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The notion of abstract convex structure generalizes the standard notion of convexity in linear spaces. We consider abstract convex structures that are combinatorial objects studied in various areas of mathematics and convex algebras as introduced in [8] and we study a general definition of convex preferences. Relations defined by aggregation of orderings are considered.

Market-implied ratings gained importance as efficient early warnings of official credit rating migrations. We build a two-dimensional implied rating system based on bonds and CDS spreads, and test its forecast performances on a set of worldwide sovereign issuers. We show that the method efficiently uses the information from both markets, and is able to outperform implied ratings based on recent machine learning techniques. (c) 2022 Elsevier Inc. All rights reserved.

Experimental game theory studies the behavior of agents who face a stream of one-shot games as a form of learning. Most literature focuses on a single recurring identical game. This paper embeds single-game learning in a broader perspective, where learning can take place across similar games. We posit that agents categorize games into a few classes and tend to play the same action within a class. The agent’s categories are generated by combining game features (payoffs) and individual motives. An individual categorization is experience-based, and may change over time. We demonstrate our approach by testing a robust (parameter-free) model over a large body of independent experimental evidence over $$2 \times 2$$ 2 × 2 symmetric games. The model provides a very good fit across games, performing remarkably better than standard learning models.

The purpose of this paper is to introduce new methods to measure the indirect control power of firms in complex corporate shareholding structures using the concept of power indices from cooperative game theory. The proposed measures vary in desirable properties satisfied, as well as in the bargaining models of power indices used to construct them. Hence, they can be used to produce different pictures of the coalitional strength of firms in control of other firms in mutual shareholding networks with the presence of cycles. Precisely, in the framework of Karos and Peters from 2015, ten power indices substitute the original Shapley and Shubik power index in a modular fashion. In this way, we obtain a set of new measures called aggregated indices. The float shareholders typically hold less than 5 percent of the outstanding shares, which is an uncertain element of indirect control in complex shareholding structures. The fuzzy number seems appropriate to model these shareholders’ behavior. The novelty is that we model the behavior of float using Z-fuzzy numbers. The new methods are tested in an example.

We show that the difference between the natural rate of interest and the current level of monetary policy stance, which we label Convergence Gap (CG), contains information that is valuable for bond predictability. Adding CG in forecasting regressions of bond excess returns significantly raises the R2, and restores countercyclical variation in bond risk premia that is otherwise missed by forward rates. Consistent with the argument that CG captures the effect of real imbalances on the path of rates, our factor has predictive ability for real bond excess returns. The importance of the gap remains robust out-of-sample and in countries other than the United States. Furthermore, its inclusion brings significant economic gains in the context of dynamic conditional asset allocation. This paper was accepted by Gustavo Manso, finance.

Quantum information technology is rapidly commercialising today. It requires urgent attention to the strategic management of this technology by business leaders and policy makers.

We study a class of sequential non-revelation mechanisms in which hospitals make simultaneous take-it-or-leave-it offers to doctors. We prove that all pure strategy subgame perfect Nash equilibrium outcomes are stable, but the inclusion of contracts shrinks the set of equilibrium outcomes. Our analysis reveals the existence of an advantage in setting the terms of the relationship that is absent from the model without contracts. The mechanisms of this class are outcome equivalent and implement the set of stable allocations in a subgame perfect Nash equilibrium when competitive rivalry is present. The equilibrium outcomes form a lattice when preferences are substitutable.

The paper computes and analyses some relevant indicators of economic performance of Italian farms producing/not producing renewable energy, and compares the economic results of the two set of farms. The source of data is the European Farm Accountant Data Network; the farms belonging to this network are analysed in relation to their structural differences, type of farming, geographical areas, economic size, as well as the type of renewable energy produced. After an in-depth statistical investigation, the main economic ratios are computed and analysed using also multivariate regression models, with a special focus on the production of solar and biogas energy. In terms of land and labour productivity and fixed factor remuneration, the results show that farms producing renewable energy perform better than the other farms. This positive effect is particularly accentuated in large companies that produce biogas, followed by farms that produce solar energy. There are still many obstacles that limit the production of renewable energy in agriculture; among these, still insufficient research and information on best practices in agriculture and, in Italy, the complexity and dispersion of the institutional legislative framework and of the public support systems. However, the need to increase the production of renewable energy has become a priority for many European countries both in the short- and in the medium term, especially in light of recent events related to the war in Ukraine.

