Mariana Macedo

Rachael Colley, Umberto Grandi, César Hidalgo et al.

In rank aggregation, members of a population rank issues to decide which are collectively preferred. We focus instead on identifying divisive issues that express disagreements among the preferences of individuals. We analyse the properties of our divisiveness measures and their relation to existing notions of polarisation. We also study their robustness under incomplete preferences and algorithms for control and manipulation of divisiveness. Our results advance our understanding of how to quantify disagreements in collective decision-making.

Mariana Macedo, Ronaldo Menezes, Alessio Cardillo

We investigate how parenthood and marriage (two major life events) reshape urban mobility patterns, an aspect overlooked in traditional `average citizen' mobility models. Leveraging US census data, we analyse whether these life transitions create distinct urban experiences. Parenthood introduces new priorities including caregiving responsibilities, work-life balance adjustments, and access to family-friendly environments. Similarly, marriage introduces new dynamics including shared household decision-making, potential dual-income benefits, combined residential preferences, and shifts in social networks and lifestyle patterns. Our analysis demonstrates that cities vary significantly in how mobility can be accommodated by different household arrangements: some better accommodate either single individuals (Houston, Virginia Beach) or married people (Atlanta, Baltimore), whereas others favour parents (Cincinnati, Chicago). This classification becomes increasingly relevant for individuals and families as remote work expands relocation possibilities. We find that parents and married individuals face different mobility costs and amenity access patterns compared to their counterparts, with variations consistent across multiple null model tests. This research advances urban planning discourse by advocating for tailored design strategies addressing diverse demographic needs rather than one-size-fits-all approaches.

Lydia Taw, Nishant Gurrapadi, Mariana Macedo et al.

Computational swarm intelligence consists of multiple artificial simple agents exchanging information while exploring a search space. Despite a rich literature in the field, with works improving old approaches and proposing new ones, the mechanism by which complex behavior emerges in these systems is still not well understood. This literature gap hinders the researchers' ability to deal with known problems in swarms intelligence such as premature convergence, and the balance of coordination and diversity among agents. Recent advances in the literature, however, have proposed to study these systems via the network that emerges from the social interactions within the swarm (i.e., the interaction network). In our work, we propose a definition of the interaction network for the Artificial Bee Colony (ABC) algorithm. With our approach, we captured striking idiosyncrasies of the algorithm. We uncovered the different patterns of social interactions that emerge from each type of bee, revealing the importance of the bees variations throughout the iterations of the algorithm. We found that ABC exhibits a dynamic information flow through the use of different bees but lacks continuous coordination between the agents.

Marcos Oliveira, Diego Pinheiro, Mariana Macedo et al.

Swarm intelligence is the collective behavior emerging in systems with locally interacting components. Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems such as robustness, scalability, and flexibility. Yet, we do not know why swarm-based algorithms work well and neither we can compare the different approaches in the literature. The lack of a common framework capable of characterizing these several swarm-based algorithms, transcending their particularities, has led to a stream of publications inspired by different aspects of nature without a systematic comparison over existing approaches. Here, we address this gap by introducing a network-based framework---the interaction network---to examine computational swarm-based systems via the optics of the social dynamics of such interaction network; a clear example of network science being applied to bring further clarity to a complicated field within artificial intelligence. We discuss the social interactions of four well-known swarm-based algorithms and provide an in-depth case study of the Particle Swarm Optimization. The interaction network enables researchers to study swarm algorithms as systems, removing the algorithm particularities from the analyses while focusing on the structure of the social interactions.