Santa Fe Institute

A bstract We generalize recent results in two-dimensional Jackiw-Teitelboim gravity to study factorization of the Hilbert space of eternal black holes in quantum gravity with a negative cosmological constant in any dimension. We approach the problem by computing the trace of two-sided observables as a sum over a recently constructed family of semiclassically well-controlled black hole microstates. These microstates, which contain heavy matter shells behind the horizon and form an overcomplete basis of the Hilbert space, exist in any theory of gravity with general relativity as its low energy limit. Using this representation of the microstates, we show that the trace of operators dual to functions of the Hamiltonians of the left and right holographic CFTs factorizes into a product over left and right factors to leading order in the semiclassical limit. Under certain conditions this implies factorization of the Hilbert space.

We study the large-time behavior of an ensemble of entities obeying replicator-like stochastic dynamics with mean-field interactions as a model for a primordial ecology. We prove the propagation-of-chaos property and establish conditions for the strong persistence of the N-replicator system and the existence of invariant distributions for a class of associated McKean–Vlasov dynamics. In particular, our results show that, unlike typical models of neutral ecology, fitness equivalence does not need to be assumed but emerges as a condition for the persistence of the system. Further, neutrality is associated with a unique Dirichlet invariant probability measure. We illustrate our findings with some simple case studies, provide numerical results, and discuss our conclusions in the light of Neutral Theory in ecology.

The authors' proposal for the evolutionary origins of historical myths does not hold up to scrutiny, as illustrated by a simple mathematical model. Group-level explanations, such as defining the conditions for in-group membership, are dismissed by the authors but are far more plausible, as illustrated by the ongoing war in Ukraine.

Historical archives for the Roman Monarchic and Republican periods (753–29 BCE) offer a highly resolved series of observations of environmental and climatic phenomena in Central Italy. This paper presents a new collection of these historical archives, gathering 319 observations across the period. We introduce the historical character of these archives and point out aspects affecting their analysis and interpretation for reconstruction of past environmental and climatic conditions in Italy in the latter half of the first millenium BCE. Archival information is seen to be generally reliable from the fifth century BCE onward, providing a valuable source about regional past climate. The historical archives’ anecdotal nature along with complexities of their formation and transmission encourage cautious and closely contextualized interpretation, and we advocate the use of this information most of all to understand Romans’ changing experience of environment and climate. We offer comparison of this data to current understanding of regional climate conditions based on scientific proxies, especially speleothems and marine cores. These records show some convergence with the historical archives, and we discuss the possibility that this may reflect a relatively warm, wet climate period (Roman Warm Period) in Italy coterminous with Rome’s initial phase of imperial expansion.

Osteology plays an indispensable role in understanding the normal patterns of different species, serving as the foundation for zoological understanding. Mazama nana is well-known in Brazil; however, basic morphological descriptions of the species are scarce, while Subulo gouazoubira, currently revalidated as Mazama gouazoubira, is also prevalent in Brazil and has recently been the subject of various phylogenetic studies. In this respect, in the present study, 19 cervid heads (16 Subulo gouazoubira and three Mazama nana) were osteologically prepared. Next, computed tomographies (CTs) were performed to enhance the assessment of bone accidents, as well as internal structures and foramina through three-dimensional (3D) reconstruction techniques due to the difficult visibility in intact specimens. A comparison was then made between the reconstructions, digital photographs, and CT cross-sections of the skull, which enabled the visualization of anatomical peculiarities and exclusivities, such as nasolacrimal-maxillary fenestra, paranasal sinuses, and thin bone architecture, as well as the unique shape of cranial bones, compared to other species that exhibit a unique and genuine phenotype.

