Policy as a struggle for meaning: disentangling knowledge translation across international health contexts

Over the last decade, research in medical science has focused on knowledge translation and diffusion of best practices to enable improved health outcomes. However, there has been less attention given to the role of policy in influencing the translation of best practice across different national contexts. This paper argues that the underlying set of public discourses of healthcare policy significantly influences its development with implications for the dissemination of best practices. Our research uses Critical Discourse Analysis to examine the policy discourses surrounding the treatment of stroke across Canada and the U.K. It focuses in specific on how concepts of knowledge translation, user empowerment, and service innovation construct different accounts of the health service in the two countries. These findings provide an important yet overlooked starting point for understanding the role of policy development in knowledge transfer and the translation of science into health practice. © 2011 Operational Research Society. All rights reserved.

The impact of government policy on technology transfer: An aircraft industry case study

This case study explores the interaction between domestic and foreign governmental policy on technology transfer with the goal of exploring the long-term impacts of technology transfer. Specifically, the impact of successive licensing of fighter aircraft manufacturing and design to Japan in the development of Japan's aircraft industry is reviewed. Results indicate Japan has built a domestic aircraft industry through sequential learning with foreign technology transfers from the United States, and design and production on domestic fighter aircraft. This process was facilitated by governmental policies in both Japan and the United States. Published by Elsevier B.V.

Code-specific policy gradient rules for spiking neurons

Although it is widely believed that reinforcement learning is a suitable tool for describing behavioral learning, the mechanisms by which it can be implemented in networks of spiking neurons are not fully understood. Here, we show that different learning rules emerge from a policy gradient approach depending on which features of the spike trains are assumed to influence the reward signals, i.e., depending on which neural code is in effect. We use the framework of Williams (1992) to derive learning rules for arbitrary neural codes. For illustration, we present policy-gradient rules for three different example codes - a spike count code, a spike timing code and the most general "full spike train" code - and test them on simple model problems. In addition to classical synaptic learning, we derive learning rules for intrinsic parameters that control the excitability of the neuron. The spike count learning rule has structural similarities with established Bienenstock-Cooper-Munro rules. If the distribution of the relevant spike train features belongs to the natural exponential family, the learning rules have a characteristic shape that raises interesting prediction problems.

On-line policy optimisation of Bayesian spoken dialogue systems via human interaction

A partially observable Markov decision process has been proposed as a dialogue model that enables robustness to speech recognition errors and automatic policy optimisation using reinforcement learning (RL). However, conventional RL algorithms require a very large number of dialogues, necessitating a user simulator. Recently, Gaussian processes have been shown to substantially speed up the optimisation, making it possible to learn directly from interaction with human users. However, early studies have been limited to very low dimensional spaces and the learning has exhibited convergence problems. Here we investigate learning from human interaction using the Bayesian Update of Dialogue State system. This dynamic Bayesian network based system has an optimisation space covering more than one hundred features, allowing a wide range of behaviours to be learned. Using an improved policy model and a more robust reward function, we show that stable learning can be achieved that significantly outperforms a simulator trained policy. © 2013 IEEE.

City versus national energy use: implications for urban energy policy and strategies

The global trend towards urbanization means that over half of the world's population now lives in cities. Cities use energy in different proportions to national energy use averages, typically corresponding to whether a country is industrialized or developing. Cities in industrialized countries tend to use less energy per capita than the national average while cities in developing countries use more. This paper looks at existing World Bank data in respect to urban energy consumption, the emissions inventory work done by New York City, and discusses how this data highlights the need for a focus on: energy policy for buildings in industrialized cities; masterplanning and new construction standards in developing cities; and how urban energy policy can become more effective in reducing urban greenhouse gas emissions.