George-Rafael Domenikos and Lock Yue Chew
Complexities 2026, 2(3), 23

Generalized Schelling models extend the classical relocation framework by introducing additional agent attributes, interaction rules, and mobility constraints. We study four variants within a common lattice formulation: a modified movement-only baseline, a local affiliation-interaction model, a model with local and random long-range interactions, and a monetary model in which movement and communication consume resources. The simulations use a 64×64 lattice and are evaluated over 30 independently seeded runs per scenario, with means and 95% confidence intervals reported throughout. At each step, occupied agents are classified as bound, activated, or, in the monetary scenario, trapped. Their conditional affiliation distributions are evaluated using Shannon entropy and a shape-weighted diagnostic whose fitted components distinguish normal-like, heavy-tailed, and bimodal structures. The diagnostic is interpreted jointly with its fitted weights and the underlying histograms rather than as a unique scalar reconstruction of shape. Validation against the bimodality coefficient, Esteban–Ray polarization, Moran’s I, and sign assortativity separates distributional polarization from spatial segregation. The interaction-driven scenarios develop strongly polarized two-lobed bound-state distributions, while the monetary model additionally produces resource-constrained dynamical arrest. The results are robust across independent initial conditions and random streams; separate extended-horizon checks to 500 steps showed no qualitative reversal. The same regimes persist on 96×96 and 128×128 lattices and under one-factor variations of satisfaction, vacancy, long-range-contact, and monetary-cost parameters. The framework therefore complements Shannon entropy by exposing distributional geometry that uncertainty alone does not encode.

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Mayo AM, Carrigee C, Harrison AB, Mayo ML, Pilkiewicz KR

PLOS Complex Syst 3(10): e0000136.

In a famous Chinese parable, a man is shown both heaven and hell. In the latter, a group of men sit around a lavish banquet table, unable to eat because their chopsticks are too long to reach their mouths. In the former, the exact same scenario plays out, except here the men feed each other. In this paper, we compare the behavior of cockroaches to that of vibrating brush-head robots, known as bristlebots, as groups of each attempt to squeeze through a narrow passage. Although the bots and the bugs have different means of locomotion, they navigate their environment in a similar fashion. As in the parable, however, a small of amount of regard for others leads to a much happier outcome. The robots try to push past each other and form logjams that block up the passage, whereas the roaches practice patience and stop and wait for those in front of them to move through the passage first. This result may seem innocuous, but it demonstrates how simple organisms, incapable of sophisticated decision-making, require only the slightest amount of sociality to collectively solve complex problems, such as how to smoothly navigate a challenging environment as a cohesive unit.In a famous Chinese parable, a man is shown both heaven and hell. In the latter, a group of men sit around a lavish banquet table, unable to eat because their chopsticks are too long to reach their mouths. In the former, the exact same scenario plays out, except here the men feed each other. In this paper, we compare the behavior of cockroaches to that of vibrating brush-head robots, known as bristlebots, as groups of each attempt to squeeze through a narrow passage. Although the bots and the bugs have different means of locomotion, they navigate their environment in a similar fashion. As in the parable, however, a small of amount of regard for others leads to a much happier outcome. The robots try to push past each other and form logjams that block up the passage, whereas the roaches practice patience and stop and wait for those in front of them to move through the passage first. This result may seem innocuous, but it demonstrates how simple organisms, incapable of sophisticated decision-making, require only the slightest amount of sociality to collectively solve complex problems, such as how to smoothly navigate a challenging environment as a cohesive unit.

Read the full article at: journals.plos.org

Shamil Chandaria, Arvo Muñoz Morán, Fernando Rosas, Anil Seth, Henry Shevlin, Marcus Hutter, Thore Graepel, Adam Bales, Iulia Comsa, Murray Shanahan, Ruben Laukkonen, Morten Kringelbach, Chris Frith, Shane Legg
The question of AI consciousness is one of the most urgent pre-emptive problems in philosophy and computer science, yet progress is hampered by a cacophony of competing theories that often talk past each other. Separating the hard problem from the mapping problem allows the deepest metaphysical disagreements to be set aside: granting that experience supervenes on a system’s organisation, the tractable question becomes at which grain of description that supervenience base sits. We extend Marr’s three levels of analysis into a five-level hierarchy of functional descriptions (behavioural, computational, intrinsic causal-structural, organismic, and organism-environment) grounded in supervenience, coarse-graining, and multiple realisability. The major theories of consciousness are positioned within this hierarchy according to which level they take to be critical, and for each level we develop operationalisable indicators and assess current AI systems against them. A Bayesian model then combines theoretical credences with indicator evidence into an overall credence in a system’s capacity for consciousness. In illustrative assessments, the verdict for current LLMs is driven as much by where theoretical credence is placed as by how the evidence is read: under different stipulated readings and credence distributions, assessments range from below 0.01 to roughly 0.8, showing sensitivity to assumptions. Finally, the consciousness indicators at each level closely overlap with the architectural features needed for general intelligence, suggesting that increasingly capable AI may become a stronger candidate for consciousness. The framework supports a structured agnosticism, in which theoretical commitments are made explicit, credences are updated as evidence accumulates, and assessments take the form of aggregated probabilities rather than verdicts.

Read the full article at: arxiv.org