Regular physical activity is essential for the healthy development of children, and sports clubs are one of the main drivers of regular exercise. Previous studies have demonstrated that public subsidies can increase participation rates in sports clubs. The effectiveness of funding in increasing participation rates depends on multiple factors, such as geographic location, the size of the sports club, and the socio-economic conditions of the population. Here, we show how an optimal allocation of government funds to sports facilitators (e.g., sports clubs) can be achieved using a data-driven simulation model that maximizes children’s access to sports facilities. We compile a dataset for all 1,854 football clubs in Austria, including estimates for their budgets, geolocations, tallies, and the age profiles of their members. We find a characteristic sublinear relationship between the number of active club members and the budget, which depends on the socio-economic conditions of the club’s municipality. In the model, where we assume this relationship to be causal, we evaluate different funding strategies. We show that an optimization strategy, where funds are distributed based on regional socio-economic characteristics and club budgets, outperforms a naive approach by up to 117% in attracting children to sports clubs with 5 million euros of additional funding. Our results suggest that the impact of public funding strategies can be substantially increased by tailoring them to regional socio-economic characteristics in an evidence-based and individualized way.

Transcription factor binding sites (TFBSs) are important sources of evolutionary innovations. Understanding how evolution navigates the sequence space of such sites can be achieved by mapping TFBS adaptive landscapes. In such a landscape, an individual location corresponds to a TFBS bound by a transcription factor. The elevation at that location corresponds to the strength of transcriptional regulation conveyed by the sequence. Here, we develop an in vivo massively parallel reporter assay to map the landscape of bacterial TFBSs. We apply this assay to the TetR repressor, for which few TFBSs are known. We quantify the strength of transcriptional repression for 17,765 TFBSs and show that the resulting landscape is highly rugged, with 2092 peaks. Only a few peaks convey stronger repression than the wild type. Non-additive (epistatic) interactions between mutations are frequent. Despite these hallmarks of ruggedness, most high peaks are evolutionarily accessible. They have large basins of attraction and are reached by around 20% of populations evolving on the landscape. Which high peak is reached during evolution is unpredictable and contingent on the mutational path taken. This in-depth analysis of a prokaryotic gene regulator reveals a landscape that is navigable but much more rugged than the landscapes of eukaryotic regulators.

We propose a new performance attribution framework that decomposes a constrained portfolio’s holdings, expected returns, variance, expected utility, and realized returns into components attributable to (1) the unconstrained mean-variance optimal portfolio; (2) individual static constraints; and (3) information, if any, arising from those constraints. A key contribution of our framework is the recognition that constraints may contain information that is correlated with returns, in which case imposing such constraints can affect performance. We extend our framework to accommodate estimation risk in portfolio construction using Bayesian portfolio analysis, which allows one to select constraints that improve—or are least detrimental to—future performance. We provide simulations and empirical examples involving constraints on environmental, social, and governance portfolios. Under certain scenarios, constraints may improve portfolio performance relative to a passive benchmark that does not account for the information contained in these constraints. This paper was accepted by Kay Giesecke, finance. Funding: Research funding from the National Key Research and Development Program of China [Grant 2022YFA1007900], the National Natural Science Foundation of China [Grants 12271013, 72342004], Peking University’s Fundamental Research Funds for the Central Universities, and the MIT Laboratory for Financial Engineering is gratefully acknowledged. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05365 .

We consider the question of whether thermodynamic macrostates are objective consequences of dynamics, or subjective reflections of our ignorance of a physical system. We argue that they are both; more specifically, that the set of macrostates forms the unique maximal partition of phase space which (1) is consistent with our observations (a subjective fact about our ability to observe the system) and (2) obeys a Markov process (an objective fact about the system’s dynamics). We review the ideas of computational mechanics, an information-theoretic method for finding optimal causal models of stochastic processes, and argue that macrostates coincide with the “causal states” of computational mechanics. Defining a set of macrostates thus consists of an inductive process where we start with a given set of observables, and then refine our partition of phase space until we reach a set of states which predict their own future, i.e. which are Markovian. Macrostates arrived at in this way are provably optimal statistical predictors of the future values of our observables.

