A Growth Model for the Quadruple Helix Innovation Theory

We propose a theoretical growth model with which to frame analytically the Quadruple Helix Innovation Theory (QHIT). The aim is to emphasise the investment in innovation transmission mechanisms in terms of economic growth and productivity gains, in one-high-technology sector, by stressing the role played by the helices of the Quadruple Helix Innovation Model: Academia and Technological Infrastructures, Firms of Innovation, Government and Civil Society. In the existing literature, the relationship between the helices and respective impacts on economic growth does not appear clear. Results are fragile due to data weakness and the inexistence of a theoretical framework to specify the relationship between the helices. Hence our motivation for providing the QHIT with a theoretical growth model. Our intent is to model the importance of emerging, dynamically adaptive, and transdisciplinary knowledge and innovation ecosystems to economic growth. We find that higher economic growth rate is obtained as a result of an increase in synergies and complementarities between different productive units, or an increase in productive government expenditure.Economic Growth, Quadruple Helix Innovation Model, Innovation Ecosystems

Every team deserves a second chance:Identifying when things go wrong

Voting among different agents is a powerful tool in problem solving, and it has been widely applied to improve the performance in finding the correct answer to complex problems. We present a novel benefit of voting, that has not been observed before: we can use the voting patterns to assess the performance of a team and predict their final outcome. This prediction can be executed at any moment during problem-solving and it is completely domain independent. We present a theoretical explanation of why our prediction method works. Further, contrary to what would be expected based on a simpler explanation using classical voting models, we argue that we can make accurate predictions irrespective of the strength (i.e., performance) of the teams, and that in fact, the prediction can work better for diverse teams composed of different agents than uniform teams made of copies of the best agent. We perform experiments in the Computer Go domain, where we obtain a high accuracy in predicting the final outcome of the games. We analyze the prediction accuracy for three different teams with different levels of diversity and strength, and we show that the prediction works significantly better for a diverse team. Since our approach is domain independent, it can be easily applied to a variety of domains