Stefano Nasini | IESEG School of Management (original) (raw)
Papers by Stefano Nasini
Social Networks, 2017
Exponential random models have been widely adopted as a general probabilistic framework for compl... more Exponential random models have been widely adopted as a general probabilistic framework for complex networks and recently extended to embrace broader statistical settings such as dynamic networks, valued networks or two-mode networks. Our aim is to provide a further step into the generalization of this class of models by considering sample spaces which involve both families of networks and nodal properties verifying combinatorial constraints. We propose a class of probabilistic models for the joint distribution of nodal properties (demographic and behavioral characteristics) and network structures (friendship and professional partnership). It results in a general and flexible modeling framework to account for homophily in social structures. We present a Bayesian estimation method based on the full characterization of their sample spaces by systems of linear constraints. This provides an exact simulation scheme to sample from the likelihood, based on linear programming techniques. After a detailed analysis of the proposed statistical methodology, we illustrate our approach with an empirical analysis of co-authorship of journal articles in the field of neuroscience between 2009 and 2013.
SIAM Journal on Optimization, 2017
One of the most efficient interior-point methods for some classes of block-angular structured pro... more One of the most efficient interior-point methods for some classes of block-angular structured problems solves the normal equations by a combination of Cholesky factorizations and preconditioned conjugate gradient for, respectively, the block and linking constraints. In this work we show that the choice of a good preconditioner depends
on geometrical properties of the constraints structure. In particular, it is seen that the principal angles between the subspaces generated by the diagonal blocks and the linking constraints can be used to estimate ex-ante the efficiency of the preconditioner. Numerical validation is provided with some generated optimization problems. An application to
the solution of multicommodity network flow problems with nodal capacities and equal flows is also presented.
Journal of Informetrics, 2017
In the context of research collaboration and co-authorship, we studied scholars' scientific achie... more In the context of research collaboration and co-authorship, we studied scholars' scientific achievements and success, based on their collection of shared publications. By means of a novel regression model, which exploits the two-mode structure of co-authorship, we translated paper scientific impact into author professional achievement, to simultaneously account for the effect of paper properties (access status, funding bodies, etc.) as well as author demographic and behavioral characteristics (gender, nationality) on academic success and impact. After a detailed analysis of the proposed statistical procedure, we illustrated our approach with an empirical analysis of a co-authorship network based on 1007 scientific articles.
European Journal of Operational Research, 2019
Most mathematical programming models for investment selection and portfolio management rely on ce... more Most mathematical programming models for investment selection and portfolio management rely on centralized decisions about both budget allocation in different (real and financial) investment options and portfolio composition within the different options. However, in more realistic market scenarios investors do not directly select the portfolio composition, but only provide guidelines and requirements for the investment procedure. Financial intermediaries are then responsible for the detailed portfolio management, resulting in a hierarchical investor-intermediary decision setting. In this work, a bi-level mixed-integer quadratic optimization problem is proposed for the decentralized selection of a portfolio of financial securities and real investments. Single-level reformulation techniques are presented, along with valid-inequalities which allow speeding-up their resolution procedure, when large-scale instances are taken into account. We conducted computational experiments on large historical stock market data from the Center for Research in Security Prices to validate and compare the proposed bi-level investment framework (and the resulting single-level reformulations), under different levels of investor’s and intermediary’s risk aversion and control. The empirical tests reveled the impact of decentralization on the investment performance, and provide a comparative analysis of the computational effort corresponding to the proposed solution approaches.
Designing reliable networks consists in finding topological structures, which are able to success... more Designing reliable networks consists in finding topological structures, which are able to successfully carry out desired processes and operations. When this set of activities performed within a network are unknown and the only available information is a probabilistic model reflecting topological network features, highly probable networks are regarded as "reliable", in the sense of being consistent with those probabilistic model. In this paper we are studying the reliability maximization, based on the Exponential Random Graph Model (ERGM), whose statistical properties has been widely used to capture complex topological feature of real-world networks. Under such models the probability of a network is maximized when specified structural properties appear in the network. However, the search of maximally reliable (highly probable) networks might result in difficult combinatorial optimization problems and an important goal of this work is to translate them into solvable systems ...
Social networks is a recent area of research motivated by the empirical study of real-world netwo... more Social networks is a recent area of research motivated by the empirical study of real-world networks, such as social relations, protein interaction, neuronal connections, etc. As closed-form probabilistic models of networks are often not available, the ability of randomly generating networks verifying specific constraints might be useful. The purpose is to develop optimization-based procedures to randomly generate networks with structural constraints, within the probabilistic framework of conditional uniform models. Based on the characterization of families of networks by means of systems of linear constraints, polynomial-time methods to generate networks with specified structural properties are constructed. The computational results suggest that the proposed methods can represent a general framework for the efficient simulation of random networks even beyond the models analyzed in this Master Thesis.
