The role of population games in the design of optimization-based controllers: a large-scale insight

Cotutela Universitat Politècnica de Catalunya i Universidad de los AndesPremi CEA Springer Award 2017, a la millor tesi d'enginyeria de control a EspanyaEuropean PhD Award on Control for Complex and Heterogeneous Systems, atorgat pel European Embedded Control InstituteThis thesis is mainly devoted to the study of the role of evolutionary-game theory in the design of distributed optimization-based controllers. Game theoretical approaches have been used in several engineering fields, e.g., drainage wastewater systems, bandwidth allocation, wireless networks, cyber security, congestion games, wind turbines, temperature control, among others. On the other hand, a specific class of games, known as population games, have been mainly used in the design of controllers to manage a limited resource. This game approach is suitable for resource allocation problems since, under the framework of full-potential games, the population games can satisfy a unique coupled constraint while maximizing a potential function. First, this thesis discusses how the classical approach of the population games can contribute and complement the design of optimization-based controllers. Therefore, this dissertation assigns special interest on how the features of the population-game approach can be exploited extending their capabilities in the solution of distributed optimization problems. In addition, density games are studied in order to consider multiple coupled constraints and preserving the non-centralized information requirements. Furthermore, it is established a close relationship between the possible interactions among agents in a population with the constrained information sharing among different local controllers. On the other hand, coalitional games are discussed focusing on the Shapley power index. This power index has been used to assign an appropriate rewarding to players in function of their contributions to all possible coalitions. Even though this power index is quite useful in the engineering context, since it involves notions of fairness and/or relevance (how important players are), the main difficulty of the implementation of the Shapley value in engineering applications is related to the high computational burden. Therefore, this dissertation studies the Shapley value in order to propose an alternative manner to compute it reducing computational time, and a different way to find it by using distributed communication structures is presented. The studied game theoretical approaches are suitable for the modeling of rational agents involved in a strategic constrained interaction, following local rules and making local decisions in order to achieve a global objective. Making an analogy, distributed optimization-based controllers are composed of local controllers that compute optimal inputs based on local information (constrained interactions with other local controllers) in order to achieve a global control objective. In addition to this analogy, the features that relate the Nash equilibrium with the Karush-Kuhn-Tucker conditions for a constrained optimization problem are exploited for the design of optimization-based controllers, more specifically, for the design of model predictive controller. Moreover, the design of non-centralized controllers is directly related to the partitioning of a system, i.e., it is necessary to represent the whole system as the composition of multiple sub-systems. This task is not a trivial procedure since several considerations should be taken into account, e.g., availability of information, dynamical coupling in the system, regularity in the amount of variables for each sub-system, among others. Then, this doctoral dissertation also discusses the partitioning problem for large-scale systems and the role that this procedure plays in the design of distributed optimization-based controllers. Finally, dynamical partitioning strategies are presented with distributed population-games-based controllers. Some engineering applications are presented to illustrate and test the performance of all the proposed control strategies, e.g., the Barcelona water supply network, multiple continuous stirred tank reactors, system of multiple unmanned aerial vehicles.Esta tesis doctoral consiste principalmente en el estudio del rol que desempeña la teoría de juegos evolutiva en el diseño de controladores distribuidos basados en optimización. Diversos enfoques de la teoría de juegos han sido usados en múltiples campos de la ingeniera, por ejemplo, en sistemas de drenaje urbano, para la asignación de anchos de banda, en redes inalámbricas, en ciber-seguridad, en juegos de congestión, turbinas eólicas, control de temperatura, entre otros. Por otra parte, una clase especifica de juegos, conocidos como juegos poblacionales, se han usado principalmente en el diseño de controladores encargados de determinar la apropiada asignación de recursos. Esta clase de juegos es apropiada para problemas de distribución dinámica de recursos dado que, en el contexto de juegos poblacionales, los juegos poblacionales pueden ser usados para maximizar una función potencial mientras se satisface una restricción acoplada. Primero, esta tesis doctoral presenta como el enfoque clásico de los juegos poblacionales pueden contribuir y complementar en el diseño de controladores basados en optimización. Posteriormente, esta disertación concentra su atención en cómo las características de los juegos poblacionales pueden ser aprovechadas y extendidas para dar solución a problemas de optimización de forma distribuida. Adicionalmente, los juegos con dependencia de densidad son estudiados con el fin de considerar múltiples restricciones mientras se preservan las características no centralizadas de los requerimientos de información. Finalmente, se establece una estrecha relación entre las posibles interacciones de los agentes en una población y las restricciones de intercambio de información entre diversos controladores locales. También, se desarrolla una discusión sobre los juegos cooperativos y el índice de poder conocido como el valor de Shapley. Este índice de poder ha sido usado para la apropiada asignación de beneficios para un jugador en función de sus contribuciones a todas las posibles coaliciones que pueden formarse. Aunque este índice de poder es de gran utilidad en el contexto ingenieril, ya que involucra nociones de justicia y/o relevancia, la principal dificultad para implementar el valor de Shapley en aplicaciones de ingeniería está asociado a los altos costos computacionales para encontrarlo. En consecuencia, esta disertación doctoral estudia el valor de Shapley con el fin de ofrecer una alternativa para calcular