4,348 research outputs found
Logical disagreement : an epistemological study
While the epistemic significance of disagreement has been a popular topic in epistemology for at least a decade, little attention has been paid to logical disagreement. This monograph is meant as a remedy. The text starts with an extensive literature review of the epistemology of (peer) disagreement and sets the stage for an epistemological study of logical disagreement. The guiding thread for the rest of the work is then three distinct readings of the ambiguous term ‘logical disagreement’. Chapters 1 and 2 focus on the Ad Hoc Reading according to which logical disagreements occur when two subjects take incompatible doxastic attitudes toward a specific proposition in or about logic. Chapter 2 presents a new counterexample to the widely discussed Uniqueness Thesis. Chapters 3 and 4 focus on the Theory Choice Reading of ‘logical disagreement’. According to this interpretation, logical disagreements occur at the level of entire logical theories rather than individual entailment-claims. Chapter 4 concerns a key question from the philosophy of logic, viz., how we have epistemic justification for claims about logical consequence. In Chapters 5 and 6 we turn to the Akrasia Reading. On this reading, logical disagreements occur when there is a mismatch between the deductive strength of one’s background logic and the logical theory one prefers (officially). Chapter 6 introduces logical akrasia by analogy to epistemic akrasia and presents a novel dilemma. Chapter 7 revisits the epistemology of peer disagreement and argues that the epistemic significance of central principles from the literature are at best deflated in the context of logical disagreement. The chapter also develops a simple formal model of deep disagreement in Default Logic, relating this to our general discussion of logical disagreement. The monograph ends in an epilogue with some reflections on the potential epistemic significance of convergence in logical theorizing
PRIMED for Sport Coaching: A Mixed-Methods Pilot Study of a Six-Week Intervention
This six-week pilot study was conducted using grounded theory from “What Works in Character Education” (Berkowitz & Bier, 2014) and specifically the “PRIMED for Character Education” framework (Berkowitz, 2021) applied to 11 high school sport coaches. The three key ideas of focus were on whether the PRIMED framework could increase the coach-participants’ commitment to character education, self-efficacy as character educators, and self-identification as Servant Leaders in an effort to “nurture the flourishing of human goodness” (Berkowitz, 2021) of our youth and, in this case, specifically, high school student-athletes.
With millions of youth involved in sport in North America and across the world, the potential positive impact for good that sport coaches can play in the development of character is significant. The relevant literature in coaching for character training programs points to two major gaps in the literature: the need to establish grounded theory around what constitutes effective character education training programs for sport coaches, and a means to combat the “fade-out” effect prevalent in coaching for character training programs. It is integral that coaches are provided effective, evidence-based, training programs from which to influence their coaching behaviors and practices for character development.
This mixed methods (six-week) pilot study with high school sport coaches was the first of its kind to utilize the grounded, evidence-based theory of the PRIMED for Character Education framework while applying it to sport coaching. Qualitative research was the prioritized method of data collection in this study, though the quantitative research data (though not statistically tested due to small sample size) also contributed important findings.
The primary findings of this short six-week pilot study with 11 high school sport coaches provide strong evidence to suggest that the PRIMED for Coaching for Character framework was applicable and relevant to their coaching and a way to increase their commitment to and self-efficacy for character education, as well as their self-identification as Servant Leaders with their sports teams. The two secondary findings of relevance to the field revealed that short interventions (brief orientation and length of time of study) could be effective; and the innovation of “weekly text prompts” could provide a possible solution to combat the “fade-out” effect. The findings from this pilot study can be built upon in future studies to enhance coaching for character training programs to benefit the millions of youth participating in sport each year
“Everything Seems to Be More Final in Live Matches”: Player Experience of Over the Board Chess and Digital Chess
In its long history, chess has seen various changes. The most recent change being the digitisation of chess, which has transformed the beloved tabletop game to a digital one. Today, chess is played by millions and most commonly it is played over the board or digitally. Despite this, whether the player experience of digital chess differs from the one in over the board chess has not been studied.
Thus, the objective of this thesis is to determine whether the player experience of over the board chess is different from that of digital chess and if so, what are the differences in the player experience. An online survey was conducted to collect data, and the data was in turn analysed with the method of thematic analysis.
