Identifying Critical States through the Relevance Index

The identification of critical states is a major task in complex systems, and the availability of measures to detect such conditions is of utmost importance. In general, criticality refers to the existence of two qualitatively different behaviors that the same system can exhibit, depending on the values of some parameters. In this paper, we show that the relevance index may be effectively used to identify critical states in complex systems. The relevance index was originally developed to identify relevant sets of variables in dynamical systems, but in this paper, we show that it is also able to capture features of criticality. The index is applied to two prominent examples showing slightly different meanings of criticality, namely the Ising model and random Boolean networks. Results show that this index is maximized at critical states and is robust with respect to system size and sampling effort. It can therefore be used to detect criticality

Information-Theoretic Methods for Identifying Relationships among Climate Variables

Information-theoretic quantities, such as entropy, are used to quantify the amount of information a given variable provides. Entropies can be used together to compute the mutual information, which quantifies the amount of information two variables share. However, accurately estimating these quantities from data is extremely challenging. We have developed a set of computational techniques that allow one to accurately compute marginal and joint entropies. These algorithms are probabilistic in nature and thus provide information on the uncertainty in our estimates, which enable us to establish statistical significance of our findings. We demonstrate these methods by identifying relations between cloud data from the International Satellite Cloud Climatology Project (ISCCP) and data from other sources, such as equatorial pacific sea surface temperatures (SST).Comment: Presented at the Earth-Sun System Technology Conference (ESTC 2008), Adelphi, MD. http://esto.nasa.gov/conferences/estc2008/ 3 pages, 3 figures. Appears in the Proceedings of the Earth-Sun System Technology Conference (ESTC 2008), Adelphi, M

Innovation Indicators: for a critical reflection on their use in Low- and Middle-Income Countries (LMICs)

It has been widely recognized that innovation is an important driver of economic growth. Many Low- and Middle-Income Countries (LMICs) have adopted innovation indicators to monitor innovation performance and to evaluate the impact of innovation policies. This paper argues that innovation indicators should be customized to the different socio-economic structures of LMICs. For this, the definition of innovation needs to be relevant to the multitude of innovation actors and processes in LMICs. LMICs also need to build competences not only in the construction of innovation indicators within their statistical systems, but also in the use of these indicators by among others policy makers. Especially as the fourth edition of the Oslo Manual (OM 2018) has broadened the scope of “innovation”, opening up policy space for LMICs to accommodate the diversity in their national systems of innovation and to develop accompanying innovation indicators.JEL Classification Codes: O38, O32, O29, P47http://www.grips.ac.jp/list/jp/facultyinfo/iizuka-michiko

Criticality of mostly informative samples: A Bayesian model selection approach

We discuss a Bayesian model selection approach to high dimensional data in the deep under sampling regime. The data is based on a representation of the possible discrete states $s$, as defined by the observer, and it consists of $M$ observations of the state. This approach shows that, for a given sample size $M$, not all states observed in the sample can be distinguished. Rather, only a partition of the sampled states $s$ can be resolved. Such partition defines an {\em emergent} classification $q_s$ of the states that becomes finer and finer as the sample size increases, through a process of {\em symmetry breaking} between states. This allows us to distinguish between the $resolution$ of a given representation of the observer defined states $s$, which is given by the entropy of $s$, and its $relevance$ which is defined by the entropy of the partition $q_s$. Relevance has a non-monotonic dependence on resolution, for a given sample size. In addition, we characterise most relevant samples and we show that they exhibit power law frequency distributions, generally taken as signatures of "criticality". This suggests that "criticality" reflects the relevance of a given representation of the states of a complex system, and does not necessarily require a specific mechanism of self-organisation to a critical point.Comment: 31 pages, 7 figure