este índice de poder reduciendo los costos computacionales e incluso contemplando estructuras distribuidas de comunicación. Los enfoques de la teoría de juegos estudiados son apropiados para el modelamiento de agentes racionales involucrados en una interacción estratégica con restricciones, siguiendo reglas locales y tomando decisiones locales para alcanzar un objetivo global. Realizando una analogía, los controladores distribuidos basados en optimización están compuestos por controladores locales que calculan acciones óptimas basados en información local (considerando interacciones restringidas con otros controladores locales) con el fin de alcanzar un objetivo global. Adicional a esta analogía, las características que relacionan el equilibrio de Nash con las condiciones de Karush-Kuhn-Tucker en un problema de optimizaciones con restricciones son aprovechadas para el diseño de controladores basados en optimización, más específicamente, para el diseño de controladores predictivos. Por otra parte, el diseño de controladores no centralizados está directamente relacionado con el particionado de un sistema, es decir, es necesario representar el sistema en su totalidad por medio del conjunto de varios sub-sistemas. Esta tarea no es un procedimiento trivial puesto que es necesario tener en cuenta varias consideraciones, por ejemplo, la disponibilidad de información, el acople dinámico en el sistema, la regularidad en cuanto a la cantidad de variables en cada sub-sistema, entre otras. Por lo tanto, esta disertación doctoral también desarrolla una discusión alrededor del problema de particionado para sistemas de gran escala y respecto al rol que este procedimiento de particionado juega en el diseño de controladores distribuidos basados en optimización. Finalmente, se presentan estrategias de particionado dinámico junto con controladores basados en juegos poblacionales. Algunas aplicaciones en ingeniería son usadas para ilustrar y probar los controladores diseñados por medio de las contribuciones novedosas basadas en teoría de juegos, estas son, la red de agua potable de Barcelona, múltiples reactores, sistema compuesto por varios vehículos aéreos no tripulados y un sistema de distribución de agua.Aquesta tesi doctoral consisteix principalment en l'estudi del paper que exerceix la teoria de jocs evolutiva en el disseny de controladors distribuïts basats en optimització. Diversos enfocaments de la teoria de jocs han estat usats en múltiples camps de l'enginyeria, per exemple, en sistemes de drenatge urbà, per a l’assignació d'amples de banda, en xarxes sense fils, a ciber-seguretat, en jocs de congestió, turbines eòliques, control de temperatura, entre altres. D'altra banda, una classe especifica de jocs, coneguts com jocs poblacionals, s'han fet servir principalment en el disseny de controladors encarregats de determinar l'apropiada assignació de recursos. Aquesta classe de jocs és apropiada per a problemes de distribució dinàmica de recursos atès que, en el context de jocs poblacionals, aquests poden ser usats per a maximitzar una funció potencial mentre es satisfà una restricció acoblada. Primer, aquesta tesi doctoral presenta com l'enfocament clàssic dels jocs poblacionals poden contribuir i complementar en el disseny de controladors basats en optimització. Posteriorment, aquesta dissertació concentra la seva atenció en com les característiques dels jocs poblacionals poden ser aprofitades i esteses per donar solució a problemes d’optimització de forma distribuïda. Addicionalment, els jocs amb dependència de densitat són estudiats amb la _finalitat de considerar múltiples restriccions mentre es preserven les característiques no centralitzades dels requeriments d’informació. Finalment, s'estableix una estreta relació entre les possibles interaccions dels agents en una població i les restriccions d'intercanvi d’informació entre diversos controladors locals. També, es desenvolupa una discussió sobre els jocs cooperatius i l’índex de poder conegut com el valor de Shapley. Aquest índex de poder ha estat usat per l'apropiada assignació de beneficis per a un jugador en funció de les seves contribucions a totes les possibles coalicions que poden formar-se. Encara que aquest índex de poder es de gran utilitat en el context de l'enginyeria, ja que involucra nocions de justícia i/o rellevància, la principal dificultat per implementar el valor de Shapley en aplicacions d'enginyeria està associat als alts costos computacionals per trobar-lo. En conseqüència, aquesta dissertació doctoral estudia el valor de Shapley per tal d'oferir una alternativa per calcular aquest índex de poder reduint els costos computacionals i fins i tot contemplant estructures distribuïdes de comunicació. Els enfocaments de la teoria de jocs estudiats són apropiats per al modelatge d'agents racionals involucrats en una interacció estratègica amb restriccions, seguint regles locals i prenent decisions locals per assolir un objectiu global. Realitzant una analogia, els controladors distribuïts basats en optimització estan compostos per controladors locals que calculen accions optimes basats en informació local (considerant interaccions restringides amb altres controladors locals) per tal d'assolir un objectiu global. Addicional a aquesta analogia, les característiques que relacionen l'equilibri de Nash amb les condicions de Karush-Kuhn-Tucker en un problema d’optimització amb restriccions són aprofitades per al disseny de controladors basats en optimització, més específicament, per al disseny de controladors predictius. D'altra banda, el disseny de controladors no centralitzats està directament relacionat amb la partició d'un sistema, és a dir, cal representar el sistema en la seva totalitat per mitjà del conjunt de diversos sub-sistemes. Aquesta tasca no és un procés trivial, ja que cal tenir en compte diverses consideracions, per exemple, la disponibilitat d’informació, l'acoblament dinàmic en el sistema, i la regularitat pel que fa a la quantitat de variables en cada sub-sistema, entre d'altres. Per tant, aquesta dissertació doctoral també desenvolupa una discussió al voltant del problema de partició per a sistemes de gran escala i respecte al paper que aquest procediment de partició juga en el disseny de controladors distribuïts basats en optimització. Finalment, es presenten estratègies de partició dinàmic juntament amb controladors basats en jocs poblacionals. Algunes aplicacions en enginyeria són usades per il·lustrar i provar els controladors dissenyats per mitjà de les contribucions noves basades en teoria de jocs, aquestes són: la xarxa d'aigua potable de Barcelona, múltiples reactors, sistema compost per diversos vehicles aeris no tripulats i un sistema de distribució d'aigua.Award-winningPostprint (published version

