The unlicensed spectrum utilized by Wi-Fi can be regarded as an economic commons in many deployments. Operators of Wi-Fi-enabled devices are usually non-cooperative, vying for spectral resources when in close range to each other, typically adopting a strategy of maximizing their transmission power. With an ever-growing number of wireless devices, this will ultimately lead to depletion of the spectrum, unless players collaborate. Previous studies used cooperative game theory to explore various collaboration strategies, enabled by the presence of some central authority or controller that executes an agreed-upon interference mitigation policy. However, the regulatory nature of unlicensed spectrum dictates that players cannot be forced into such collaboration. Most deployments therefore involve a mix of cooperative and non-cooperative players. In this paper, we propose a new way of modeling use cases involving a central authority or controller by combining non-cooperative and cooperative game theory. Our model uses the non-cooperative concept of Nash equilibriums as well as the cooperative concept of Nash bargaining. To the best of our knowledge, this paper is the first to propose a hybrid non-cooperative and cooperative game theoretic model for communication networks that offers the players the opportunity to strategize between non-cooperative and cooperative natures. It is successfully applied to the case of a densely-populated apartment block. The results show that, if only a subset of players joins the collaboration, most of the remaining non-joining players may obtain an SINR that is worse than what they would have obtained in the fully non-cooperative scenario; they are punished for their uncooperative behavior.

It is well-known that the cost of parcel delivery can be reduced by designing routes that take into account the uncertainty surrounding customers’ presences. Thus far, routing problems with stochastic customer presences have relied on the assumption that all customer presences are independent from each other. However, the notion that demographic factors retain predictive power for parcel-delivery efficiency suggests that shared characteristics can be exploited to map dependencies between customer presences. This paper introduces the correlated probabilistic traveling salesman problem (CPTSP). The CPTSP generalizes the traveling salesman problem with stochastic customer presences, also known as the probabilistic traveling salesman problem (PTSP), to account for potential correlations between customer presences. I propose a generic and flexible model formulation for the CPTSP using copulas that maintains computational and mathematical tractability in high-dimensional settings. I also present several adaptations of existing exact and heuristic frameworks to solve the CPTSP effectively. Computational experiments on real-world parcel-delivery data reveal that correlations between stochastic customer presences do not always affect route decisions, but could have a considerable impact on route cost estimates. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0005 .