Old‐age protection for women in the Spanish pension system

The example of Spain confirms the common view that contributory pension systems reproduce inequalities between the sexes that result from the nature of labour market structures and the sharing of family responsibilities. In general, women who stay at home are not entitled to their own pensions and are dependent on benefits of lower value such as survivors' pensions (derived entitlements) or non‐contributory pensions. In turn, women who work outside the home accrue lower entitlements than men and, consequently, lower old‐age or disability pensions (personal entitlements). The purpose of this article is to examine the figures for pension distribution by sex in Spain, review some of the pension policies that have been implemented since 2000, and propose direct action for progress in the transition from derived entitlements to personal entitlements. These proposals are designed to promote sex equality, defined as the right to equal well‐being and financial security in old age.     

A review of representation issues and modeling challenges with influence diagrams

Since their introduction in the mid 1970s, influence diagrams have become a de facto standard for representing Bayesian decision problems. The need to represent complex problems has led to extensions of the influence diagram methodology designed to increase the ability to represent complex problems. In this paper, we review the representation issues and modeling challenges associated with influence diagrams. In particular, we look at the representation of asymmetric decision problems including conditional distribution trees, sequential decision diagrams, and sequential valuation networks. We also examine the issue of representing the sequence of decision and chance variables, and how it is done in unconstrained influence diagrams, sequential valuation networks, and sequential influence diagrams. We also discuss the use of continuous chance and decision variables, including continuous conditionally deterministic variables. Finally, we discuss some of the modeling challenges faced in representing decision problems in practice and some software that is currently available.     

Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables

Many real-world optimization problems involve two different subsets of variables: decision variables, and those variables which are not present in the cost function but constrain the solutions, and thus, must be considered during optimization. Thus, dependencies between and within both subsets of variables must be considered. In this paper, an estimation of distribution algorithm (EDA) is implemented to solve this type of complex optimization problems. A Gaussian Bayesian network is used to build an abstraction model of the search space in each iteration to identify patterns among the variables. As the algorithm is initialized from data, we introduce a new hyper-parameter to control the influence of the initial data in the decisions made during the EDA execution. The results show that our algorithm improves the cost function more than the expert knowledge does.