Papers

Inference of phylogenetic trees from sequences plays a central role in understanding evolutionary relationships, with applications ranging from tracking pathogen spread to reconstructing the history of life. Conventionally, practitioners obtain posteriors over trees using likelihood-based methods like MCMC. However, likelihoods are only available for simple evolutionary models with restrictive assumptions. For realistic evolutionary models, inference using conventional methods is prohibitively expensive, inaccurate, or even impossible. In practice, practitioners settle for using as flexible a conventional method as possible, even though they expect it is misspecified. In principle, we could use simulation-based inference: simulate tree and sequence data from an evolutionary model and train a neural network to predict the posterior over trees given sequences. However it is a mathematical challenge to represent and train complex posterior distributions over trees. Here we present a flexible family of conditional generative models over trees. Our models iteratively build trees using three natural paradigms – top-down, middle-out, or bottom-up – and are trained using a discrete diffusion framework. For real and synthetic datasets of up to $65$ taxa and $>10^5$ tokens, our models recover MCMC posteriors much more accurately than the previous state-of-the-art simulation-based method Phyloformer 2. Finally, we compare to IQ-Tree, a popular flexible-but-misspecified conventional method. Our models match its tree estimates on simple data, but on more realistic data, our method is reliably more accurate.

Maximum compatibility provides a criterion for phylogenetic reconstruction in which the objective is to retain a maximum set of mutually compatible characters. For binary characters, pairwise compatibility can be represented by a weighted graph whose vertices correspond to characters and whose edges connect compatible pairs. A leading algorithm by Cherry [BMC Bioinformatics, 18(1):127, 2017] uses this representation to identify maximum compatible character sets as maximum weight cliques. We formulate the problem as maximum weight independent set enumeration on the complement graph and use deterministic postprocessing to map sampled bit strings to maximal feasible solutions. To explore the prospects of quantum computational advantage, we further construct bespoke correction-in-the-loop Quantum Approximate Optimisation Algorithm instances and find that traditional Lagrange multiplier choices may not be optimal for the enumeration problem on noisy devices. We also introduce lossless graph reductions with reconstruction rules that preserve the complete set of optimal solutions. Our results indicate that graph reduction and sampling can be effectively combined to address the enumeration bottleneck in maximum compatibility phylogeny, while retaining exact reconstruction when the residual optimum set is completely resolved. We enhance Cherry's approach to maximum compatibility by introducing graph reduction techniques and quantum sampling. Our classical benchmarks show a clear difference between finding one optimum and recovering the optimum set in practice. We demonstrate the utility of our methods on graphs constructed from SARS-CoV-2 sequences.

Developing resilient crops requires models that accurately capture how plant growth dynamics emerge from the continuous interplay between genetic and environmental factors. While high-throughput phenotyping platforms generate data for dynamic growth models, current modelling approaches show limitations: process-based crop growth models (CGMs) capture environmental dynamics but are difficult to scale across many genotypes because they require calibration of many parameters, while quantitative genetic models allow modelling many genotypes but often summarise time-varying environments using static covariates. To address this gap, we propose a data-driven framework to identify reduced systems of ordinary differential equations (ODEs) describing crop growth dynamics as functions of environmental variables and a controllable number of genotype-specific coefficients (GSCs). Using the Sparse Identification of Nonlinear Dynamics (SINDy) method, we infer model structure directly from data via sparse regression on a physiologically informed library of candidate functions. In this framework, GSCs parameterise cultivar-specific differences in response to environmental conditions. The resulting models are linear in these GSCs, making them compatible with mixed-model and genomic prediction approaches. We evaluate the approach on an in silico dataset generated with WOFOST for five potato cultivars across twelve growing seasons. Our identified ODEs generalise to held-out years and achieve $R^2 > 0.9$ for end-of-season yield prediction across held-out years when compared to full-scale WOFOST simulations. These results provide a proof of concept for SINDy-based identification of parsimonious dynamic crop models from longitudinal crop-growth data.

