Michele Braccini, Paolo Baldini, Andrea Roli

Artificial Life

Biological cells are usually operating in conditions characterized by intercellular signaling and interaction, which are supposed to strongly influence individual cell dynamics. In this work, we study the dynamics of interacting random Boolean networks, focusing on attractor properties and response to perturbations. We observe that the properties of isolated critical Boolean networks are substantially maintained also in interaction settings, while interactions bias the dynamics of chaotic and ordered networks toward that of critical cells. The increase in attractors observed in multicellular scenarios, compared to single cells, allows us to hypothesize that biological processes, such as ontogeny and cell differentiation, leverage interactions to modulate individual and collective cell responses.

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Federica De Domenico , Fabio Caccioli , Giacomo Livan , Guido Montagna and Oreste Nicrosini

Royal Society Open Science, July 2024 Volume 11 Issue 7

Participants in socio-economic systems are often ranked based on their performance. Rankings conveniently reduce the complexity of such systems to ordered lists. Yet, it has been shown in many contexts that those who reach the top are not necessarily the most talented, as chance plays a role in shaping rankings. Nevertheless, the role played by chance in determining success, i.e. serendipity, is underestimated, and top performers are often imitated by others under the assumption that adopting their strategies will lead to equivalent results. We investigate the tradeoff between imitation and serendipity in an agent-based model. Agents in the model receive payoffs based on their actions and may switch to different actions by either imitating others or through random selection. When imitation prevails, most agents coordinate on a single action, leading to non-meritocratic outcomes, as a minority of them accumulate the majority of payoffs. Yet, such agents are not necessarily the most skilled ones. When serendipity dominates, instead, we observe more egalitarian outcomes. The two regimes are separated by a sharp transition, which we characterize analytically in a simplified setting. We discuss the implications of our findings in a variety of contexts, ranging from academic research to business.

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Stuart J Bartlett

I introduce a new approach to semantic information based upon the influence of erasure operations (interventions) upon distributions of a system’s future trajectories through its phase space. Semantic (meaningful) information is distinguished from syntactic information by the property of having some intrinsic causal power on the future of a given system. As Shannon famously stated, syntactic information is a simple property of probability distributions (the elementary Shannon expression), or correlations between two subsystems and thus does not tell us anything about the meaning of a given message. Kolchinsky & Wolpert (2018) introduced a powerful framework for computing semantic information, which employs interventions upon the state of a system (either initial or dynamic) to erase syntactic information that might influence the viability of a subsystem (such as an organism in an environment). In this work I adapt this framework such that rather than using the viability of a subsystem, we simply observe the changes in future trajectories through a system’s phase space as a result of informational interventions (erasures or scrambling). This allows for a more general formalisation of semantic information that does not assume a primary role for the viability of a subsystem (to use examples from Kolchinsky & Wolpert (2018), a rock, a hurricane, or a cell). Many systems of interest have a semantic component, such as a neural network, but may not have such an intrinsic connection to viability as living organisms or dissipative structures. Hence this simple approach to semantic information could be applied to any living, non-living or technological system in order to quantify whether a given quantity of syntactic information within it also has semantic or causal power.

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Carlos Gershenson

I present a personal account of self-organizing systems. As such, it is necessarily biased and partial. Nevertheless, it should be useful to motivate useful discussions. The relevant contribution is not my attempts at answering questions (maybe all my answers are wrong), but the steps towards framing relevant questions to better understand self-organization, information, complexity, and emergence. With this aim, I start with a notion and examples of self-organizing systems (what?), continue with their properties and related concepts (how?), and close with applications (why?).

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