Corporate Misgovernance at the World Bank

We test for evidence of corporate misgovernance at the World Bank. Most major decisions at the World Bank are made by its Board of Executive Directors. However, in any given year the majority of the Bank's member countries do not get a chance to serve on this powerful body. In this paper, we empirically investigate whether board membership leads to higher funding from the World Bank's two main development financing institutions, the International Bank for Reconstruction and Development (IBRD) and the International Development Association (IDA). We find that developing countries serving on the Board of Executive Directors can expect an approximate doubling of funding from the IBRD. In absolute terms, countries serving on the board are rewarded with an average $60 million "bonus" in IBRD loans. This is more likely driven by soft forces like boardroom culture rather than by the power of the vote itself. We find no significant effect in IDA funding.

Qualitative System Identification from Imperfect Data

Experience in the physical sciences suggests that the only realistic means of understanding complex systems is through the use of mathematical models. Typically, this has come to mean the identification of quantitative models expressed as differential equations. Quantitative modelling works best when the structure of the model (i.e., the form of the equations) is known; and the primary concern is one of estimating the values of the parameters in the model. For complex biological systems, the model-structure is rarely known and the modeler has to deal with both model-identification and parameter-estimation. In this paper we are concerned with providing automated assistance to the first of these problems. Specifically, we examine the identification by machine of the structural relationships between experimentally observed variables. These relationship will be expressed in the form of qualitative abstractions of a quantitative model. Such qualitative models may not only provide clues to the precise quantitative model, but also assist in understanding the essence of that model. Our position in this paper is that background knowledge incorporating system modelling principles can be used to constrain effectively the set of good qualitative models. Utilising the model-identification framework provided by Inductive Logic Programming (ILP) we present empirical support for this position using a series of increasingly complex artificial datasets. The results are obtained with qualitative and quantitative data subject to varying amounts of noise and different degrees of sparsity. The results also point to the presence of a set of qualitative states, which we term kernel subsets, that may be necessary for a qualitative model-learner to learn correct models. We demonstrate scalability of the method to biological system modelling by identification of the glycolysis metabolic pathway from data

Predicting Financial Market Crashes Using Ghost Singularities

This is the author accepted manuscript.The final version is available from Public Library of Science via the DOI in this recordWe analyse the behaviour of a non-linear model of coupled stock and bond prices exhibiting periodically collapsing bubbles. By using the formalism of dynamical system theory, we explain what drives the bubbles and how foreshocks or aftershocks are generated. A dynamical phase space representation of that system coupled with standard multiplicative noise rationalises the log-periodic power law singularity pattern documented in many historical financial bubbles. The notion of ‘ghosts of finite-time singularities’ is introduced and used to estimate the end of an evolving bubble, using finite-time singularities of an approximate normal form near the bifurcation point. We test the forecasting skill of this method on different stochastic price realisations and compare with Monte Carlo simulations of the full system. Remarkably, the approximate normal form is significantly more precise and less biased. Moreover, the method of ghosts of singularities is less sensitive to the noise realisation, thus providing more robust forecasts.This project has received funding from the European Unions Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie grant agreement No 643073

A generalized 2D-dynamical mean-field Ising model with a rich set of bifurcations (inspired and applied to financial crises)

This is the final version of the article. Available from the publisher via the DOI in this record.We analyze an extended version of the dynamical mean-field Ising model. Instead of classical physical representation of spins and external magnetic field, the model describes traders' opinion dynamics. The external field is endogenized to represent a smoothed moving average of the past state variable. This model captures in a simple set-up the interplay between instantaneous social imitation and past trends in social coordinations. We show the existence of a rich set of bifurcations as a function of the two parameters quantifying the relative importance of instantaneous versus past social opinions on the formation of the next value of the state variable. Moreover, we present a thorough analysis of chaotic behavior, which is exhibited in certain parameter regimes. Finally, we examine several transitions through bifurcation curves and study how they could be understood as specific market scenarios. We find that the amplitude of the corrections needed to recover from a crisis and to push the system back to “normal” is often significantly larger than the strength of the causes that led to the crisis itself.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 643073