Probability generating functions (PGFs) are a fundamental tool in mathematical epidemiology to characterize the stochastic evolution of epidemics via branching process theory. However, they are rarely used as operational tools in real-time during an outbreak, the main barrier being a lack of a systematic, computational methodology to describe epidemiological time series, such as cases or hospital admissions over time. Here, we introduce a general and computationally efficient framework for outbreak analytics based on PGFs. We first develop a method to encode joint distributions of incidences and their noisy measurements by realistic surveillance systems. Then, by transforming PGFs to cumulant generating functions (CGFs), we enable efficient evaluation of these joint distributions. We demonstrate the practicality of our approach for Bayesian inference and short-term forecasts, offering a significant computational advantage over simulation-based methods. Altogether, our framework operationalizes generating functions and paves the way for their broader adoption in computational epidemiology.

Epidemiological models often take the form of systems of differential equations of varying levels of complexity. In multi-strain models, complexity arises from the interaction of diverse pathogen types that may exhibit different patterns of cross-immunity due to the host's immune system. Where previous work has tended to focus on theoretical analysis of this complexity, or model simplification, less effort has been given to efficient numerical exploration of full multi-strain disease dynamics. Here, we propose a computational framework for representing multi-strain disease transmission models, through a mathematically transparent hard-coded mapping between sets and integers. By mapping infection histories into Boolean matrices, the framework eliminates explicit iteration over strain combinations while preserving key biological processes, including sequential infection and partial cross-immunity. Benchmark comparisons of numerical solutions of our matrix formulation and a set-indexed implementation demonstrate substantial performance gains. Across models with increasing numbers of strains, the matrix formulation consistently reduces runtime, with a speedup exceeding two orders of magnitude for systems with 11 strains. We present an application of this approach to the case of COVID-19 in Indonesia, focusing on the replacement of the Delta variant by Omicron. We hope that our approach represents a robust computational approach to automatically generate diverse multi-strain epidemic models that can be solved in a computationally efficient manner for a variety of scientific and public-health questions.

Does evolutionary entropy depend on the width of the age classes used to describe a population? We show that the apparent dependence on temporal resolution is a consequence of the loss of information about reproductive timing within discrete classes. When this information is retained, corresponding continuous and discrete representations yield the same cycle entropy, generation time, evolutionary entropy, and reproductive potential in a common unit of time. When within-class timing is unknown, a uniform local reconstruction yields a logarithmic correction. For constant post-maturity hazard and fertility, continuous exponential and discrete geometric descriptions correspond exactly at every time scale. Thus, for a fixed reference duration, evolutionary entropy is a property of the reproductive process, not of its temporal partition. These results provide a mathematical basis for comparing demographic models across different temporal scales by identifying the information required to separate differences in reproductive organisation from effects of the chosen age-class representation.

Learning agents, whether humans or algorithms, adapt to one another rather than to a fixed environment. Selfish learning need not produce a good outcome for the group, especially when learners face a social dilemma. The iterated prisoner's dilemma (IPD) is the classical example, but it is usually studied in populations of agents who imitate successful strategies. Here, we instead study selfish gradient ascent learners on the two-dimensional space of reactive strategies. A reactive strategy is described by two continuous traits: reciprocity, the chance of cooperating after the co-player cooperates, and forgiveness, the chance of cooperating after the co-player defects. Each learner updates its strategy in the direction that most rapidly improves its payoff against all other learners, by an amount governed by its learning rate. We show that heterogeneity in learning rates can qualitatively change the long-term outcome: a population with uniform learning rates converges to always-defect (ALLD), whereas a population with variable learning rates approaches generous-tit-for-tat (GTFT), which produces nearly optimal collective payoffs. In simulations, every population passes through a transient state of minimal forgiveness, in which the distribution of reciprocity determines the outcome. Variable learning rates promote cooperation because slow learners preserve high reciprocity until the population reaches this state. We prove sufficient conditions on the distribution of reciprocity for convergence to ALLD or to tit-for-tat (TFT). Variable learning rates also allow cooperation to recover from perturbation, which is provably impossible under uniform learning. These results suggest that purely selfish and myopic learning can produce persistent cooperation provided the population has intrinsic variability in learning rates.

