Giovanni Paolo Crespi

In un panorama economico sempre più frammentato e complesso, come si definisce e come si misura oggi il valore? Il volume raccoglie contributi scientifici e riflessioni maturate dal dialogo tra il mondo accademico, l'istat e le istituzioni, mettendo in luce la duplice dimensione della crescita: quella globale, legata alle grandi dinamiche dei mercati, e quella locale, profondamente radicata nei territori. Ne emerge una lettura integrata della ricchezza delle nazioni come fenomeno complesso e multidimensionale. La ricchezza non si esaurisce nei flussi economici correnti, ma prende forma nella capacità di governare risorse strategiche, costruire reti produttive resilienti, accumulare patrimonio nel tempo e adattarsi a un contesto globale in continuo mutamento. Il volume rafforza così la consapevolezza che ricchezza, competitività e resilienza delle economie dipendono da un intreccio sempre più stretto tra risorse materiali, strutture produttive, relazioni internazionali, capacità di accumulazione e qualità delle reti d'impresa.

In this paper, we provide a systematization of the equilibrium concepts for vector-valued games. In general abstract setting, we give the definitions of Nash equilibrium, weak Nash equilibrium, and proper Nash equilibrium, we investigate the relationships among them, and we provide existence results for proper Nash equilibria and weak Nash equilibria. The link between the introduced equilibrium concepts and Nash equilibria of a game with scalar payoffs obtained by means of linear scalarization is investigated. For a vector matrix game, we show that the notions of proper Nash equilibrium and Nash equilibrium are equivalent, we prove existence of both Nash equilibria and weak Nash equilibria, we give characterizations of proper and weak best correspondence sets respectively, by defining characterizing functions from the matrices and the preference cones, and finally we list all best correspondence sets. The given definitions are illustrated by means of examples.

A robust game is a distribution-free model to handle ambiguity generated by a bounded set of possible realizations of the values of players' payoff functions. The players are worst-case optimizers and a solution, called robust-optimization equilibrium, is guaranteed by standard regularity conditions. The paper investigates the sensitivity to the level of uncertainty of this equilibrium focusing on robust games with no private information. Specifically, we prove that a robust-optimization equilibrium is an epsilon-Nash equilibrium of the nominal counterpart game, where epsilon measures the extra profit that a player would obtain by reducing his level of uncertainty. Moreover, given an epsilon-Nash equilibrium of a nominal game, we prove that it is always possible to introduce uncertainty such that the epsilon-Nash equilibrium is a robust-optimization equilibrium. These theoretical insights increase our understanding on how uncertainty impacts on the solutions of a robust game. Solutions that can be extremely sensitive to the level of uncertainty as the worst-case approach introduces non-linearity in the payoff functions. An example shows that a robust Cournot duopoly model can admit multiple and asymmetric robust-optimization equilibria despite only a symmetric Nash equilibrium exists for the nominal counterpart game.

Set-valued extensions of vector-valued functions are used to investigate the relations between weak efficiency and variational inequalities (both Stampacchia and Minty type) which allows to apply the complete lattice framework from set optimization. Since the seminal work of Giannessi, it has been a challenge to generalize scalar results to the vector case. In this effort, some notions of generalized derivatives for vector-valued functions have been introduced, either in the form of set-valued functions or introducing appropriate notions of infinite elements in vector spaces. Switching the focus to set optimization in conlinear spaces, we propose a Dini-type derivative, that keeps the same set-valued form of the optimization problem.

Via a family of monotone scalar functions, a preorder on a set is extended to its power set and then used to construct a hull operator and a corresponding complete lattice of sets. Functions mapping into the preordered set are extended to complete lattice-valued ones, and concepts for exact and approximate solutions for corresponding set optimization problems are introduced and existence results are given. Well-posedness for complete lattice-valued problems is introduced and characterized. The new approach is compared with existing ones in vector and set optimization. Its relevance is shown by means of many examples from multicriteria decision making, statistics, and mathematical economics and finance.

We study a vector-valued game with uncertainty in the pay-off functions. We reduce the notion of Nash equilibrium to a robust set optimization problem and we define accordingly the notions of robust Nash equilibria and weak robust Nash equilibria. Existence results for the latter are proved and a comparison between the former and the analogous notion in Yu and Liu (J Optim Theory Appl 159:272–280, 2013) is shown with an example. The proposed definition of weak robust Nash equilibrium is weaker than that already introduced in Yu and Liu (2013). On the contrary, the robust Nash equilibrium we introduce is not comparable with the notion of robust equilibrium in Yu and Liu (2013), that is defined componentwise. Nevertheless, by means of an example, we show that our notion has some advantages, avoiding some pitfalls that occurs with the other.

Risk measures are defined as functionals of the portfolio loss distribution, thus implicitly assuming the knowledge of such a distribution. However, in practical applications, the need for estimation arises and with it the need to study the effects of mis-specification errors, as well as estimation errors on the final conclusion. In this paper we focus on the qualitative robustness of a sequence of estimators for set-valued risk measures. These properties are studied in detail for two well-known examples of set-valued risk measures: the value-at-risk and the maximum average value-at-risk. Our results illustrate, in particular, that estimation of set-valued value-at-risk can be given in terms of random sets. Moreover, we observe that historical set-valued value-at-risk, while failing to be sub-additive, leads to a more robust procedure than alternatives such as the maximum likelihood average value at-risk.