The findings show that most chess players think the player experience of the two chesses is different. A small section of the respondents feel that the experience is identical. Furthermore, those who find a difference report a variety of differences which can be categorised into five themes. In general, the respondents prefer the experience of over the board chess more than the digital version as they see it more social and competitive. Digital chess, for most, is lacking in the social aspects and has thus the worse experience. However, digital chess is often seen as the optimal version for faster games of chess
Impulse, Spring 2023
Page 2| Dewey Rollag RememberedPage 4| Basu to Develop Testing PlatformPage 5| Faculty NewsPage 6| Targeted Donation Comes at Right TimePage 8| SDSU Robotics Receives $10k GrantPage 9| SDSU\u27s Lunar Project Catches Eye of NASA JudgesPage 10| Mark Gronowski - The Balancing ActPage 13| Matt Dentlinger - First Team Academic All AmericanPage 14| Jocelyn Tanner - Competitive On and Off the PitchPage 14| Student SuccessPage 16| Electrical Engineering Students Top PeersPage 18| College of Engineering StatisticsPage 20| Engineering Undergrads from CompanyPage 22| Engineering a Family Affair for Mother, DaughterPage 24| Dennis Helder - Distinguished EngineerPage 27| Corporate PartnersPage 28| Alumni NewsPage 30| McComish Helping Power Change with FellowshipPage 32| Bender Family Commitment ContinuesPage 34| Dean\u27s ClubPage 36| Development\u27s Director\u27s Messagehttps://openprairie.sdstate.edu/coe_impulse/1073/thumbnail.jp
Planeswalking: Magic: The Gathering Across Analog and Digital Platforms
This dissertation analyzes the relationship between Wizards of the Coast\u27s trading card game Magic: The Gathering and its digital adaptations. I used critical technocultural, ludic discourse analysis, and ludic textual analysis to examine the analog trading card game and digital adaptations. I examined an archive of paratextual media including trade magazines, developer blogs, game reviews, and player guides. I chose Magic for its long history, impact on the analog game industry, and the sheer number of adaptations that have been produced. This analysis begins by introducing a method for describing analog to digital adaptations called Adaptation Mapping. Adaptation mapping describes adaptations as a relationship between how the interface of the game is remediated and the degree to which a game represents the thematic and ludic experiences of the original. Then I examine the narrative framework that allows Magic to tell stories through both its theme and mechanics. Identifying the figure of the Planeswalker as a key component in how narrative functions in Magic, I trace the development of the planeswalker as a player analog to independent original characters under the purview of Wizards of the Coast. The adaptations provide a backdrop for this change and highlights the way that the same mechanical and algorithmic systems can characterize both player and official characters within Magics ecosystem. This shift highlights the way that marketing is approached and influences the design of the game. Finally, I examine how digital adaptations are intwined with ludic platform economy that has emerged through the 2010s. The apparatus that allows for capital to flow through the community is coopted via adaptation and remediated in ways that redirect capital back towards Wizards of the Coast as the platform owner. Analog to digital adaptation is a critical juncture in examining the impact of platformization on play and games
OddAssist - An eSports betting recommendation system
It is globally accepted that sports betting has been around for as long as the sport itself. Back in
the 1st century, circuses hosted chariot races and fans would bet on who they thought would
emerge victorious. With the evolution of technology, sports evolved and, mainly, the
bookmakers evolved. Due to the mass digitization, these houses are now available online, from
anywhere, which makes this market inherently more tempting. In fact, this transition has
propelled the sports betting industry into a multi-billion-dollar industry that can rival the sports
industry.
Similarly, younger generations are increasingly attached to the digital world, including
electronic sports – eSports. In fact, young men are more likely to follow eSports than traditional
sports. Counter-Strike: Global Offensive, the videogame on which this dissertation focuses, is
one of the pillars of this industry and during 2022, 15 million dollars were distributed in
tournament prizes and there was a peak of 2 million concurrent viewers. This factor, combined
with the digitization of bookmakers, make the eSports betting market extremely appealing for
exploring machine learning techniques, since young people who follow this type of sports also
find it easy to bet online.
In this dissertation, a betting recommendation system is proposed, implemented, tested, and
validated, which considers the match history of each team, the odds of several bookmakers and
the general feeling of fans in a discussion forum.
The individual machine learning models achieved great results by themselves. More specifically,
the match history model managed an accuracy of 66.66% with an expected calibration error of
2.10% and the bookmaker odds model, with an accuracy of 65.05% and a calibration error of
2.53%.
