Harm, hype and evidence: ELSI research and policy guidance.

There has been much investment in research on the ethical, legal and social issues (ELSI) associated with genetic and genomic research. This research should inform the development of the relevant policy. So far, much of the relevant policy - such as in the areas of patents, genetic testing and genetic discrimination - seems to be informed more by speculation of harm and anecdote than by available evidence. Although a quest for evidence cannot always be allowed to delay policy choice, it seems axiomatic to us that policy options are improved by the incorporation of evidence.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Policy-induced technology adoption: Evidence from the U.S. lead phasedown

Theory suggests that economic instruments, such as pollution taxes or tradable permits, can provide more efficient technology adoption incentives than conventional regulatory standards. We explore this issue for an important industry undergoing dramatic decreases in allowed pollution - the U.S. petroleum industry's phasedown of lead in gasoline. Using a duration model applied to a panel of refineries from 1971-1995, we find that the pattern of technology adoption is consistent with an economic response to market incentives, plant characteristics, and alternative policies. Importantly, evidence suggests that the tradable permit system used during the phasedown provided incentives for more efficient technology adoption decisions.

Factors influencing malaria control policy-making in Kenya, Uganda and Tanzania.

BACKGROUND: Policy decisions for malaria control are often difficult to make as decision-makers have to carefully consider an array of options and respond to the needs of a large number of stakeholders. This study assessed the factors and specific objectives that influence malaria control policy decisions, as a crucial first step towards developing an inclusive malaria decision analysis support tool (MDAST). METHODS: Country-specific stakeholder engagement activities using structured questionnaires were carried out in Kenya, Uganda and Tanzania. The survey respondents were drawn from a non-random purposeful sample of stakeholders, targeting individuals in ministries and non-governmental organizations whose policy decisions and actions are likely to have an impact on the status of malaria. Summary statistics across the three countries are presented in aggregate. RESULTS: Important findings aggregated across countries included a belief that donor preferences and agendas were exerting too much influence on malaria policies in the countries. Respondents on average also thought that some relevant objectives such as engaging members of parliament by the agency responsible for malaria control in a particular country were not being given enough consideration in malaria decision-making. Factors found to influence decisions regarding specific malaria control strategies included donor agendas, costs, effectiveness of interventions, health and environmental impacts, compliance and/acceptance, financial sustainability, and vector resistance to insecticides. CONCLUSION: Malaria control decision-makers in Kenya, Uganda and Tanzania take into account health and environmental impacts as well as cost implications of different intervention strategies. Further engagement of government legislators and other policy makers is needed in order to increase funding from domestic sources, reduce donor dependence, sustain interventions and consolidate current gains in malaria.

A tale of two market failures: Technology and environmental policy

Market failures associated with environmental pollution interact with market failures associated with the innovation and diffusion of new technologies. These combined market failures provide a strong rationale for a portfolio of public policies that foster emissions reduction as well as the development and adoption of environmentally beneficial technology. Both theory and empirical evidence suggest that the rate and direction of technological advance is influenced by market and regulatory incentives, and can be cost-effectively harnessed through the use of economic-incentive based policy. In the presence of weak or nonexistent environmental policies, investments in the development and diffusion of new environmentally beneficial technologies are very likely to be less than would be socially desirable. Positive knowledge and adoption spillovers and information problems can further weaken innovation incentives. While environmental technology policy is fraught with difficulties, a long-term view suggests a strategy of experimenting with policy approaches and systematically evaluating their success. © 2005 Elsevier B.V. All rights reserved.

Environmental policy and technological change

The relationship between technological change and environmental policy has received increasing attention from scholars and policy makers alike over the past ten years. This is partly because the environmental impacts of social activity are significantly affected by technological change, and partly because environmental policy interventions themselves create new constraints and incentives that affect the process of technological developments. Our central purpose in this article is to provide environmental economists with a useful guide to research on technological change and the analytical tools that can be used to explore further the interaction between technology and the environment. In Part 1 of the article, we provide an overview of analytical frameworks for investigating the economics of technological change, highlighting key issues for the researcher. In Part 2, we turn our attention to theoretical analysis of the effects of environmental policy on technological change, and in Part 3, we focus on issues related to the empirical analysis of technology innovation and diffusion. Finally, we conclude in Part 4 with some additional suggestions for research.

GIS-based Association Between PM10 and Allergic Diseases in Seoul: Implications for Health and Environmental Policy.

