Exploring Network Effects of Point-to-Point Networks: An Investigation of the Spatial Entry Patterns of Southwest Airlines

This paper explores network effects in Point-to-Point airline networks by examining the spatial entry patterns of Southwest airlines during the 1990-2006 period. Estimation results from a spatial probit model reveal clear spatial dependence in profitability across different routes served by the carrier. Detailed investigation suggests two main sources of network effects, namely: (1) airport and regional presence, and (2) substitutability of markets. Findings of the paper suggest also that the network effects embedded in Southwest’s Point-to-Point network have many distinguishing features as compared to those identified in a typical Hub-and-Spoke network. This study brings some fresh insights on airline network effects in general, as well as explaining the pattern of aggressive network expansions of LCCs in particular.Point-to-Point Networks, spatial entry patterns, Southwest airlines, spatial probit model

Comparison principle for stochastic heat equations driven by α\alpha-stable white noises

For a class of non-linear stochastic heat equations driven by $\alpha$-stable white noises for $\alpha\in(1,2)$ with Lipschitz coefficients, we first show the existence and pathwise uniqueness of $L^p$-valued c\`{a}dl\`{a}g solutions to such a equation for $p\in(\alpha,2]$ by considering a sequence of approximating stochastic heat equations driven by truncated $\alpha$-stable white noises obtained by removing the big jumps from the original $\alpha$-stable white noises. If the $\alpha$-stable white noise is spectrally one-sided, under additional monotonicity assumption on noise coefficients, we prove a comparison theorem on the $L^2$-valued c\`{a}dl\`{a}g solutions of such a equation. As a consequence, the non-negativity of the $L^2$-valued c\`{a}dl\`{a}g solution is established for the above stochastic heat equation with non-negative initial function

Existence of weak solutions to stochastic heat equations driven by truncated α\alpha-stable white noises with non-Lipschitz coefficients

We consider a class of stochastic heat equations driven by truncated $\alpha$-stable white noises for $1<\alpha<2$ with noise coefficients that are continuous but not necessarily Lipschitz and satisfy globally linear growth conditions. We prove the existence of weak solution, taking values in two different spaces, to such an equation using a weak convergence argument on solutions to the approximating stochastic heat equations. For $1<\alpha<2$ the weak solution is a measure-valued c\`{a}dl\`{a}g process. However, for $1<\alpha<5/3$ the weak solution is a c\`{a}dl\`{a}g process taking function values, and in this case we further show that for $0<p<5/3$ the uniform $p$-th moment for $L^p$-norm of the weak solution is finite, and that the weak solution is uniformly stochastic continuous in $L^p$ sense and satisfies a flow property

Long-range social influence in phone communication networks on offline adoption decisions

We use high-resolution mobile phone data with geolocation information and propose a novel technical framework to study how social influence propagates within a phone communication network and affects the offline decision to attend a performance event. Our fine-grained data are based on the universe of phone calls made in a European country between January and July 2016. We isolate social influence from observed and latent homophily by taking advantage of the rich spatial-temporal information and the social interactions available from the longitudinal behavioral data. We find that influence stemming from phone communication is significant and persists up to four degrees of separation in the communication network. Building on this finding, we introduce a new “influence” centrality measure that captures the empirical pattern of influence decay over successive connections. A validation test shows that the average influence centrality of the adopters at the beginning of each observational period can strongly predict the number of eventual adopters and has a stronger predictive power than other prevailing centrality measures such as the eigenvector centrality and state-of-the-art measures such as diffusion centrality. Our centrality measure can be used to improve optimal seeding strategies in contexts with influence over phone calls, such as targeted or viral marketing campaigns. Finally, we quantitatively demonstrate how raising the communication probability over each connection, as well as the number of initial seeds, can significantly amplify the expected adoption in the network and raise net revenue after taking into account the cost of these interventions

Airline Horizontal Mergers and Productivity: Empirical Evidence from a Natural Experiment in China

The identification of possible efficiency gains is a core issue in the analysis of mergers. However, empirical studies are generally subject to bias caused by merger endogeneity. In the early 2000s, the Chinese government pursued a strategy of merging small firms in key industries to create large enterprise groups. Mergers created by this policy provide a rare natural experiment to investigate the effect of mergers. We take the opportunity to apply the difference-in-differences approach to identify the effect of mergers on the efficiency of Chinese airlines. Overall, our analysis suggests that the mergers increased the productivity of Chinese airlines

