Author: cxdig

Anticipatory Agents in Causal Bubbles: Reconciling Quantum Bayesianism, Rosen’s Anticipatory Systems, and Pragmatic Constructivism 

Michael Lissack

This paper presents a unified theoretical framework that reconciles four apparently disparate approaches: Quantum Bayesianism (QBism), Robert Rosen’s theory of Anticipatory Systems, the causal bubbles interpretation of quantum mechanics, and pragmatic constructivism through Hans Vaihinger’s philosophy of ‘as if.’ We demonstrate that these frameworks converge on a fundamental insight: reality emerges from a relational causal structure-the pattern of influences that determine what can affect what-rather than from external observation. The QBist agent exemplifies a Rosen Anticipatory System operating within a causal bubble, wherein the quantum wave function serves as a heuristic fiction-an ‘as if’ construct-used for anticipatory modeling within the agent’s architecture rather than for ontological description. This synthesis resolves longstanding quantum paradoxes, provides a naturalized account of final causality, and extends to encompass human cognition and artificial intelligence as distinct instantiations of the same anticipatory pattern. We argue that physical laws function as normative standards for coherent anticipation that acquire constraining force through selective pressure, and that this relational ontology bridges quantum physics, theoretical biology, epistemology, and cognitive science, dissolving apparent conflicts between these domains into perspectives on a shared structure.

Read the full article at: papers.ssrn.com

CfP: Variational, Nonequilibrium, and Optimization Principles of the Coevolution of Structure and Dynamics in Complex Systems

Complex systems fascinate because of the way dynamic microscopic interactions give rise to striking, often unexpected macroscopic structures: convection cells in fluids, patterns in ecosystems, networks in societies, and organization in biology. What unites these diverse examples is the deep link between how the agents in systems move and what structure emerges. While diverse approaches have been proposed, in addition, a unifying language may lie in variational principles and optimal control in stochastic and dissipative regimes which can offer a powerful language for understanding this interplay.

Action principles are among the most unifying ideas in science: from Lagrangian mechanics to quantum field theory, they describe how nature selects pathways. The stochastic-dissipative extensions of the principle of least action in the form of path integrals, such as by Onsager-Machlup and more recent versions provide a natural framework for describing how agents and processes, obeying fundamental physical laws, select the most probable and efficient pathways under constraints. These pathways not only govern system dynamics but also generate—and are constrained by—emergent structures. Feedback between dynamics and structure thus shapes evolution, with frozen accidents and historical contingencies balanced against tendencies toward action-efficient configurations. If dynamics select the most probable, efficient pathways, then structure itself may be seen as the lasting imprint of such pathways. Can such principles also help explain the emergence of complexity?

This Collection aims to gather theoretical, computational, and empirical contributions that advance the use of variational principles to explain and predict structure–dynamics interplay in complex systems. By doing so, we hope to move toward general non-equilibrium thermodynamics capable of grounding complexity science in physics while connecting to diverse domains of application. Contributions are welcome across disciplines, from mathematics and physics to biology, engineering, and social sciences. Themes may include, but are not limited to:

  • Stochastic and dissipative formulations of variational principles.
  • Path integrals and optimal control.
  • Structure formation in non-equilibrium thermodynamics.
  • Agent-based simulations and computational models.
  • Empirical case studies from physical, chemical, biological, or social systems.
  • Comparative perspectives with non-variational approaches.

The aim is to advance a physics-grounded framework for understanding how complex structures emerge and persist under dynamic constraints. The objective of this Collection is to foster dialogue among researchers working on different manifestations of the same fundamental questions: How do dynamics give rise to structure, how structure determines dynamics, and how can variational principles provide the key to understanding this process across scales and systems? Can variational pathways explain the emergence of complex structures from dynamics across nature and society?

More at: www.nature.com

APCNCS 2026 – Asia-Pacific Summer School and Conference on Networks and Complex Systems

9-12 June, 2026
Nanyang Technological University, Singapore

The number of scientists working on networks and complex systems in the Asia-Pacific region is increasing, but high-level conferences in these areas remain limited to NetSci, NetSciX, the International Conference on Computational Science (ICCS), and Conference on Complex Systems (CCS). Asia-Pacific scientists, especially postdocs and PhD students in these areas therefore have limited opportunities to attend these conferences. This leads to a lack of exposure of Asia-Pacific scientists to good work done elsewhere in the world, and of scientists from other parts of the world to good work done in Asia-Pacific, and seriously hampers the academic growths of Asia-Pacific scientists. Recently, we have been encouraged by the strong turnout of Indian scientists at all levels at the NetSciX 2025 conference in Indore, India. We can sense that the younger scientists treasured this opportunity to share their work. Unfortunately, it is impossible to bring these flagship conferences to Asia-Pacific every year. At best, we can host one such conference in Asia once every three to four years.

This prompted us to start an Asia-Pacific Conference on Networks and Complex Systems beginning next year (2-5 Jun 2026 in Singapore) to cater to these unmet demands. With this conference, we would always have a platform to present our recent work and meet up with colleagues. While we will always invite the most exciting speakers from all around the world, our goal is to include more invited speakers from Asia-Pacific. We also aim to give as many participants as possible (including PhD students) a chance to give oral presentations. We will rotate this conference series around Asia-Pacific, where most venues are more affordable compared to Europe or North America. Hopefully, more Asian scientists would be able to attend, even if they have limited funding. This is especially true of PhD students and postdoctoral researchers, many of whom can only attend the local editions of the conference.

Finally, to ensure that the conference is relevant and high-level, we will be advised by an International Advisory Committee of eminent scientists in the fields of networks and complex systems. Additionally, to ensure that good organizational practices are shared after they are developed, besides local organizers the organizing team for a given year will also comprise core members from the organizing team for the next year.

More at: apcncs2026.github.io

Copy or collaborate? How networks impact collective problem solving

Gülşah Akçakır, John C. Lang & P. J. Lamberson
npj Complexity volume 2, Article number: 35 (2025)

Collaboration enables groups to solve problems beyond the reach of their individual members in contexts ranging from research and development to high-energy physics. While communication networks play a pivotal role in group success, there is a longstanding debate on the optimal network topology for solving complex problems. Prior research reaches contradictory conclusions–some studies suggest networks that slow information transmission help maintain diversity, leading groups to explore more of the problem space and find better solutions in the long run, while others argue that networks that maximize communication efficiency allow groups to exploit known solutions, boosting overall performance. Many existing models assume that individuals use their network connections only to copy better-performing group members, but we show that such groups often perform worse than if individuals worked independently. Instead, our model introduces a crucial distinction: in addition to copying, individuals can actively collaborate, leveraging diverse perspectives to uncover solutions that would otherwise remain inaccessible. Our findings reveal that the optimal network structure depends on the balance between copying and collaboration. When copying dominates, inefficient, exploration-focused networks lead to better outcomes. However, when individuals primarily collaborate, highly connected, efficient networks win out. We also show how groups can reap the benefits of both strategies by employing a collaborate first-copy later heuristic in highly connected networks. The results offer new insights into how organizations should be structured to maximize problem-solving performance across different contexts.

Read the full article at: www.nature.com