Mining and Incentive Concession Contracts

This paper studies the design of a mining concession contract as a multi-period autoselection problem where production is the depletion of a non renewable resource. As compared to symmetric information, we show that overproduction (resp. underproduction) is optimal in the initial phase (resp. terminal phase ) of the resource extraction program. Also, asymmetric information lengthens the contract duration but reduces the scarcity rent. Finally, when there are several agents competing for contract bid, we show that optimal auctioning could be used to award the concession, assigning the lowest cost agent to carry out the extraction.ADVERSE SELECTION; EXHAUSTIBILITY; OVERPRODUCTION

Performance Evaluation - Annual Report Year 3

This report describes the work done and results obtained in third year of the CATNETS project. Experiments carried out with the different configurations of the prototype are reported and simulation results are evaluated with the CATNETS metrics framework. The applicability of the Catallactic approach as market model for service and resource allocation in application layer networks is assessed based on the results and experience gained both from the prototype development and simulations. --Grid Computing

Taxing Emissions, Not Income: How to Moderate the Regional Impact of Federal Environment Policy

Canadian policymakers have the policy tools needed to ameliorate the regional economic harm that taxing GHG emissions can cause. A price on GHG emissions will affect Canadian provinces differently, possibly undermining support for a policy that incurs regional transfers of income. The authors recommend returning to the provinces the revenues collected through auctioned emissions permits, so that they may offer personal and corporate income tax relief, all to moderate the regional impact of GHG carbon policy. Allowing provinces to retain the revenues collected from auctioned emissions permits would achieve a greater degree of regional equity than the other policy options.Economic Growth and Innovation, GHG emissions, GHG carbon policy. Canadian federal policy, regional impacts of climate policy

Datamining for Web-Enabled Electronic Business Applications

Web-Enabled Electronic Business is generating massive amount of data on customer purchases, browsing patterns, usage times and preferences at an increasing rate. Data mining techniques can be applied to all the data being collected for obtaining useful information. This chapter attempts to present issues associated with data mining for web-enabled electronic-business

Proof-of-Concept Application - Annual Report Year 2

This document first gives an introduction to Application Layer Networks and subsequently presents the catallactic resource allocation model and its integration into the middleware architecture of the developed prototype. Furthermore use cases for employed service models in such scenarios are presented as general application scenarios as well as two very detailed cases: Query services and Data Mining services. This work concludes by describing the middleware implementation and evaluation as well as future work in this area. --Grid Computing

Moving from Data-Constrained to Data-Enabled Research: Experiences and Challenges in Collecting, Validating and Analyzing Large-Scale e-Commerce Data

Widespread e-commerce activity on the Internet has led to new opportunities to collect vast amounts of micro-level market and nonmarket data. In this paper we share our experiences in collecting, validating, storing and analyzing large Internet-based data sets in the area of online auctions, music file sharing and online retailer pricing. We demonstrate how such data can advance knowledge by facilitating sharper and more extensive tests of existing theories and by offering observational underpinnings for the development of new theories. Just as experimental economics pushed the frontiers of economic thought by enabling the testing of numerous theories of economic behavior in the environment of a controlled laboratory, we believe that observing, often over extended periods of time, real-world agents participating in market and nonmarket activity on the Internet can lead us to develop and test a variety of new theories. Internet data gathering is not controlled experimentation. We cannot randomly assign participants to treatments or determine event orderings. Internet data gathering does offer potentially large data sets with repeated observation of individual choices and action. In addition, the automated data collection holds promise for greatly reduced cost per observation. Our methods rely on technological advances in automated data collection agents. Significant challenges remain in developing appropriate sampling techniques integrating data from heterogeneous sources in a variety of formats, constructing generalizable processes and understanding legal constraints. Despite these challenges, the early evidence from those who have harvested and analyzed large amounts of e-commerce data points toward a significant leap in our ability to understand the functioning of electronic commerce.Comment: Published at http://dx.doi.org/10.1214/088342306000000231 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org