Infancia y Aprendizaje, 2013
This study aimed to design an intervention programme using language play for preschoolers and to ... more This study aimed to design an intervention programme using language play for preschoolers and to assess its impact on the conceptualization of writing through phonemic awareness tasks, rhyme, letter knowledge and writing. We used a quasi-experimental design with a pre-test/post-test control group. The sample included 47 children between the ages of 5 and 6 years old, 23 in the intervention group and 24 in the control group. The activities for the intervention programme incorporated language play in learning situations from children's literature texts. The results indicate a positive impact of the programme: the intervention group had progress on tasks of phonological awareness and letter knowledge. This development was particularly important in children of lower initial performance, who managed to achieve similar levels of performance as the rest of the group during the post-test.
In this paper we study the problem of network discovery and influence propagation and propose an ... more In this paper we study the problem of network discovery and influence propagation and propose an integrated approach for the analysis of lead-lag synchronization in multiple choices. Network models for the processes by which decisions propagate through social interaction have been studied before, but only a few consider unknown structures of interacting agents. In fact, while individual choices are typically observed, inferring individual influences -- who influences who -- from sequences of dynamic choices requires strong modeling assumptions on the cross-section dependencies of the observed panels. We propose a class of parametric models which extends the vector autoregression to the case of pairwise influences between individual choices over multiple items and supports the analysis of influence propagation. After uncovering a collection of theoretical properties (conditional moments, parameter sensitivity, identifiability and estimation), we provide an economic application to music broadcasting, where a set of songs are diffused over radio stations; we infer station-to-station influences based on the proposed methodology and assess the propagation effect of initial launching stations to maximize songs diffusion. Both on the theoretical and empirical side, the proposed approach connects fields which are traditionally treated as separated areas: the problem of network discovery and the one of influence propagation.
Demographic and behavioral characteristics of journal authors are important indicators of homophi... more Demographic and behavioral characteristics of journal authors are important indicators of homophily in co-authorship networks. In the presence of correlations between adjacent nodes (assortative mixing), combining the estimation of the individual characteristics and the network structure results in a well-fitting model, which is capable to provide a deep understanding of the linkage between individual and social properties. This paper aims to propose a novel probabilistic model for the joint distribution of nodal properties (authors’ demographic and behavioral characteristics) and network structure (co-authorship connections), based on the nodal similarity effect. A Bayesian approach is used to estimate the model parameters, providing insights about the probabilistic properties of the observed data set. After a detailed analysis of the proposed statistical methodology, we illustrate our approach with an empirical analysis of co-authorship of 1007 journal articles indexed in the ISI Web of Science database in the field of neuroscience between 2009 and 2013.
The analysis of markets with indivisible goods and fixed exogenous prices has played an important... more The analysis of markets with indivisible goods and fixed exogenous prices has played an important role in economic models, especially in relation to wage rigidity and unemployment. This research report provides a mathematical and computational details associated to the mathematical programming based approaches proposed by Nasini et al. to study pure exchange economies where discrete amounts of commodities are exchanged at fixed prices. Barter processes, consisting in sequences of elementary reallocations of couple of commodities among couples of agents, are formalized as local searches converging to equilibrium allocations. A direct application of the analyzed processes in the context of computational economics is provided, along with a Java implementation of the approaches described in this research report.
Network analysis is of great interest for the study of social, biological and technological netwo... more Network analysis is of great interest for the study of social, biological and technological networks, with applications, among others, in business, marketing, epidemiology and telecommunications. Researchers are often interested in assessing whether an observed feature in some particular network is expected to be found within families of networks under some hypothesis (named conditional random networks, i.e., networks satisfying some linear constraints). This work presents procedures to generate networks with specified structural properties which rely on the solution of classes of integer optimization problems. We show that, for many of them, the constraints matrices are totally unimodular, allowing the efficient generation of conditional random networks by polynomial time interior-point methods. The computational results suggest that the proposed methods can represent a general framework for the efficient generation of random networks even beyond the models analyzed in this paper. This work also opens the possibility for other applications of mathematical programming in the analysis of complex networks.
Presentations by Stefano Nasini
Complex networks is a recent area of research motivated by the empirical study of realworld netwo... more Complex networks is a recent area of research motivated by the empirical study of realworld networks, such as social relations, protein interaction, neuronal connections, etc. As closed-form probabilistic models of networks are often not available, the ability of randomly generating networks verifying specific constraints might be useful. The purpose of this work is to develop optimization-based procedures to randomly generate networks with structural constraints, within the probabilistic framework of conditional uniform models. Based on the characterization of families of networks by means of systems of linear constraints, polynomialtime methods to generate networks with specified structural properties are constructed.
