American political culture has virtually precluded public discussion about the fundamental weaknesses of capitalism, forcing media reformers to argue defensively that commercial broadcasting is a special case of market failure. This investigation questions the historical inevitability of commercialized mass media structure by examining how the ideology of media professionalism is deployed in public debate over noncommercial uses of mass media resources. The work of John Dewey and Walter Lippmann frame a theoretical understanding of how professional autonomy works in opposition to community, and thus how professionalization works in opposition to a shared democratic sphere. Relying on the fundamental concepts of discursive formations studied in depth by Michel Foucault, three case studies analyze historic moments (the invention of listener support by Lewis Hill, the rise of news reporting by community television volunteers, and the introduction of media literacy in K-12 public education) that offer evidence of discursive breaks within the constructions of professionalism that support commercialization, and what those breaks suggest about the re-instantiation of the historical inevitability of the commercial regime. The conclusion discusses how conditions have led us to a point of deprofessionalization, a state in which media consumers disarm the notion of professionalism before it can be deployed as a governing relation, and how deproduction of authoritative texts effectively contains the power of professionalized norms. INDEX WORDS: Professionalism, Professionalization, Media reform, Commercialization, Noncommercial media, Dewey-Lippmann debate, Lewis Hill, Community television, Media literacy, Deproduction, Deprofessionalization

The ever-expanding volume of data generated by network devices such as smartphones, personal computers, and sensors has significantly contributed to the remarkable advancements in artificial intelligence (AI) and machine learning (ML) algorithms. However, effectively processing and learning from this extensive data usually requires substantial computational capabilities centralized in a server. Moreover, concerns regarding data privacy arise when collecting training data from distributed network devices. To address these challenges, collaborative ML with decentralized data has emerged as a promising solution for large-scale machine learning across distributed devices, driven by the parallel computing and learning trends. Collaborative and distributed ML can be broadly classified into two types: server-based and fully decentralized, based on whether the model aggregation is coordinated by a parameter server or performed in a decentralized manner through peer-to-peer communication. In cases where communication between devices occurs over wireless links, which are inherently imperfect, unreliable, and resource-constrained, how can we design communication protocols to achieve the best learning performance? This thesis investigates decentralized learning using decentralized stochastic gradient descent, an established algorithm for decentralized ML, in a novel setting with imperfect and constrained communication. "Imperfect" implies that communication can fail and "constrained" implies that communication resources are limited. The communication across a link between two devices is modeled as a binary event with either success or failure, depending on if multiple neighbouring devices are transmitting information. To compensate for communication failures, every communication round can have multiple communication slots, which are limited and must be carefully allocated over the learning process. The quality of communication is quantified by introducing normalized throughput, describing the ratio of successful links in a communication round. To decide when devices should broadcast, both random and deterministic medium access policies have been developed with the goal of maximizing throughput, which has shown very efficient learning performance. Finally, two schemes for allocating communication slots over communication rounds have been defined and simulated: Delayed-Allocation and the Periodic-Allocation schemes, showing that it is better to allocate slots late rather than early, and neither too frequently nor infrequently which can depend on several factors and requires further study

We study games where a team of agents needs to collaborate against an adversary to achieve a common goal. The agents make their moves simultaneously, and they have different perceptions about the system state after each move, due to different sensing capabilities. Each agent can only act based on its own experiences, since no communication is assumed during the game. However, before the game begins, the agents can agree on some strategy. A strategy is winning if it guarantees that the agents achieve their goal regardless of how the opponent acts. Identifying a winning strategy, or determining that none exists, is known as the strategy synthesis problem. In this thesis, we only consider a simple objective where the agents must force the game into a given state. Much of the literature is focused on strategies that either rely on that the agents (a) can remember everything that they have perceived or (b) can only remember the last thing that they have perceived. The strategy synthesis problem is (in the general case) undecidable in (a) and has exponential running time in (b). We are interested in the middle, where agents can have finite memory. Specifically, they should be able to keep a finite-state machine, which they update when they make new observations. In our case, the internal state of each agent represents its knowledge about the state of affairs. In other words, an agent is able to update its knowledge, and act based on it. We propose an algorithm for constructing the finite-state machine for each agent, and assigning actions to the internal states before the game begins. Not every winning strategy can be found by the algorithm, but we are convinced that the ones found are valid ones. An important building block for the algorithm is the knowledge-based subset construction (KBSC) used in the literature, which we generalise to games with multiple agents. With our construction, the game can be reduced to another game, still with uncertain state information, but with less or equal uncertainty. The construction can be applied arbitrarily many times, but it appears as if it stabilises (so that no new knowledge is gained) after only a few steps. We discuss this and other interesting properties of our algorithm in the final chapters of this thesis. / Vi studerar spel där ett lag agenter behöver samarbeta mot en motståndare för att uppnå ett mål. Agenterna agerar samtidigt, och vid varje steg av spelet så har de olika uppfattning om spelets tillstånd. De antas inte kunna kommunicera under spelets gång, så agenterna kan bara agera utifrån sina egna erfarenheter. Innan spelet börjar kan agenterna dock komma överrens om en strategi. En sådan strategi är vinnande om den garanterar att agenterna når sitt mål oavsett hur motståndaren beter sig. Att hitta en vinnande strategi är känt som syntesproblemet. I den här avhandlingen behandlar vi endast ett enkelt mål där agenterna måste tvinga in spelet i ett givet tillstånd. Mycket av litteraturen handlar om strategier där agenterna antingen antas (a) kunna minnas allt som de upplevt eller (b) bara kunna minnas det senaste de upplevt. Syntesproblemet är (i det generella fallet) oavgörbart i (a) och tar exponentiell tid i (b). Vi är intressede av fallet där agenter kan ha ändligt minne. De ska kunna ha en ändlig automat, som de kan uppdatera när de får nya observationer. I vårt fall så representerar det interna tillståndet agentens kunskap om spelets tillstånd. En agent kan då uppdatera sin kunskap och agera utifrån den. Vi föreslår en algoritm som konstruerar en ändlig automat åt varje agent, samt instruktioner för vad agenten ska göra i varje internt tillstånd. Varje vinnande strategi kan inte hittas av algoritmen, men vi är övertygade om att de som hittas är giltiga. En viktig byggsten är den kunskapsbaserade delmängskonstruktionen (KBSC), som vi generaliserar till spel med flera agenter. Med vår konstruktion kan spelet reduceras till ett annat spel som har mindre eller lika mycket osäkerhet. Detta kan göras godtyckligt många gånger, men det verkar som om att ingen ny kunskap tillkommer efter bara några gånger. Vi diskuterar detta vidare tillsammans med andra intressanta egenskaper hos algoritmen i de sista kapitlen i avhandlingen.