Incentive Mechanisms for Managing and Controlling Cyber Risks: The Role of Cyber Insurance and Resource Pooling

Faced with a myriad of costly and frequent cyber threats, organizations not only invest in software security mechanisms such as firewalls and intrusion detection systems but increasingly also turn to cyber insurance which has emerged as an accepted risk mitigation mechanism and allows purchasers of insurance policies to transfer their risks to the insurer. Insurance is fundamentally a method of risk transfer, which in general does not reduce the overall risk and may provide disincentives for firms to strengthen their security; an insured may lower its effort after purchasing coverage, a phenomenon known as moral hazard. As cyber insurance is a common method for cyber risk management, it is critical to be able to use cyber insurance as both a risk transfer mechanism and an incentive mechanism for firms to increase their security efforts. This is the central focus and main goal of this dissertation. Specifically, we consider two features of cybersecurity and their impact on the subsequent insurance contract design problem. The first is the interdependent nature of cybersecurity, whereby one entity's state of security depends not only on its own effort but also on the effort of others in the same eco-system (e.g., vendors and suppliers). The second is our ability to perform an accurate quantitative assessment of security posture at a firm-level by combining recent advances in Internet measurement and machine learning techniques. The first feature, i.e., the risk interdependence among firms is an interesting aspect that makes this contract problem different from what is typically seen in the literature: how should policies be designed for firms with dependent risk relationships? We show security interdependence leads to a profit opportunity for the insurer, created by the inefficient effort levels exerted by the insureds who do not account for risk externalities when insurance is not available. Security pre-screening then enables effective premium discrimination: firms with better security conditions may get a discount on their premium payment. This type of contract allows the insurer to take advantage of the profit opportunity by incentivizing insureds to increase their security effort and improve the state of network security. We show this conclusion holds even when an insurer has the ability to seek loss recovery when an incident can be attributed to a third party. By embedding these concepts in a practical rate-schedule based underwriting framework we show that these results can be readily implemented in existing practice. While pre-screening is an effective method to incentivize effort, the insureds may lower their efforts after the pre-screening and post-contract, within the policy period, in yet another manifestation of moral hazard. We show that this can be mitigated through periodic screening combined with premium adjustment, effectively resulting in an active policy that has built-in contingencies, and the actual premium payable is realized over time based on the screening results. Outside the context of insurance, the study of inefficient security investment and how to design incentives is commonly formulated as an interdependent security game. In a departure from typical taxation and subsidy based mechanisms, we consider resource pooling as a way to incentivize effort in a network of interdependent agents, by allowing agents to invest in themselves as well as in other agents. We show that the interaction of strategic and selfish agents under resource pooling improves the agents' efforts as well as their utilities.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155236/1/khalili_1.pd

