Compliance with International Soft Law

2020 ◽  
pp. 49-64
Author(s):  
Michael D'Rosario ◽  
John Zeleznikow
Keyword(s):  
Neural Network ◽  
Legal System ◽  
Goodness Of Fit ◽  
Predictive Accuracy ◽  
Estimation Method ◽  
System Structure ◽  
Soft Law ◽  
Network Modelling ◽  
Democratic Systems

The present article considers the importance of legal system origin in compliance with ‘international soft law,' or normative provisions contained in non-binding texts. The study considers key economic and governance metrics on national acceptance an implementation of the first Basle accord. Employing a data set of 70 countries, the present study considers the role of market forces and bilateral and multi-lateral pressures on implementation of soft law. There is little known about the role of legal system structure-related variables as factors moderating the implementation of multi-lateral agreements and international soft law, such as the 1988 accord. The present study extends upon research within the extant literature by employing a novel estimation method, a neural network modelling technique, with multi-layer perceptron artificial neural network (MPANN). Consistent with earlier studies, the article identifies a significant and positive effect associated with democratic systems and the implementation of the Basle accord. However, extending upon traditional estimation techniques, the study identifies the significance of savings rates and government effectiveness in determining implementation. Notably, the method is able to achieve a superior goodness of fit and predictive accuracy in determining implementation.

scholarly journals Compliance with International Soft Law

2018 ◽  
Vol 9 (3) ◽  
pp. 1-15 ◽  
Author(s):  
Michael D'Rosario ◽  
John Zeleznikow
Keyword(s):  
Neural Network ◽  
Legal System ◽  
Goodness Of Fit ◽  
Predictive Accuracy ◽  
Estimation Method ◽  
System Structure ◽  
Soft Law ◽  
Network Modelling ◽  
Data Set

The present article considers the importance of legal system origin in compliance with ‘international soft law,' or normative provisions contained in non-binding texts. The study considers key economic and governance metrics on national acceptance an implementation of the first Basle accord. Employing a data set of 70 countries, the present study considers the role of market forces and bilateral and multi-lateral pressures on implementation of soft law. There is little known about the role of legal system structure-related variables as factors moderating the implementation of multi-lateral agreements and international soft law, such as the 1988 accord. The present study extends upon research within the extant literature by employing a novel estimation method, a neural network modelling technique, with multi-layer perceptron artificial neural network (MPANN). Consistent with earlier studies, the article identifies a significant and positive effect associated with democratic systems and the implementation of the Basle accord. However, extending upon traditional estimation techniques, the study identifies the significance of savings rates and government effectiveness in determining implementation. Notably, the method is able to achieve a superior goodness of fit and predictive accuracy in determining implementation.

scholarly journals Predicting Credit Rating Migration Employing Neural Network Models

2020 ◽  
pp. 65-82
Author(s):  
Michael D'Rosario ◽  
Calvin Hsieh
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Credit Rating ◽  
Predictive Accuracy ◽  
Estimation Method ◽  
Network Models ◽  
Legal Framework ◽  
Neural Network Models ◽  
Network Modelling ◽  
Extant Literature

Credit rating migration ranks amongst the most pertinent issues concerning institutional lenders and investors alike. There are a number of studies that have employed both parametric and non-parametric methodologies to forecast credit rating migration, employing machine learning methods; and notably, artificial intelligence methods becoming increasingly popular. The present study extends upon research within the extant literature employing a novel estimation method, a neural network modelling technique, herewith the MPANN (multi-layer neural network). Consistent with the extant literature, the present article identifies that the legal framework and system of taxation enacted within a polity are pertinent to predicting rating migration. However, extending upon traditional estimation techniques the study identifies that a number of different model calibrations achieve greater predictive accuracy than traditional parametric regression. Notably, the method is able to achieve superior goodness of fit and predictive accuracy in determining credit rating migration than models employed within the extant literature.

scholarly journals Predicting Credit Rating Migration Employing Neural Network Models

2018 ◽  
Vol 9 (4) ◽  
pp. 70-85
Author(s):  
Michael D'Rosario ◽  
Calvin Hsieh
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Credit Rating ◽  
Predictive Accuracy ◽  
Estimation Method ◽  
Network Models ◽  
Legal Framework ◽  
Neural Network Models ◽  
Network Modelling ◽  
Extant Literature

Credit rating migration ranks amongst the most pertinent issues concerning institutional lenders and investors alike. There are a number of studies that have employed both parametric and non-parametric methodologies to forecast credit rating migration, employing machine learning methods; and notably, artificial intelligence methods becoming increasingly popular. The present study extends upon research within the extant literature employing a novel estimation method, a neural network modelling technique, herewith the MPANN (multi-layer neural network). Consistent with the extant literature, the present article identifies that the legal framework and system of taxation enacted within a polity are pertinent to predicting rating migration. However, extending upon traditional estimation techniques the study identifies that a number of different model calibrations achieve greater predictive accuracy than traditional parametric regression. Notably, the method is able to achieve superior goodness of fit and predictive accuracy in determining credit rating migration than models employed within the extant literature.

