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The paper analyzes the optimal lockdown policy using the SQAIRD model over a network with three population groups (young, adult, and old). We show that different lockdown policies may be justified by different socioeconomic structures (objective cost functions that are either convex or concave). We also show that a lockdown policy is always better than a laissez-faire policy, and a targeted policy specific to each group outperforms a uniform policy. In our benchmark example, we consider the case of Italy. Our simulations show that: (a) a lockdown policy is always better than the laissez-faire policy because it limits the costs generated by the pandemic in an uncontrolled situation; (b) a group-specific targeted lockout policy is more effective than a uniform policy to the extent that the groups differ. The latter is a less expensive targeted policy (as it optimally minimizes direct, indirect, and vaccination costs), and it is equally effective in controlling the pandemic. One finding of particular interest is that the optimal lockdown rate should be higher for the young and elderly than for adults. This is motivated by the fact that younger individuals are more likely to spread the virus in question asymptomatically. [ABSTRACT FROM AUTHOR]

Accurate modeling of the patient flow within an Emergency Department (ED) is required by all studies dealing with the increasing and well-known problem of overcrowding. Since Discrete Event Simulation (DES) models are often adopted with the aim of assessing solutions for reducing the impact of this worldwide phenomenon, an accurate estimation of the service time of the ED processes is necessary to guarantee the reliability of the results. However, simulation models concerning EDs are frequently affected by missing data, thus requiring a proper estimation of some unknown parameters. In this paper, a simulation-based optimization approach is used to estimate the incomplete data in the patient flow within an ED by adopting a model calibration procedure. The objective function of the resulting minimization problem represents the deviation between simulation output and real data, while the constraints ensure that the response of the simulation is sufficiently accurate according to the precision required. Data from a real case study related to a big ED in Italy is used to test the effectiveness of the proposed approach. The experimental results show that the model calibration allows recovering the missing parameters, thus leading to an accurate DES model. [ABSTRACT FROM AUTHOR]

Since the mid 1990s some European countries (including Italy) implemented a Notional Defined Contribution (NDC) pension system. Such a system is based on pay-as-you-go funding, while the pension amount is a function of the individual lifelong contribution. Despite many appealing features, the NDC system presents some drawbacks: first, it is vulnerable to demographic and economic shocks compromising the financial sustainability; second, it could fail to guarantee adequate pension benefits to pensioners. In order to reduce the first limit, automatic balance mechanisms (ABMs) have been proposed in literature and also implemented in Sweden, while solutions that combine financial sustainability and social adequacy have been applied only in a pay-as-you-go point system. The aim of this paper is to insert into the Italian NDC architecture ABMs that preserve social adequacy under financial sustainability constraints. Godinez-Olivares et al. (Insur Math Econ 69:117–126, 2016) built ABMs for a Defined Benefit pension system using nonlinear optimization techniques to calculate the optimal paths of the control variables representing the main drivers of the system: contribution rate, retirement age and indexation of pensions. Following this line of research, we have developed a nonlinear optimization model for the Italian NDC system based on three control variables: pensions indexation, notional rate and contribution rate. The objective function considers both social adequacy and contribution rate sustainability, under liquidity and sustainability constraints. In the numerical application we apply the model to the Italian pension system and test the sensitivity of the results to different economic scenarios and objective function parameters. [ABSTRACT FROM AUTHOR]

This paper presents a micro–macro framework to derive a credit crunch indicator for the Italian manufacturing sector. Using qualitative firm-level data over the years 2008–2018, nonlinear discrete panel data techniques are first applied in order to identify the loan supply curve controlling for firm-specific observable characteristics. In the subsequent step, the variation of the estimated supply curve that cannot be explained by proxies for loan demand is interpreted as the degree of credit squeeze prevailing in the economy at a given point in time. The empirical evidence shows that credit crunch episodes are less likely to occur during periods of sustained economic growth, or when credit availability for the manufacturing sector is relatively abundant. In contrast, a tight monetary policy stance or a worsening of the quality of banking balance sheets tend to increase the likelihood of experiencing a credit squeeze. [ABSTRACT FROM AUTHOR]

Even in their everyday lives people are expected to make difficult decisions objectively and rationally, no matter how complex or uncertain the situation. In this research, we study how the format of presentation and the amount of presented information concerning risky events influence the decision-making process, and the propensity to take risk in decision makers. The results of an exploratory survey conducted in Finland and in Italy suggest that decision-making behavior changes according to the way the information is presented. We provide experimental evidence that different representations of expected outcomes create distinct cognitive biases and as a result affect the decisions made. This identified change in the perception of risk has, to the best of our knowledge, not been identified nor directly studied previously in the scientific literature. The paper thus presents novel insights into managerial decision-making that are potentially relevant for decision support theory, with implications to decision-makers and for information providers. Understanding the impact of various forms of presentation of risk is crucial in being able to convey information clearly and in a way that avoids misunderstandings. The implications of the results on being able to avoid opportunistic manipulation of decisions, are also of great concern in many application areas. Social networks are more and more frequently being used as a source of information and in this context it is crucial to acknowledge the effect that different ways of presenting and communicating risky outcomes may have on the behavior of the target group. Here presented results may, for example, be highly relevant for marketing and advertising that is conducted by using social media or social networks. [ABSTRACT FROM AUTHOR]

An individual investor has to decide how to allocate his/her savings from a retirement perspective. This problem covers a long-term horizon. In this paper we consider a 40-year horizon formulating a multi-criteria multistage program with stochastic dominance constraints in an intermediate stage and in the final stage. As we are dealing with a real problem and we have formulated the model in cooperation with a commercial Italian bank, the intermediate stage corresponds to a possible withdrawal allowed by the Italian pension system. The sources of uncertainty considered are: the financial returns, the interest rate evolution, the investor's salary process and a considerable withdrawal event. We include a set of portfolio constraints according to the pension plan regulation. The objective of the model is to minimize the Average Value at Risk Deviation measure and to satisfy wealth goals. Three different wealth target formulations are considered: a deterministic wealth target (i.e. a comparison between the accumulated average wealth and a fixed threshold) and two stochastic dominance relations-the first order and the second order-introducing a benchmark portfolio and then requiring the optimal portfolio to dominate the benchmark. In particular, we prove that solutions obtained under stochastic dominance constraints ensure a safer allocation while still guaranteeing good returns. Moreover, we show how the withdrawal event affects the solution in terms of allocation in each of the three frameworks. Finally, the sensitivity and convergence of the stochastic solutions and computational issues are investigated. [ABSTRACT FROM AUTHOR]

This paper presents a case study of a railway timetable optimization for the very dense Simplon corridor, a major railway connection in the Alps between Switzerland and Italy. The key to deal with the complexity of this scenario is the use of a novel aggregation-disaggregation method. Starting from a detailed microscopic representation as it is used in railway simulation, the data is transformed by an automatic procedure into a less detailed macroscopic representation, that is sufficient for the purpose of capacity planning and amenable to state-of-the-art integer programming optimization methods. This macroscopic railway network is saturated with trains. Finally, the optimized timetable is re-transformed to the microscopic level in such a way that it can be operated without any conflicts among the train paths. Using this micro-macro aggregation-disaggregation approach in combination with integer programming methods, it becomes for the first time possible to generate a profit maximal and conflict free timetable for the complete Simplon corridor over an entire day by a simultaneous optimization of all trains requests. In addition, this also allows us to undertake a sensitivity analysis of various problem parameters. [ABSTRACT FROM AUTHOR]