Persistent viral infections arise from complex interactions between viral replication, host cell responses, and ongoing viral evolution. A general framework linking viral fitness to persistence dynamics is lacking. Here, we develop for the first time a mathematical model of viral persistence that takes into consideration viral fitness variations. Essential to the model is the partition of the classical fitness parameter into three components: replicative, infective, and dispersal fitness. The model was initially inspired by a new experiment on hepatitis C virus (HCV) persistence, established in human hepatoma cells, also reported in this work. This experiment documents two strikingly different viral trajectories depending on the initial replicative fitness of the viral population used to establish persistence. The dynamical model describes the interactions among uninfected cells, infected cells, and infectious virions, and it incorporates, through a continuum, two alternative mechanisms of viral release from cells: budding and lysis. Analysis of the model reveals that viral fitness parameters organise infection outcomes into distinct dynamical regimes. Low replicative and dispersal fitness values lead to viral extinction, whereas high values enable persistence through either stable coexistence or recurrent infection waves. The space of fitness discloses a hierarchy among these parameters, with replicative and dispersal fitness being able to trigger important shifts in the outcome of the infection as opposed to infective fitness. The model identifies trade-offs between replication and dispersal that shape viral production and predicts slow dynamical regimes in which infection may persist despite low detectable viral loads. It provide candidates mechanisms capable of generating the persistence patterns observed experimentally.

Invasive alien species (IAS) generate substantial ecological and economic damages that often transcend political boundaries, creating strong incentives for coordinated management. We develop a two-region ecological-economic model of IAS control that integrates prevention and eradication measures with population dynamics characterized by logistic growth and an Allee effect. We derive analytical results on the interaction between ecological thresholds and strategic behaviour and compare outcomes under cooperation (central planner) and non-cooperation (open loop Nash equilibrium and Markovian Nash equilibrium). We show that, under realistic control costs, neither prevention nor eradication can eliminate invasive populations in finite time, and complete prevention of cross-border spread is generally not optimal. In the absence of an Allee effect, the zero-population equilibrium is unstable, implying long-run persistence of the invasive species. By contrast, the presence of an Allee effect fundamentally alters system dynamics by making extinction locally stable and creating a basin of attraction within which eradication becomes feasible. This finding highlights the economic significance of ecological thresholds and demonstrates how delays in management can shift populations beyond critical levels, after which eradication is unlikely regardless of subsequent control efforts. Numerical simulations further show that cooperative management expands the set of initial conditions leading to eradication and consistently reduces aggregate social costs relative to decentralized management. The results provide a formal rationale for rapid intervention, precautionary regulation, and stronger multilateral cooperation in IAS management, particularly in jurisdictions such as the European Union where listing and control actions may be delayed pending evidence of harm.

Targeted vaccination often prioritizes individuals who contribute most to transmission. However, this rationale may change under leaky protection, whereby vaccination reduces susceptibility at each exposure rather than fully preventing infection. Here, we use a survey-derived individual-level exposure framework for 2,279 residents to simulate infection dynamics across ranges of transmission intensity and vaccine efficacy. We compare two vaccination strategies: the high-exposure strategy (prioritizing highly exposed individuals) and the oldest-first strategy (prioritizing older individuals). As transmission intensity changes, we identify three patterns in the relative advantages of these two strategies. At low transmission, the high-exposure strategy outperforms the oldest-first strategy in reducing both epidemic peak size and final infection proportion. At intermediate transmission, it lowers epidemic peak size but increases final infection proportion, revealing a trade-off between these metrics. At high transmission, differences between the strategies narrow, and the oldest-first strategy has a slight advantage for both metrics. Overall, our findings highlight the importance of considering vaccine failure mode when evaluating vaccination policies under different transmission dynamics.

Rate-induced tipping ("R-tipping") in ecological modelling is characterised by too-rapid change of an environmental parameter causing collapse of a population without any bifurcation being crossed ("B-tipping"). A well-known example of a slow-fast predator-prey model in which R-tipping is observed [Vanselow, Wieczorek, Feudel, Journal of Theoretical Biology, 479, 64-72 (2019)] suffers from unecological "resurgence", whereby populations driven to functional extinction recover towards stable coexistence. We propose an analytically tractable model, incorporating a strong Allee effect into the prey dynamics in a slow-fast Lotka-Volterra-type predator-prey system, which induces bistability and thus renders the extinction state a genuine attractor. Applying geometric singular perturbation theory (GSPT), we describe the dynamics of the extended model that is obtained by "ramping" of the inverse prey carrying capacity. We show the presence of a parabolic-shaped, folded critical manifold which admits a folded saddle singularity, the strong canard of which separates solutions that track the moving coexistence state from those that tip to extinction. The analytical simplicity of our model allows us to derive explicit expressions for the critical rate that separates "tracking" from "tipping" dynamics. Finally, we argue that our system represents a minimal model for rate-induced tipping to extinction, in that it incorporates three essential ingredients: bistability, a folded critical manifold, and a folded-saddle-type canard.

