Search

Complex Adaptive Systems (CAS) is an interdisciplinary and dynamic modelling approach for the study of today’s global challenges. It is used for the explanation, description, and prediction of behaviours of system components and the system at large. To understand and assess the quality of research in which CAS models are designed and used, a thorough understanding of the meanings of ‘validity’ from social science research methodology and ‘validation’ from simulation modelling is needed. In this paper, we first describe the modelling process. Then, we analyse the concepts ‘validity’ and ‘validation’ as used in a set of research methodology textbooks and a set of modelling textbooks. We present one single map that integrates validity as characteristic of the model input, the modelling process, model validation, and the validity of the model built. The map is illustrated by means of one example. The terminology proposed in the map allows to describe and distinguish between the validity of primary research used for input in the model, how the quality of the modelling depends on structural and behavioural validation, and, how the assessment of the validity of the model is informed by these types of validation plus research with independent data.

This chapter provides an overview of applications and trends in benefit transfer for the United States. Benefit transfer has been widely applied in the United States for several decades. Initially it was used to value recreation at public water resource projects and on public lands. Now it is used extensively for benefit-cost analysis of new environmental regulations, and for monetizing natural resource damages and small oil spills. Groups that use benefit transfer, once primarily federal agencies, have grown to now include state agencies, consulting firms, and non-governmental organizations. As the underlying body of information continues to expand through primary research, new benefit transfer methods are developed to take advantage of this growth in information. Benefit transfer has become an important valuation tool in the United States where applications are expected to continue in the future.

On completion of the main quantitative and qualitative analysis of both the evader and general population samples in Chaps. 5–8, this chapter follows by providing further statistical testing as well as a comparative analysis of the two taxpayer groups. Initially the data analysis employed Chi-square statistical tests which investigated the relationship, if any, between the selected compliance variables themselves. (e.g. tax fairness and tax awareness) in order to address the additional primary research questions posed (PRQ 7 to PRQ 21). Second, similar Chi-square tests were conducted upon the two samples with regard to the selected demographic variables in order to address the secondary research questions posed (SRQ1-1 to SRQ8-7). Third, a comparative analysis of the two taxpayer samples employing Mann–Whitney statistical tests with regard to the compliance and demographic variables was conducted.

Previous studies of adaptive capacity have shown the value in utilising past climate events to explore the experiences in mobilising adaptive and coping responses. The case events used in each case study area served as reference points of climate variability and as useful indications for the impact of extremes in a future, warmer climate. The exploration of past experiences in relation to climate related extreme events acted as a means to understand and assess adaptive capacity. This chapter details the extreme events used as focussing events for the exploration of adaptive capacity, as well as the attenuating management challenges which frame the context within which adaptive capacity must be mobilised. This chapter provides an in depth account of the focussing events, in the context of climate change impact projections, as based on interview data, archival data and primary research utilising meteorological and climate model data.

For several decades now, a new inductive method, meta-analysis, is all the rage in social and medical sciences. Meta-analyses, that is, statistical reviews of the results of primary studies concerning a test hypothesis, set new standards of excellence on what counts as strong evidence. In the current prevailing mood in medical and behavioural sciences, it is only a properly conducted, up-to-date meta-analysis that licenses detachment of hypotheses from the host of evidential claims made in individual studies, which claims may be inconclusive or contradictory with each other. My goal in this chapter is to see the extent to which judgment aggregation methods subsume meta-analytic ones. To this end, I derive a generalized version of the classical Condorcet Jury Theorem, and I contend that one can model at least some meta-analytic procedures using this theorem.

The participation, status, and advancement of women in academic science and engineering have been pressing social concerns in the United States, particularly over the past 25 years. The concern is rooted in two basic sets of issues: the provision of human resources for the science and engineering workforce, and social equity in access to and rewards for professional participation in these fields. As human resources, women are important to the size, creativity, and diversity of the scientific and engineering workforce, broadly (Hanson, 1996; Pearson & Fechter, 1994). Women faculty, specifically, contribute to the culture and climate of the university and the development of students’ capacities and potential in science and engineering—with potential consequences for future generations of scientists and engineers. The percentages of women faculty are positively associated with percentages of women students who are undergraduate majors in mathematical sciences (Sharpe & Sonnert, 1999), majors in science and engineering (Canes & Rosen, 1995), and majors and recipients of bachelor’s degrees in life sciences, physical sciences, and engineering (Sonnert et al., 2007). This provides empirical support for the long-standing discussion about women faculty as “role models” for undergraduate women in scientific (and other) fields (Astin & Sax, 1996; Hackett et al., 1989; Stake & Noonan, 1983; Xie & Shauman, 1997). In graduate education in science and engineering, women faculty are consequential because of whom they train and the ways in which they do so. In a survey of 1,215 faculty in doctoral granting departments in five science and engineering fields, women faculty reported acting as primary research advisors for a larger number of women graduate students than did men, and also had larger number of women students on their research teams. Further, women faculty put significantly more emphasis upon giving help to advisees across areas, not only in designing, executing, and publishing research but also in gaining social capacities, including participating in laboratory meetings, making presentations, and interacting with faculty (Fox, 2003a). The status and advancement of women faculty in science and engineering is a pressing, national issue also because of related concerns of social equity (or inequity)