Blockchain and artificial intelligence enabled peer-to-peer energy trading in smart grids

Peer-to-peer (P2P) energy trading allows smart grid-connected parties to trade renewable energy with each other. It is widely considered a scheme to mitigate the supplydemand imbalances during peak-hour. In a P2P energy trading system, users (e.g., prosumers, Electric Vehicles (EV)) increase their utility by trading energy securely with each other at a lower price than that of the main grid. However, three challenges hinder the development of secured P2P energy trading systems. First, there is a lack of implicit trust and transparency between trading participants because they do not know each other. Second, P2P energy trading systems cannot offer an intelligent trading strategy that could maximize users’ (agents’) utility. This is because the agents may lack previous trading experience data that enable them to select an optimal trading strategy. Third, the current energy trading platforms are mainly centralized, which makes them vulnerable to malicious attacks and Single point of failure (SPOF). This may interrupt the transaction validation mechanism when the system is compromised, and the central database is unavailable. [...

Game-theoretic approaches for smart prosumer communities

Global warming is endangering the Earth’s ecosystem. It is imperative for humanity to limit greenhouse gas emissions in order to combat rising global average temperatures. Demand-side management (DSM) schemes have widely been analysed in the context of the future smart grid. Often they are based on game-theoretic approaches to schedule the electricity consumption of its participants such that it results in small peak-to-average ratios (PAR) of the aggregated load. In order to guarantee high comfort levels for the consumer, we investigate DSM schemes on the basis of individually owned energy storage systems. The scheduling of these batteries is incentivised by a specific pricing function offered to the users. Within this thesis we cover various aspects for these type of management schemes. Firstly, we design a simple game-theoretic scheduling mechanism and analyse how the battery model, more specifically the round-trip efficiency, affects the outcome. From the simulations we find the importance of highly efficient energy storage systems for the engagement of participants. Secondly, the simple scheduling mechanism is replaced with a more advanced dynamic game, that models fine-grained control over the battery. For this novel game, we derive an analytical solution for the best response of a user, considerably speeding up the solution algorithm for the game. Furthermore, a comparison between the two games also shows the improvements in reducing the PAR of the aggregated load. Based on the augmented game, we investigate the resilience of the equilibrium solution with respect to inevitable real-world forecasting errors. One of the main findings of this thesis is reflected in the results showing the robustness of the schedules for a large number of simulated scenarios and even in the worst-case. Thirdly, we explicitly deal with the finite horizon effect that occurs due to the fixed time frame of the game mechanism. This eventually leads to a DSM system which results in a mean PAR of the aggregated load close to the optimum. Further studies show that these outcomes can be achieved due to the interaction of the households. Individual scheduling of batteries reduces the potential reduction of PAR and is especially detrimental for the robustness against forecasting errors. Fourthly, the developed model is analysed with respect to cyber-physical attacks. We develop a novel type of data-injection attack on the forecasted data and show their impact. After suggesting suitable monitoring strategies to the utility company, a game-theoretic model is employed to understand their decision making process. Finally, we investigate which battery size is optimal for such a DSM scheme. The respective experiments give insight into the different factors that determine the sizing of the battery. From the results we can infer that certain types of users only require a small scale battery system to achieve considerable gains. Overall, this thesis provides an in-depth analysis of a demand-side management scheme that can be employed by prosumers all around the world in the nearest future. Furthermore, the experiments give insights to utility companies to focus on community approaches and how they can mitigate potential cyber attacks