Combining the models through stacking increased the accuracy to 67.62% but worsened the
expected calibration error to 5.19%. On the other hand, merging the datasets and training a
new, stronger model on that data improved the accuracy to 66.81% and had an expected
calibration error of 2.67%.
The solution is thoroughly tested in a betting simulation encapsulating 2500 matches. The
system’s final odd is compared with the odds of the bookmakers and the expected long-term
return is computed. A bet is made depending on whether it is above a certain threshold. This
strategy called positive expected value betting was used at multiple thresholds and the results
were compared.
While the stacking solution did not perform in a betting environment, the match history model
prevailed with profits form 8% to 90%; the odds model had profits ranging from 13% to 211%;
and the dataset merging solution profited from 11% to 77%, all depending on the minimum
expected value thresholds.
Therefore, from this work resulted several machine learning approaches capable of profiting
from Counter Strike: Global Offensive bets long-term.É globalmente aceite que as apostas desportivas existem há tanto tempo quanto o próprio
desporto. Mesmo no primeiro século, os circos hospedavam corridas de carruagens e os fãs
apostavam em quem achavam que sairia vitorioso, semelhante às corridas de cavalo de agora.
Com a evolução da tecnologia, os desportos foram evoluindo e, principalmente, evoluíram as
casas de apostas. Devido à onda de digitalização em massa, estas casas passaram a estar
disponíveis online, a partir de qualquer sítio, o que torna este mercado inerentemente mais
tentador. De facto, esta transição propulsionou a indústria das apostas desportivas para uma
indústria multibilionária que agora pode mesmo ser comparada à indústria dos desportos.
De forma semelhante, gerações mais novas estão cada vez mais ligadas ao digital, incluindo
desportos digitais – eSports. Counter-Strike: Global Offensive, o videojogo sobre o qual esta
dissertação incide, é um dos grandes impulsionadores desta indústria e durante 2022, 15
milhões de dólares foram distribuídos em prémios de torneios e houve um pico de espectadores
concorrentes de 2 milhões. Embora esta realidade não seja tão pronunciada em Portugal, em
vários países, jovens adultos do sexo masculino, têm mais probabilidade de acompanharem
eSports que desportos tradicionais. Este fator, aliado à digitalização das casas de apostas,
tornam o mercado de apostas em eSports muito apelativo para a exploração técnicas de
aprendizagem automática, uma vez que os jovens que acompanham este tipo de desportos têm
facilidade em apostar online.
Nesta dissertação é proposto, implementado, testado e validado um sistema de recomendação
de apostas que considera o histórico de resultados de cada equipa, as cotas de várias casas de
apostas e o sentimento geral dos fãs num fórum de discussão – HLTV. Deste modo, foram
inicialmente desenvolvidos 3 sistemas de aprendizagem automática.
Para avaliar os sistemas criados, foi considerado o período de outubro de 2020 até março de
2023, o que corresponde a 2500 partidas. Porém, sendo o período de testes tão extenso, existe
muita variação na competitividade das equipas. Deste modo, para evitar que os modelos
ficassem obsoletos durante este período de teste, estes foram re-treinados no mínimo uma vez
por mês durante a duração do período de testes.
O primeiro sistema de aprendizagem automática incide sobre a previsão a partir de resultados
anteriores, ou seja, o histórico de jogos entre as equipas. A melhor solução foi incorporar os
jogadores na previsão, juntamente com o ranking da equipa e dando mais peso aos jogos mais
recentes. Esta abordagem, utilizando regressão logística teve uma taxa de acerto de 66.66%
com um erro expectável de calibração de 2.10%.
O segundo sistema compila as cotas das várias casas de apostas e faz previsões com base em
padrões das suas variações. Neste caso, incorporar as casas de aposta tendo atingido uma taxa
de acerto de 65.88% utilizando regressão logística, porém, era um modelo pior calibrado que o
modelo que utilizava a média das cotas utilizando gradient boosting machine, que exibiu uma
taxa de acerto de 65.06%, mas melhores métricas de calibração, com um erro expectável de
2.53%.
