Biological variation: back to basics

Biological variation: back to basics

The Role of Wealth in Gain and Loss Perception: An Empirical Analysis

People with significantly different initial starting capitals may perceive gains and losses differently. In order to test this hypothesis, we consider and compare two samples of investors: retail investors as those with a maximum of €500,000 worth of assets under management (AUM) and private investors as those with more than €500,000 AUM. Based on the answers obtained from specifically devised questionnaires, we test the differences in gain and loss perception and check the level of satisfaction/dissatisfaction in situations of gain and loss. The results obtained demonstrate that private and retail investors perceive gains and losses differently

Switch-out and switch-in: what motivates the decision makers in Italian occupational pension funds?

To switch presumes two kinds of transactions carried out by the same person: on the one hand, the decision to exit an investment line (switch-out) and, on the other hand, the decision to enter into a new investment line (switch-in). What motivates the decision makers? This paper, considering a sample of Italian occupational pension funds, investigates the impact of short-term and long-term performance on the switch decision process and whether the same performance can lead investors to make opposite switch decisions. Some irrational behaviors are identified

In mezzo al guado. La governance subregionale tra «vecchie» province e «nuove» aree vaste

An ongoing change in regional governance triggered by the Delrio Law (No. 56/2014) led to a differentiated landscape across the country. This reshaping consists more in provisional, inertial and somehow random tendencies than in governance models that are consolidating. What emerges is a not homogeneous system under a variable geometry. The final result is that although transitional, these governance models can mark a divide be-tween first movers – which started a rescaling process – and late comer regions – which did not and thus lag behind, still in midstream. Yet, like all policy legacies, this divide can significantly impact future centre-periphery relations

Multi-Task Attentive Residual Networks for Argument Mining

We explore the use of residual networks and neural attention for argument mining and in particular link prediction. The method we propose makes no assumptions on document or argument structure. We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble. We evaluate it on a challenging data set consisting of user-generated comments, as well as on two other datasets consisting of scientific publications. On the user-generated content dataset, our model outperforms state-of-the-art methods that rely on domain knowledge. On the scientific literature datasets it achieves results comparable to those yielded by BERT-based approaches but with a much smaller model size.Comment: 12 pages, 2 figures, submitted to IEEE Transactions on Neural Networks and Learning System

Attention in Natural Language Processing

Attention is an increasingly popular mechanism used in a wide range of neural architectures. The mechanism itself has been realized in a variety of formats. However, because of the fast-paced advances in this domain, a systematic overview of attention is still missing. In this article, we define a unified model for attention architectures in natural language processing, with a focus on those designed to work with vector representations of the textual data. We propose a taxonomy of attention models according to four dimensions: the representation of the input, the compatibility function, the distribution function, and the multiplicity of the input and/or output. We present the examples of how prior information can be exploited in attention models and discuss ongoing research efforts and open challenges in the area, providing the first extensive categorization of the vast body of literature in this exciting domain

Argumentative Link Prediction using Residual Networks and Multi-Objective Learning.

We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. We propose a domain-agnostic method that makes no assumptions on document or argument structure. We evaluate our method on a challenging dataset consisting of user-generated comments collected from an online platform. Results show that our model outperforms an equivalent deep network and offers results comparable with state-of-the-art methods that rely on domain knowledge

Does menu design influence retirement investment choices? Evidence from Italian occupational pension funds

Previous research has demonstrated that consumers’ decisions regarding supplementary pensions could be affected by biases. Bernatzi and Thaler’s experiment demonstrated that menu design can influence pension fund enrollment decisions, in that participants appear to adopt a naïve heuristic, i.e., “extremeness aversion”. Using a database of 27 occupational pension funds from 2007 to 2011, representing 1,732,530 employees, this study asked whether menu design affected Italian workers’ choices regarding the supplementary pension system as a result of the new rules enacted by the regulator in 2007. Most enrolled workers opted for the median investment line. I discuss the possible relevance of this result to public policy, in particular the possibility of including these preferences in the regulations, with the aim of benefiting employees