The Innocuous Impact of Pan-European General Principles of Good Administration on Hungarian Law and Legal Practice

2020 ◽  
pp. 449-477
Author(s):  
Petra Lea Láncos
Keyword(s):  
Human Rights ◽  
Legal System ◽  
Case Law ◽  
Soft Law ◽  
Full Potential ◽  
Council Of Europe ◽  
Legislation And Jurisprudence ◽  
European Court ◽  
The Impact

This chapter discusses the influence of the pan-European principles of good administration in the Hungarian legal system. It discloses that while the impact and role of these pan-European principles, in particular that of the case law of the European Court of Human Rights, are growing in Hungarian legislation and jurisprudence, clear traces of them are still difficult to discern. It also finds that, despite some influence stemming from the Council of Europe (CoE) in the codification concepts underlying recent procedural reforms, the full potential to that effect is far from being realized. In particular, reliance on soft law instruments of the CoE remains problematic, in part due to legal formalism inherited from the country’s socialist past.

scholarly journals Legitimising data-driven models: exemplification of a new data-driven mechanistic modelling framework

2013 ◽  
Vol 17 (7) ◽  
pp. 2827-2843 ◽  
Author(s):  
N. J. Mount ◽  
C. W. Dawson ◽  
R. J. Abrahart
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Goodness Of Fit ◽  
Relative Sensitivity ◽  
Data Driven ◽  
Network Modelling ◽  
Core Element ◽  
Mechanistic Modelling ◽  
Modelling Framework ◽  
Artificial Neural

Abstract. In this paper the difficult problem of how to legitimise data-driven hydrological models is addressed using an example of a simple artificial neural network modelling problem. Many data-driven models in hydrology have been criticised for their black-box characteristics, which prohibit adequate understanding of their mechanistic behaviour and restrict their wider heuristic value. In response, presented here is a new generic data-driven mechanistic modelling framework. The framework is significant because it incorporates an evaluation of the legitimacy of a data-driven model's internal modelling mechanism as a core element in the modelling process. The framework's value is demonstrated by two simple artificial neural network river forecasting scenarios. We develop a novel adaptation of first-order partial derivative, relative sensitivity analysis to enable each model's mechanistic legitimacy to be evaluated within the framework. The results demonstrate the limitations of standard, goodness-of-fit validation procedures by highlighting how the internal mechanisms of complex models that produce the best fit scores can have lower mechanistic legitimacy than simpler counterparts whose scores are only slightly inferior. Thus, our study directly tackles one of the key debates in data-driven, hydrological modelling: is it acceptable for our ends (i.e. model fit) to justify our means (i.e. the numerical basis by which that fit is achieved)?

scholarly journals Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning

2017 ◽  
Vol 372 (1711) ◽  
pp. 20160049 ◽  
Author(s):  
Anna C. Schapiro ◽  
Nicholas B. Turk-Browne ◽  
Matthew M. Botvinick ◽  
Kenneth A. Norman
Keyword(s):  
Neural Network ◽  
Statistical Learning ◽  
Entorhinal Cortex ◽  
Learning Systems ◽  
Network Modelling ◽  
Cognitive Sciences ◽  
Trade Off ◽  
Rapid Learning ◽  
Modelling Approach

A growing literature suggests that the hippocampus is critical for the rapid extraction of regularities from the environment. Although this fits with the known role of the hippocampus in rapid learning, it seems at odds with the idea that the hippocampus specializes in memorizing individual episodes. In particular, the Complementary Learning Systems theory argues that there is a computational trade-off between learning the specifics of individual experiences and regularities that hold across those experiences. We asked whether it is possible for the hippocampus to handle both statistical learning and memorization of individual episodes. We exposed a neural network model that instantiates known properties of hippocampal projections and subfields to sequences of items with temporal regularities. We found that the monosynaptic pathway—the pathway connecting entorhinal cortex directly to region CA1—was able to support statistical learning, while the trisynaptic pathway—connecting entorhinal cortex to CA1 through dentate gyrus and CA3—learned individual episodes, with apparent representations of regularities resulting from associative reactivation through recurrence. Thus, in paradigms involving rapid learning, the computational trade-off between learning episodes and regularities may be handled by separate anatomical pathways within the hippocampus itself. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’.

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