The Mutual Interaction of Online and Offline Identities in Massively Multiplayer Online Communities: A Study of EVE Online Players

This phenomenological study was conducted to examine the ways that individuals experience massively multiplayer online games, and the interaction between online and offline identities. Ten members of the EVE Online community were interviewed about their experiences of the boundaries and crossovers between offline self and online character. Interviews were transcribed, coded, and analyzed for consistent themes. Themes drawn from the data fell into three over- arching categories: the Appeal of EVE, or the player motivations and qualities of the game environment that influence player investment; Self/Character Interaction, describing the ways in which online and offline identities interact; and Moral Dilemmas, in which players describe their thoughts and reactions to the moral ambiguity of EVE Online. Appeal of EVE contained the themes of Importance of Social Interaction, My Choices Matter, Algerism, and EVE Relationships are Meaningful. Self/Character Interaction contained the themes of My Character and I Are the Same, My Character and I Are Different, Parallels, Friction Between Selves, One Identity Learning From the Other, and Intersections. Moral Dilemmas contained the themes of My Prosocial Choices, Someone Else’s Antisocial Choices, and Morality is Ambiguous. A final theme, not associated with any of the three categories, but present throughout all of them, was Emotional Content. These results were compared and contrasted with existing literature, and conclusions were drawn about the parallel processes between online and offline selves

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 300)

This bibliography lists 232 reports, articles and other documents introduced into the NASA scientific and technical information system in July 1987

Instructional Logistics and Chunque-Based Learning Systems

Existing instructional design and curriculum design strategy components were synthesized to provide a comprehensive set of design models for the development of learning systems. The term instructional logistics was coined to define the management of student progress through a series of customized learning experiences. Strategies were developed for the design of student-centered learning systems by partitioning a curriculum into meaningful and manageable pieces (called chunques) and by manipulating those pieces to create personalized and individualized paths through a series of self-contained learning experiences. Strategies were developed to organize a collection of chunques into a path based on initial simplified mental models designed specifically to make the subject matter more appropriate for novice learners. Two types of paths were proposed: paths created prior to instruction based on the best guess at what is optimal for the particular circumstances (anticipatory paths) and paths modified on the fly based on diagnostic information gathered during the learning process (adaptive paths). Curriculum design decisions were based on two propositions: that curriculum decisions can be categorized as value-laden decisions, based on some conception of worth, or as technical decisions, based on instructional needs. The three souls proposition was developed, which proposes that educational goals can be categorized as education-to-be, sagacity-to-know, or training-to-do

Quantum Economic Theory of Intelligence

The Quantum Economics Intelligence Initiative, spearheaded by Quantum Economist PhDs. Kaiola M Liu integrates insights from seminal thinkers like Einstein, Archimedes, Adam Smith, Nick Land, and Sun Tzu. By applying principles of quantum mechanics, this forward-looking project aims to redefine economic modeling, exploring real-world applications and potential benefits. The initiative encompasses foundational studies, economic model applications, incorporation of quantum computing, and analysis of contemporary economic philosophies. Keywords - Quantum Mechanics, Economics, Technological Advancements, Philosophy

Making Sense of Successful Global Teams

Global teams tend to underperform. Teamwork often frustrates members compromising the results as well as employee motivation. In practice, bad results are often camouflaged, and both management and team members lack insight into what is really driving teams and why they do not reach their goals. The underlying metaphor our economic model is built on is the “machine” where people instead of active agents with true influence are implicitly seen as resources, executors of processes and walking curriculum vitaes to be aligned in precise ways to achieve often arbitrary goals and to meet unrealistic expectations. This study takes a critical stand towards this mainstream view and applies reflexive methodology, the lens of sensemaking as well as the metaphor and the narrative as rhetorical devices to study how and why global teams form and evolve the way they do over time. The insights of this study are based on an experimental methodology studying many teams from a close range, and reveal how different structurally identical well-performing global teams executing the same tasks can be. Teams when studied from within, are dynamic phenomena rather than static sums of their parts. Alternative team metaphors, such as the “chain gang”, “dysfunctional family”, “sandbox”, “scouts” and “master cooks”, for instance, emerge. The very different team dynamics are in part explained by how successful team members are at social sensemaking – establishing shared understandings around such basic concepts as “leadership”, “good communication” and “team goals”. Individual team members and their capability and willingness to engage in self-reflection and their decisions to act or not to act on what may first appear mundane events, can have huge influence over what their teams become. Sustainably successful teams work both on the task and the team itself and consider the team as a constant work-in-progress and not a fixed entity. This study proposes innovative ways of looking at and studying global teams. People, team members, can be considered active agents, capable human beings on whose sensemaking paths depend on what these teams become and how they evolve over time

Probabilistic Commonsense Knowledge

Commonsense knowledge is critical to achieving artificial general intelligence. This shared common background knowledge is implicit in all human communication, facilitating efficient information exchange and understanding. But commonsense research is hampered by its immense quantity of knowledge because an explicit categorization is impossible. Furthermore, a plumber could repair a sink in a kitchen or a bathroom, indicating that common sense reveals a probable assumption rather than a definitive answer. To align with these properties of commonsense fundamentally, we want to not only model but also evaluate such knowledge human-like using abstractions and probabilistic principles. Traditional combinatorial probabilistic models, e.g., probabilistic graphical model approaches, have limitations to modeling large-scale probability distributions containing thousands or even millions of commonsensical events. On the other hand, although embedding-based representation learning has the advantage of generalizing to large combinations of events, they suffer from producing consistent probabilities under different styles of queries. Combining benefits from both sides, we introduce probabilistic box embeddings, which represent joint probability distributions on a learned latent space of geometric embeddings. By using box embeddings, it is now possible to handle queries with intersections, unions, and negations in a way similar to Venn diagram reasoning, which has faced difficulty even when using large language models. Meanwhile, existing evaluations do not reflect the probabilistic nature of commonsense knowledge. The popular multiple-choice evaluation style often misleads us into the paradigm that commonsense solved. To fill in the gap, we propose a method of retrieving commonsense related question answer distributions from human annotators as well as a novel method of generative evaluation. We utilize these approaches in two new commonsense datasets. Finally, we draw a connection between the-state-of-art NLP models --- large language models and their ability to perform commonsense reasoning tasks. According to the previous study, large language models would make inconsistent predictions while given different input texts for plausible commonsense situations. We intend to evaluate their performance using more rigorous probabilistic measurements

Knowledge Management: A Primer

Knowledge Management is an expanding field of study. In this paper we clarify and explain some of the terms and concepts that underlie this field. In particular we discuss knowledge and its related philosophies; how the sociotechnical aspects of organizations can assist in knowledge management and how communities of practice can thus be supported; how knowledge can be valued in an organization; and the idea of intellectual capital. We conclude that knowledge management is not an easy \u27fix\u27 to an organisation\u27s problems. Implemented well it can increase productivity, improve worker collaboration, and shorten product development times. Implemented badly it may incur significant costs without delivering these benefits

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse