Complexity Thoughts: Issue #36
Unraveling complexity: building knowledge, one paper at a time
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Dall-e 3 representation of this issue’s content
Foundations of network science and complex systems
We expect our work to be of broad interest in not only computational sciences and nonequilibrium thermodynamics but also areas such as biological physics, where our results may apply to descriptions of information processing by living systems
Developing a thermodynamic theory of computation is a challenging task at the interface of nonequilibrium thermodynamics and computer science. In particular, this task requires dealing with difficulties such as stochastic halting times, unidirectional (possibly deterministic) transitions, and restricted initial conditions, features common in real-world computers. Here, we present a framework which tackles all such difficulties by extending the martingale theory of nonequilibrium thermodynamics to generic nonstationary Markovian processes, including those with broken detailed balance and/or absolute irreversibility. We derive several universal fluctuation relations and second-law-like inequalities that provide both lower and upper bounds on the intrinsic dissipation (mismatch cost) associated with any periodic process—in particular, the periodic processes underlying all current digital computation. Crucially, these bounds apply even if the process has stochastic stopping times, as it does in many computational machines. We illustrate our results with exhaustive numerical simulations of deterministic finite automata processing bit strings, one of the fundamental models of computation from theoretical computer science. We also provide universal equalities and inequalities for the acceptance probability of words of a given length by a deterministic finite automaton in terms of thermodynamic quantities, and outline connections between computer science and stochastic resetting. Our results, while motivated from the computational context, are applicable far more broadly.
Ecosystems and Evolution
The evolution of multicellular life spurred evolutionary radiations, fundamentally changing many of Earth’s ecosystems. Yet little is known about how early steps in the evolution of multicellularity affect eco-evolutionary dynamics. Through long-term experimental evolution, we observed niche partitioning and the adaptive divergence of two specialized lineages from a single multicellular ancestor. Over 715 daily transfers, snowflake yeast were subjected to selection for rapid growth, followed by selection favouring larger group size. Small and large cluster-forming lineages evolved from a monomorphic ancestor, coexisting for over ~4,300 generations, specializing on divergent aspects of a trade-off between growth rate and survival. Through modelling and experimentation, we demonstrate that coexistence is maintained by a trade-off between organismal size and competitiveness for dissolved oxygen. Taken together, this work shows how the evolution of a new level of biological individuality can rapidly drive adaptive diversification and the expansion of a nascent multicellular niche, one of the most historically impactful emergent properties of this evolutionary transition.
A formal test of the theory of universal common ancestry
This review by W. Ford Doolittle might be interesting to read before the paper, to understand the state of the art at the time of writing (especially about the role of horizontal gene transfer in the evolution of the tree of life).
Nature is not obliged to behave parsimoniously. — W. Ford Doolittle
Universal common ancestry (UCA) is a central pillar of modern evolutionary theory1. As first suggested by Darwin2, the theory of UCA posits that all extant terrestrial organisms share a common genetic heritage, each being the genealogical descendant of a single species from the distant past3,4,5,6. The classic evidence for UCA, although massive, is largely restricted to ‘local’ common ancestry—for example, of specific phyla rather than the entirety of life—and has yet to fully integrate the recent advances from modern phylogenetics and probability theory. Although UCA is widely assumed, it has rarely been subjected to formal quantitative testing7,8,9,10, and this has led to critical commentary emphasizing the intrinsic technical difficulties in empirically evaluating a theory of such broad scope1,5,8,9,11,12,13,14,15. Furthermore, several researchers have proposed that early life was characterized by rampant horizontal gene transfer, leading some to question the monophyly of life11,14,15. Here I provide the first, to my knowledge, formal, fundamental test of UCA, without assuming that sequence similarity implies genetic kinship. I test UCA by applying model selection theory5,16,17 to molecular phylogenies, focusing on a set of ubiquitously conserved proteins that are proposed to be orthologous. Among a wide range of biological models involving the independent ancestry of major taxonomic groups, the model selection tests are found to overwhelmingly support UCA irrespective of the presence of horizontal gene transfer and symbiotic fusion events. These results provide powerful statistical evidence corroborating the monophyly of all known life.
Neuroscience
Flexible information routing by transient synchrony
[..] in coupled circuits with transient synchrony, frequency tracking and out-of-phase locking of oscillatory bursts are emergent features of the large-scale circuit's collective dynamics
Perception, cognition and behavior rely on flexible communication between microcircuits in distinct cortical regions. The mechanisms underlying rapid information rerouting between such microcircuits are still unknown. It has been proposed that changing patterns of coherence between local gamma rhythms support flexible information rerouting. The stochastic and transient nature of gamma oscillations in vivo, however, is hard to reconcile with such a function. Here we show that models of cortical circuits near the onset of oscillatory synchrony selectively route input signals despite the short duration of gamma bursts and the irregularity of neuronal firing. In canonical multiarea circuits, we find that gamma bursts spontaneously arise with matched timing and frequency and that they organize information flow by large-scale routing states. Specific self-organized routing states can be induced by minor modulations of background activity.