The marked difference in reported human Lyme disease cases between the northeastern and southeastern United States remains a significant concern. Differences in biodiversity and the questing behavior of Ixodes scapularis, the primary vector of Borrelia burgdorferi, the causative agent of Lyme disease, have been proposed as key drivers of geographic variation in disease incidence. In this study, we investigate how these differences, together with ratio-dependent tick-host contact rates, contribute to the north-south gap in Lyme disease cases. Building on our previous work, we develop a discrete-time system of difference equations to model infection dynamics between ticks and their sylvatic hosts. We derive an expression for the basic reproductive number and vary key parameters to assess their impact on infection persistence. We further examine the effects of these mechanisms on nymphal infection prevalence (NIP) and the density of infected nymphs (DIN), two key metrics for predicting disease risk. Our results indicate that under baseline conditions, southern host communities exhibit lower infection prevalence and reduced potential for pathogen invasion, consistent with observed regional patterns. However, southern host communities can support sustained transmission with different questing behaviors. Notably, this qualitative north-south difference emerges using parameter values obtained from the literature, without calibrating the model to reproduce geographic differences in disease prevalence. In addition, ratio-dependent host-finding success shows a saturating effect on the basic reproductive number, compared to the traditional assumption of density-independent host-finding success. These results highlight the importance of biodiversity, tick questing behavior, and using multiple metrics when assessing Lyme disease risk.

Nonpharmaceutical interventions (NPIs), such as mask wearing, can reduce epidemic spread, but their effectiveness depends on whether people adopt and sustain them. The incentive to adhere changes with infection risk and with the behavior of others. Yet it remains unclear how epidemiological protection and social reinforcement determine the strategic structure of infection control, and how epidemic feedback changes that structure over time. Here, we develop a socio-epidemiological model in which prevalence changes behavioral incentives while adherence changes transmission, separating private protection from source control and incorporating conformity. At a given prevalence, private protection favors adherence, source control creates opportunities for free-riding, and conformity reinforces whichever behavior is already common, generating distinct adherence games. Because adherence reduces transmission and prevalence, the population can move between these games as the epidemic unfolds. This feedback can stabilize partial adherence, create alternative behavioral norms, or generate recurrent epidemic-behavior cycles. Moreover, interventions with the same population-level effect on transmission at a given adherence level can produce different long-term outcomes when protection is distributed differently between the individual and others. Thus, voluntary infection control is a feedback-evolving game whose outcome depends not only on epidemiological efficacy but also on who is protected and how behavior is socially reinforced.

Characterizing the genetic architecture of phenotypic variation is central to human genetics, yet trait polygenicity—the diversity of genetic variants responsible for a heritable trait's variation in a sample—is rarely quantified formally. O'Connor and Sella (2026) addressed this need by proposing four polygenicity measures that are quasi-arithmetic means and therefore satisfy a set of principles they argue are desirable for polygenicity measures. Here, I show that these four measures are points on a continuum corresponding to the Hill numbers, a family of ecological diversity statistics offering a tunable emphasis on variants with small versus large contributions. I further show that the Hill numbers are the only quasi-arithmetic means suitable for measuring polygenicity because they are the only ones that are "effective numbers" of variants. The connection to the Hill numbers clarifies the relationships among O'Connor and Sella's four polygenicity measures: all four are effective numbers of variants, and those that give greater weight to variants with small contributions yield higher estimates of polygenicity. It also connects polygenicity to results in the ecological diversity literature, including the replication principle, a criterion for how such measures should behave when independent sets of variants are combined. Together, these results offer a unified framework for interpreting, comparing, and selecting measures of trait polygenicity.

