A Negotiation Style Recommender Based on Computational Ecology in Open Negotiation Environments

The system described herein represents the first example of a recommender system in digital ecosystems where agents negotiate services on behalf of small companies. The small companies compete not only with price or quality, but with a wider service-by-service composition by subcontracting with other companies. The final result of these offerings depends on negotiations at the scale of millions of small companies. This scale requires new platforms for supporting digital business ecosystems, as well as related services like open-id, trust management, monitors and recommenders. This is done in the Open Negotiation Environment (ONE), which is an open-source platform that allows agents, on behalf of small companies, to negotiate and use the ecosystem services, and enables the development of new agent technologies. The methods and tools of cyber engineering are necessary to build up Open Negotiation Environments that are stable, a basic condition for predictable business and reliable business environments. Aiming to build stable digital business ecosystems by means of improved collective intelligence, we introduce a model of negotiation style dynamics from the point of view of computational ecology. This model inspires an ecosystem monitor as well as a novel negotiation style recommender. The ecosystem monitor provides hints to the negotiation style recommender to achieve greater stability of an open negotiation environment in a digital business ecosystem. The greater stability provides the small companies with higher predictability, and therefore better business results. The negotiation style recommender is implemented with a simulated annealing algorithm at a constant temperature, and its impact is shown by applying it to a real case of an open negotiation environment populated by Italian companies

Service offshoring and the skill composition of labor demand

This paper studies the effects of service offshoring on the skill composition of labor demand, using novel comparable data for nine Western European countries between 1990 and 2004. The empirical analysis delivers three main results. First, service offshoring is skill-biased, because it increases the demand for high and medium skilled labor and decreases the demand for low skilled labor. Second, the effects of service offshoring are similar to those of material offshoring, both qualitatively and quantitatively. Third, the economic magnitude of these effects is not large.

Employment effects of service offshoring: evidence from matched firms

This paper studies the effects of service offshoring on the level and skill composition of domestic employment, using a rich data set of Italian firms and propensity score matching techniques. The results show that service offshoring has no effect on the level of employment but changes its composition in favor of high skilled workers.