Strategic and Blockchain-based Market Decisions for Cloud Computing

The cloud computing market has been in the center of attention for years where cloud providers strive to survive by either competition or cooperation. Some cloud providers choose to compete in the market that is dominated by few large providers and try to maximize their profit without sacrificing the service quality which leads to higher user ratings. Many research proposals tried to contribute to the cloud market competition. However, the majority of these proposals focus only on pricing mechanisms, neglecting thus the cloud service quality and users satisfaction. Meanwhile, cloud providers intend to form cloud federations to enhance their services quality and revenues. Nevertheless, traditional centralized cloud federations have strict challenges that might hinder the members' motivation to participate in, such as formation of stable coalitions with long-term commitments, participants' trustworthiness, shared revenue, and security of the managed data and services. For a stable and trustworthy federation, it is vital to avoid blind-trust on the claimed SLA guarantees from the members and monitor the quality of service considering the various characteristics of cloud services. This thesis aims to tackle the issues of cloud computing market from the two perspectives of competition and cooperation by: 1) modeling and solving the conflicting situation of revenue, user ratings and service quality, to improve the providers position in the market and increase the future users' demand; 2) proposing a user-centric game theoretical framework to allow the new and smaller cloud providers to have a share in the market and increase users satisfaction through providing high quality and added-value services; 3) motivating the cloud providers to adopt a coopetition behavior through a novel, fully distributed blockchain-based federation's structure that enables them to trade their computing resources through smart contracts; 4) introducing a new role of oracle as a verifier agent to monitor the quality of service and report to the smart contract agents deployed on the blockchain while optimizing the cost of using oracles; and 5) developing a Bayesian bandit learning oracles reliability mechanism to select the oracles smartly and optimize the cost and reliability of utilized oracles. All of the contributions are validated by simulations and implementations using real-world data

Strategic decision-making on low-carbon technology and network capacity investments using game theory

