Introduction to the Special Issues on Societal Metabolism: Blending New Insights from Complex System Thinking with Old Insights from Biophysical Analyses of the Economic Process

Population and Environment -     

Understanding the socio-economic distribution of multiple deprivation: An application of self-organising maps

In this paper we seek to contribute to recent efforts to develop and implement multi-dimensional approaches to social exclusion by applying self-organising maps (SOMs) to a set of material deprivation indicators from the Irish component of EU-SILC. The first stage of our analysis involves the identification of sixteen clusters that confirm the multi-dimensional nature of deprivation in contemporary Ireland and the limitations of focusing solely on income. In going beyond this mapping stage, we consider both patterns of socio-economic differentiation in relation to cluster membership and the extent to which such membership contributes to our understanding of economic stress. Our analysis makes clear the continuing importance of traditional forms of stratification relating to factors such as income, social class and housing tenure in accounting for patterns of multiple deprivation. However, it also confirms the role of acute life events and life cycle and location influences. Most importantly, it demonstrates that conclusions relating to the relative impact of different kinds of socio-economic influences are highly dependent on the form of deprivation being considered. Our analysis suggests that debates relating to the extent to which poverty and social exclusion have become individualized should take particular care to distinguish between different kinds of outcomes. Further analysis demonstrates that the SOM approach is considerably more successful than a comparable latent class analysis in identifying those exposed to subjective economic stress.     

The Multidimensionality of Child Poverty: Evidence from Afghanistan

This paper examines multidimensional poverty among children in Afghanistan using the Alkire-Foster method. Several previous studies have underlined the need to separate children from their adult nexus when studying poverty and treat them according to their own specificities. From the capability approach, child poverty is understood to be the lack of freedom to do and to be what children themselves value and have reason to value. The case of Afghanistan is particularly relevant as years of conflict aggravated by several severe droughts, political insecurity, bad governance and ongoing violence have significantly increased poverty in the country. The paper discusses the relevant dimensions when analysing child poverty and uses data from a survey carried out by Handicap International which contains information on dimensions of children’s wellbeing that is typically missing in standard surveys. Ten dimension are considered in this paper: health, care and love, material deprivation, food security, social inclusion, education, freedom from economic and non-economic exploitation, shelter and environment, autonomy, and mobility. Our results show that younger children, those living in rural areas, girls and disabled children are the most deprived.     

ECONOMIC DETERMINANTS OF THE TIMING OF PREFERENTIAL TRADE AGREEMENT FORMATIONS AND ENLARGEMENTS

Preferential trade agreements (PTAs) have proliferated over the past 60 years. While a small number of recent studies have examined empirically the economic determinants of the likelihood of a pair of countries having a PTA, this study explains empirically the timing of all PTA formations and enlargements from 1950 through 2006 using duration analysis. Our main and novel goal is to predict (in‐ and out‐of‐sample) a substantive share of these 1,560 PTA events using a parsimonious model with mainly economic variables, taking selection dynamics into account. Our analysis reveals that we can predict correctly in‐sample the actual year of entry into force for 26% of the 1,560 bilateral PTA formations/enlargements in the period 1950–2006 among 10,518 pairings of 146 countries using only a few economic and political variables. Moreover, we can predict correctly in‐sample 57% of these PTA events within a 10‐year window leading up to the event using this model. The model also performs well out‐of‐sample for the near term (82%), but not if the out‐of‐sample period is very long. We conclude with an evaluation of the model's ability to predict the timing of the North American Free Trade Agreement, the European Union's formation and enlargements, and the model's ten most likely post‐2006 PTA events. (JEL F14, F15)     

Evaluation of strategies to mitigate contagion spread using social network characteristics

Computer simulation is an effective tool for assessing mitigation strategies, with recent trends concentrating on agent-based techniques. These methods require high computational efforts in order to simulate enough scenarios for statistical significance. The population individuals and their contacts determined by agent-based simulations form a social network. For some network structures it is possible to gain high accuracy estimates of contagion spread based on the connection structure of the network, an idea that is utilized in this work. A representative social network constructed from the 2006 census of the Greater Toronto Area (Ontario, Canada) of 5 million individuals in 1.8 million households is used to demonstrate the efficacy of our approach. We examine the effects of six mitigation strategies with respect to their ability to contain disease spread as indicated by pre- and post-vaccination reproduction numbers, mean local clustering coefficients and degree distributions. One outcome of the analysis provides evidence supporting the design of mitigation strategies that aim to fragment the population into similarly sized components. While our analysis is framed in the context of pandemic disease spread, the approach is applicable to any contagion such as computer viruses, rumours, social trends, and so on.     