Quorum sensing (QS) is classically understood as a mechanism by which bacterial cells sense their own density, but environmental fluctuations make any single cell's estimate unreliable. An alternative view holds that populations pool individual estimates to reach reliable collective decisions. We investigate how a two-autoinducer toggle switch network enables this collective adaptation in fluctuating environments. At the single-cell level, the toggle switch is bistable. Membrane permeability controls autoinducer secretion and is the evolving trait. Using an individual-based model of a spatially structured population, we show that permeability evolves through a collective mechanism. Environmental frequency is the primary determinant of evolutionary outcome. Under strong asymmetry, the dominant trait reaches a high plateau. The trait linked to the rare environment stays low. It rises smoothly as its environment becomes more frequent. Total permeability stays close to a fixed budget; environmental frequency mainly sets how it is split between the two traits. When the two environments are equally frequent, no single trait wins. Both traits coexist, and noise asymmetry between the two sensing channels biases the levels at which they coexist rather than selecting a winner. Coexistence is also the slowest outcome to reach. Higher noise speeds adaptation, but the evolved permeability is non-monotonic in noise, peaking at an intermediate value. Earlier work found that more noise favors collective sensing as long as cells are correct on average. We develop a two-trait adaptive-dynamics model that unifies these regimes. Away from symmetry, it reduces to a closed one-dimensional equation that captures the individual-based results. At symmetry, weak cross-repression gives a single coexistence attractor, consistent with replicate populations maintaining both traits.

Using the recently published global census of the biomass of wild terrestrial mammals, we perform a detailed mathematical statistical analysis of its distribution over $N_s=4795$ species, drawing on tools developed in economics to characterize wealth inequality. We show that the Lorenz curve of the mammal biomass distribution is characterized by a large Gini coefficient $G=0.944$ exceeding the inequality reported for wealth distribution among world countries. This distribution is compared to the predictions of the Wealth Thermalization Hypothesis (WTH), in which species biomass values are treated as energy levels populated according to a Rayleigh-Jeans (RJ) steady-state distribution. We show that an RJ extended spectral model reproduces the real Lorenz and Pareto curves over four orders of magnitude of biomass variation, capturing the strong condensation of biomass among the rare, heavy-bodied species and its near-absence among the vast majority of light-bodied ones. These results extend the WTH framework, previously validated on distributions of human economic origin, to a biological distribution shaped by ecological and evolutionary constraints, and place the extreme rarity of large-bodied mammal species within the same statistical mathematical description as the oligarchic concentration of wealth in human societies. We finally discuss possible ecological mechanisms that may contribute to the observed distribution, including interspecific interactions, food availability, and competition.

Migration and selection jointly shape evolutionary outcomes in spatially structured populations. Migration can vary over time as ecological shifts, such as changes in sea level, alter habitat connectivity. Here, we study the impact of periodic migration on selection within a geographically subdivided population. Using a two-deme Moran model with spatially heterogeneous selection, we analyze a scenario in which a mutant genotype is advantageous in one habitat and disadvantageous in the other. Keeping the time-averaged migration rate fixed, we vary the period, relative phase, and directional asymmetry of migration and examine the resulting steady-state mutant frequency. We find that migration periodicity generally drives the population toward neutrality: relative to constant migration, it decreases the mutant frequency when mutants are advantageous on average and increases it when they are deleterious, without reversing the direction of selection in the parameter regimes examined. This effect is strongest for out-of-phase migration, which models seasonal migration, and is further enhanced by directional asymmetry. The response to the migration period is generally non-monotonic, with an intermediate characteristic period producing the largest deviation from constant-migration behavior under in-phase migration. These results arise from the coupling between migration and local selection in Moran process: temporal modulation of migration changes not only the exchange of individuals between habitats but also the local population composition on which selection acts. Our results show that populations with identical mean migration rates can show different evolutionary outcomes depending on the temporal organization of gene flow.

Phylogenetic networks generalize phylogenetic trees as models of evolutionary history, allowing lineages to merge as well as to diverge. For many types of genetic data the root position of such a network cannot be recovered, so that only a semi-directed network can be inferred: a mixed graph in which only the edges entering a reticulation vertex are directed. A common strategy for inferring such a network is to first infer the subnetwork it induces on each set of $k\geq 3$ of its leaves, called a $k$-net, and then to assemble these pieces. This can only succeed if the $k$-nets determine the network, in which case that network is said to be encoded by its $k$-nets. Semi-directed networks of level-1 and 2, those whose biconnected components contain at most one, respectively two, reticulations, are known to be encoded by their $4$-nets, or quarnets, whereas level-3 networks are not. Even so, in this paper we show that level-3 semi-directed networks are encoded by their $5$-nets, or quinnets, and we characterize the limitation of quarnets exactly: we show that a single previously reported counterexample captures the only obstruction, every other level-3 network being encoded by its quarnets. Our proofs rest on a collection of encoding results for individual structural features of a network, which we establish for networks of arbitrary level and which are of independent interest.