Modeling airport capacity choice with real options

This study analyzes optimal choice of the airport capacity to invest immediately (the prior capacity) and the size of real option to acquire for possible future expansion. Facing demand uncertainty, an airport first chooses its prior capacity and real option, and then later chooses its final capacity and airport charge once demand is observed. Our analytical results show that if demand uncertainty is low and capacity and real option costs are relatively high, an airport will not acquire a real option. Otherwise, an airport can use a real option to improve its expected profit or social welfare. Both the magnitude of profit or welfare gain and the optimal size of the real option increase with demand uncertainty. A higher real option cost leads to a larger prior capacity and smaller real option, whereas a higher capital cost leads to lower prior capacity. A profit-maximizing airport would choose a smaller prior capacity and real option than a welfare-maximizing airport. Competition in the airline market promotes airport capacity investments and the adoption of real options by profit-maximizing airports, whereas airport commercial services increase prior capacity but not real option

Microenvironment Signals and Mechanisms in the Regulation of Osteosarcoma

Osteosarcoma (OS) is the most common malignant primary bone tumor in children and adolescents and features rapid development, strong metastatic ability, and poor prognosis. It has been well established that diverse genetic aberrations and metabolic alterations confer the tumorigenesis and development of OS. The intricate metabolism and vascularization that contributes to the nutrient and structural support for tumor progression should be thoroughly clarified to help us gain novel insights into OS and its clinical diagnoses and treatments. With regard to the complex bone extracellular matrix (ECM) and local cell populations, we intend to illustrate the interrelationship between various microenvironmental signals and the different stages of OS evolution. Solid evidence has noted two crucial factors of the OS microenvironment in the acquisition of stem cell phenotypes - transforming growth factor-β1 (TGF-β1) signaling and hypoxia. Different cell subtypes in the local environment might also serve as unique contributors that interact with each other and communicate with distant cells, thus participating in local invasion and metastasis. Proper models have been established and improved to reveal the evolutionary footsteps of how normal cells transform into a neoplastic state and progress toward malignancy

Characterizing the Intra-Vineyard Variation of Soil Bacterial and Fungal Communities

Vineyard soil microbial communities potentially mediate grapevine growth, grape production as well as wine terroir. Simultaneously assessing shifts of microbial community composition at the intra-vineyard scale allows us to decouple correlations among environmental variables, thus providing insights into vineyard management. Here we investigated bacterial and fungal community compositions and their relationships with edaphic properties in soils collected from a commercial vineyard at four different soil depths (0–5, 5–10, 10–20, and 20–40 cm). Soil organic carbon (SOC) content, invertase activity and phosphatase activity decreased along depth gradient in the 0–20 cm soil fraction (P &lt; 0.001). The soil bacterial biomass and α-diversity were significantly higher than those of fungi (P ≤ 0.001). Statistical analyses revealed that SOC content, pH, C/N ratio and total phosphorus (TP) were significant determinants of soil bacterial (R = 0.494, P = 0.001) and fungal (R = 0.443, P = 0.001) community structure. The abundance of dominated bacterial phyla (Proteobacteria, Acidobacteria and Actinobacteria) and fungal phyla (Ascomycota, Zygomycota and Basidiomycota) slightly varied among all soil samples. Genus Lactococcus, which comprised 2.72% of the soil bacterial community, showed increasing pattern with depth. Importantly, Candidatus Nitrososphaera, Monographella and Fusarium were also detected with high abundances in soil samples, indicating their ecological function in soil nitrogen cycle and the potential risk in grapevine disease. Overall, this work detected the intra-vineyard variation of bacterial and fungal communities and their relationships with soil characteristics, which was beneficial to vineyard soil management and grapevine disease prevention

Learning to infer structures of network games

Strategic interactions between a group of individuals or organisations can be modelled as games played on networks, where a player’s payoff depends not only on their actions but also on those of their neighbours. Inferring the network structure from observed game outcomes (equilibrium actions) is an important problem with numerous potential applications in economics and social sciences. Existing methods mostly require the knowledge of the utility function associated with the game, which is often unrealistic to obtain in real-world scenarios. We adopt a transformer-like architecture which correctly accounts for the symmetries of the problem and learns a mapping from the equilibrium actions to the network structure of the game without explicit knowledge of the utility function. We test our method on three different types of network games using both synthetic and real-world data, and demonstrate its effectiveness in network structure inference and superior performance over existing methods