Social Networks, 2017
Exponential random models have been widely adopted as a general probabilistic framework for compl... more Exponential random models have been widely adopted as a general probabilistic framework for complex networks and recently extended to embrace broader statistical settings such as dynamic networks, valued networks or two-mode networks. Our aim is to provide a further step into the generalization of this class of models by considering sample spaces which involve both families of networks and nodal properties verifying combinatorial constraints. We propose a class of probabilistic models for the joint distribution of nodal properties (demographic and behavioral characteristics) and network structures (friendship and professional partnership). It results in a general and flexible modeling framework to account for homophily in social structures. We present a Bayesian estimation method based on the full characterization of their sample spaces by systems of linear constraints. This provides an exact simulation scheme to sample from the likelihood, based on linear programming techniques. After a detailed analysis of the proposed statistical methodology, we illustrate our approach with an empirical analysis of co-authorship of journal articles in the field of neuroscience between 2009 and 2013.
SIAM Journal on Optimization, 2017
One of the most efficient interior-point methods for some classes of block-angular structured pro... more One of the most efficient interior-point methods for some classes of block-angular structured problems solves the normal equations by a combination of Cholesky factorizations and preconditioned conjugate gradient for, respectively, the block and linking constraints. In this work we show that the choice of a good preconditioner depends
on geometrical properties of the constraints structure. In particular, it is seen that the principal angles between the subspaces generated by the diagonal blocks and the linking constraints can be used to estimate ex-ante the efficiency of the preconditioner. Numerical validation is provided with some generated optimization problems. An application to
the solution of multicommodity network flow problems with nodal capacities and equal flows is also presented.
Journal of Informetrics, 2017
In the context of research collaboration and co-authorship, we studied scholars' scientific achie... more In the context of research collaboration and co-authorship, we studied scholars' scientific achievements and success, based on their collection of shared publications. By means of a novel regression model, which exploits the two-mode structure of co-authorship, we translated paper scientific impact into author professional achievement, to simultaneously account for the effect of paper properties (access status, funding bodies, etc.) as well as author demographic and behavioral characteristics (gender, nationality) on academic success and impact. After a detailed analysis of the proposed statistical procedure, we illustrated our approach with an empirical analysis of a co-authorship network based on 1007 scientific articles.
European Journal of Operational Research, 2019
Most mathematical programming models for investment selection and portfolio management rely on ce... more Most mathematical programming models for investment selection and portfolio management rely on centralized decisions about both budget allocation in different (real and financial) investment options and portfolio composition within the different options. However, in more realistic market scenarios investors do not directly select the portfolio composition, but only provide guidelines and requirements for the investment procedure. Financial intermediaries are then responsible for the detailed portfolio management, resulting in a hierarchical investor-intermediary decision setting. In this work, a bi-level mixed-integer quadratic optimization problem is proposed for the decentralized selection of a portfolio of financial securities and real investments. Single-level reformulation techniques are presented, along with valid-inequalities which allow speeding-up their resolution procedure, when large-scale instances are taken into account. We conducted computational experiments on large historical stock market data from the Center for Research in Security Prices to validate and compare the proposed bi-level investment framework (and the resulting single-level reformulations), under different levels of investor’s and intermediary’s risk aversion and control. The empirical tests reveled the impact of decentralization on the investment performance, and provide a comparative analysis of the computational effort corresponding to the proposed solution approaches.
Designing reliable networks consists in finding topological structures, which are able to success... more Designing reliable networks consists in finding topological structures, which are able to successfully carry out desired processes and operations. When this set of activities performed within a network are unknown and the only available information is a probabilistic model reflecting topological network features, highly probable networks are regarded as "reliable", in the sense of being consistent with those probabilistic model. In this paper we are studying the reliability maximization, based on the Exponential Random Graph Model (ERGM), whose statistical properties has been widely used to capture complex topological feature of real-world networks. Under such models the probability of a network is maximized when specified structural properties appear in the network. However, the search of maximally reliable (highly probable) networks might result in difficult combinatorial optimization problems and an important goal of this work is to translate them into solvable systems ...
Social networks is a recent area of research motivated by the empirical study of real-world netwo... more Social networks is a recent area of research motivated by the empirical study of real-world networks, such as social relations, protein interaction, neuronal connections, etc. As closed-form probabilistic models of networks are often not available, the ability of randomly generating networks verifying specific constraints might be useful. The purpose is to develop optimization-based procedures to randomly generate networks with structural constraints, within the probabilistic framework of conditional uniform models. Based on the characterization of families of networks by means of systems of linear constraints, polynomial-time methods to generate networks with specified structural properties are constructed. The computational results suggest that the proposed methods can represent a general framework for the efficient simulation of random networks even beyond the models analyzed in this Master Thesis.
