Simulator Development - Annual Report Year 3

This document describes the progress of the simulator development with in the third year of the CATNETS project. The refinement of the simulator as well as a detailed guide to conducting simulations is presented. --Grid Computing

Rational bidding using reinforcement learning: an application in automated resource allocation

The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms – one centralized and one decentralized

Motivating Organizational Search

This paper investigates the value of high-powered incentives for motivating search for novelty in business organizations. While organizational search critically depends on the individual efforts of employees, motivating search effort is challenged by problems of unobservable behavior and the misalignment of individual and organizational interests. Prior work on organizational design thus suggests that stronger incentives can overcome these problems and make organizations more innovative. To address this conjecture, we develop a computational model of organizational search that rests on two opposing effects of high-powered incentives: On the one hand, they promote higher effort by increasing the potential rewards from search; on the other hand, they increase the competition among ideas, as the ability of an organization to implement and remunerate good ideas is limited by its resource base. Our results indicate that low-powered incentives are effective in generating a sufficient stream of incremental innovations, but that they also result in a shortage of more radical innovations. Stronger incentives, in contrast, do not systematically foster radical innovations either, but instead create a costly oversupply of good ideas. Nonetheless, higher-powered incentives can still be effective in small firms and if strong persistence is required to develop a new idea. Based on the analysis of our model, we develop a set of propositions that appear to be consistent with extant evidence and point to new avenues for empirical research.Organizational search, incentives, innovation, agent-based simulation