This paper addresses the challenge that, under China's current mosquito-control policy for chikungunya—clearing stagnant water, killing adult mosquitoes, and preventing bites—epidemics can still recur and continue to rise. By constructing a vector–host dynamical model that incorporates asymptomatic infections and a screening rate, we derive approximate analytical expressions for the epidemic peak value and peak time, and reveal the intrinsic link between short-term transients and long-term outcomes. Simulations across three scenarios—human-seeded, mosquito-seeded, and joint human–mosquito-seeded—show that (i) even when the vector population is effectively suppressed, asymptomatic carriers can still sustain transmission; (ii) the effect of the screening rate on the peak time is non-monotonic, and there exists a critical threshold that maximizes the delay of the epidemic; and (iii) moderate screening can both suppress the first wave and avoid leaving an excessively large susceptible pool that would amplify subsequent waves. Accordingly, we upgrade the original policy to a Twelve-character strategy for prevention, mitigation, and control—clearing stagnant water, killing adult mosquitoes, preventing bites, and screening cases early—providing theoretical support for chikungunya and other mosquito-borne diseases.

We're often in a situation where we have models of measles transmission that are stuck at a coarse population scale. This paper is about increasing their resolution or, said differently, creating consistent models for the population's subcommunities. We pay special attention to the fact that even if the full population is isolated, subcommunities are open to measles importations and related consequences of population movement. It turns out that the resolution issue can be productively framed as a constrained optimization problem, where the constraint is aggregation to the coarse model. That mathematical scaffolding allows us to make epidemiologically motivated approximations concentrated at the large population scale, where they're more valid, which leaves the estimates at the fine subcommunity scale atypically unstructured. We develop the idea in the context of the pre-vaccination-era 60 city UK dataset, building transmission models for all 60 cities. We find that they have intuitive seasonality, volatility, and importation elements, but that they depend on nontrivial dynamics in their case reporting probabilities. Potential interpretations of those surveillance processes are discussed in some detail, and we also describe how they influence inference on the balance between competing cryptic transmission and transient elimination dynamics.

Stochasticity shapes evolutionary dynamics in finite populations beyond the reach of infinite-population theories. Using a stochastic birth–death framework on random regular networks, we establish evolutionary stability criteria for finite structured populations playing three-strategy games and identify critical degree thresholds governing invasion dynamics. Our framework goes beyond two-strategy evolutionary games to reveal that the initial composition of multi-type mutants, together with population structure and stochasticity, shapes the evolutionary fate of the resident population.

Approximate solutions are found for Feller-like neutral multi-type branching diffusions $\big{(}\mathbf{X}(t)\big{)}_{t \in \mathbb{R}_{\ge 0}}$ in the limit of small mutation rates. The method employed involves solving approximations to the Laplace transformed forward Kolmogorov equation by integrating along characteristics. To leading order in the scale $\theta$ of the overall mutation rate the super-critical diffusion is found to collapse onto a line density aligned with the stationary left eigenvector of the rate matrix following a rapid change of behaviour at a critical time $t_{\rm c}$, which has a weak logarithmic dependence on $\theta$. First order in $\theta$ approximations to the density and moments of $\mathbf{X}(t)$ are also determined. The first-order approximation corresponds to allowing at most one mutation in the coalescent tree of a sample and is valid for $t < t_{\rm c}$ in the supercritical case, and more generally for the critical and sub-critical cases.

Groups of individuals can solve collective problems more accurately than any single member, by aggregating their opinions. Recent theoretical work has identified individual-level reward schemes that allow uninformed individuals to evolve collective intelligence from the bottom up, through social learning. Yet these results are restricted to linear prediction problems and simple averaging, while the decision tasks that real groups confront are often non-linear, and the institutions that aggregate opinions are seldom single-layer averages: districts elect representatives who in turn vote on policy, referees advise editors who decide on publication. Here we develop a framework for the evolution of collective intelligence in multi-layer voting populations, where individuals observe limited information and groups recursively aggregate their opinions by majority rule. We prove that single-layer voting cannot solve non-linear classification problems under any individual reward scheme. We then identify a "marginal feedback" payoff structure, which rewards individuals only when their opinion is pivotal in their group, and at every layer above them. This reward scheme induces a layered population to evolve accurate collective solutions to complex, non-linear decision tasks through individual-level peer imitation alone. The collective behavior that emerges is equivalent to a multi-layer perceptron in machine learning. Our results provide a naturalistic account of hierarchical institutions, in which the outsize importance of swing voters is the incentive that sustains collective accuracy; and they identify the credit-assignment rule in machine learning as not just an engineered solution but a natural evolutionary outcome.