Infancia y Aprendizaje, 2013
This study aimed to design an intervention programme using language play for preschoolers and to ... more This study aimed to design an intervention programme using language play for preschoolers and to assess its impact on the conceptualization of writing through phonemic awareness tasks, rhyme, letter knowledge and writing. We used a quasi-experimental design with a pre-test/post-test control group. The sample included 47 children between the ages of 5 and 6 years old, 23 in the intervention group and 24 in the control group. The activities for the intervention programme incorporated language play in learning situations from children's literature texts. The results indicate a positive impact of the programme: the intervention group had progress on tasks of phonological awareness and letter knowledge. This development was particularly important in children of lower initial performance, who managed to achieve similar levels of performance as the rest of the group during the post-test.
In this paper we study the problem of network discovery and influence propagation and propose an ... more In this paper we study the problem of network discovery and influence propagation and propose an integrated approach for the analysis of lead-lag synchronization in multiple choices. Network models for the processes by which decisions propagate through social interaction have been studied before, but only a few consider unknown structures of interacting agents. In fact, while individual choices are typically observed, inferring individual influences -- who influences who -- from sequences of dynamic choices requires strong modeling assumptions on the cross-section dependencies of the observed panels. We propose a class of parametric models which extends the vector autoregression to the case of pairwise influences between individual choices over multiple items and supports the analysis of influence propagation. After uncovering a collection of theoretical properties (conditional moments, parameter sensitivity, identifiability and estimation), we provide an economic application to music broadcasting, where a set of songs are diffused over radio stations; we infer station-to-station influences based on the proposed methodology and assess the propagation effect of initial launching stations to maximize songs diffusion. Both on the theoretical and empirical side, the proposed approach connects fields which are traditionally treated as separated areas: the problem of network discovery and the one of influence propagation.
Demographic and behavioral characteristics of journal authors are important indicators of homophi... more Demographic and behavioral characteristics of journal authors are important indicators of homophily in co-authorship networks. In the presence of correlations between adjacent nodes (assortative mixing), combining the estimation of the individual characteristics and the network structure results in a well-fitting model, which is capable to provide a deep understanding of the linkage between individual and social properties. This paper aims to propose a novel probabilistic model for the joint distribution of nodal properties (authors’ demographic and behavioral characteristics) and network structure (co-authorship connections), based on the nodal similarity effect. A Bayesian approach is used to estimate the model parameters, providing insights about the probabilistic properties of the observed data set. After a detailed analysis of the proposed statistical methodology, we illustrate our approach with an empirical analysis of co-authorship of 1007 journal articles indexed in the ISI Web of Science database in the field of neuroscience between 2009 and 2013.
The analysis of markets with indivisible goods and fixed exogenous prices has played an important... more The analysis of markets with indivisible goods and fixed exogenous prices has played an important role in economic models, especially in relation to wage rigidity and unemployment. This research report provides a mathematical and computational details associated to the mathematical programming based approaches proposed by Nasini et al. to study pure exchange economies where discrete amounts of commodities are exchanged at fixed prices. Barter processes, consisting in sequences of elementary reallocations of couple of commodities among couples of agents, are formalized as local searches converging to equilibrium allocations. A direct application of the analyzed processes in the context of computational economics is provided, along with a Java implementation of the approaches described in this research report.
Network analysis is of great interest for the study of social, biological and technological netwo... more Network analysis is of great interest for the study of social, biological and technological networks, with applications, among others, in business, marketing, epidemiology and telecommunications. Researchers are often interested in assessing whether an observed feature in some particular network is expected to be found within families of networks under some hypothesis (named conditional random networks, i.e., networks satisfying some linear constraints). This work presents procedures to generate networks with specified structural properties which rely on the solution of classes of integer optimization problems. We show that, for many of them, the constraints matrices are totally unimodular, allowing the efficient generation of conditional random networks by polynomial time interior-point methods. The computational results suggest that the proposed methods can represent a general framework for the efficient generation of random networks even beyond the models analyzed in this paper. This work also opens the possibility for other applications of mathematical programming in the analysis of complex networks.
Complex networks is a recent area of research motivated by the empirical study of realworld netwo... more Complex networks is a recent area of research motivated by the empirical study of realworld networks, such as social relations, protein interaction, neuronal connections, etc. As closed-form probabilistic models of networks are often not available, the ability of randomly generating networks verifying specific constraints might be useful. The purpose of this work is to develop optimization-based procedures to randomly generate networks with structural constraints, within the probabilistic framework of conditional uniform models. Based on the characterization of families of networks by means of systems of linear constraints, polynomialtime methods to generate networks with specified structural properties are constructed.