The Health Status of Southern Children: A Neglected Regional Disparity

Purpose: Great variations exist in child health outcomes among states in the United States, with southern states consistently ranked among the lowest in the country. Investigation of the geographical distribution of children’s health status and the regional factors contributing to these outcomes has been neglected. We attempted to identify the degree to which region of residence may be linked to health outcomes for children with the specific aim of determining whether living in the southern region of the United States is adversely associated with children’s health status. Methods: A child health index (CHI) that ranked each state in the United States was computed by using statespecific composite scores generated from outcome measures for a number of indicators of child health. Five indicators for physical health were chosen (percent low birth weight infants, infant mortality rate, child death rate, teen death rate, and teen birth rates) based on their historic and routine use to define health outcomes in children. Indicators were calculated as rates or percentages. Standard scores were calculated for each state for each health indicator by subtracting the mean of the measures for all states from the observed measure for each state. Indicators related to social and economic status were considered to be variables that impact physical health, as opposed to indicators of physical health, and therefore were not used to generate the composite child health score. These variables were subsequently examined in this study as potential confounding variables. Mapping was used to redefine regional groupings of states, and parametric tests (2-sample t test, analysis of means, and analysis-of-variance F tests) were used to compare the means of the CHI scores for the regional groupings and test for statistical significance. Multiple regression analysis computed the relationship of region, social and economic indicators, and race to the CHI. Simple linear-regression analyses were used to assess the individual effect of each indicator. Results: A geographic region of contiguous states, characterized by their poor child health outcomes relative to other states and regions of the United States, exists within the “Deep South” (Mississippi, Louisiana, Arkansas, Tennessee, Alabama, Georgia, North Carolina, South Carolina, and Florida). This Deep-South region is statistically different in CHI scores from the US Census Bureau– defined grouping of states in the South. The mean of CHI scores for the Deep-South region was \u3e1 SD below the mean of CHI scores for all states. In contrast, the CHI score means for each of the other 3 regions were all above the overall mean of CHI scores for all states. Regression analysis showed that living in the Deep- South region is a stronger predictor of poor child health outcomes than other consistently collected and reported variables commonly used to predict children’s health. Conclusions: The findings of this study indicate that region of residence in the United States is statistically related to important measures of children’s health and may be among the most powerful predictors of child health outcomes and disparities. This clarification of the poorer health status of children living in the Deep South through spatial analysis is an essential first step for developing a better understanding of variations in the health of children. Similar to early epidemiology work linking geographic boundaries to disease, discovering the mechanisms/pathways/causes by which region influences health outcomes is a critical step in addressing disparities and inequities in child health and one that is an important and fertile area for future research. The reasons for these disparities may be complex and synergistically related to various economic, political, social, cultural, and perhaps even environmental (physical) factors in the region. This research will require the use and development of new approaches and applications of spatial analysis to develop insights into the societal, environmental, and historical determinants of child health that have been neglected in previous child health outcomes and policy research. The public policy implications of the findings in this study are substantial. Few, if any, policies identify these children as a high-risk group on the basis of their region of residence. A better understanding of the depth and breadth of disparities in health, education, and other social outcomes among and within regions of the United States is necessary for the generation of policies that enable policy makers to address and mitigate the factors that influence these disparities. Defining and clarifying the regional boundaries is also necessary to better inform public policy decisions related to resource allocation and the prevention and/or mitigation of the effects of region on child health. The identification of the Deep South as a clearly defined sub-region of the Census Bureau’s regional definition of the South suggests the need to use more culturally and socially relevant boundaries than the Census Bureau regions when analyzing regional data for policy development

Improving Task-Parameterised Movement Learning Generalisation with Frame-Weighted Trajectory Generation

Learning from Demonstration depends on a robot learner generalising its learned model to unseen conditions, as it is not feasible for a person to provide a demonstration set that accounts for all possible variations in non-trivial tasks. While there are many learning methods that can handle interpolation of observed data effectively, extrapolation from observed data offers a much greater challenge. To address this problem of generalisation, this paper proposes a modified Task-Parameterised Gaussian Mixture Regression method that considers the relevance of task parameters during trajectory generation, as determined by variance in the data. The benefits of the proposed method are first explored using a simulated reaching task data set. Here it is shown that the proposed method offers far-reaching, low-error extrapolation abilities that are different in nature to existing learning methods. Data collected from novice users for a real-world manipulation task is then considered, where it is shown that the proposed method is able to effectively reduce grasping performance errors by ${\sim30\%}$ and extrapolate to unseen grasp targets under real-world conditions. These results indicate the proposed method serves to benefit novice users by placing less reliance on the user to provide high quality demonstration data sets.Comment: 8 pages, 6 figures, submitted to 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Fiscal illusion and cyclical government expenditure: State government expenditure in the United States

