Mikhail Prokopenko, Paul C. W. Davies, Michael Harré, Marcus Heisler, Zdenka Kuncic, Geraint F. Lewis, Ori Livson, Joseph T. Lizier, Fernando E. Rosas

We study open-ended evolution by focusing on computational and information-processing dynamics underlying major evolutionary transitions. In doing so, we consider biological organisms as hierarchical dynamical systems that generate regularities in their phase-spaces through interactions with their environment. These emergent information patterns can then be encoded within the organism’s components, leading to self-modelling “tangled hierarchies”. Our main conjecture is that when macro-scale patterns are encoded within micro-scale components, it creates fundamental tensions (computational inconsistencies) between what is encodable at a particular evolutionary stage and what is potentially realisable in the environment. A resolution of these tensions triggers an evolutionary transition which expands the problem-space, at the cost of generating new tensions in the expanded space, in a continual process. We argue that biological complexification can be interpreted computation-theoretically, within the Gödel–Turing–Post recursion-theoretic framework, as open-ended generation of computational novelty. In general, this process can be viewed as a meta-simulation performed by higher-order systems that successively simulate the computation carried out by lower-order systems. This computation-theoretic argument provides a basis for hypothesising the biological arrow of time.

Read the full article at: arxiv.org

Kevin M. Stoltz, Cliff A. Joslyn

Boolean networks are a valuable class of discrete dynamical systems models, but they remain fundamentally limited by their inability to capture multi-way interactions in their components. To remedy this limitation, we propose a model of Boolean hypernetworks, which generalize standard Boolean networks. Utilizing the bijection between hypernetworks and bipartite networks, we show how Boolean hypernetworks generalize standard Boolean networks. We derive ensembles of Boolean hypernetworks from standard random Boolean networks and simulate the dynamics of each. Our results indicate that several properties of Boolean network dynamics are affected by the addition of multi-way interactions, and that these additions can have stabilizing or destabilizing effects.

Read the full article at: arxiv.org

Sean M. Carroll & Achyuth Parola

We consider emergence from the perspective of dynamics: states of a system evolving with time. We focus on the role of a decomposition of wholes into parts, and attempt to characterize relationships between levels without reference to whether higher-level properties are “novel” or “unexpected.” We offer a classification of different varieties of emergence, with and without new ontological elements at higher levels.

Read the full article at: philarchive.org

YI ZHAO, THOMAS P. WYTOCK, KIMBERLY A. REYNOLDS, AND ADILSON E. MOTTER 
SCIENCE ADVANCES
28 Aug 2024
Vol 10, Issue 35

Irreversibility, in which a transient perturbation leaves a system in a new state, is an emergent property in systems of interacting entities. This property has well-established implications in statistical physics but remains underexplored in biological networks, especially for bacteria and other prokaryotes whose regulation of gene expression occurs predominantly at the transcriptional level. Focusing on the reconstructed regulatory network of Escherichia coli, we examine network responses to transient single-gene perturbations. We predict irreversibility in numerous cases and find that the incidence of irreversibility increases with the proximity of the perturbed gene to positive circuits in the network. Comparison with experimental data suggests a connection between the predicted irreversibility to transient perturbations and the evolutionary response to permanent perturbations.

Read the full article at: www.science.org

Ilya Horiguchi, Takahide Yoshida, Takashi Ikegami

Recent advancements in Large Language Models (LLMs) have spurred a surge of interest in leveraging these models for game-theoretical simulations, where LLMs act as individual agents engaging in social interactions. This study explores the potential for LLM agents to spontaneously generate and adhere to normative strategies through natural language discourse, building upon the foundational work of Axelrod’s metanorm games. Our experiments demonstrate that through dialogue, LLM agents can form complex social norms, such as metanorms-norms enforcing the punishment of those who do not punish cheating-purely through natural language interaction. The results affirm the effectiveness of using LLM agents for simulating social interactions and understanding the emergence and evolution of complex strategies and norms through natural language. Future work may extend these findings by incorporating a wider range of scenarios and agent characteristics, aiming to uncover more nuanced mechanisms behind social norm formation.

Read the full article at: arxiv.org