Driving complementarity in interdisciplinary research: a reflection

ABSTRACT

The value of interdisciplinary research is increasingly recognised by the research community. Funding bodies are nowadays specifically encouraging that research they fund is interdisciplinary in nature. However, what is often branded as interdisciplinary research is in fact a network of researchers working to deliver a research output. In this research note, we share our experience of what we think can be labelled “truly interdisciplinary”. We share some best practice tips for those who are interested to go through a similar metamorphosis as we did as a team, learning from each other’s ways of working. Overall, it has been a very positive experience and a revelation that has had a big impact on each author’s view on how to conduct research as an integrated multidisciplinary team, as it has shifted our mental models and broadened our thinking, capacity to reflect, and critical analysis. We hope that our research note clearly shows the benefits of truly interdisciplinary research and motivates other researchers to follow our pathway of transformation.     

Model-based clustering with determinant-and-shape constraint

Statistics and Computing - Model-based approaches to cluster analysis and mixture modeling often involve maximizing classification and mixture likelihoods. Without appropriate constrains on the...     

Who’s About to Leave? A Global Survey of Aspirations and Intentions to Migrate

This article analyses aspirations and intentions to migrate based on Gallup World Poll for the period 2010–2015. We estimate individual‐level traits associated with aspirations and intentions to migrate across groups of countries in different regions and with different income levels. This paper brings together previous hypotheses regarding migration aspirations and tests them under several specifications, while keeping separate findings according to migration aspirations and intentions to appreciate differences between them. Being dissatisfied with one’s own standard of living is associated with a higher probability of desiring to move, while the relation with the actual preparation to migrate is less clear. Some individual traits remain significant across (almost) all specifications: being male, foreign‐born, highly educated, and having networks abroad are associated with higher probability of preparing for international migration. Aspects related to one’s economic situation are not consistently significant across specifications, signalling the importance of contextual analyses for these factors.     

Applying the Consensual Method of Estimating Poverty in a Low Income African Setting

Social Indicators Research - We present the first study of multidimensional poverty in Benin using the consensual or socially perceived necessities approach. There is a remarkable level consensus...     

Interpersonal Distances in Group Walking

The spatial organization of 1,020 groups comprised of adolescents and young adults, observed in an ecological setting while walking, was analyzed. Observations were made in an urban environment where walking speed could be considered. The results showed that male dyads and triads tended to walk abreast less often than female dyads. Mixed dyads walked abreast more often than same-sex dyads; and the males preceded the females in two-thirds of the cases. The male groups walked at a higher rate of speed than the female groups. Walking speed was correlated to misalignment between group members when walking. The most frequent spatial arrangement in triads was a “<” formation (as seen from above, while the walking direction was from left to right), with the middle individual positioned slightly behind in comparison to the lateral individuals. Groups comprised of more than three individuals tended to split themselves into single individuals, dyads, and triads.     

Urban growth in China: past, prospect, and its impacts

Rapid growth and globalization of the domestic economy have dramatically accelerated urbanization in China, resulting in significant environmental impacts and challenges for sustainable development. Using a multistate model accounting for distributional aspects of age, sex, education, and migration in rural and urban regions, we estimate the magnitude of urbanization in China through 2030 and examine some major associated sustainability issues. Results indicate that: (1) for a range of assumptions, China’s urban population will nearly double from 2000 to 2030; (2) the labor force will constitute a larger share of total population in urban areas than rural due to internal migration of younger workers—this appears particularly true for the mega-urban metropolises of Beijing and Shanghai; (3) rural populations will experience more aging than urban; and (4) level of education among China’s rural labor force will remain low, which could pressure China’s industrial structural transition from an agricultural to a service-based economy.     

Internal slackening scoring methods

We deal with the ranking problem of the nodes in a directed graph. The bilateral relationships specified by a directed graph may reflect the outcomes of a sport competition, the mutual reference structure between websites, or a group preference structure over alternatives. We introduce a class of scoring methods for directed graphs, indexed by a single nonnegative parameter α. This parameter reflects the internal slackening of a node within an underlying iterative process. The class of so-called internal slackening scoring methods, denoted by λ ^α , consists of the limits of these processes. It is seen that λ⁰ extends the invariant scoring method, while λ ^∞ extends the fair bets scoring method. Method λ¹ corresponds with the existing λ-scoring method of Borm et al. (Ann Oper Res 109(1):61–75, 2002) and can be seen as a compromise between λ⁰ and λ ^∞ . In particular, an explicit proportionality relation between λ ^α and λ¹ is derived. Moreover, the internal slackening scoring methods are applied to the setting of social choice situations where they give rise to a class of social choice correspondences that refine both the Top cycle correspondence and the Uncovered set correspondence.     

Some results for maximum likelihood estimation of adjusted relative risks

Maximum likelihood (ML) estimation of relative risks via log-binomial regression requires a restricted parameter space. Computation via non linear programming is simple to implement and has high convergence rate. We show that the optimization problem is well posed (convex domain and convex objective) and provide a variance formula along with a methodology for obtaining standard errors and prediction intervals which account for estimates on the boundary of the parameter space. We performed simulations under several scenarios already used in the literature in order to assess the performance of ML and of two other common estimation methods.     

Confidence biases and learning among intuitive Bayesians

We design a double-or-quits game to compare the speed of learning one’s specific ability with the speed of rising confidence as the task gets increasingly difficult. We find that people on average learn to be overconfident faster than they learn their true ability and we present an intuitive-Bayesian model of confidence which integrates confidence biases and learning. Uncertainty about one’s true ability to perform a task in isolation can be responsible for large and stable confidence biases, namely limited discrimination, the hard–easy effect, the Dunning–Kruger effect, conservative learning from experience and the overprecision phenomenon (without underprecision) if subjects act as Bayesian learners who rely only on sequentially perceived performance cues and contrarian illusory signals induced by doubt. Moreover, these biases are likely to persist since the Bayesian aggregation of past information consolidates the accumulation of errors and the perception of contrarian illusory signals generates conservatism and under-reaction to events. Taken together, these two features may explain why intuitive Bayesians make systematically wrong predictions of their own performance.