Medical AI is transforming clinical practice, yet adoption faces non-technological barriers: epistemic, ethical, and social. This collection addresses a ...

Artificial agents are examined within simulated environments to elucidate the emergence of collective behaviors and decision-making processes under diverse environmental pressures and population structures. Using an Agent-Based Modeling (ABM) framework, the simulation tracked 157,097 iterations across four agent types: cooperators, defectors, super-reciprocators, and free riders; while analyzing 12 core metrics, including alignment indices, coherence, and environmental stress. The results revealed distinct phase transitions in behavior, with low-density populations (d < 0.4) supporting strong consensus formation and higher densities (d > 0.8) leading to fragmentation and increased competition. Agent alignment consistently ranged between 0.28 and 0.37, reflecting partial but stable consensus across conditions. Cooperative behaviors emerged and persisted only when resource availability exceeded a critical threshold (RG ≥ 6), underscoring the role of resource abundance in sustaining collective intelligence. Through interventions such as network topology changes and cognitive plasticity adjustments, agents demonstrated emergent behavioral patterns that arose from their rule-based interactions. It is important to note that these patterns, while complex, do not constitute “sophisticated decision-making” in the sense of genuine intelligence or understanding. Rather, they represent emergent properties of the system, collective behaviors that cannot be reduced to individual agent rules but emerge from their interactions under specific environmental conditions. This distinction is crucial for avoiding misattribution of intelligence to rule-following systems, even when those systems produce complex outputs. These findings provide insights into the mechanisms driving emergent intelligence in artificial systems and their implications for the governance and ethical design of future AI agents.

YouTube video by AyudaFuturo

Bio-hybrid robotics&#8212;systems integrating living tissues with artificial mechanisms&#8212;challenge conventional ethical frameworks due to their ontological ambiguity and technological novelty. Wh...

Artificial agents are examined within simulated environments to elucidate the emergence of collective behaviors and decision-making processes under diverse environmental pressures and population struc...

El XI Congreso Internacional de ALICE, tiene como eje la gobernabilidad y la inteligencia artificial. En Granada, España, Septiembre 2025.

In this article, I explored the application of large language models (LLMs) in analysing linguistic colexification and ambiguity within bioethical scenarios. By employing word embeddings derived from ...

Este artículo examina la explicación inferencialista de la representación científica de Mauricio Suárez a la luz de avances recientes en neurociencia e inteligencia artificial (NeuroAI). Aunque aporta...

En los últimos años se han intensificado las voces que alertan sobre la necesidad de regular nuevos derechos humanos que protejan la mente y, en concreto, la información y los datos almacenados en aqu...

A few million words suffice for children to acquire language. Yet, the brain mechanisms underlying this unique ability remain poorly understood. To...

En los últimos años se han intensificado las voces que alertan sobre la necesidad de regular nuevos derechos humanos que protejan la mente y, en concreto, la información y los datos almacenados en aqu...