The Allocation of Resources by Interest Groups: Lobbying, Litigation and Administrative Regulation

One of the central concerns about American policy making institutions is the degree to which political outcomes can be influenced by interested parties. While the literature on interest group strategies in particular institutions - legislative, administrative, and legal - is extensive, there is very little scholarship which examines how the interdependencies between institutions affects the strategies of groups. In this paper we examine in a formal theoretical model how the opportunity to litigate administrative rulemaking in the courts affects the lobbying strategies of competing interest groups at the rulemaking stage. Using a resource-based view of group activity, we develop a number of important insights about each stage that cannot be observed by examining each one in isolation. We demonstrate that lobbying effort responds to the ideology of the court, and the responsiveness of the court to resources. In particular, (1) as courts become more biased toward the status quo, interest group lobbying investments become smaller, and may be eliminated all together, (2) as interest groups become wealthier, they spend more on lobbying, and (3) as the responsiveness of courts to resources decreases, the effect it has on lobbying investments depends on the underlying ideology of the court

The Allocation of Resources by Interest Groups: Lobbying, Litigation and Administrative Regulation

One of the central concerns about American policy-making institutions is the degree to which political outcomes can be influenced by interested parties. While the literature on interest group strategies in particular institutions - legislative, administrative, and legal is extensive, there is very little scholarship which examines how the interdependencies between institutions affects the strategies of groups. In this paper we examine in a formal theoretical model, how the opportunity to litigate administrative rulemaking in the courts affects the lobbying strategies of competing interest groups at the rulemaking stage. Using a resource-based view of group activity, we develop a number of important insights about each stage - which cannot be observed by examining each one in isolation. We demonstrate that lobbying effort responds to the ideology of the court, and the responsiveness of the court to resources. In particular, 1) as courts become more biased toward the status quo, interest group lobbying investments become smaller, and may be eliminated all together, 2) as interest groups become wealthier, they spend more on lobbying, and 3) as the responsiveness of courts to resources decreases, the effect it has on lobbying investments depends on the underlying ideology of the court.

Distributed production planning and control agent based system

A model of an Agent based Production Planning and Control (PPC) system able to be dynamically adaptable to local and distributed utilization of production resources and materials is presented. The PPC system is based on the selection of resources to deal with one order of different quantities of one product each time. In this way it is build one scheduling solution for that particular order. The production resources are selected and scheduled using a multiagent system supported by an implementation of the Smith Contract Net, using Java Spaces technology. The multiagent system is based on three main agents: Client, Resource and Manager. These agents negotiate the final product, and the correspondent components, requested by the client. An order for each product (component) triggers a process of dynamic design of a production system to fulfill that particular order. This system exists till the end of the order

Vision-Based Corrosion Identification Using Data-Driven Semantic Segmentation Techniques

Corrosion is a natural process that degrades metal-made materials. Its detection is of primordial importance for quality control and for ensuring longevity of metal-made objectsin various contexts, in particular in industrial environments. Different techniques for corrosion identification including ultrasonic testing, radio-graphic testing, and magnetic flux leakage have been proposed in the past. However, these require the use of costlyand heavy equipment onsite for successful data acquisition. An under-explored alternative is to deploy conventional lightweight and inexpensive camera systems and computer vision based methods to tackle the former problem. In this work we present a detailed benchmark of four state-of-the-art supervised semantic segmentation techniques, for vision-based pixel-level corrosion identification. We focus our study on four, recently proposed deep learning architectures which have surpassed human-level accuracy on various visual tasks. The results demonstrate that the former approaches may be used for the problem of segmenting highly irregular patterns in industrial settings, such as corrosion, with high accuracy rates

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info:eu-repo/semantics/publishedVersio