The degree of uncertainty associated with the value of a company plays a relevant role in valuation analysis. We propose an original and robust methodology for company market valuation, which replaces the traditional point estimate of the conventional Discounted Cash Flow model with a probability distribution of fair values that convey information about both the expected value of the company and its intrinsic uncertainty. Our methodology depends on two main ingredients: an econometric model for company revenues and a set of firm-specific balance sheet relations that are estimated using historical data. We explore the effectiveness and scope of our methodology through a series of statistical exercises on publicly traded U.S. companies. At the firm level, we show that the fair value distribution derived with our methodology constitutes a reliable predictor of the company’s future abnormal returns. At the market level, we show that a long-short valuation (LSV) factor, built using buy-sell recommendations based on the fair value distribution, contains information not accessible through the traditional market factors. The LSV factor significantly increases the explanatory and the predictive power of factor models estimated on portfolios and individual stock returns.

We study a mean field game in continuous time over a finite horizon, T, where the state of each agent is binary and where players base their strategic decisions on two, possibly competing, factors: the willingness to align with the majority (conformism) and the aspiration of sticking with the own type (stubbornness). We also consider a quadratic cost related to the rate at which a change in the state happens: changing opinion may be a costly operation. Depending on the parameters of the model, the game may have more than one Nash equilibrium, even though the corresponding N-player game does not. Moreover, it exhibits a very rich phase diagram, where polarized/unpolarized, coherent/incoherent equilibria may coexist, except for T small, where the equilibrium is always unique. We fully describe such phase diagram in closed form and provide a detailed numerical analysis of the N-player counterpart of the mean field game. In this finite dimensional setting, the equilibrium selected by the population of players is always coherent (favoring the subpopulation whose type is aligned with the initial condition), but it does not necessarily minimize the cost functional. Rather, it seems that, among the coherent ones, the equilibrium prevailing is the one that most benefits the underdog subpopulation forced to change opinion.

Education plays a central role in tackling consumers' food waste. However, research on children's food waste at school tends to focus on quantification and logistical factors rather than on the impact of interventions. Furthermore, behavioural elements, including imitation, tend to be neglected despite their well-established role in the food realm. To contribute to filling this gap, we assess the short and long-term impact on food waste levels of a lesson about the environmental consequences of food waste. Innovating on the literature, we control for both behavioural factors and social influence. For this purpose, we developed a longitudinal protocol that factors in altruistic concerns elicited through economic experiments, and the influence of parents and classmates assessed through parents' questionnaires and network questions. We apply the protocol to a sample of 420 Italian primary school students from 20 classes. The lesson was implemented in half of the classes, randomly selected. We find that the lesson only reduces self-declared food waste in the short-term but the impact does not persist after some months. Concerns for the environmental implications of food waste increase significantly, and this effect is still present in the longer-term. Neither the parents' approach to wasting food nor the degree of students' pro-social motivations make a significant difference. In turn, students' food waste is found to align with that of the students sitting nearby in the school canteen, suggesting that imitation through direct observation of behaviours plays an important role. These results call policymakers to take advantage of network effects in social settings to favour the replication of pro-environmental behaviours.

PurposeThis study aims to identify different consumer segments to address the strategies that can be adopted by companies and policymakers to increase the consumption of safer foods and reduce the negative externalities caused by pesticides. More than 3,000 consumers were involved in the survey, of which more than 1,000 completed in all parts.Design/methodology/approachThe complexity of the topic required a multidimensional approach. Therefore, the authors modelled the decision support system by proposing a decision rule-based approach to analyse consumers' food purchasing choices. More precisely, the authors referred to the dominance-based rough set approach (DRSA).FindingsBased on the DRSA results, three consumer segments were identified: green consumers, integrated pest management (IPM)-informed and active consumers, and potential low-pesticide consumers for which different policy implications have been highlighted.Research limitations/implicationsDespite the high number of survey respondents, further research should seek to obtain data from a more balanced sample. Furthermore, different methods of analysis could be applied and the results compared.Practical implicationsIdentification and promotion of managerial and public policies to increase the consumption of low pesticide food.Social implicationsThe main social implications can be summarised in the greater knowledge and awareness of the environmental aspects related to food, recognition of the intrinsic quality and/or functionality of food.Originality/valueThe authors contribute to the literature in two ways. First, the authors refer to the DRSA, an innovative approach in the context of consumer analysis. Second, based on the decision rules, the authors identify three consumer segments to which specific tools can be addressed.

