Networks and Spatial Economics最新文献

In this paper, we introduce a novel approach to approximate the solution of variational inequality problems without relying on the monotonicity assumption. We propose a two-step inertial modified projection and contraction method for solving quasi-monotone and without-monotone variational inequalities in real Hilbert spaces. We establish a weak convergence result for the proposed method under suitable conditions. Additionally, numerical examples and a network equilibrium flow problem are provided to illustrate the effectiveness of our method and compare it with recent related methods in the literature.

With rapid regional development and urbanization, many public and private facilities and infrastructures (e.g., sirens, cellphone base stations, bike sharing stations, wind turbines, etc.) require regular renovation or supplementation. Evaluating existing facility efficiency and expanding to new facility locations are of broad interest among stakeholders, including businesses, urban planners, government agencies, and the public more generally. Such evaluation can be used to improve overall social accessibility, equity and efficiency by reconfiguring or adding new facilities in the best way possible. A regularly distributed lattice is often viewed as an optimal configuration given important observed properties and characteristics. In this paper, we formulate a spatial optimization model to evaluate spatial coverage efficiency. Specifically, given two sets of points, the model seeks the optimal location and orientation of an idealized lattice to align with an existing facility configuration. The distance between existing facilities and the ideally configured lattice under the optimal alignment represents efficiency. An iterative heuristic based on gradient descent and spatial indexing is developed to solve this problem. Extensive computational experience demonstrates the importance of this problem and the effectiveness of the derived solution approach, as well as highlights assistance provided to decision makers in identifying inefficiencies as well as improving existing infrastructure service systems.

The topic of regional resilience has gained notoriety in regional economics and economic geography in recent years. The main aim is to understand why regions react differently to adverse shocks. To this end, several empirical studies have analyzed the impacts economic crises have had on the performance of regional economies and found considerably asymmetric results. Similarly, this study analyzes the duration of the 2008 crisis in Brazilian regions and aims to identify the characteristics that allowed some regions to rapidly recover from the crisis. However, the main novelty of this study lies in the fact that to do so, statistical tools from survival analysis were used. The findings suggest that having more educated workers and being located within metropolitan regions are two characteristics that contribute to faster recovery.

Innovation networks play a key role in advancing knowledge transfer, collaboration, and technological progress across sectors and regions. Central to the understanding of the mechanisms driving such networks is their dynamic evolution and structure. Much of the literature explores spatial and socio-economic drivers of innovation networks, focusing on geographic, institutional, and cultural influences. However, many of these studies tend to overlook the intricate properties that govern the behavior and dynamics of these networks. This study seeks to address this research gap, delving deeper by investigating Chinese intercity innovation networks between 2007 and 2018. Specifically, we examine: (i) the preferential attachment dynamics within intercity innovation networks, (ii) transitivity effects that underscore the interconnectedness of these networks, and (iii) the persistence and recurrence of connections. We find that cities show indeed a remarkable tendency to form ties with others that already have numerous connections. Such transitivity effects are important in highlighting the formation of innovation clusters. Moreover, the influence of link memory suggests that past collaborations significantly determine future partnerships, similar to the persistent nature of relationships in agglomeration theories.

This paper models duopolistic competition between an online retailer and a physical store retailer with the online retailer modelled as a firm with uniform delivered pricing policy and the physical store as a firm with a mill pricing policy. Both firms seek to maximize their respective profits through an appropriate choice of location and price. The market is assumed to be given by a “uniform path” - a tree whose node weights and arc lengths are equal. Modelling reality, we consider two alternate types of transportation costs faced by the online retailer: either dependent on the relative location of the firm and a customer or independent of it. Beginning with the framework of a Stackelberg, i.e., sequential game, optimal location and price strategies are analytically derived for both sequences of market entry by the two competitors. Cases in which the leader faces the first entry paradox or can become a monopolist by strategically deterring entry by the follower are delineated. Thereafter, Nash Equilibrium solutions to the simultaneous game are identified. Salient insights from the results include: (a) the competitive pressure faced by the physical store in the presence of online competition (b) the inability of an online retailer to compete in “large” markets under the first type of transportation cost and (c) the advantage to the physical retailer of being a market leader.

In this paper, we study the proximal point algorithm with inertial extrapolation to approximate a solution to the quasi-convex pseudo-monotone equilibrium problem. In the proposed algorithm, the inertial parameter is allowed to take both negative and positive values during implementations. The possibility of the choice of negative values for the inertial parameter sheds more light on the range of values of the inertial parameter for the proximal point algorithm. Under standard assumptions, we prove that the sequence of iterates generated by the proposed algorithm converges to a solution of the equilibrium problem when the bifunction is strongly quasi-convex in its second argument. Sublinear and linear rates of convergence are also given under standard conditions. Numerical results are reported for both cases of negative and positive inertial factor of the proposed algorithm and comparison with related algorithm is discussed.