Multiple epidemic waves are often taken as evidence that simple compartmental models are structurally inadequate. Yet the key question is not whether a simple model can reproduce multiple peaks, but how much time-varying structure is minimally required to preserve observed epidemic-wave patterns and what can legitimately be inferred from such a representation. Using the susceptible-infectious-removed (SIR) model, we develop a framework for peak-preserving dynamic parsimony linking dynamical systems, inverse problems, statistical inference, and infectious-disease modeling. We establish exact multi-wave representability under time-varying transmission, characterize conditions for epidemic peaks and resurgence, and show the incidence-only non-identifiability of time-varying transmission and removal rates. Our main methodological contribution is minimal peak-preserving complexity (MPPC): the smallest dimension of a positive time-varying transmission function whose posterior-predictive trajectories preserve salient features, including peak count, timing, height, trough depth, and whole-curve agreement. We estimate MPPC using peak-aware approximate Bayesian computation and evaluate it through six reproducible stress tests, a Philippine COVID-19 reported-case proof of concept, and short-term future-knot projection. Across diverse data-generating mechanisms, low-dimensional time-varying SIR models can preserve aggregate epidemic-wave topology with modest dynamic complexity. However, accurate trajectory compression does not imply recovery of the underlying mechanism or reliable long-term counterfactual prediction. The framework therefore separates four often-conflated questions: representability, minimal dynamic complexity, mechanistic identifiability, and projection validity, providing a basis for parsimonious modeling of complex epidemic trajectories.

A mutant gene that appears in a finite population will, in the long run, either disappear from it or become fixed, so that all members of the population will carry a copy of the mutant gene. Determining the fixation probability of a mutant gene is thus a prerequisite for modeling evolutionary dynamics in finite populations. Within the diffusion theory framework, the mutant's competition with the wild-type genes is described by the closed-form solution of a one-dimensional backward Kolmogorov equation for the mutant fixation probability, derived by Motoo Kimura in 1962. However, in asexual populations the rate of adaptation and the relative importance of alternative evolutionary pathways are shaped by clonal interference - the competition between several lineages arising from distinct beneficial or deleterious mutations. Quantitative descriptions of clonal interference require computing fixation probabilities in populations where multiple alleles are present simultaneously - a formidable mathematical problem which amounts to solving a multi-dimensional backward Kolmogorov equation. Here, we employ fluctuation theorems (a set of exact equalities in non-equilibrium statistical mechanics) in order to re-derive Kimura's fixation probability formula. When extended to multi-allele systems, fluctuation theorems provide a constraint which must be satisfied by any valid set of fixation probabilities. Moreover, we present a recursive analytical solution of the multi-allele fixation probability problem. We use our formalism to study the fate of a novel beneficial mutation in a clonal interference scenario, discovering substantial deviations between our exact treatment and the mean-field approximation in which the target mutation competes with a single effective genotype. Our formalism can be used to replace both explicit simulations and approximate analytical results in models of asexual evolution.

Let $\cP_{\ell,k}$ be the set of rooted binary phylogenetic networks with $\ell$ labelled leaves, $k$ reticulations and no parallel edges. We write $|\cP_{\ell,k}|=W_k(\ell)+D_k(\ell)$, where the weighted count $W_k(\ell)$ adds the inverse orders of the leaf-fixing automorphism groups and the defect $D_k(\ell)$ collects the remainder. The weighted count satisfies, for every $k$, a recursion over the source layers of the tree-component structure that involves neither a list of component graphs nor any distinction between symmetric and asymmetric configurations, and its exponential generating function is a Laurent polynomial in $\sqrt{1-2x}$. Automorphism groups of networks are $2$-groups, elementary abelian for $k\le5$ but not in general. For $k\le5$ the defect is the weighted count of networks with a distinguished involution, which obeys an extension of the same recursion. An exact symbolic evaluation of the two recursions yields $|\cP_{\ell,k}|$ in closed form for $k\le5$, the case $k=5$ being new; it reproduces the published counts for $k\le4$ and corrects a coefficient in a published generating function for $k=3$. For every $k$, uniformly over explicit ranges of $k$, we prove that the non-tree-child networks are a fraction $2k(k-1)/\ell$ of the tree-child networks to leading order, which gives the third term of the asymptotic expansion of $|\cP_{\ell,k}|$. We also prove that reticulation-visible networks exceed tree-child networks by the fraction $k(k-1)/\ell$, and that a uniformly random network in $\cP_{\ell,k}$ has a nontrivial automorphism with probability $k(k-1)/(4\ell^3)$ to leading order.

