arXiv subjects

Giuliano Punzo

Publications and source records attributed to Giuliano Punzo.

5 recordsLinked to original sources

arXiv2026

Node-Wise Dynamic Optimal Control for Evolutionary Games on General Multilayer Networks

Promoting cooperative behaviour amongst decision makers has key implications for the long term sustainability of social systems. Incentives can promote cooperation in situations where defection is more favourable. Previous research has identified optimal decentralised incentives in structured populations where the social environment is described by a generic single-layer networks. Optimality is here intended in the sense of cost minimisation. Here, we provide and optimal incentive strategy for structured populations in general network, a rather unexplored case. Further, we look at populations where, through the network structure, each player interact with one set of neighbours while contributing to an opinion diffusion dynamics from a second set of neighbours. Hence we cover the case of multilayer networks. To fill this gap, we provide a solution to the optimal control problem by solving the Hamilton-Jacobi-Bellman equation and derive an analytic solution for distributing incentives in multilayer networks. By implementing the dynamics of the networked Prisoner's Dilemma Game, we provide a feedback control loop that yields an optimal incentive distribution over time. The dynamic incentive depends on the current state of cooperation, the game-layer network, the strategy-diffusion layer, and the payoff matrix design. We found that the optimal incentive is node-wise, unique to each node on the network, influenced by its relative position according to the adjacency matrices of both layers. We also found that the optimal solution is not exclusively reward or punishment. While one node can receive a reward, the other node may receive punishment at the same time, depending on its current level of cooperation, relative to other nodes. We provide analytic solutions and numerical validations for the cases studied, comparing our results to the existing literature.

math.OC↗

arXiv2024

Oligopoly Game Stabilisation Through Multilayer Congestion Dynamics

International trade and logistics are subject to factors including geopolitical instability, climate change, and black swan events such as the unforeseen closure of the Suez Canal. The problem of predicting local price change under modification of an underlying transport network or change in supply characteristics unites elements of game theory, network theory and transport. The Cournot Oligopoly models economic actors as rational players attempting to maximise profit by optimising supply quantities with analytical results now consolidated about equilibrium characteristics where transport conditions are fixed. Similarly, where supply and demand are fixed, the routing of goods in a transport network can be analytically solved through a traffic assignment problem. Hence we can solve the coupled Cournot-congestion problem by means of a 2-layer network. Where the layers are linked, inter-layer feedback wherein players attempt to maximise their utility occurs. In this respect we find players benefit from taking advantage of non-simultaneous responses to the market rather than moving to a new equilibrium. We draw conclusions about the nature of equilibria, finding that the concave utility curve property results in unique and stable equilibrium for each uncoupled layer, while linked layers have a non-unique stable equilibria for which general solutions are stated.

physics.soc-ph↗

arXiv2022

Link Count Data-driven Static Traffic Assignment Models Through Network Modularity Partitioning

Accurate static traffic assignment models are important tools for the assessment of strategic transportation policies. In this article we present a novel approach to partition road networks through network modularity to produce data-driven static traffic assignment models from loop detector data on large road systems. The use of partitioning allows the estimation of the key model input of Origin-Destination demand matrices from flow counts alone. Previous network tomography-based demand estimation techniques have been limited by the network size. The amount of partitioning changes the Origin-Destination estimation optimisation problems to different levels of computational difficulty. Different approaches to utilising the partitioning were tested, one which degenerated the road network to the scale of the partitions and others which left the network intact. Applied to a subnetwork of England's Strategic Road Network and other test networks, our results for the degenerate case showed flow and travel time errors are reasonable with a small amount of degeneration. The results for the non-degenerate cases showed that similar errors in model prediction with lower computation requirements can be obtained when using large partitions compared with the non-partitioned case. This work could be used to improve the effectiveness of national road systems planning and infrastructure models.

eess.SY↗

arXiv2022

A Novel Approach to Climate Resilience of Infrastructure Networks

With a changing climate, the frequency and intensity of extreme weather events are likely to increase, posing a threat to infrastructure systems' resilience. The response of infrastructure systems to localised failures depends on whether assets are affected randomly, in a targeted strategic way, or any way in between. More than that, infrastructure decisions today, including new routes or improvements to existing assets, will underpin the behaviour of the systems over the next century. It is important to separate and analyse the case of climate-based disruptions and how they affect systems' resilience. This paper presents a probabilistic resilience assessment framework where failure scenarios and network disruptions are generated using weather profile data from climate prediction models with component-level fragility functions. A case study is then carried out to quantify the resilience of Great Britain's railway passenger transport system to high-temperature-related track buckling under the Representative Concentration Pathway 8.5 (RCP8.5) climate change scenario. A 95-year horizon on the resilience of the railway system is drawn. The results also reveal the non-linear responses of the railway system to the increasing temperature and show that models considering random asset failures overestimate the system's resilience.

eess.SY↗

arXiv2019

Network Communities of Dynamical Influence

Fuelled by a desire for greater connectivity, networked systems now pervade our society at an unprecedented level that will affect it in ways we do not yet understand. In contrast, nature has already developed efficient networks that can instigate rapid response and consensus, when key elements are stimulated. We present a technique for identifying these key elements by investigating the relationships between a system's most dominant eigenvectors. This approach reveals the most effective vertices for leading a network to rapid consensus when stimulated, as well as the communities that form under their dynamical influence. In applying this technique, the effectiveness of starling flocks was found to be due, in part, to the low outdegree of every bird, where increasing the number of outgoing connections can produce a less responsive flock. A larger outdegree also affects the location of the birds with the most influence, where these influentially connected birds become more centrally located and in a poorer position to observe a predator and, hence, instigate an evasion manoeuvre. Finally, the technique was found to be effective in large voxel-wise brain connectomes where subjects can be identified from their influential communities.

cs.SI↗