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Luana Ferraz Alvarenga

Publications and source records attributed to Luana Ferraz Alvarenga.

2 recordsLinked to original sources

The Power of Altruism in Sticker Economics: Generosity Minimizes Collective Costs and Overprotective Norms Fuel Inefficiency

Collecting the FIFA World Cup sticker album presents a classic public-goods and collective-action dilemma, in which completing a collection on one's own is highly inefficient. To evaluate how localized community norms shape collective efficiency, we use agent-based modeling and Monte Carlo simulations, parameterized with empirical field observations from exchange meetups in Natal, Brazil. Reflecting the tournament's recent expansion, the Panini 2026 album features 980 individual stickers, including 68 metallic specials. We contrast a standard baseline economy (1:2 special-to-normal exchange ratio) with an overprotective, strict strategy (exclusive special-for-special trading) and an altruistic, generous strategy (in which advanced players surrender needed duplicates to assist peers). Our findings reveal that overprotective rules trap liquidity and drive network-wide inefficiency. The strict strategy increases median completion costs by 10 packs and severely penalizes the least fortunate 5\% of collectors, adding 20 packs in large cities and 30 in small communities. Conversely, widespread generosity optimizes network liquidity and dramatically compresses the long tail of bad luck. Introducing the generous strategy reduces required purchases for the 5th percentile by 90 packs in large-scale configurations and 130 packs in smaller clusters. Furthermore, widespread altruism triggers a strong functional coupling that effectively synchronizes completion rates across the network. This study demonstrates that while rigid, protective norms degrade collective welfare, generosity successfully mitigates pack-draw variance, transforming an expensive, isolated hobby into a resilient, highly efficient public good.

cs.GT↗

How VADER is your AI? Towards a definition of artificial intelligence systems appropriate for regulation

Artificial intelligence (AI) has driven many information and communication technology (ICT) breakthroughs. Nonetheless, the scope of ICT systems has expanded far beyond AI since the Turing test proposal. Critically, recent AI regulation proposals adopt AI definitions affecting ICT techniques, approaches, and systems that are not AI. In some cases, even works from mathematics, statistics, and engineering would be affected. Worryingly, AI misdefinitions are observed from Western societies to the Global South. In this paper, we propose a framework to score how validated as appropriately-defined for regulation (VADER) an AI definition is. Our online, publicly-available VADER framework scores the coverage of premises that should underlie AI definitions for regulation, which aim to (i) reproduce principles observed in other successful technology regulations, and (ii) include all AI techniques and approaches while excluding non-AI works. Regarding the latter, our score is based on a dataset of representative AI, non-AI ICT, and non-ICT examples. We demonstrate our contribution by reviewing the AI regulation proposals of key players, namely the United States, United Kingdom, European Union, and Brazil. Importantly, none of the proposals assessed achieve the appropriateness score, ranging from a revision need to a concrete risk to ICT systems and works from other fields.

cs.AI↗