We model the isotropic depinning transition of a domain-wall using a two dimensional Ginzburg-Landau scalar field instead of a directed elastic string in a random media. An exact algorithm accurately targets both the critical depinning field and the critical configuration for each sample. For random bond disorder of weak strength $\Delta$, the critical field scales as $\Delta^{4/3}$ in agreement with the predictions for the quenched Edwards-Wilkinson elastic model. However, critical configurations display overhangs beyond a characteristic length $l_{\tt 0} \sim \Delta^{-\alpha}$, with $\alpha\approx 2.2$, indicating a finite-size crossover. At the large scales, overhangs recover the orientational symmetry which is broken by directed elastic interfaces. We obtain quenched Edwards-Wilkinson exponents below $l_{\tt 0}$ and invasion percolation depinning exponents above $l_{\tt 0}$. A full picture of domain wall isotropic depinning in two dimensions is hence proposed.

Drawing inspiration from honeybee swarms' nest-site selection process, we assess the ability of a kilobot robot swarm to replicate this captivating example of collective decision-making. Honeybees locate the optimal site for their new nest by aggregating information about potential locations and exchanging it through their waggle-dance. The complexity and elegance of solving this problem relies on two key abilities of scout honeybees: self-discovery and imitation, symbolizing independence and interdependence, respectively. We employ a mathematical model to represent this nest-site selection problem and program our kilobots to follow its rules. Our experiments demonstrate that the kilobot swarm can collectively reach consensus decisions in a decentralized manner, akin to honeybees. However, the strength of this consensus depends not only on the interplay between independence and interdependence but also on critical factors such as swarm density and the motion of kilobots. These factors enable the formation of a percolated communication network, through which each robot can receive information beyond its immediate vicinity. By shedding light on this crucial layer of complexity --the crowding and mobility conditions during the decision-making--, we emphasize the significance of factors typically overlooked but essential to living systems and life itself.

Collective decision-making is a widespread phenomenon in both biological and artificial systems, where individuals reach a consensus through social interactions. While traditional models of opinion dynamics and contagion focus on pairwise interactions, recent research emphasizes the importance of including higher-order group interactions and autonomous behavior to better reflect real-world complexity. In this work, we introduce a collective decision-making model inspired by social insects. In our framework, uncommitted agents can explore options independently and become committed, while social interactions influence these agents to prefer options already accepted by the group. Our model extends classical contagion models by incorporating multiple, mutually exclusive options and distinguishing between pairwise and higher-order social influences. Using simulations and analytical mean-field solutions, we show that higher-order interactions are essential for breaking symmetry in systems with equally valid options. We find that pairwise communication alone can cause decision deadlock, but adding group interactions allows the system to overcome stalemates and reach consensus. Our results emphasize the important roles of autonomous behavior and higher-order structures in collective decision-making. These insights could help us better understand social systems and design decision protocols for artificial swarms.