Effective communication to improve udder health: can social science help?

Improved udder health requires consistent application of appropriate management practices by those involved in managing dairy herds and the milking process. Designing effective communication requires that we understand why dairy herd managers behave in the way they do and also how the means of communication can be used both to inform and to influence. Social sciences- ranging from economics to anthropology - have been used to shed light on the behaviour of those who manage farm animals. Communication science tells us that influencing behaviour is not simply a question of „getting the message across‟ but of addressing the complex of factors that influence an individual‟s behavioural decisions. A review of recent studies in the animal health literature shows that different social science frameworks and methodologies offer complementary insights into livestock managers‟ behaviour but that the diversity of conceptual and methodological frameworks presents a challenge for animal health practitioners and policy makers who seek to make sense of the findings – and for researchers looking for helpful starting points. Data from a recent study in England illustrate the potential of „home-made‟ conceptual frameworks to help unravel the complexity of farmer behaviour. At the same time, though, the data indicate the difficulties facing those designing communication strategies in a context where farmers believe strongly that they are already doing all they can reasonably be expected to do to minimise animal health risks.

Dealing with complexity: an autopoietic view of the People's Planning Campaign, Kerala

Complexity is integral to planning today. Everyone and everything seem to be interconnected, causality appears ambiguous, unintended consequences are ubiquitous, and information overload is a constant challenge. The nature of complexity, the consequences of it for society, and the ways in which one might confront it, understand it and deal with it in order to allow for the possibility of planning, are issues increasingly demanding analytical attention. One theoretical framework that can potentially assist planners in this regard is Luhmann's theory of autopoiesis. This article uses insights from Luhmann's ideas to understand the nature of complexity and its reduction, thereby redefining issues in planning, and explores the ways in which management of these issues might be observed in actual planning practice via a reinterpreted case study of the People's Planning Campaign in Kerala, India. Overall, this reinterpretation leads to a different understanding of the scope of planning and planning practice, telling a story about complexity and systemic response. It allows the reinterpretation of otherwise familiar phenomena, both highlighting the empirical relevance of the theory and providing new and original insight into particular dynamics of the case study. This not only provides a greater understanding of the dynamics of complexity, but also produces advice to help planners implement structures and processes that can cope with complexity in practice.

The first five years of the EU Impact Assessment system: a risk economics perspective on gaps between rationale and practice

In 2003 the European Commission started using Impact Assessment (IA) as the main empirical basis for its major policy proposals. The aim was to systematically assess ex ante the economic, social and environmental impacts of EU policy proposals. In parallel, research proliferated in search for theoretical grounds for IAs and in an attempt to evaluate empirically the performance of the first sets of IAs produced by the European Commission. This paper combines conceptual and evaluative studies carried out in the first five years of EU IAs. It concludes that the great discrepancy between rationale and practice calls for a different theoretical focus and a higher emphasis on evaluating empirically crucial risk economics aspects of IAs, such as the value of statistical life, price of carbon, the integration of macroeconomic modelling and scenario analysis.

Time complexity analysis of the stochastic diffusion search

The Stochastic Diffusion Search algorithm -an integral part of Stochastic Search Networks is investigated. Stochastic Diffusion Search is an alternative solution for invariant pattern recognition and focus of attention. It has been shown that the algorithm can be modelled as an ergodic, finite state Markov Chain under some non-restrictive assumptions. Sub-linear time complexity for some settings of parameters has been formulated and proved. Some properties of the algorithm are then characterised and numerical examples illustrating some features of the algorithm are presented.