A leading explanation for why humans cooperate with strangers is indirect reciprocity: when conduct is observed, individuals earn good reputations by cooperating with the deserving. Previous research has identified many "moral systems" that sustain cooperation through indirect reciprocity - each pairing a strategy for how to act with a norm for how to judge others - yet what makes them succeed, and whether that success generalizes beyond the donation game, has remained unclear. Here we evaluate all 1,048,576 moral systems whose norms condition on both players' reputations and actions, across every two-player social dilemma. We find that every highly stable, cooperative system pairs a "discriminating" strategy - one that cooperates with the good and defects against the bad when in good standing - with a "consistent" norm that deems good exactly those who act as the strategy prescribes. Such systems "practice what they preach" and preach what they practice. In the limit of small errors, only one strategy paired with a consistent norm remains stable across all social dilemmas: the "contrite discriminator", which cooperates to make amends when its standing is bad. Our results unify decades of research on indirect reciprocity, revealing moral consistency as the common ingredient of every stable, cooperative moral system.

Whether a fitted mortality model needs a Makeham term, the non-negative constant representing background mortality, is commonly decided with a likelihood-ratio test. Because the constant cannot be negative, testing its absence places the parameter on the boundary of its range, and the usual chi-squared calibration does not apply. Assuming independent Poisson death counts and standard regularity conditions, we show that when the constant is the only parameter on a boundary the statistic converges to an equal mixture of a point mass at zero and a chi-squared distribution with one degree of freedom, so that at the 5% level the critical value is 2.71 rather than 3.84. We give conditions under which the correction holds for Makeham models, verify them for Gompertz-Makeham and gamma-Gompertz-Makeham, and quantify the information the data carry about the constant, which fixes the local power of the test, the smallest term it can detect, and how fast detectability falls as the age window narrows. When a gamma-frailty variance is estimated and its true value is also zero, the limit is no longer a chi-squared mixture and the usual correction rejects too often. Monte Carlo experiments show that the conventional cutoff rejects at half the nominal level, that a correctly calibrated test can still miss a term at its own detection threshold in most samples, and that excess rejections under an estimated zero frailty variance persist as exposure grows.

Selective advantage leads to the proliferation of a species into an ecosystem. On the other hand, self-propulsion can lead to dynamic clustering of individuals. To analyze the intricate effects produced by such competing processes, we introduce a minimal model for a proliferating active population, where run-and-tumble migration is combined with a stochastic birth-death process. Beginning with the microscopic dynamics of the particles, we derive the corresponding hydrodynamic equations and validate them through numerical simulations. We demonstrate that the inclusion of activity leads to the emergence of novel morphological patterns, which are sensitive to the specific rules governing migration. Furthermore, we show that activity significantly alters the characteristics of invasion waves, increasing their propagation speed. However, beyond a threshold, excessive activity disrupts the wave-like nature of the invasion process. We also extend our study to two dimensions and analyze the instabilities that lead to pattern formation in such systems.

We propose and study a compartmental epidemiological model that incorporates both waning immunity and mutation-driven immune escape into a single-strain framework. The model, termed SIRCm, extends the SIRC (Susceptible-Infected-Recovered-Cross-immune) model, in which recovered individuals pass through an intermediate cross-immune class before returning to full susceptibility. In SIRCm, both the rate of immune waning and the rate of immune escape are amplified by a feedback function tied to the current level of infection. We study two formulations of this feedback, a transmission-driven model, in which mutation opportunities arise at the point of new infection, and a prevalence-driven model, in which they arise in proportion to the infected population alone. For both models and any feedback strength, we prove existence of an endemic equilibrium using persistence theory, and characterize its behavior analytically in the weak and strong-feedback limits, recovering the SIRC and SIS models, respectively, as limiting cases. Unlike the SIRC model, we show that the SIRCm endemic equilibrium is unstable in certain parameter regimes. This leads to novel behavior, even in the absence of seasonal forcing.