For a nation somewhat obsessed with property and property rights it is surprising that there has been a comparative dearth of published material on the system of housing provision and housing policy in Ireland, whereas in most other western European countries, particularly the United Kingdom, Sweden, Denmark and the Netherlands, sophisticated housing research infrastructures have been developed, not only in the universities but also in national and local government and in non-governmental sectors. Recently this gap in publication has started to be filled. For example, Norris and Winston (2004) have produced a comprehensive overview of Irish housing policy developments over the past decade, while in late 2004 the National Economic and Social Council (2004) published an analysis of housing policy with a particular focus on affordability and land policy. Despite these publications, however, it is still true to say that the amount of original primary research on housing issues is meagre and unbalanced. We know, for example, a good deal about social housing and the tenants who live in this sector (Fahey, 1999), which accounts for less that 10 per cent of all housing, but our in-depth knowledge of the owner-occupied sector, which accounts for 80 per cent of all housing, is paltry in comparison. This lack of information and analysis is anomalous in view of the fact that since the early 1990s housing has become one of the central economic, social and environmental issues in Ireland. This centrality stems directly from the importance of housing in providing basic shelter and accommodation, its role as a home, its role as a financial investment, its role in economic development and its role in shaping our urban and rural environment. While these are general attributes, they have been brought into even greater prominence in the past decade by the extraordinary surge in housing output across the state, generated by the economic boom and population growth. Not only have our urban centres

The scope and innovative nature of the proposed domains and indicators of children’s well-being are in many ways both reasons for hope and reasons for despair. When looking at the list of proposed indicators in the previous chapter, it seems the most vital aspect of their acceptance and use is the existence of data, or at least the possibility of identifying sources of information and an adequate way to collect the data.

During the last 30 years, clinical trial methodology has evolved from the interface of epidemiology, biostatistics, and clinical medicine to a specialty with its own experts, terminology, computer programs, books, and journals. During much of this time, randomized clinical trials for the treatment of primary biliary cirrhosis (PBC) were being conducted1–5. This chapter discusses three important issues in the design of therapeutic trials of PBC: choice of the primary research issue, sample size and power calculations, and choice of the primary response variable. It is hoped that consideration of these issues will result in well-planned trials in PBC and will serve a note of caution to investigators before embarking on a trial that may not be capable of achieving its goal.

Clinicians and policy-makers are increasingly turning to reviews rather than primary research for information to guide decision-making. Reviews are necessary because of the continuing “explosion” in the primary literature, and the difficulty of accessing some primary material. In addition reviews can “create” new evidence. The combination and synthesis of the results of smaller studies can provide answers to questions which have not otherwise been adequately answered by the individual results of small studies. Sound decisions about health care must be based on as high a proportion as possible of the available evidence. For example, a systematic review a decade ago of all the available randomized controlled trials of corticosteroids would have shown that a short course of this inexpensive treatment substantially reduces the risk of neonatal morbidity and death when given to women expected to give birth prematurely. For many years this evidence was not taken into account, and consequently tens of thousands of babies suffered unnecessarily and neonatal care has been more expensive than it need have been.

Meta-analysis is more than a statistical technique for pooling data from multiple primary research reports. Instead it is a wholly new and precise discipline for gathering and transferring the results of primary research projects in a valid and understandable format so that they can be applied to patient care by physicians and those who set medical policy.

The conference now about to be concluded has presented a wide spectrum of coordinate efforts, of accomplishments, projects, and viewpoints. As is only natural and even purposeful in a first colloquium of this scope, there are also some loose ends which we hope to tie together later. Many experienced scientists found it worth participating in the subject which is more organization rather than primary research, and this is a very encouraging feature. Most of us will have deplored, in one context or other, the lack of reliable data compilations; yet not much could be done before efficient computer and storage facilities were at hand, and we may also have nourished the hope that someone else would undertake the tedious and often thankless job of compilation. We are grateful that the invitation to convene the first Numerical Data conference came from the Strasbourg Observatory, where a large-scale and multi-purpose program has been successfully developing in recent years.

The primary research activities of our team at the Laboratoire de Psychobiologie de l’Enfant in Paris involve the development of social communication during the first 3 to 4 years of life. We have focused on what might be called the hidden face of early social competence. More precisely, we have been involved in the functional analysis of transitory adaptation — the examination of behavior that either declines or disappears during the early stages of development.