Policy entrepreneurship in the development of public sector strategy: the case of London health reform

The development of health policy is recognized as complex; however, there has been little development of the role of agency in this process. Kingdon developed the concept of policy entrepreneur (PE) within his ‘windows’ model. He argued inter-related ‘policy streams' must coincide for important issues to become addressed. The conjoining of these streams may be aided by a policy entrepreneur. We contribute by clarifying the role of the policy entrepreneur and highlighting the translational processes of key actors in creating and aligning policy windows. We analyse the work in London of Professor Sir Ara Darzi as a policy entrepreneur. An important aspect of Darzi's approach was to align a number of important institutional networks to conjoin related problems. Our findings highlight how a policy entrepreneur not only opens policy windows but also yokes together a network to make policy agendas happen. Our contribution reveals the role of clinical leadership in health reform.

A framework for analysing business performance, firm innovation and related contextual factors: Perceptions of managers and policy makers in two European regions

The aim of this paper is to propose a novel reference framework that can be used to study how different kinds of innovation can result in better business performance and how external factors can influence both the firm's capacity to innovate and innovation itself. The value of the framework is demonstrated as it is applied in an exploratory study of the perceptions of public policy makers and managers from two European regions - the Veneto Region in Italy and the East of England in the UK. Amongst other things, the data gathered suggest that managers are generally less convinced than public policy makers, that the innovativeness of a firm is affected by factors over which policy makers have some control. This finding poses the question "what, if any, role can public policy makers play in enhancing a company's competitiveness by enabling it to become more innovative?".

Gaussian processes for fast policy optimisation of POMDP-based dialogue managers

Modelling dialogue as a Partially Observable Markov Decision Process (POMDP) enables a dialogue policy robust to speech understanding errors to be learnt. However, a major challenge in POMDP policy learning is to maintain tractability, so the use of approximation is inevitable. We propose applying Gaussian Processes in Reinforcement learning of optimal POMDP dialogue policies, in order (1) to make the learning process faster and (2) to obtain an estimate of the uncertainty of the approximation. We first demonstrate the idea on a simple voice mail dialogue task and then apply this method to a real-world tourist information dialogue task. © 2010 Association for Computational Linguistics.

On-line policy optimisation of spoken dialogue systems via live interaction with human subjects

Statistical dialogue models have required a large number of dialogues to optimise the dialogue policy, relying on the use of a simulated user. This results in a mismatch between training and live conditions, and significant development costs for the simulator thereby mitigating many of the claimed benefits of such models. Recent work on Gaussian process reinforcement learning, has shown that learning can be substantially accelerated. This paper reports on an experiment to learn a policy for a real-world task directly from human interaction using rewards provided by users. It shows that a usable policy can be learnt in just a few hundred dialogues without needing a user simulator and, using a learning strategy that reduces the risk of taking bad actions. The paper also investigates adaptation behaviour when the system continues learning for several thousand dialogues and highlights the need for robustness to noisy rewards. © 2011 IEEE.

A policy dimension required for technology roadmapping: Learning from the emergence of Chinese wind turbine industry

Innovation policies play an important role throughout the development process of emerging industries in China. Existing policy and industry studies view the emergence process as a black-box, and fail to understand the impacts of policy to the process along which it varies. This paper aims to develop a multi-dimensional roadmapping tool to better analyse the dynamics between policy and industrial growth for new industries in China. Through reviewing the emergence process of Chinese wind turbine industry, this paper elaborates how policy and other factors influence the emergence of this industry along this path. Further, this paper generalises some Chinese specifics for the policy-industry dynamics. As a practical output, this study proposes a roadmapping framework that generalises some patterns of policy-industry interactions for the emergence process of new industries in China. This paper will be of interest to policy makers, strategists, investors and industrial experts. Copyright © 2013 Inderscience Enterprises Ltd.

Policy optimisation of POMDP-based dialogue systems without state space compression

The partially observable Markov decision process (POMDP) has been proposed as a dialogue model that enables automatic improvement of the dialogue policy and robustness to speech understanding errors. It requires, however, a large number of dialogues to train the dialogue policy. Gaussian processes (GP) have recently been applied to POMDP dialogue management optimisation showing an ability to substantially increase the speed of learning. Here, we investigate this further using the Bayesian Update of Dialogue State dialogue manager. We show that it is possible to apply Gaussian processes directly to the belief state, removing the need for a parametric policy representation. In addition, the resulting policy learns significantly faster while maintaining operational performance. © 2012 IEEE.