Human behavior
A mechanistic model of gossip, reputations, and cooperation
our model offers a mechanistic justification for the common assertion that gossip can facilitate cooperation by indirect reciprocity
Social reputations facilitate cooperation: those who help others gain a good reputation, making them more likely to receive help themselves. But when people hold private views of one another, this cycle of indirect reciprocity breaks down, as disagreements lead to the perception of unjustified behavior that ultimately undermines cooperation. Theoretical studies often assume population-wide agreement about reputations, invoking rapid gossip as an endogenous mechanism for reaching consensus. However, the theory of indirect reciprocity lacks a mechanistic description of how gossip actually generates consensus. Here, we develop a mechanistic model of gossip-based indirect reciprocity that incorporates two alternative forms of gossip: exchanging information with randomly selected peers or consulting a single gossip source. We show that these two forms of gossip are mathematically equivalent under an appropriate transformation of parameters. We derive an analytical expression for the minimum amount of gossip required to reach sufficient consensus and stabilize cooperation. We analyze how the amount of gossip necessary for cooperation depends on the benefits and costs of cooperation, the assessment rule (social norm), and errors in reputation assessment, strategy execution, and gossip transmission. Finally, we show that biased gossip can either facilitate or hinder cooperation, depending on the direction and magnitude of the bias. Our results contribute to the growing literature on cooperation facilitated by communication, and they highlight the need to study strategic interactions coupled with the spread of social information.
Many studies have used mobile device location data to model SARS-CoV-2 dynamics, yet relationships between mobility behavior and endemic respiratory pathogens are less understood. We studied the effects of population mobility on the transmission of 17 endemic viruses and SARS-CoV-2 in Seattle over a 4-year period, 2018-2022. Before 2020, visits to schools and daycares, within-city mixing, and visitor inflow preceded or coincided with seasonal outbreaks of endemic viruses. Pathogen circulation dropped substantially after the initiation of COVID-19 stay-at-home orders in March 2020. During this period, mobility was a positive, leading indicator of transmission of all endemic viruses and lagging and negatively correlated with SARS-CoV-2 activity. Mobility was briefly predictive of SARS-CoV-2 transmission when restrictions relaxed but associations weakened in subsequent waves. The rebound of endemic viruses was heterogeneously timed but exhibited stronger, longer-lasting relationships with mobility than SARS-CoV-2. Overall, mobility is most predictive of respiratory virus transmission during periods of dramatic behavioral change and at the beginning of epidemic waves.
Perspectives
A third transition in science?
An evolving biosphere is a propagating construction, not an entailed deduction.
Since Newton, classical and quantum physics depend upon the ‘Newtonian paradigm’. The relevant variables of the system are identified. For example, we identify the position and momentum of classical particles. Laws of motion in differential form connecting the variables are formulated. An example is Newton’s three laws of motion. The boundary conditions creating the phase space of all possible values of the variables are defined. Then, given any initial condition, the differential equations of motion are integrated to yield an entailed trajectory in the prestated phase space. It is fundamental to the Newtonian paradigm that the set of possibilities that constitute the phase space is always definable and fixed ahead of time. This fails for the diachronic evolution of ever-new adaptations in any biosphere. Living cells achieve constraint closure and construct themselves. Thus, living cells, evolving via heritable variation and natural selection, adaptively construct new-in-the-universe possibilities. We can neither define nor deduce the evolving phase space: we can use no mathematics based on set theory to do so. We cannot write or solve differential equations for the diachronic evolution of ever-new adaptations in a biosphere. Evolving biospheres are outside the Newtonian paradigm. There can be no theory of everything that entails all that comes to exist. We face a third major transition in science beyond the Pythagorean dream that ‘all is number’ echoed by Newtonian physics. However, we begin to understand the emergent creativity of an evolving biosphere: emergence is not engineering.
Oldies but goldies
Complex Patterns in a Simple System
Numerical simulations of a simple reaction-diffusion model reveal a surprising variety of irregular spatiotemporal patterns. These patterns arise in response to finite-amplitude perturbations. Some of them resemble the steady irregular patterns recently observed in thin gel reactor experiments. Others consist of spots that grow until they reach a critical size, at which time they divide in two. If in some region the spots become overcrowded, all of the spots in that region decay into the uniform background.