Fernando Rosas, Alexander Boyd, Manuel Baltieri

Recent work proposes using world models to generate controlled virtual environments in which AI agents can be tested before deployment to ensure their reliability and safety. However, accurate world models often have high computational demands that can severely restrict the scope and depth of such assessments. Inspired by the classic `brain in a vat’ thought experiment, here we investigate ways of simplifying world models that remain agnostic to the AI agent under evaluation. By following principles from computational mechanics, our approach reveals a fundamental trade-off in world model construction between efficiency and interpretability, demonstrating that no single world model can optimise all desirable characteristics. Building on this trade-off, we identify procedures to build world models that either minimise memory requirements, delineate the boundaries of what is learnable, or allow tracking causes of undesirable outcomes. In doing so, this work establishes fundamental limits in world modelling, leading to actionable guidelines that inform core design choices related to effective agent evaluation.

Read the full article at: arxiv.org

Evangelos Pournaras, Srijoni Majumdar, Thomas Wellings, Joshua C. Yang, Fatemeh B. Heravan, Regula Hänggli Fricker, Dirk Helbing

Voting methods are instrumental design element of democracies. Citizens use them to express and aggregate their preferences to reach a collective decision. However, voting outcomes can be as sensitive to voting rules as they are to people’s voting choices. Despite the significance and inter-disciplinary scientific progress on voting methods, several democracies keep relying on outdated voting methods that do not fit modern, pluralistic societies well, while lacking social innovation. Here, we demonstrate how one can upgrade real-world democracies, namely by using alternative preferential voting methods such as cumulative voting and the method of equal shares designed for a proportional representation of voters’ preferences. By rigorously assessing a new participatory budgeting approach applied in the city of Aarau, Switzerland, we unravel the striking voting outcomes of fair voting methods: more winning projects with the same budget and broader geographic and preference representation of citizens by the elected projects, in particular for voters who used to be under-represented, while promoting novel project ideas. We provide profound causal evidence showing that citizens prefer proportional voting methods, which possess strong legitimacy without the need of very technical specialized explanations. We also reveal strong underlying democratic values exhibited by citizens who support fair voting methods such as altruism and compromise. These findings come with a global momentum to unleash a new and long-awaited participation blueprint of how to upgrade democracies.

Read the full article at: arxiv.org

Linzhuo Li, Yiling Lin, Lingfei Wu

Using large-scale citation data and a breakthrough metric, the study systematically evaluates the inevitability of scientific breakthroughs. We find that scientific breakthroughs emerge as multiple discoveries rather than singular events. Through analysis of over 40 million journal articles, we identify multiple discoveries as papers that independently displace the same reference using the Disruption Index (D-index), suggesting functional equivalence. Our findings support Merton’s core argument that scientific discoveries arise from historical context rather than individual genius. The results reveal a long-tail distribution pattern of multiple discoveries across various datasets, challenging Merton’s Poisson model while reinforcing the structural inevitability of scientific progress.

Read the full article at: arxiv.org

It started as a fantasy, then a promise — inspired by biology and animated by the ideas of physicists — and grew to become a powerful research tool. Now artificial intelligence has evolved into something else: a junior colleague, a partner in creativity, an impressive if unreliable wish-granting genie. It has changed everything, from how we relate to data and truth, to how researchers devise experiments and mathematicians think about proofs. In this special series, we explore how AI is changing what it means to do science and math, and what it means to be a scientist.

Read the full article at: www.quantamagazine.org

Michael Levin

Advanced Intelligent Systems,

Recent discussions and debate around artificial intelligence (AI) and its status are notably incomplete, missing the implications of highly relevant aspects of the emerging fields of diverse intelligence (DI) and synthetic morphology, as well as of basic facts of developmental biology. Herein, it is argued that human flourishing is impossible without an appreciation of the space of possible beings and of the ways in which today’s intelligent machine debates are about universal existential questions facing biological beings, not just AI. The inevitable arrival of a wide set of unconventional bodies and minds as humans modify and create new forms will disrupt untenable old narratives of what people are and how to recognize their sentient allies in unfamiliar guises. Herein, the issues engendered by the advent of AI from the perspective of the field of DI and the evolutionary history of the bodies and minds are discussed.

Read the full article at: advanced.onlinelibrary.wiley.com