In recent years, renewable energy technologies have been increasingly adopted and seen as key to humanity’s efforts to reduce greenhouse gases emissions and combat climate change. Yet, a side effect is that renewables have reached high penetration rates in many areas, leading to undesired curtailment, especially if existing grid infrastructure is insufficient and renewable energy generated cannot be exported at areas of high energy demand. The issue of curtailment is compelling at remote areas, where renewable resources are abundant, such as in windy islands. Not only renewable production is wasted, but often curtailment comes with high costs for renewable energy developers and energy end-users. In fact, procedures on how generators access the grid and how curtailment is applied, are key factors that affect the decisions of investors about generation and grid capacity installed. Part of this thesis studies the properties of widely used curtailment rules, applied in several countries including the UK, and their effect on strategic interactions between self-interested and profit-maximising low-carbon technology investors. The work develops a game-theoretic framework to study the effects of curtailment on the profitability of existing renewable projects and future developments. More specifically, work presented in this thesis determines the upper bounds of tolerable curtailment at a given location that allows for profitable investments. Moreover, the work studies the effect of various curtailment strategies on the capacity factor of renewable generators and the effects of renewable resource spatial correlation on the resulting curtailment. In fact, power network operators face a significant knowledge gap about how to implement curtailment rules that achieve desired operational objectives, but at the same time minimise disruption and economic losses for renewable generators. In this context, this thesis shows that fairness and equal sharing of imposed curtailment among generators is important to achieve maximisation of the renewable generation capacity installed at a certain area. A new rule is proposed that minimises disruption and the number of curtailment events a generator needs to respond to, while achieving fair allocation of curtailment between generators of unequal ratings. While curtailment can be reduced by smart grid techniques, a long term solution is increasing the network capacity. Grid reinforcements, however, are expensive and costs weight to all energy consumers. For this reason, debate in the energy community has focused on ways to attract private investment in grid reinforcement. A key knowledge gap faced by regulators is how to incentivise such projects, that could prove beneficial, especially in cases where several distributed generators can use the same power line to access the main grid, against the payment of a transmission fee. This thesis develops methods from empirical and algorithmic game theory to provide solutions to this problem. Specifically, a two-location model is considered, where excess renewable generation and demand are not co-located, and where a private renewable investor constructs a power line, providing also access to other generators, against a charge for transmission. In other words, the privately developed line is shared among all generators, a principle known as ‘common access’ line rules. This formulation may be studied as a Stackelberg game between transmission and local generation capacity investors. Decisions on optimal (and interdependent) renewable capacities built by investors, affect the resulting curtailment and profitability of projects and can be determined in the equilibrium of the game. A first approach to study the behaviour of investors at the game equilibrium, assumed a simple model, based on average values of renewable production and demand over a larger time horizon. This assumption allowed for an initial examination of the Stackelberg game equilibrium, by achieving an analytical, closed-form solution of the equilibrium and the investigation of its properties for a wide range of cost parameters. Next, a refined model is developed, able to capture the stochastic nature of renewable production and variability of energy demand. A theoretical analysis of the game is presented along with an estimation of the equilibrium by utilisation of empirical game-theoretic techniques and production/demand data from a real network upgrade project in the UK. The proposed method is general, and can be applied to similar case studies, where there is excess of renewable generation capacity, and where sufficient data is available. In practice, however, available data may be erroneous or experience significant gaps. To deal with data issues, a method for generating time series data is developed, based on Gibbs sampling. This attains an iterative simulation analysis with different time series data as an input (Markov Chain Monte Carlo), thus achieving the exploration of the solution space for multiple future scenarios and leading to a reduction of the uncertainty with regards to the investment decisions taken. Energy storage can reduce curtailment or defer network upgrades. Hence, the last part of this thesis proposes a model consisted of a line investor, local generators and a third independent storage player, who can absorb renewable production, that would otherwise have been curtailed. The model estimates optimal transmission, generation and storage capacities for various financial parameters. The value of storage is determined by comparing the energy system operation with and without energy storage. All models proposed in this thesis, are validated and applied to a practical setting of a grid reinforcement project, in the UK, and a large dataset of real wind speed measurements and demand. In summary, the research work studies the interplay among self-interested and indepen dent low-carbon investors, at areas of excess renewable capacity with network constraints and high curtailment. The work proposes a mechanism for setting transmission charges that ensures that the transmission line gets built, but investors from the local community, can also benefit from investing in renewable energy and energy storage. Overall, the results of this work show how game-theoretic techniques can help energy system stakeholders to bridge the knowledge gap about setting optimal curtailment rules and determining appropriate transmission charges for privately developed network infrastructure.Engineering and Physical Sciences Research Council (EPSRC

Global Perspectives on NGO Communication for Social Change

This book examines the central role media and communication play in the activities of Non-Governmental Organizations (NGOs) around the globe, how NGOs communicate with key publics, engage stakeholders, target political actors, enable input from civil society, and create participatory opportunities. An international line-up of authors first discuss communication practices, strategies, and media uses by NGOs, providing insights into the specifics of NGO programs for social change goals and reveal particular sets of tactics NGOs commonly employ. The book then presents a set of case studies of NGO organizing from all over the world—ranging from Sudan via Brazil to China – to illustrate the particular contexts that make NGO advocacy necessary, while also highlighting successful initiatives to illuminate the important spaces NGOs occupy in civil society. This comprehensive and wide-ranging exploration of global NGO communication will be of great interest to scholars across communication studies, media studies, public relations, organizational studies, political science, and development studies, while offering accessible pieces for practitioners and organizers

Global Perspectives on NGO Communication for Social Change

This book examines the central role media and communication play in the activities of Non-Governmental Organizations (NGOs) around the globe, how NGOs communicate with key publics, engage stakeholders, target political actors, enable input from civil society, and create participatory opportunities. An international line-up of authors first discuss communication practices, strategies, and media uses by NGOs, providing insights into the specifics of NGO programs for social change goals and reveal particular sets of tactics NGOs commonly employ. The book then presents a set of case studies of NGO organizing from all over the world—ranging from Sudan via Brazil to China – to illustrate the particular contexts that make NGO advocacy necessary, while also highlighting successful initiatives to illuminate the important spaces NGOs occupy in civil society. This comprehensive and wide-ranging exploration of global NGO communication will be of great interest to scholars across communication studies, media studies, public relations, organizational studies, political science, and development studies, while offering accessible pieces for practitioners and organizers