Complexity Thoughts: Issue #22
Unraveling complexity: building knowledge, one paper at a time
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Dall-e 3 representation of this issue’s content
In a Nutshell
In this issue, we explore of stochastic thermodynamics, with a novel measure to quantify the time-reversal asymmetry in systems and establishing thermodynamic bounds on this asymmetry. We then shift to the dynamics of group consensus, highlighting the role of cross-inhibition in achieving it even among asocial or strongly opinionated minorities. Challenging the traditional “edge of chaos hypothesis”, we have a study showing that real-world biological systems exhibit a higher degree of order and robustness against perturbations than previously thought. We also touch how stigmergy in termite nests can drive self-organization, and the unexpected cognitive capabilities of birds with small, non-cortical brains, redefining our understanding of complex cognition. And more.
Foundations of network science and complex systems
Thermodynamic bounds on time-reversal asymmetry
Our bound directly connects the time-reversal asymmetry with its cause
If you are interested in stochastic thermodynamics you know that the irreversibility of a (nonequilibrium) process emerges from fluctuations in stochastic trajectories. Irreversibility is related to time-reversal asymmetry and implies a thermodynamic cost: the latter is difficult to infer from partial information obtained experimentally.
Quantifying irreversibility of a system using finite information constitutes a major challenge in stochastic thermodynamics. We introduce an observable that measures the time-reversal asymmetry between two states after a given time lag. Our central result is a bound on the time-reversal asymmetry in terms of the total cycle affinity driving the system out of equilibrium. This result leads to further thermodynamic bounds on the asymmetry of directed fluxes, on the asymmetry of finite-time cross-correlations, and on the cycle affinity of coarse-grained dynamics.
It is not trivial to reach a consensus in a group of units, especially when social behavior cannot be given for granted. The authors propose a model to reach consensus even in situations where we’d not expect it to emerge. The result is relevant, since it might provide some explanation for observations in empirical complex systems — e.g., in social insect colonies such as bees or in complex brains (see here) — characterized by signalling of inhibitory information.
The topic is rather intricate and interesting, and I warmly recommend to read the whole special issue recently dedicated to liquid and solid brains (see here for the introduction) where an attempt to map (dis)similarities across complex systems, and much more, is performed.
Example cognitive networks. The figure illustrates four classes of cognitive networks, based on whether or not actual neurons are present or absent and on the physical organization of the network. [→ Source]
Such a simple change in individual behaviour revealed a dramatic change in the collective dynamics of the swarm, enabling system stability and symmetry-breaking
Strongly opinionated minorities can have a dramatic impact on the opinion dynamics of a large population. Two factions of inflexible minorities, polarised into two competing opinions, could lead the entire population to persistent indecision. Equivalently, populations can remain undecided when individuals sporadically change their opinion based on individual information rather than social information. Our analysis compares the cross-inhibition model with the voter model for decisions between equally good alternatives, and with the weighted voter model for decisions among alternatives characterised by different qualities. Here we show that cross-inhibition, contrary to the other two models, is a simple mechanism that allows the population to reach a stable majority for one alternative even in the presence of a relatively high amount of asocial behaviour. The results predicted by the mean-field models are confirmed by experiments with swarms of 100 locally interacting robots. This work suggests an answer to the longstanding question of why inhibitory signals are widespread in natural systems of collective decision making, and, at the same time, it proposes an efficient mechanism for designing resilient swarms of minimalistic robots.
Biological Systems
Models of Cell Processes are Far from the Edge of Chaos
Complex biological systems, like yeast cells, operate in a delicate balance between rigid order and disorder, while adapting to environmental and internal cues without chaotic behavior, and this balance is crucial for evolutionary adaptability. In fact, some degree of phenotypic mutability in biological systems is beneficial for their adaptability and evolution, although excessive mutability can hinder evolutionary progress or even cause population collapse.
Traditionally, there is an hypothesis about criticality in a living system: this relates to some boundary between order and disorder in living systems, with the “ideal state” being at this boundary. This is what it is usually meant with the expression “at the edge of chaos”. However, real-world biological models are different from theoretical ones: they exhibit non-random characteristics such as canalization, functional redundancy, and higher occurrences of source nodes, necessitating caution in applying theoretical models directly.
Here’s where this paper enters into the game: at variance with the criticality hypothesis, the authors find that real systems demonstrate greater robustness to perturbations than previously thought, especially in terms of long-term effects. While individual subsystems of a cell are highly ordered, critical behavior might emerge at larger scales through the interaction of various modules, pushing for further research into multiscale models.
Complex living systems are thought to exist at the “edge of chaos” separating the ordered dynamics of robust function from the disordered dynamics of rapid environmental adaptation. Here, a deeper inspection of 72 experimentally supported discrete dynamical models of cell processes reveals previously unobserved order on long time scales, suggesting greater rigidity in these systems than was previously conjectured. We find that propagation of internal perturbations is transient in most cases, and that even when large perturbation cascades persist, their phenotypic effects are often minimal. Moreover, we find evidence that stochasticity and desynchronization can lead to increased recovery from regulatory perturbation cascades. Our analysis relies on new measures that quantify the tendency of perturbations to spread through a discrete dynamical system. Computing these measures was not feasible using current methodology; thus, we developed a multipurpose CUDA-based simulation tool, which we have made available as the open-source Python library cubewalkers. Based on novel measures and simulations, our results suggest that—contrary to current theory—cell processes are ordered and far from the edge of chaos.
Metastatic cells exploit their stoichiometric niche in the network of cancer ecosystems
ecological analysis of individuals as ecosystems could be essential to understanding tumor biology, evolution, and clinical progression, providing a framework for potential therapeutic targets
Metastasis is a nonrandom process with varying degrees of organotropism—specific source-acceptor seeding. Understanding how patterns between source and acceptor tumors emerge remains a challenge in oncology. We hypothesize that organotropism results from the macronutrient niche of cells in source and acceptor organs. To test this, we constructed and analyzed a metastatic network based on 9303 records across 28 tissue types. We found that the topology of the network is nested and modular with scale-free degree distributions, reflecting organotropism along a specificity/generality continuum. The variation in topology is significantly explained by the matching of metastatic cells to their stoichiometric niche. Specifically, successful metastases are associated with higher phosphorus content in the acceptor compared to the source organ, due to metabolic constraints in proliferation crucial to the invasion of new tissues. We conclude that metastases are codetermined by processes at source and acceptor organs, where phosphorus content is a limiting factor orchestrating tumor ecology.
Self-organized biotectonics of termite nests
Stigmergy in termite societies is a fascinating concept where units don't rely on direct communication (like tactile or visual) to share information. Instead, they communicate indirectly through their building activities. When termites work, they leave behind a specific stimulus — like a pheromone they secrete — on the surface they're building on. Other termites sense this stimulus and respond to it, guiding their own building actions. This process allows termites to coordinate their activities and construct complex structures without needing a central command or explicit communication between individuals. The core of complexity!
These structures are not merely the byproduct of animal behavior, however, since they also play a central role in regulating the flow of information necessary for their own construction and function
Termite nests are a remarkable example of functional self-organization that show how structure and function emerge on multiple length and time scales in ecophysiology. To understand the process by which this arises, we document the labyrinthine architecture within the subterranean nests of the African termite Apicotermes lamani and develop a simple mathematical model that relies on the physical and biological interactions between termites, pheromones, and mud in the nest. Our model explains the formation of parallel floors connected by linear and helical ramps, consistent with observations of real nests. In describing this multiagent system, we elucidate principles of physical and behavioral coupling with relevance to swarm intelligence and architectural design.
Neuro & Cognitive Sciences
This is an Opinion piece, but it poses some interesting questions while providing some potential explanations. Not size but content matters.
Since corvids, parrots, and great apes show equal abilities for complex cognition, the idea that large and isocortical brains are a prerequisite for complex cognition is challenged
Many cognitive neuroscientists believe that both a large brain and an isocortex are crucial for complex cognition. Yet corvids and parrots possess non-cortical brains of just 1–25 g, and these birds exhibit cognitive abilities comparable with those of great apes such as chimpanzees, which have brains of about 400 g. This opinion explores how this cognitive equivalence is possible. We propose four features that may be required for complex cognition: a large number of associative pallial neurons, a prefrontal cortex (PFC)-like area, a dense dopaminergic innervation of association areas, and dynamic neurophysiological fundaments for working memory. These four neural features have convergently evolved and may therefore represent ‘hard to replace’ mechanisms enabling complex cognition.