Critical-layer structures and mechanisms in elastoinertial turbulence

Simulations of elastoinertial turbulence (EIT) of a polymer solution at low Reynolds number are shown to display localized polymer stretch fluctuations. These are very similar to structures arising from linear stability (Tollmien-Schlichting (TS) modes) and resolvent analyses: i.e., critical-layer structures localized where the mean fluid velocity equals the wavespeed. Computation of self-sustained nonlinear TS waves reveals that the critical layer exhibits stagnation points that generate sheets of large polymer stretch. These kinematics may be the genesis of similar structures in EIT.Comment: 5 pages, 4 figures; Accepted in Physical Review Letter

Estimation of Pan Evaporation from Climatological Data

A new formula and coefficients for climatological factors was developed for estimating pan evaporation based on an analysis of data from 23 states in the U. S. and from five other countries. This formula may be written: Ev=K R Ct Cw Ch Cs Ce Cm in which K is a dimensionless constant R is the theoretical radiation reaching the outer atmosphere, expressed as equivalent evaporation in the same units~ , as Ev. Ct, Cw, Ch,and Cs are dimensionless sub-coefficients for temperature , wind, humidity, and sunshine. Ce is a sub-coefficient for elevation, and Cm is a monthly coefficient Equations were developed for all of the coefficients and table s were prepared to facilitate the application of the formula. The study was a continuation of a thesis study by Patil (1962) who analyzed mE}-re than 3,200 months of records from 40 stations in the western states to develop a formula and equations for the coefficients of climatological factors . Later, a separate analysis was made of the same data by Mathison (1963) using similar, but different procedures. The present study differs from those of Patil and Mathison in two major respects. The data includes, in addition to those used by Patil and Mathison. 507 months of records from the stations in the Midwest and southern states, Puerto Rico, Panama, Hawaii, Canada, Alaska, Nigeria, and Peru. These data extended the range of the weather factors. The second difference was i.n the analytical procedure. A technique suggested by Grassi (1964) was used to develop coefficients for temperature, hum[dity, and sunshine. These coefficients are more independent of other climatic factors than those developed by Patil and Mathison

THE EFFECT OF NUTRITION ON MENTAL HEALTH: A FOCUS ON INFLAMMATORY MECHANISMS

Neuropsychiatric disorders are closely associated with a persistent low-grade inflammatory state. This suggests that the development of psychopathology is not only limited to the brain, but rather involves an additional systemic aspect, accounting for the large body of evidence demonstrating co-presentation of mental illness with chronic inflammatory conditions and metabolic syndromes. Studies have shown that inflammatory processes underlie the development of neuropsychiatric symptoms, with recent studies revealing not only correlative, but causative relationships between the immune system and psychopathology. Lifestyle factors such as diet and exercise may influence psychopathology, and this may occur via a bidirectional relationship. Mental illness may prevent health-seeking behaviours such as failing to maintain a balanced diet, whilst adopting a ‘healthy’ diet rich in fruits, vegetables and fish alongside nutritional supplementation correlates with a reduction in psychiatric symptoms in patients. Obesity and the gut microbiome have proven to be further factors which play an important role in inflammatory signalling and the development of psychiatric symptoms. In a related paper we focus on the role of exercise (another significant lifestyle factor) on mental health (Venkatesh et al. 2020). Lifestyle modifications which target diet and nutrition may prove therapeutically beneficial for many patients, especially in treatment-resistant subgroups. The current evidence base provides equivocal evidence, however future studies will prove significant, as this is a highly attractive therapeutic avenue, due to its cost efficacy, low side effect profile and preventative potential. By promoting lifestyle changes and addressing the limitations and barriers to adoption, these therapies may prove revolutionary for mental health conditions