O terceiro sistema, baseia-se no sentimento dos fãs no fórum HLTV. Primeiramente, é utilizado
o GPT 3.5 para extrair o sentimento de cada comentário, com uma taxa geral de acerto de
84.28%. No entanto, considerando apenas os comentários classificados como conclusivos, a taxa de acerto é de 91.46%. Depois de classificados, os comentários são depois passados a um
modelo support vector machine que incorpora o comentador e a sua taxa de acerto nas partidas
anteriores. Esta solução apenas previu corretamente 59.26% dos casos com um erro esperado
de calibração de 3.22%.
De modo a agregar as previsões destes 3 modelos, foram testadas duas abordagens.
Primeiramente, foi testado treinar um novo modelo a partir das previsões dos restantes
(stacking), obtendo uma taxa de acerto de 67.62%, mas com um erro de calibração esperado
de 5.19%. Na segunda abordagem, por outro lado, são agregados os dados utilizados no treino
dos 3 modelos individuais, e é treinado um novo modelo com base nesse conjunto de dados
mais complexo. Esta abordagem, recorrendo a support vector machine, obteve uma taxa de
acerto mais baixa, 66.81% mas um erro esperado de calibração mais baixo, 2.67%.
Por fim, as abordagens são postas à prova através de um simulador de apostas, onde sistema
cada faz uma previsão e a compara com a cota oferecia pelas casas de apostas. A simulação é
feita para vários patamares de retorno mínimo esperado, onde os sistemas apenas apostam
caso a taxa esperada de retorno da cota seja superior à do patamar.
Esta cota final é depois comparada com as cotas das casas de apostas e, caso exista uma casa
com uma cota superior, uma aposta é feita. Esta estratégia denomina-se de apostas de valor
esperado positivo, ou seja, apostas cuja cota é demasiado elevada face à probabilidade de se
concretizar e que geram lucros a longo termo. Nesta simulação, os melhores resultados, para
uma taxa de mínima de 5% foram os modelos criados a partir das cotas das casas de apostas,
com lucros entre os 13% e os 211%; o dos dados históricos que lucrou entre 8% e 90%; e por
fim, o modelo composto, com lucros entre os 11% e os 77%.
Assim, deste trabalho resultaram diversos sistemas baseados em machine learning capazes de
obter lucro a longo-termo a apostar em Counter Strike: Global Offensive
Esports events: classification and impact of business model of video games on size
Esports events are not commonly researched in academic literature. This research aims to provide a higher degree of understanding around esports, esports events and their size, and to develop a framework for future research. Video game business models are considered, as their link with esports is not often examined. Overwatch is also investigated as a case study of a single video game and its associated esport. The methodology employed is based on mixed methods. A pragmatic approach is utilised, adjusting the research philosophy based on the most suitable approach for each part of the study. The research design evolves based on the findings and the methods used in the previous chapters. Chapter 4 utilises a mixed methods approach, chapters 5 and 6 both use a quantitative method, before chapter 7 uses a qualitative, case study like technique. Chapter 4 explores the determination of a framework to measure size of events and such framework is created, with 1 event being classed as ''giga'', 16 as ''mega'', 15 as ''major'', and 11 as ''minor''. Chapter 5 undertakes a similar pursuit but utilising an index to rank sizes. There are no large differences in score or in class, and there is a high degree of correlation between the index and the classification from the previous chapter. Chapter 6 explores event size vs. video game business model, finding that events associated to buy-to-play and free-to-play games have a larger size than events associated to pay-to-pay games. Chapter 7 analyses Overwatch and concludes that a switch to a free-to-play model would be beneficial for Overwatch, and for its associated esport Overwatch League. A number of recommendations are made as a result of the research undertaken. Better collection and organisation of data on esports would be beneficial for future research. A centralised governing body would help with a number of aspects in esports. More research could be undertaken into business models, into the implication of choosing one over another and switching between them. A research centre at the European level would also be beneficial, as would the growth of formal structures around esports and esports research
Transformative Education: How Can You Become a Better College Teacher?
Transformative Education presents a comprehensive approach to college teaching that stresses both the presentation of topical coverage AND the development of critical thinking skills. The book focuses on several key points in the learning process such as student preparation for class, student engagement during class, and student review and organization of the material after class. The book discusses the urgent need for more and better high-quality college education, a goal that can be achieved by a methodical approach to gradual teaching improvement.https://scholarship.richmond.edu/bookshelf/1396/thumbnail.jp
- …