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The unpredictability of gameplay
The Unpredictability of Gameplay explores the many forms of unpredictability in games and proposes a comprehensive theoretical framework for understanding and categorizing non-deterministic game mechanics. Rather than viewing all game mechanics with unpredictable outcomes as a single concept, Mark R. Johnson develops a three-part typology for such mechanics, distinguishing between randomness, chance, and luck in gameplay, assessing games that range from grand strategy and MMORPGs to slot machines and card games. He also explores forms of unanticipated unpredictability, where elements of games fail to function as intended and create new forms of gameplay in the process. Covering a range of game concepts using these frameworks, The Unpredictability of Gameplay then explores three illustrative case studies: 1) procedural generation, 2) replay value and grinding, and 3) player-made practices designed to reduce the level of luck in non-deterministic games. Throughout, Johnson demonstrates the importance of looking more deeply at unpredictability in games and game design and the various ways in which unpredictability manifests while offering an invaluable tool for game scholars and game designers seeking to integrate unpredictability into their work.
Book
A Review on PMsub.2.5 Sources, Mass Prediction, and Association Analysis: Research Opportunities and Challenges
Air pollution has long been one of the most life-threatening issues which has led to massive amounts of premature human death due to fatal diseases and environmental disasters. Several Sustainable Development Goals (SDGs) set up by the United Nations coincide with the solutions for air pollution reduction. To reach a sustainable future, researchers have conducted many theoretical analyses or case studies of air pollution at different places on the globe and proposed prudent strategies for obtaining an equilibrium between socioeconomic development and air pollution reduction. This research selected a substantial number of articles and existing review papers published between 2013 and 2024 and organized these publications into subfields. This research was focused on filling the gap between existing reviews and the state-of-the-art technologies in the last decade. To be informative and contextual, this review presented a pathway for readers to comprehend the research in three contiguous phases of air pollution analysis, from compositional apportionment and mass prediction of pollution to disclosing associations between pollution concentration and natural or anthropogenic factors. At the end of this review, the author highlighted several research fields which have been overlooked in previous reviews but will be increasingly important in the future.
Journal Article
Everyday chaos : technology, complexity, and how we're thriving in a new world of possibility
Modern science, the Internet, big data, and AI are each saying the same thing to us: the world is -- and always has been -- far more complex and unpredictable than we've allowed ourselves to see. As a result we're undergoing a sea change in our understanding of how things happen, and in our deepest strategies for predicting, preparing for, and managing our lives and our businesses. For example, machine learning allows us to make better predictions (think the weather, stock performance, online clicks) but we know less about why those predictions are right--and we need to get used to that. And in fact, over the past twenty years we've been unintentionally developing strategies that avoid anticipating what will happen so we don't have to depend on unreliable revenue forecasts, assumptions about customer needs, and hypotheses about how a product will be used. By embracing these strategies, we're flourishing by creating yet more possibilities and yet more unpredictability. In wide-ranging stories and characteristically all-encompassing syntheses, technology researcher, internet expert, and philosopher David Weinberger reveals the trends that hide in so many aspects of our lives--and shows us how they matter.-- Provided by publisher
Book
Suboptimal foraging theory
Optimal foraging theory (OFT) is based on the ecological concept that organisms select behaviors that convey future fitness, and on the mathematical concept of optimization: finding the alternative that provides the best value of a fitness measure. As implemented in, for example, state-based dynamic modeling, OFT is powerful for one key problem of modern ecology: modeling behavior as a tradeoff among competing fitness elements such as growth, risk avoidance, and reproductive output. However, OFT is not useful for other modern problems such as representing feedbacks within systems of interacting, unique individuals: When we need to model foraging by each of many individuals that interact competitively or synergistically, optimization is impractical or impossible—there are no optimal behaviors. For such problems we can, however, still use the concept of future fitness to model behavior by replacing optimization with less precise (but perhaps more realistic) techniques for ranking alternatives. Instead of simplifying the systems we model until we can find optimal behavior, we can use theory based on inaccurate predictions, coarse approximations, and updating to produce good behavior in more complex and realistic contexts. This so-called state- and prediction-based theory (SPT) can, for example, produce realistic foraging decisions by each of many unique, interacting individuals when growth rates and predation risks vary over space and time. Because SPT lets us address more natural complexity and more realistic problems, it is more easily tested against more kinds of observation and more useful in management ecology. A simple foraging model illustrates how SPT readily accommodates complexities that make optimization intractable. Other models use SPT to represent contingent decisions (whether to feed or hide, in what patch) that are tradeoffs between growth and predation risk, when both growth and risk vary among hundreds of patches, vary unpredictably over time, depend on characteristics of the individuals, are subject to feedbacks from competition, and change over the daily light cycle. Modern ecology demands theory for tradeoff behaviors in complex contexts that produce feedbacks; when optimization is infeasible, we should not be afraid to use approximate fitness-seeking methods instead.
Journal Article