Coopetition and innovation. Lessons from worker cooperatives in the Spanish machine tool industry

This is an electronic version of the accepted paper in Journal of Business & Industrial Marketing[EN] Purpose – This paper aims to investigate how the implementation of the inter-cooperation principle among Spanish machine-tool cooperatives helps them to coopete–collaborate with competitors, in their innovation and internationalization processes and achieve collaborative advantages. Design/methodology/approach – The paper uses a multi-case approach based on interviews with 15 CEOs and research and development (R&D) managers, representing 14 Spanish machine tool firms and institutions. Eight of these organizations are worker-cooperatives.. Findings – Worker -cooperatives achieve advantages on innovation and internationalization via inter-cooperation (shared R&D units, joint sales offices, joint after-sale services, knowledge exchange and relocation of key R&D technicians and managers). Several mutual bonds and ties among cooperatives help to overcome the risk of opportunistic behaviour and knowledge leakage associated to coopetition. The obtained results give some clues explaining to what extent and under which conditions coopetitive strategies of cooperatives are transferable to other types of ownership arrangements across sectors. Practical implications – Firms seeking cooperation with competitors in their R&D and internationalization processes can learn from the coopetitive arrangements analyzed in the paper. Social implications – Findings can be valuable for sectoral associations and public bodies trying to promote coopetition and alliances between competitors as a means to benefit from collaborative advantages. Originality/value – Focusing on an “ideal type” of co-operation -cooperative organisationsand having access to primary sources, the paper shows to what extent (and how) strong coopetitive structures and processes foster innovation and internationalization

Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course. A proof-of-principle study

Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients

Ethical intuitionism and the linguistic analogy

It is a central tenet of ethical intuitionism as defended by W. D. Ross and others that moral theory should reflect the convictions of mature moral agents. Hence, intuitionism is plausible to the extent that it corresponds to our well-considered moral judgments. After arguing for this claim, I discuss whether intuitionists offer an empirically adequate account of our moral obligations. I do this by applying recent empirical research by John Mikhail that is based on the idea of a universal moral grammar to a number of claims implicit in W. D. Ross’s normative theory. I argue that the results at least partly vindicate intuitionism

EvoTanks: co-evolutionary development of game-playing agents

This paper describes the EvoTanks research project, a continuing attempt to develop strong AI players for a primitive 'Combat' style video game using evolutionary computational methods with artificial neural networks. A small but challenging feat due to the necessity for agent's actions to rely heavily on opponent behaviour. Previous investigation has shown the agents are capable of developing high performance behaviours by evolving against scripted opponents; however these are local to the trained opponent. The focus of this paper shows results from the use of co-evolution on the same population. Results show agents no longer succumb to trappings of local maxima within the search space and are capable of converging on high fitness behaviours local to their population without the use of scripted opponents

Factorized Q-Learning for Large-Scale Multi-Agent Systems

Deep Q-learning has achieved significant success in single-agent decision making tasks. However, it is challenging to extend Q-learning to large-scale multi-agent scenarios, due to the explosion of action space resulting from the complex dynamics between the environment and the agents. In this paper, we propose to make the computation of multi-agent Q-learning tractable by treating the Q-function (w.r.t. state and joint-action) as a high-order high-dimensional tensor and then approximate it with factorized pairwise interactions. Furthermore, we utilize a composite deep neural network architecture for computing the factorized Q-function, share the model parameters among all the agents within the same group, and estimate the agents' optimal joint actions through a coordinate descent type algorithm. All these simplifications greatly reduce the model complexity and accelerate the learning process. Extensive experiments on two different multi-agent problems demonstrate the performance gain of our proposed approach in comparison with strong baselines, particularly when there are a large number of agents.Comment: 7 pages, 5 figures, DAI 201