This paper investigates the implications for portfolio theory of using multivariate semiparametric estimators and a copula-based approach, especially when the number of risky assets becomes substantial. Parametric, nonparametric, and semiparametric regression methods are compared to approximate their returns in large-scale portfolio selection problems. Semiparametric regression models are used to prove that, under certain assumptions, the variability of the errors decreases as the number of factors increases. Moreover, a copula principal component analysis (PCA)-based approach is proposed, and its superiority to the classical Pearson PCA approach is demonstrated. Empirical analyses validate the suggested approaches and evaluate the impact of different approximation methods on portfolio selection problems. Here, the ex-ante sample paths of several portfolio strategies aiming to maximize portfolio wealth using either reward-risk or drawdown-based performance measures are compared. The results show that the proposed methodologies outperform the traditional approach for out-of-sample portfolios, especially when the dependence structure is represented by the Pearson linear correlation.

The paper considers the pricing of credit default swaps (CDSs) using a revised version of the credit risk model proposed in Cathcart and El-Jahel (2003). Default occurs either the first time a signaling process breaches a threshold barrier or unexpectedly at the first jump of a Cox process. The intensity of default depends on the risk-free interest rate, which follows a Vasicek process, instead of a Cox-Ingersoll-Ross process as in the original model. This offers two advantages. On the one hand, it allows us to account for negative interest rates which are recently observed, on the other hand, it simplifies the formula for pricing CDSs. The goodness of fit of the model is tested using a dataset of CDS credit spreads related to European companies. The results obtained show a rather satisfactory agreement between theoretical predictions and market data, which is identical to the one obtained with the original model. In addition, the values of the calibrated parameters result to be stable over time and the semi-closed form solution ensures a very fast implementation.

Humans are capable of solving cooperation problems following social norms. Social norms dictate appropriate behaviour and judgement on others in response to their previous actions and reputation. Recently, the so-called leading eight norms have been identified from many potential social norms that can sustain cooperation through a reputation-based indirect reciprocity mechanism. Despite indirect reciprocity being claimed to extend direct reciprocity in larger populations where direct experiences cannot be accumulated, the success of social norms have been analysed in models with global information and evolution. This study is the first to analyse the leading eight norms with local information and evolution. We find that the leading eight are robust against selfish players within most scenarios and can maintain a high level of cooperation also with local information and evolution. In fact, local evolution sustains cooperation under a wider set of conditions than global evolution, while local reputation does not hinder cooperation compared to global reputation. Four of the leading eight norms that do not reward justified defection offer better chances for cooperation with quick evolution, reputation with noise, larger networks, and when unconditional defectors enter the population.

We study Nash equilibria for a sequence of symmetric $N$-player stochastic games of finite-fuel capacity expansion with singular controls and their mean-field game (MFG) counterpart. We construct a solution of the MFG via a simple iterative scheme that produces an optimal control in terms of a Skorokhod reflection at a (state-dependent) surface that splits the state space into action and inaction regions. We then show that a solution of the MFG of capacity expansion induces approximate Nash equilibria for the $N$-player games with approximation error $\varepsilon$ going to zero as $N$ tends to infinity. Our analysis relies entirely on probabilistic methods and extends the well-known connection between singular stochastic control and optimal stopping to a mean-field framework.

Given a real valued functional T on the space of bounded random variables, we investigate the problem of the existence of a conditional version of nonlinear means. We follow a seminal idea by Chisini (1929), defining a mean as the solution of a functional equation induced by T. We provide sufficient conditions which guarantee the existence of a (unique) solution of a system of infinitely many functional equations, which will provide the so called Conditional Chisini mean. We apply our findings in characterizing the scalarization of conditional Risk Measures, an essential tool originally adopted by Detlefsen and Scandolo (2005) to deduce the robust dual representation.

