A Game-Theoretic Analysis of the Off-Switch Game

The off-switch game is a game theoretic model of a highly intelligent robot interacting with a human. In the original paper by Hadfield-Menell et al. (2016), the analysis is not fully game-theoretic as the human is modelled as an irrational player, and the robot's best action is only calculated under unrealistic normality and soft-max assumptions. In this paper, we make the analysis fully game theoretic, by modelling the human as a rational player with a random utility function. As a consequence, we are able to easily calculate the robot's best action for arbitrary belief and irrationality assumptions

Game-theoretic infrastructure sharing in multioperator cellular networks

©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The introduction of fourth-generation wireless technologies has fueled the rapid development of cellular networks, significantly increasing the energy consumption and the expenditures of mobile network operators (MNOs). In addition, network underutilization during low-traffic periods (e.g., night zone) has motivated a new business model, namely, infrastructure sharing, which allows the MNOs to have their traffic served by other MNOs in the same geographic area, thus enabling them to switch off part of their network. In this paper, we propose a novel infrastructure-sharing algorithm for multioperator environments, which enables the deactivation of underutilized base stations during low-traffic periods. Motivated by the conflicting interests of the MNOs and the necessity for effective solutions, we introduce a game-theoretic framework that enables the MNOs to individually estimate the switching-off probabilities that reduce their expected financial cost. Our approach reaches dominant strategy equilibrium, which is the strategy that minimizes the cost of each player. Finally, we provide extensive analytical and experimental results to estimate the potential energy and cost savings that can be achieved in multioperator environments, incentivizing the MNOs to apply the proposed scheme.Peer ReviewedPostprint (author's final draft

Maximizing Profit in Green Cellular Networks through Collaborative Games

In this paper, we deal with the problem of maximizing the profit of Network Operators (NOs) of green cellular networks in situations where Quality-of-Service (QoS) guarantees must be ensured to users, and Base Stations (BSs) can be shared among different operators. We show that if NOs cooperate among them, by mutually sharing their users and BSs, then each one of them can improve its net profit. By using a game-theoretic framework, we study the problem of forming stable coalitions among NOs. Furthermore, we propose a mathematical optimization model to allocate users to a set of BSs, in order to reduce costs and, at the same time, to meet user QoS for NOs inside the same coalition. Based on this, we propose an algorithm, based on cooperative game theory, that enables each operator to decide with whom to cooperate in order to maximize its profit. This algorithms adopts a distributed approach in which each NO autonomously makes its own decisions, and where the best solution arises without the need to synchronize them or to resort to a trusted third party. The effectiveness of the proposed algorithm is demonstrated through a thorough experimental evaluation considering real-world traffic traces, and a set of realistic scenarios. The results we obtain indicate that our algorithm allows a population of NOs to significantly improve their profits thanks to the combination of energy reduction and satisfaction of QoS requirements.Comment: Added publisher info and citation notic

The Three Doors Problem...-s

I argue that we must distinguish between: (0) the Three-Doors-Problem Problem [sic], which is to make sense of some real world question of a real person. (1) a large number of solutions to this meta-problem, i.e., many specific Three-Doors-Problem problems, which are competing mathematizations of the meta-problem (0). Each of the solutions at level (1) can well have a number of different solutions: nice ones and ugly ones; correct ones and incorrect ones. I discuss three level (1) solutions, i.e., three different Monty Hall problems; and try to give three short correct and attractive solutions. These are: an unconditional probability question; a conditional probability question; and a game-theory question. The meta-message of the article is that applied statisticians should beware of solution-driven science.Comment: Submitted to Springer Lexicon of Statistics. Version 2: some minor improvement

Game-theoretic Resource Allocation Methods for Device-to-Device (D2D) Communication

Device-to-device (D2D) communication underlaying cellular networks allows mobile devices such as smartphones and tablets to use the licensed spectrum allocated to cellular services for direct peer-to-peer transmission. D2D communication can use either one-hop transmission (i.e., in D2D direct communication) or multi-hop cluster-based transmission (i.e., in D2D local area networks). The D2D devices can compete or cooperate with each other to reuse the radio resources in D2D networks. Therefore, resource allocation and access for D2D communication can be treated as games. The theories behind these games provide a variety of mathematical tools to effectively model and analyze the individual or group behaviors of D2D users. In addition, game models can provide distributed solutions to the resource allocation problems for D2D communication. The aim of this article is to demonstrate the applications of game-theoretic models to study the radio resource allocation issues in D2D communication. The article also outlines several key open research directions.Comment: Accepted. IEEE Wireless Comms Mag. 201

An exact solution method for binary equilibrium problems with compensation and the power market uplift problem

We propose a novel method to find Nash equilibria in games with binary decision variables by including compensation payments and incentive-compatibility constraints from non-cooperative game theory directly into an optimization framework in lieu of using first order conditions of a linearization, or relaxation of integrality conditions. The reformulation offers a new approach to obtain and interpret dual variables to binary constraints using the benefit or loss from deviation rather than marginal relaxations. The method endogenizes the trade-off between overall (societal) efficiency and compensation payments necessary to align incentives of individual players. We provide existence results and conditions under which this problem can be solved as a mixed-binary linear program. We apply the solution approach to a stylized nodal power-market equilibrium problem with binary on-off decisions. This illustrative example shows that our approach yields an exact solution to the binary Nash game with compensation. We compare different implementations of actual market rules within our model, in particular constraints ensuring non-negative profits (no-loss rule) and restrictions on the compensation payments to non-dispatched generators. We discuss the resulting equilibria in terms of overall welfare, efficiency, and allocational equity