The role of science in policy making

This thesis aims to consider the role played by science in policy making. Firstly, two decision models are considered, synoptic rationality which depends heavily on formal information and comprehensive planning, and disjointed incrementalism, under which decisions are made in a fragmented and remedial manner via the interaction of interested partisans and with little necessity for formal information. Secondly, different descriptions of scientific activity are discussed and a broadly Kuhnian view of science is supported, with what is regarded as a `fact' being heavily influenced by social factors. It is suggested that scientific controversies are more likely to occur in policy related science but for reasons that are intrinsic to science rather than due to some correctable aberration. A number of case studies, including two `in-depth' studies into maternal deprivation and the relationship between hyperactivity and food additives, support this contention and also show that whilst scientific findings can raise issues they cannot aid in the resolution of these as the synoptic model suggests that they should. Instead information supports and legitimates value based policy views, with actual policy decisions arrived at via negotiation and aiming at a balancing of partisan pressures, as suggested by the incremental model. Not only does information not aid the resolution of policy disputes, it cannot do so. When policy is disputed, scientific findings are also likely to be disputed and further research merely attracts more highly destructive criticism. This is termed the over critical model. When policy is decided then there is reduced impetus to critically test scientific ideas; this is termed the under critical model. Both of these situations act to the detriment of science. The main conclusion drawn is that the belief that science is essential to decision making is misleading and may serve to mask rather than illuminate areas of dispute.

Place-based economic development strategy in England:filling the missing space

This paper examines the implications of a place-based economic strategy in the context of the UK Coalition government's framework for achieving local growth and the creation of Local Economic Partnerships in England. It draws on the international literature to outline the basic foundations of place-based policy approaches. It explores two key features, particularly as they relate to governance institutions and to the role of knowledge. After examining key concepts in the place-based policy literature, such as 'communities of interest' and 'capital city' and 'local elites', it shows how they might be interpreted in an English policy context. The paper then discusses a place-based approach towards an understanding of the role of knowledge, linked to debates around 'smart specialisation'. In doing so, it shows why there is an important 'missing space' in local growth between the 'national' and the 'local' and how that space might be filled through appropriate governance institutions and policy responses. Overall, the paper outlines what a place-based approach might mean in particular for Central Government, in changing its approach towards sub-national places and for local places, in seeking to realise their own potential. Furthermore, it outlines what the 'missing space' is and how it might be filled, and therefore what a place-based sub-national economic strategy might address. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Driving licensing renewal policy using neural network-based probabilistic decision support system

This paper investigates neural network-based probabilistic decision support system to assess drivers' knowledge for the objective of developing a renewal policy of driving licences. The probabilistic model correlates drivers' demographic data to their results in a simulated written driving exam (SWDE). The probabilistic decision support system classifies drivers' into two groups of passing and failing a SWDE. Knowledge assessment of drivers within a probabilistic framework allows quantifying and incorporating uncertainty information into the decision-making system. The results obtained in a Jordanian case study indicate that the performance of the probabilistic decision support systems is more reliable than conventional deterministic decision support systems. Implications of the proposed probabilistic decision support systems on the renewing of the driving licences decision and the possibility of including extra assessment methods are discussed.