This paper presents results from the SESAR ER3 Domino project. Three mechanisms are assessed at the ECAC-wide level: 4D trajectory adjustments (a combination of actively waiting for connecting passengers and dynamic cost indexing), flight prioritisation (enabling ATFM slot swapping at arrival regulations), and flight arrival coordination (where flights are sequenced in extended arrival managers based on an advanced cost-driven optimisation). Classical and new metrics, designed to capture network effects, are used to analyse the results of a micro-level agent-based model. A scenario with congestion at three hubs is used to assess the 4D trajectory adjustment and the flight prioritisation mechanisms. Two different scopes for the extended arrival manager are modelled to analyse the impact of the flight arrival coordination mechanism. Results show that the 4D trajectory adjustments mechanism succeeds in reducing costs and delays for connecting passengers. A trade-off between the interests of the airlines in reducing costs and those of non-connecting passengers emerges, although passengers benefit overall from the mechanism. Flight prioritisation is found to have no significant effects at the network level, as it is applied to a small number of flights. Advanced flight arrival coordination, as implemented, increases delays and costs in the system. The arrival manager optimises the arrival sequence of all flights within its scope but does not consider flight uncertainties, thus leading to sub-optimal actions., 20 pages, 6 figures, Journal of Air Transport Management

We introduce a new scalar coefficient to measure linear correlation between random vectors which preserves all the relevant properties of Pearson’s correlation in arbitrary dimensions. The new measure and its bounds are derived from a mass transportation approach in which the expected inner product of two random vectors is taken as a measure of their covariance and then standardized by the maximal attainable value given their marginal covariance matrices. In several simulative studies we show the limiting distribution of the empirical estimator of the newly defined index and of the corresponding rank correlation. A comparative study shows that our proposed correlation, though derived from a novel approach, behaves similarly to some of the multivariate dependence notions recently introduced in the literature. Throughout the paper, we also give some auxiliary results of independent interest in matrix analysis and mass transportation theory, including an improvement to the Cauchy-Schwarz inequality for positive definite covariance matrices.

We study agents who distill the complex world around them using cognitive frames. We assume that agents share the same frame and analyze how the frame affects their collective performance. In one-shot and repeated interactions, the frame causes agents to be either better or worse off than if they could perceive the environment in full detail: it creates a fog of cooperation or a fog of conflict. In repeated interactions, the frame is as important as agents’ patience in determining the outcome: for a fixed discount factor, when all agents choose what they perceive as their best play, there remain significant performance differences induced by different frames. A low-performing team conducting a site visit to observe a high-performing team will be mystified, sometimes observing different actions than they expected or being given unexpected reasons for the actions they expected. Finally, we distinguish between incremental versus radical changes in frames, and we develop a model of category formation to analyze challenges faced by a leader who seeks to improve the agents’ collective performance.

A recent trend in algorithm design consists of augmenting classic data structures with machine learning models, which are better suited to reveal and exploit patterns and trends in the input data so to achieve outstanding practical improvements in space occupancy and time efficiency. This is especially known in the context of indexing data structures for big data where, despite few attempts in evaluating their asymptotic efficiency, theoretical results are yet missing in showing that learned indexes are provably better than classic indexes, such as B-tree s and their variants. In this paper, we present the first mathematically-grounded answer to this problem by exploiting a link with a mean exit time problem over a proper stochastic process which, we show, is related to the space and time complexity of these learned indexes. As a corollary of this general analysis, we show that plugging this result in the (learned) PGM-index, we get a learned data structure which is provably better than B-tree s.

In this paper, we amend a multi-criteria methodology known as MURAME, to evaluate the creditworthiness of a large sample of Italian Small and Medium-sized Enterprises, using as input their balance sheet data. This methodology produces results in terms of scoring and of classification into homogeneous rating classes. A distinctive goal of this paper is to consider a preference disaggregation method to endogenously determine some parameters of MURAME, by solving a nonsmooth constrained optimization problem. Because of the complexity of the involved mathematical programming problem, for its solution we use an evolutionary metaheuristic, coupled with a specific efficient initialization. This is combined with an unconstrained reformulation of the problem, which provides a reasonable compromise between the quality of the solution and the computational burden. An extensive numerical experience is reported, comparing an exogenous choice of MURAME parameters with our approach.

In this paper we propose a hybrid metaheuristic based on Particle Swarm Optimization, which we tailor on a portfolio selection problem. To motivate and apply our hybrid metaheuristic, we reformulate the portfolio selection problem as an unconstrained problem, by means of penalty functions in the framework of the exact penalty methods. Our metaheuristic is hybrid as it adaptively updates the penalty parameters of the unconstrained model during the optimization process. In addition, it iteratively refines its solutions to reduce possible infeasibilities. We report also a numerical case study. Our hybrid metaheuristic appears to perform better than the corresponding Particle Swarm Optimization solver with constant penalty parameters. It performs similarly to two corresponding Particle Swarm Optimization solvers with penalty parameters respectively determined by a REVAC-based tuning procedure and an irace-based one, but on average it just needs less than 4% of the computational time requested by the latter procedures.