Experimental calculation of the damping ratio in buildings hosting permanent GPS stations during the recent Italian earthquakes

During the recent earthquakes in Italy, the contemporary presence of about 40 permanent GPS and 40 accelerometric stations of the national seismic network made it possible to estimate the value of the damping ratio of the buildings hosting the GPS stations. This value was calculated as the minimum of a function (parabola) constructed step by step from the relations between the ordinates of the pseudo-acceleration spectra extracted from the GPS and accelerometric measurements. For both construction material and building geometry, the results indicate values that differ by at least two percentage points from the values imposed by the technical regulations

Truthful Learning Mechanisms for Multi-Slot Sponsored Search Auctions with Externalities

Sponsored search auctions constitute one of the most successful applications of microeconomic mechanisms. In mechanism design, auctions are usually designed to incentivize advertisers to bid their truthful valuations and to assure both the advertisers and the auctioneer a non-negative utility. Nonetheless, in sponsored search auctions, the click-through-rates (CTRs) of the advertisers are often unknown to the auctioneer and thus standard truthful mechanisms cannot be directly applied and must be paired with an effective learning algorithm for the estimation of the CTRs. This introduces the critical problem of designing a learning mechanism able to estimate the CTRs at the same time as implementing a truthful mechanism with a revenue loss as small as possible compared to an optimal mechanism designed with the true CTRs. Previous work showed that, when dominant-strategy truthfulness is adopted, in single-slot auctions the problem can be solved using suitable exploration-exploitation mechanisms able to achieve a per-step regret (over the auctioneer's revenue) of order $O(T^{-1/3})$ (where T is the number of times the auction is repeated). It is also known that, when truthfulness in expectation is adopted, a per-step regret (over the social welfare) of order $O(T^{-1/2})$ can be obtained. In this paper we extend the results known in the literature to the case of multi-slot auctions. In this case, a model of the user is needed to characterize how the advertisers' valuations change over the slots. We adopt the cascade model that is the most famous model in the literature for sponsored search auctions. We prove a number of novel upper bounds and lower bounds both on the auctioneer's revenue loss and social welfare w.r.t. to the VCG auction and we report numerical simulations investigating the accuracy of the bounds in predicting the dependency of the regret on the auction parameters

ICT Adoption and Organizational Change. An Innovative Training System on Industrial Automation Systems for enhancing competitiveness of SMEs

The purpose of this paper is to introduce and discuss the benefits of on-line training on automation and innovation fields and try to explain their organizational impact on small and medium-sized enterprises (SME). Besides it tries to understand what are the main barriers for SMEs with respect to the realisation of their innovative potential and their capacity to improve internal processes by ICT adoption and organizational change. They are becoming particularly important for achieving greater productivity, lower operational costs, and higher revenues (usually characterized by reduced access to external finance, unavailability of wider distribution channels, low internationalization, etc.). The purpose of the paper is also to synthetize the experience done and the benefits of e-learning and of a specific online environment in the training process in this field. The project provides training contents to enhance participants background and some innovative simulations to improve knowledge of employees on industrial automation systems.The purpose of this paper is to introduce and discuss the benefits of on-line training on automation and innovation fields and try to explain their organizational impact on small and medium-sized enterprises (SME). Besides it tries to understand what are the main barriers for SMEs with respect to the realisation of their innovative potential and their capacity to improve internal processes by ICT adoption and organizational change. They are becoming particularly important for achieving greater productivity, lower operational costs, and higher revenues (usually characterized by reduced access to external finance, unavailability of wider distribution channels, low internationalization, etc.). The purpose of the paper is also to synthetize the experience done and the benefits of e-learning and of a specific online environment in the training process in this field. The project provides training contents to enhance participants background and some innovative simulations to improve knowledge of employees on industrial automation systems.Invited Submission

Algorithms for Strong Nash Equilibrium with More than Two Agents

Strong Nash equilibrium (SNE) is an appealing solu-tion concept when rational agents can form coalitions. A strategy profile is an SNE if no coalition of agents can benefit by deviating. We present the first general– purpose algorithms for SNE finding in games with more than two agents. An SNE must simultaneously be a Nash equilibrium (NE) and the optimal solution of mul-tiple non–convex optimization problems. This makes even the derivation of necessary and sufficient mathe-matical equilibrium constraints difficult. We show that forcing an SNE to be resilient only to pure–strategy de-viations by coalitions, unlike for NEs, is only a nec-essary condition here. Second, we show that the ap-plication of Karush–Kuhn–Tucker conditions leads to another set of necessary conditions that are not suffi-cient. Third, we show that forcing the Pareto efficiency of an SNE for each coalition with respect to coalition correlated strategies is sufficient but not necessary. We then develop a tree search algorithm for SNE finding. At each node, it calls an oracle to suggest a candidate SNE and then verifies the candidate. We show that our new necessary conditions can be leveraged to make the oracle more powerful. Experiments validate the overall approach and show that the new conditions significantly reduce search tree size compared to using NE condi-tions alone