© 2016 Scottish Economic Society. A well-established literature argues that fiscal illusion increases the level of government expenditure. This article focuses on the proposition that fiscal illusion also influences the cyclicality of government expenditure. Predictions are formed with reference to government reliance on high income elasticities of indirect tax revenues and on intergovernmental transfers. Predictions are tested with reference to the expenditures of 36 states in the United States from 1980 to 2000. Government expenditures are more likely to be procyclical when citizens systematically underestimate the cost of taxation

Business reporting: how transparency becomes a justification mechanism

This paper contributes to the discussion of the present status and future scenarios of business reporting by conducting a thorough review and analysis of state-of-the-art of the authoritative business reporting literature and also a set of specifically formulated models for voluntary reporting. By analyzing the chosen business reporting models with respect to 14 generally acknowledged themes, the paper contributes to the understanding of the myriad of arguments that are mobilized in the debate on the future of corporate reporting. Based on a qualitative content analysis of nine purposively selected business reporting models, the analysis illuminates and characterizes the argumentation for supplementary reporting. The paper posits some interesting points in connection with the motives for improving corporate reporting practices. A dichotomy between reliability through normalization and relevance through linking disclosure to value creation is emphasized. As transparency is in the eye of the beholder, the process of developing corporate reporting practices must be concerned with reaching a common understanding and agreement between producers and consumers of such disclosures. Transparency is perceived as both a key objective and outcome of comprehensive business reporting. However, the concept of transparency seems to be an empty concept merely constituting a justification mechanism for actual behaviour, i.e. that disclosure instead is driven by the signalling value for the individual company of disclosing a piece of voluntary information.No keywords;

A call for resilience index for health and social systems in Africa

This repository item contains a single issue of Issues in Brief, a series of policy briefs that began publishing in 2008 by the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future. This paper is part of the Africa 2060 Project, a Pardee Center program of research, publications and symposia exploring African futures in various aspects related to development on continental and regional scales. The views expressed in this paper are strictly those of the author and should not be assumed to represent the views of the Frederick S. Pardee Center for the Study of the Longer-Range Future or of Boston University.This policy brief explores the concept of resilience as it applies to health and social systems in Africa, and suggests that development of a multi-dimensional resilience index may help to understand and formulate policy in settings of complex emergencies. This paper is part of the Africa 2060 Project, a Pardee Center program of research, publications and symposia exploring African futures in various aspects related to development on continental and regional scales

Maximising the impact of careers services on career management skills: a review of the literature

The review identified an international body of work on the development and implementation of competency frameworks in reaction to CMS, including the ‘Blueprint’ frameworks, which are a series of inter-related national approaches to career management skills (originating in the USA and taken up subsequently, and with different emphases, by Canada, Australia, England and Scotland). There is, as yet, little empirical evidence to support the overall efficacy of CMS frameworks, but they have the advantage of setting out what needs to be learned (usually as a clear and identifiable list of skills, attributes and attitudes) and, often, how this learning is intended to happen. The international literature emphasised the iterative nature and mixture of formal and informal learning and life experiences that people needed to develop CMS. It suggested that, though there was no single intervention or group of interventions that appeared most effective in increasing CMS, there were five underpinning components of career guidance interventions that substantially increased effectiveness, particularly when combined. These included the use of narrative/writing approaches; the importance of providing a ‘safe’ environment; the quality of the adviser-client relationship; the need for flexibility in approach; the provision of specialist information and support; and clarity on the purpose and aims of action planning. The review also identified a possible emergent hierarchy around the efficacy of different modes of delivery of career guidance interventions on CMS development. Interventions involving practitioner contact and structured groups appeared more effective than self-directed interventions or unstructured groups. Computer-based interventions were found to work better when practitioner input was provided during the intervention or when they were followed up by a structured workshop session to discuss and review the results.Skills Funding Agenc