Luís Correia

Vasco V. Branco, Jandó Benedek, Lidia Pivovarova et al.

1. A hard stop for the implementation of rigorous conservation initiatives is our lack of key species data, especially occurrence data. Furthermore, researchers have to contend with an accelerated speed at which new information must be collected and processed due to anthropogenic activity. Publications ranging from scientific papers to gray literature contain this crucial information but their data are often not machine-readable, requiring extensive human work to be retrieved. 2. We present the ARETE R package, an open-source software aiming to automate data extraction of species occurrences powered by large language models, namely using the chatGPT Application Programming Interface. This R package integrates all steps of the data extraction and validation process, from Optical Character Recognition to detection of outliers and output in tabular format. Furthermore, we validate ARETE through systematic comparison between what is modelled and the work of human annotators. 3. We demonstrate the usefulness of the approach by comparing range maps produced using GBIF data and with those automatically extracted for 100 species of spiders. Newly extracted data allowed to expand the known Extent of Occurrence by a mean three orders of magnitude, revealing new areas where the species were found in the past, which mayhave important implications for spatial conservation planning and extinction risk assessments. 4. ARETE allows faster access to hitherto untapped occurrence data, a potential game changer in projects requiring such data. Researchers will be able to better prioritize resources, manually verifying selected species while maintaining automated extraction for the majority. This workflow also allows predicting available bibliographic data during project planning.

Burak Öz, Fei Wu, Luis Correia et al.

Decentralized block building mechanisms replace the monopoly of a single proposer with multiple builders. However, their censorship-resistance and fair-access benefits depend not only on the number of builders, but also on whether builders are geographically positioned to provide timely transaction coverage. We study this tension between builder location choice, user transaction coverage, and reward concentration by modeling decentralized block building as a stochastic coverage game. Builders choose regions, information sources emit transactions over a block construction round, and latency determines whether each transaction is received before the deadline. We show that the builder region game is an exact potential game and therefore admits a pure Nash equilibrium. We prove an asymptotically tight factor-2 Price of Anarchy bound, quantifying the price of decentralization from uncoordinated builder placement, and derive tight bounds on builder utility concentration, showing that the lowest-utility builder earns at least half of the highest-utility builder's payoff, and the utility-share HHI is at most 12.5% above the egalitarian benchmark. We complement the theory with simulations, studying the builder region game under richer latency and source environments. We find that welfare losses are most pronounced in intermediate regimes where peripheral sources are reachable and valuable, but selfish incentives still favor regions with strong access to high-value sources. We also find that geographic and utility concentration need not align: planner allocations can improve coverage by assigning builders to lower-payoff peripheral regions, while equilibrium outcomes can be more geographically concentrated but more utility-balanced. We connect our findings to protocol design and discuss future directions on location-market modeling and alternative reward-sharing rules.

Luis Correia, Antonio Manso

This chapter presents SMuGA, an integration of symbiogenesis with the Multiset Genetic Algorithm (MuGA). The symbiogenetic approach used here is based on the host-parasite model with the novelty of varying the length of parasites along the evolutionary process. Additionally, it models collaborations between multiple parasites and a single host. To improve efficiency, we introduced proxy evaluation of parasites, which saves fitness function calls and exponentially reduces the symbiotic collaborations produced. Another novel feature consists of breaking the evolutionary cycle into two phases: a symbiotic phase and a phase of independent evolution of both hosts and parasites. SMuGA was tested in optimization of a variety of deceptive functions, with results one order of magnitude better than state of the art symbiotic algorithms. This allowed to optimize deceptive problems with large sizes, and showed a linear scaling in the number of iterations to attain the optimum.