10 research outputs found

    Game theory and Artificial Intelligence in just preservation

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    We humans can show presumption, arrogance and many dubious traits. By virtue of being land-dwelling, dexterous, relatively intelligent, and having good communication hardware and (good) fortune, we have for recent millennia largely had dominion of our planet. Yet humans often do not treat themselves (let alone other species) particularly well. Treves et al.’s idea of a multispecies justice system — not “prioritizing humans” but “finding practical ways to work within human systems” — invites consideration

    Understanding and Measuring Collective Intelligence Across Different Cognitive Systems: An Information-Theoretic Approach

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    A dynamic intelligence test framework for evaluating AI agents

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    Tennis superstars: the relationship between star status and demand for tickets

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    Akin to other sports, professional tennis is urged to adopt a consumer-centred strategy and understand the influence of the star status of elite players on demand for its core product. Measuring the impact that tennis players have on demand for match attendance remains a key element towards achieving that goal. Using data from the Australian Open ticket sales, the authors demonstrate how individual players have influenced stadium attendance at the Grand Slam. Findings indicate that some players are associated with a strong positive impact on demand for tickets, above and beyond their performance ratings, reflecting their value to the Australian Open. The authors discuss how this star status can be used to inform business decisions related to tournament management, match scheduling, and determining player appearance fees, to ultimately drive better commercial outcomes and deliver a world-class sporting event. The findings have implications for tournament organisers, player managers and those that market player activities. © 2019 Sport Management Association of Australia and New Zealan

    Tennis influencers: the player effect on social media engagement and demand for tournament attendance

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    Understanding the interest of sports fans in professional tennis has valuable operational and marketing implications for tournament organisers, marketeers, player sponsors and the media. In sports, professional tennis in particular, the player effect on social media user engagement is still elusive. Using data from the 2019 Australian Open grand slam period, the authors examine Adler's (1985) theoretical construct in the context of sports and social media. A social listening tool is used to probe more than 2 million posts and comments mentioning elite male and female tennis players on four major social media channels: Twitter, Facebook, Instagram and YouTube, over the grand slam period. It is shown that the effect of professional tennis players on social media user engagement extends beyond their talent. A selection of players had a strong positive impact on prompting social media activity, even after accounting for factors related to their performance, the tournament rounds in which they were featured and the opponents against whom they played. Furthermore, the connection between social media research and sports economics is considered by examining the relationship between a player's effects on social media engagement and her/his differential influence on demand for tickets at the Australian Tennis Open. The authors further discuss how the social media star influence can be used, in combination with other quantitative measures, to optimise tennis tournament scheduling, determine player appearance fees and lift participation in the sport. © 2020 Elsevier Lt

    Measuring Universal Intelligence in Agent-Based Systems Using the Anytime Intelligence Test

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    Abstract. This paper aims to quantify and analyze the intelligence of artificial agent collectives. A universal metric is proposed and used to empirically measure intelligence for several different  agent  decision  controllers.  Accordingly,  the  effectiveness  of  various  algorithms  is evaluated on a per-agent basis over a selection of abstracted, canonical tasks of different algorithmic complexities. Results reflect the different settings over which cooperative multiagent systems can be significantly more intelligent per agent than others. We identify and discuss some of the factors in influencing the collective performance of these systems <br

    A new AI evaluation cosmos: ready to play the game?

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    We report on a series of new platforms and events dealing with AI evaluation that may change the way in which AI systems are compared and their progress is measured. The introduction of a more diverse and challenging set of tasks in these platforms can feed AI research in the years to come, shaping the notion of success and the directions of the field. However, the playground of tasks and challenges presented there may misdirect the field without some meaningful structure and systematic guidelines for its organization and use. Anticipating this issue, we also report on several initiatives and workshops that are putting the focus on analyzing the similarity and dependencies between tasks, their difficulty, what capabilities they really measure and – ultimately – on elaborating new concepts and tools that can arrange tasks and benchmarks into a meaningful taxonomy
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