ABSTRACT
The public pension system crisis, arising mainly from the changing demographic, has hit different countries worldwide. For governments and citizens, it is very important to have reliable information regarding pensions in order to make decisions with a maximum degree of effectiveness and to ensure a decent income in retirement. This study presents a new method for optimizing forecasts of the average pension by using the ordered weighted averaging (OWA) operator, the induced ordered weighted averaging (IOWA) operator, the generalized ordered weighted averaging (GOWA) operator, the induced generalized ordered weighted averaging (IGOWA) operator, and particular forms of the probabilistic ordered weighted averaging (POWA) operator and the quasi-arithmetic ordered weighted averaging (Quasi-OWA) operator. It also accounts for inflation or deflation, providing a more realistic assessment of the average pension. The main advantage of this approach is the possibility to include the attitudinal character of experts or decision-makers into the calculation. The study also presents an illustrative example of how to forecast the real average pension for all autonomous communities of Spain by using this new approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Intense and frequent changes increase uncertainty and complexity in decision-making. The COVID-19 pandemic exacerbates this situation. Therefore, the decision-maker seeks to reduce risks and meet these challenges. The manuscript aims to identify cause-effect relationships between variables affecting countries and changes caused by the COVID-19 pandemic and propose an algorithm to facilitate decision-making by identifying forgotten effects. The authors use thematic analysis to synthesize the semi-systematic literature review findings. The applied research uses a quantitative approach through modeling and simulation. The results highlight that the pandemic effects are associated with causes such as health care, political and economic stability, social justice, and the level of corruption. Decision-makers must prioritize the management of these variables guided by science. The main contribution is to show an algorithm that identifies forgotten effects in pandemics' socio-economic and health management, preventing future crises. In addition, the study advances the frontier of knowledge by addressing identified gaps and contributes to academia and policy makers. The most critical limitation is the number of variables included in this research. Future investigations could include analyses on the impact of climate change and sustainable development of nations and country-specific studies on the forgotten effects of the COVID-19 pandemic. © 2022 Taylor & Francis Group, LLC.
ABSTRACT
In order to help the decision-making to the owners of grocery stores of less than 150 m2 in Barcelona, this work presents a decision support system based in the Forgotten Effects Theory of 701 stores at the City. COVID-19 is changing consumption patterns and Grocery stores must adapt to the new reality by improving customer service, product exposure, cordiality… in order not to see their market share reduced due to the growth of online sales of food products, they also have large distributors as competitors who offer a greater variety of products to their customers since they have a much larger sales area. The focus in on the importance given in each District to the different parameters that we have determined. The experiment carried out allows us to determine the second-generation effects, which are more indirect. The results highlight the differences between the different Districts in the assessment of the different determinants of our study. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Humanity is experiencing intense and frequent changes caused by the COVID-19 pandemic, which increases uncertainty and complexity in decision-making. New challenges become urgent, mainly to issues related to the aging population, since the coronavirus affects the elderly more than adults. Finding alternatives to preserve the quality of life and work support of the elderly becomes a great challenge for society. In this context, science plays a vital role in proposing new solutions to solve the problems derived from this crisis, being this the primary motivation. The objectives of the article are to learn about the forgotten effects of the pandemic on economically active older adults and to indicate how fuzzy logic can help reduce risks by facilitating decision-making. The main contribution would be to correctly identify its causes and effects, such as the digital gap and job loss by this age group, and to point out corrective measures. The study's methodology is based on applied research, with a quantitative modeling and simulation approach through the Forgotten Effects Theory. The results allow us to predict and act more effectively on problems, seeking to increase the welfare, employability, and life expectancy of the elderly. The study highlights future lines of research on the subject.