PURPOSE: The role of PM10 in the development of allergic diseases remains controversial among epidemiological studies, partly due to the inability to control for spatial variations in large-scale risk factors. This study aims to investigate spatial correspondence between the level of PM10 and allergic diseases at the sub-district level in Seoul, Korea, in order to evaluate whether the impact of PM10 is observable and spatially varies across the subdistricts. METHODS: PM10 measurements at 25 monitoring stations in the city were interpolated to 424 sub-districts where annual inpatient and outpatient count data for 3 types of allergic diseases (atopic dermatitis, asthma, and allergic rhinitis) were collected. We estimated multiple ordinary least square regression models to examine the association of the PM10 level with each of the allergic diseases, controlling for various sub-district level covariates. Geographically weighted regression (GWR) models were conducted to evaluate how the impact of PM10 varies across the sub-districts. RESULTS: PM10 was found to be a significant predictor of atopic dermatitis patient count (P<0.01), with greater association when spatially interpolated at the sub-district level. No significant effect of PM10 was observed on allergic rhinitis and asthma when socioeconomic factors were controlled for. GWR models revealed spatial variation of PM10 effects on atopic dermatitis across the sub-districts in Seoul. The relationship of PM10 levels to atopic dermatitis patient counts is found to be significant only in the Gangbuk region (P<0.01), along with other covariates including average land value, poverty rate, level of education and apartment rate (P<0.01). CONCLUSIONS: Our findings imply that PM10 effects on allergic diseases might not be consistent throughout Seoul. GIS-based spatial modeling techniques could play a role in evaluating spatial variation of air pollution impacts on allergic diseases at the sub-district level, which could provide valuable guidelines for environmental and public health policymakers.

The honest broker; making sense of science in policy and politics

p.81-82

The honest broker; making sense of science in policy and politics

p.81-82

An investigation of assessment policy and practices in terms of broad social and educational purposes

Based upon relevant literature, this study investigated the assessment policy and practices for the BSc (Hons) Computing Science programme at the University of Greenwich (UOG), contextualising these in terms of broad social and educational purposes. It discusses Assessment, and then proceeds to give a critical evaluation of the assessment policy and practices at the UOG. Although this is one case study, because any of the features of the programme are generic to other programmes and institutions, it is of wider value and has further implications. The study was concluded in the summer of 2002. It concludes that overall, the programme's assessment policy and practices are well considered in terms of broad social and educational purposes, although it identifies and outlines several possible improvements, as well as raising some major issues still to be addressed which go beyond assessment practices.

A policy-definition language and prototype implementation library for policy-based autonomic systems

This paper presents work towards generic policy toolkit support for autonomic computing systems in which the policies themselves can be adapted dynamically and automatically. The work is motivated by three needs: the need for longer-term policy-based adaptation where the policy itself is dynamically adapted to continually maintain or improve its effectiveness despite changing environmental conditions; the need to enable non autonomics-expert practitioners to embed self-managing behaviours with low cost and risk; and the need for adaptive policy mechanisms that are easy to deploy into legacy code. A policy definition language is presented; designed to permit powerful expression of self-managing behaviours. The language is very flexible through the use of simple yet expressive syntax and semantics, and facilitates a very diverse policy behaviour space through both hierarchical and recursive uses of language elements. A prototype library implementation of the policy support mechanisms is described. The library reads and writes policies in well-formed XML script. The implementation extends the state of the art in policy-based autonomics through innovations which include support for multiple policy versions of a given policy type, multiple configuration templates, and meta-policies to dynamically select between policy instances and templates. Most significantly, the scheme supports hot-swapping between policy instances. To illustrate the feasibility and generalised applicability of these tools, two dissimilar example deployment scenarios are examined. The first is taken from an exploratory implementation of self-managing parallel processing, and is used to demonstrate the simple and efficient use of the tools. The second example demonstrates more-advanced functionality, in the context of an envisioned multi-policy stock trading scheme which is sensitive to environmental volatility

Generic support for policy-based self-adaptive systems

This paper presents a policy definition language which forms part of a generic policy toolkit for autonomic computing systems in which the policies themselves can be modified dynamically and automatically. Targeted enhancements to the current state of practice include: policy self-adaptation where the policy itself is dynamically modified to match environmental conditions; improved support for non autonomics-expert developers; and facilitating easy deployment of adaptive policies into legacy code. The policy definition language permits powerful expression of self-managing behaviours and facilitates a diverse policy behaviour space. Features include support for multiple versions of a given policy type, multiple configuration templates, and meta policies to dynamically select between policy instances. An example deployment scenario illustrates advanced functionality in the context of a multi policy stock trading system which is sensitive to environmental volatility.