Cortical reactivations predict future sensory responses
This highlights the idea that, more generally, when sensory experiences are sparse and punctuated by longer periods of quiet rest, offline reactivations may actively reorganize sensory-evoked response patterns to enhance the separability of population responses during distinct experiences55,58 while also potentially supporting pattern completion59, memory consolidation60, stabilization46 and associative learning18
Many theories of offline memory consolidation posit that the pattern of neurons activated during a salient sensory experience will be faithfully reactivated, thereby stabilizing the pattern1,2. However, sensory-evoked patterns are not stable but, instead, drift across repeated experiences3,4,5,6. Here, to investigate the relationship between reactivations and the drift of sensory representations, we imaged the calcium activity of thousands of excitatory neurons in the mouse lateral visual cortex. During the minute after a visual stimulus, we observed transient, stimulus-specific reactivations, often coupled with hippocampal sharp-wave ripples. Stimulus-specific reactivations were abolished by local cortical silencing during the preceding stimulus. Reactivations early in a session systematically differed from the pattern evoked by the previous stimulus—they were more similar to future stimulus response patterns, thereby predicting both within-day and across-day representational drift. In particular, neurons that participated proportionally more or less in early stimulus reactivations than in stimulus response patterns gradually increased or decreased their future stimulus responses, respectively. Indeed, we could accurately predict future changes in stimulus responses and the separation of responses to distinct stimuli using only the rate and content of reactivations. Thus, reactivations may contribute to a gradual drift and separation in sensory cortical response patterns, thereby enhancing sensory discrimination7.
Is the brain uncontrollable, like the weather?
The brain may be chaotic. Does that mean our efforts to control it are doomed?
Read this interesting piece from Nicole Rust, featuring a diverse set of opinions and ideas from several experts (disclaimer: I am in that list).
Evolution
Mutation enhances cooperation in direct reciprocity
What’s the role of direct reciprocity in the evolution of cooperation? Is it effective in repeated interactions among individuals?
Significant cooperation can evolve when the ratio of benefits to costs exceeds a certain threshold which is related to the length of memory of past interactions. It is interesting that, in scenarios where individuals remember only their most recent interaction (one-round memory), this threshold is identified as two.
Here the authors propose that intermediate mutation rates can foster high levels of cooperation, even when the benefit-to-cost ratio is close to (but still larger than) 1 and when individuals have minimal historical information. Explanation? Two factors: (1) mutation introduces diversity which weakens the evolutionary stability of non-cooperative (defector) behaviors; (2) mutation results in varied communities of cooperative individuals, which are more resilient compared to uniform groups.
We find that the variation introduced by mutations systematically favors cooperation
Evolution is not only fierce competition but also cooperation. The latter can be seen as the main architect of biological complexity. Cooperation means that one individual pays a cost for another individual to receive a benefit. Cooperation is opposed by natural selection unless a mechanism for evolution of cooperation is at work. One such mechanism is direct reciprocity which utilizes repeated interactions between the same individuals. Here, we report that intermediate mutation rates, which lead to diverse communities of cooperators, dramatically enhance the levels of cooperation in competitive settings. Therefore, mutation “cooperates” with cooperation to maximize its beneficial effect on life.
Computational Social Science
Do fossil fuel firms reframe online climate and sustainability communication? A data-driven analysis
our analysis offers observational evidence that the top polluting fossil fuel firms are responsive to online communication by NGOs on topics associated with environmental justice and climate change themes
Identifying drivers of climate misinformation on social media is crucial to climate action. Misinformation comes in various forms; however, subtler strategies, such as emphasizing favorable interpretations of events or data or reframing conversations to fit preferred narratives, have received little attention. This data-driven paper examines online climate and sustainability communication behavior over 7 years (2014–2021) across three influential stakeholder groups consisting of eight fossil fuel firms (industry), 14 non-governmental organizations (NGOs), and eight inter-governmental organizations (IGOs). We examine historical Twitter interaction data (n = 668,826) using machine learning-driven joint-sentiment topic modeling and vector autoregression to measure online interactions and influences amongst these groups. We report three key findings. First, we find that the stakeholders in our sample are responsive to one another online, especially over topics in their respective areas of domain expertise. Second, the industry is more likely to respond to IGOs’ and NGOs’ online messaging changes, especially regarding environmental justice and climate action topics. The fossil fuel industry is more likely to discuss public relations, advertising, and corporate sustainability topics. Third, we find that climate change-driven extreme weather events and stock market performance do not significantly affect the patterns of communication among these firms and organizations. In conclusion, we provide a data-driven foundation for understanding the influence of powerful stakeholder groups on shaping the online climate and sustainability information ecosystem around climate change.
Book of the week
Systems Medicine: Physiological Circuits and the Dynamics of Disease
By Uri Alon