Fuzzy System Dynamics Risk Analysis (FuSDRA) of Autonomous Underwater Vehicle Operations in the Antarctic

With the maturing of autonomous technology and better accessibility, there has been a growing interest in the use of autonomous underwater vehicles (AUVs). The deployment of AUVs for under-ice marine science research in the Antarctic is one such example. However, a higher risk of AUV loss is present during such endeavors due to the extreme operating environment. To control the risk of loss, existing risk analyses approaches tend to focus more on the AUV's technical aspects and neglect the role of soft factors, such as organizational and human influences. In addition, the dynamic and complex interrelationships of risk variables are also often overlooked due to uncertainties and challenges in quantification. To overcome these shortfalls, a hybrid fuzzy system dynamics risk analysis (FuSDRA) is proposed. In the FuSDRA framework, system dynamics models the interrelationships between risk variables from different dimensions and considers the time-dependent nature of risk while fuzzy logic accounts for uncertainties. To demonstrate its application, an example based on an actual Antarctic AUV program is presented. Focusing on funding and experience of the AUV team, simulation of the FuSDRA risk model shows a declining risk of loss from 0.293 in the early years of the Antarctic AUV program, reaching a minimum of 0.206 before increasing again in later years. Risk control policy recommendations were then derived from the analysis. The example demonstrated how FuSDRA can be applied to inform funding and risk management strategies, or broader application both within the AUV domain and on other complex technological systems.     

An Optimum Population for North and Latin America

The population of North America, which now stands at nearly 300 million people, is projected to double in about 60 years, while the population of nearly 500 million people in South America is projected to double in less than 40 years. Both of these populations obtain more than 99% of their food from the land, and this percentage will increase as these populations grow. Maintaining fertile and ample land is critical if these large populations are to be fed. Soil degradation by soil erosion is a serious problem on both continents. In addition, agricultural land is being lost to urbanization and highways because of rapid population growth. Nearly a half hectare of land is needed for urbanization for each person added to the North American population; this is already causing serious problems with agriculture in some states in the United States. The land resources that are critical for food production will be especially so if the populations of both continents double to nearly 2 billion. Land resources will also be critical when both continents deplete their fossil fuels in less than 100 years and have to turn to renewable energy sources. With about 2 billion people, there will be serious shortages of food, water, and energy resources and the standard of living will significantly decline. Our assessment suggests that for a relatively high standard of living in North and South America each continent should have no more than about 200 million people, or a total of 400 million.     

An evaluation of change-point estimators for a sequence of normal observations with unknown parameters

Performance of maximum likelihood estimators (MLE) of the change-point in normal series is evaluated considering three scenarios where process parameters are assumed to be unknown. Different shifts, sample sizes, and locations of a change-point were tested. A comparison is made with estimators based on cumulative sums and Bartlett's test. Performance analysis done with extensive simulations for normally distributed series showed that the MLEs perform better (or equal) in almost every scenario, with smaller bias and standard error. In addition, robustness of MLE to non-normality is also studied.     

Teaching Communication in a Statistical Collaboration Course: A Feasible,Project-Based,Multimodal Curriculum

Many schools offer a statistical collaboration curriculum using standard instructional methods such as lectures whereby students are taught to successfully apply their training. The process of building statisticians' collaborative skills and characteristics can be challenging due to logistical issues, time constraints, unstructured research problems, and resources. Instructors vary in their pedagogy and topics taught, and students' experiences vary. There is a dearth of literature describing how to implement a course integrating communication skills, critical thinking, collaboration, and the integration of team members in a learner-centered format. Few courses integrate behavior-based learning using role-playing, video demonstration and feedback, case-based teaching activities, and presentation of basic statistical concepts. We have developed and implemented a two-semester biostatistics collaboration course, of which the purpose is to develop the students' knowledge, skills, attitudes, and behaviors necessary to interact effectively with investigators. Our innovative curriculum uses a multimodal, project-based, experiential process to address real-world problems provided by real and/or simulated collaborators while minimizing usual challenges. Rubrics and peer evaluation forms are offered as online supplementary materials. This article describes how a collaboration curriculum focusing on communication and team practice is feasible, how it enhances skill and professionalism, and how it can be implemented at other institutions.