer in the top down approach followed by the Cornell Racing Team. The Strategic layer is charged of global route planning, the tactical layer of collision avoidance and maneuver planning, while the operational layer controls lane tracking and safe following. The simulator is the last layer. Through a COBRA interface the C++ or C# version of the simulator will be receiving commands from the Artificial Intelligence Strategic Layer concerning maneuvers such as Turn Left, Turn Right, Change Lane, Increase Speed, and Stop. The simulator induces from its current situation, using controls such as bounding boxes and the World class, pointing to every object in the World, a set of more detailed commands. Apart from writing a simplified version of the simulator in C++, we also concentrated my efforts onto finding a solution aside from dynamic programming for Path Planning and the Behavioral Modeling of Visible and Neighboring Vehicles on the road network. We have built an efficient and self-correcting C++ GUI Interface including some random moving vehicles as well as a smart vehicle named Autosmart. The Path Planning algorithm is written and implemented although may be missing a more significant round of testing. To do so, we are using the approach of game theory and artificial intelligence’s neural networks. We represent the world as nature, resulting in decisions independent of the drivers (types: turn left or right at the next intersection); nature being in this case the DARPA Challenge organizers. Moreover the drivers chose their behaviors (aggressive, altruist) on the road and keep updating their anticipations about the other players behavior and types, as mentioned above. The end result is to train these neural networks to react to previously categorized behaviors and situations by storing necessary information about the ‘game’. Every player runs its own network, although in our case we limited the simulation to one smart vehicle, Autosmart and 2 random vehicles; therefore by nature the algorithm the algorithm would lead to biased results. It is meant for simplicity since if not for programming the set of commands which lead to adequate behavior at intersections and on segments, such as being done for the smart vehicle; sometimes the random vehicles get into trouble, being too much off the road network. In most cases, the simulator will self-correct their path however. |
| Keywords: | C++; DARPA Urban Challenge; Game Theory; Artificial Intelligence; |
| JEL: | C70 |
| Date: | 2007–08–25 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:4603&r=ict |
| By: | Jeong-Yoo Kim (Kyun Hee University); Insik Min (Kyun Hee University); Christian Zimmermann (University of Connecticut) |
| Abstract: | In this paper, we study the citation decision of a scientific author. By citing a related work, authors can make their arguments more persuasive. We call this the correlation effect. But if authors cite other work, they may give the impression that they think the cited work is more competent than theirs. We call this the reputation effect. These two effects may be the main sources of citation bias. We empirically show that there exists citation bias in Economics by using data from RePEc. We also report how the citation bias differs across regions (U.S., Europe and Asia). |
| Keywords: | citation bias, correlation effect, reputation effect, signal, strategy, RePEc |
| JEL: | D81 |
| Date: | 2007–08 |
| URL: | http://d.repec.org/n?u=RePEc:uct:uconnp:2007-31&r=ict |