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Browsing Capítulos de libros by browse.metadata.categoria "Ciencias de la computación"
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- ItemA Network Approach to the Formation of Self-assembled Teams(2020) Ichhaporia, R.; Gomez Zara, Diego Alonso; DeChurch, L.; Contractor, N.Which individuals in a network make the most appealing teammates? Which invitations are most likely to be accepted? And which are most likely to be rejected? This study explores the factors that are most likely to explain the selection, acceptance, and rejection of invitations in self-assembling teams. We conducted a field study with 780 participants using an online platform that enables people to form teams. Participants completed an initial survey assessing traits, relationships, and skills. Next, they searched for and invited others to join a team. Recipients could then accept, reject, or ignore invitations. Using Exponential Random Graph Models (ERGMs), we studied how traits and social networks influence teammate choices. Our results demonstrated that (a) agreeable leaders with high psychological collectivism send invitations most frequently, (b) previous collaborators, leaders, competent workers, females, and younger individuals receive the most invitations, and (c) rejections are concentrated in the hands of a few. © 2020, Springer Nature Switzerland AG.
- ItemNo Agreement Without Loss: Learning and Social Choice in Peer Review(2023) Barcelo Baeza, Pablo; Duarte, Mauricio; Rojas González, Luis Cristóbal; Steifer, TomaszIn peer review systems, reviewers are often asked toevaluate various features of submissions, such as technical qualityor novelty. A score is given to each of the predefined features andbased on these the reviewer has to provide an overall quantitativerecommendation. It may be assumed that each reviewer has her ownmapping from the set of features to a recommendation, and thatdifferent reviewers have different mappings in mind. This introducesan element of arbitrariness known as commensuration bias. In thispaper we discuss a framework, introduced by Noothigattu, Shah andProcaccia, and then applied by the organizers of the AAAI 2022conference. Noothigattu, Shah and Procaccia proposed to aggregatereviewer’s mapping by minimizing certain loss functions, and studiedaxiomatic properties of this approach, in the sense of social choicetheory. We challenge several of the results and assumptions used intheir work and report a number of negative results. On the one hand,we study a trade-off between some of the axioms proposed and theability of the method to properly capture agreements of the majorityof reviewers. On the other hand, we show that dropping a certainunrealistic assumption has dramatic effects, including causing themethod to be discontinuous.