Many publicly available databases provide disease related data, that makes it possible to link genomic data to medical and meta-data. The cancer genome atlas (TCGA), for example, compiles tens of thousand of datasets covering a wide array of cancer types. Here we introduce an interactive and highly automatized TCGA-based workflow that links and analyses epigenomic and transcriptomic data with treatment and survival data in order to identify possible biomarkers that indicate treatment success. TREMSUCS-TCGA is flexible with respect to type of cancer and treatment and provides standard methods for differential expression analysis or DMR detection. Furthermore, it makes it possible to examine several cancer types together in a pan-cancer type approach. Parallelisation and reproducibility of all steps is ensured with the workflowmanagement system Snakemake. TREMSUCS-TCGA produces a comprehensive single report file which holds all relevant results in descriptive and tabular form that can be explored in an interactive manner. As a showcase application we describe a comprehensive analysis of the available data for the combination of patients with squamous cell carcinomas of head and neck, cervix and lung treated with cisplatin, carboplatin and the combination of carboplatin and paclitaxel. The best ranked biomarker candidates are discussed in the light of the existing literature, indicating plausible causal relationships to the relevant cancer entities.

Immune responses are induced by parasite exposure and can in turn reduce parasite burden. Despite such apparently simple rules of engagement, key drivers of within-host dynamics, including dose-dependence of defense and infection duration, have proven difficult to predict. Here, we model how varied inoculating doses interact with multi-tiered host defenses at a site of inoculation, by confronting barrier, innate, and adaptive tiers with replicating and non-replicating parasites across multiple orders of magnitude of dose. We find that, in general, intermediate parasite doses generate infections of longest duration because they are sufficient in number to breach barrier defenses, but insufficient to strongly induce subsequent tiers of defense. These doses reveal “wormholes” in defense from which parasites might profit: Deviation from the hypothesis of independent action, which postulates that each parasite has an independent probability of establishing infection, may therefore be widespread. Interestingly, our model predicts local maxima of duration at two doses–one for each tier transition. While some empirical evidence is consistent with nonlinear dose-dependencies, testing the predicted dynamics will require finer-scale dose variation than experiments usually incorporate. Our results help explain varied infection establishment and duration among differentially-exposed hosts and elucidate evolutionary pressures that shape both virulence and defense.

Much received wisdom in the conservation literature is that individual connections across community boundaries undercut natural resource management. However, when multiple communities access the same resource, these long‐distance relationships could generate interdependence and trust to motivate engagement in collective action to manage the resource. To test this, we interviewed 1317 people in 28 fishing villages in Tanzania about their participation in managing open‐access fisheries and their social relationships in each village accessing the fishery. People with more friends in other villages trusted more people in those villages and were more likely to participate in collective action to manage the shared fishery, such as reporting others for destructive fishing practices. These results show that long‐distance relationships may be a useful foundation upon which to build conservation efforts that cross community boundaries and bolster sustainable resource use.

We consider problems in the functional analysis and evolution of combinatorial chemical reaction networks as rule-based, or three-level systems. The first level consists of rules, realized here as graph-grammar representations of reaction mechanisms. The second level consists of stoichiometric networks of molecules and reactions, modeled as hypergraphs. At the third level is the stochastic population process on molecule counts, solved for dynamics of population trajectories or probability distributions. Earlier levels in the hierarchy generate later levels combinatorially, and as a result constraints imposed in earlier and smaller layers can propagate to impose order in the architecture or dynamics in later and larger layers. We develop general methods to study rule algebras, emphasizing system consequences of symmetry; decomposition methods of flows on hypergraphs including the stoichiometric counterpart to Kirchhoff’s current decomposition and work/dissipation relations studied by Wachtel et al.; and the large-deviation theory for currents in a stoichiometric stochastic population process, deriving additive decompositions of the large-deviation function that relate a certain Kirchhoff flow decomposition to the extended Pythagorean theorem from information geometry. The latter result allows us to assign a natural probabilistic cost to topological changes in a reaction network of the kind produced by selection for catalyst-substrate specificity. We develop as an example a model of biological sugar-phosphate chemistry from a rule system published by Andersen et al. It is one of the most potentially combinatorial reaction systems used by biochemistry, yet one in which two ancient, widespread and nearly unique pathways have evolved in the Calvin-Benson cycle and the Pentose Phosphate pathway, which are additionally nearly reverses of one another. We propose a probabilistic accounting in which physiological costs can be traded off against the fitness advantages that select them, and which suggests criteria under which these pathways may be optimal.

Transit functions $R:V\times V\rightarrow 2^V$ model abstract betweenness as well as binary clustering. Examples are I(u, v), the interval between u and v, comprising all points on a shortest path from u to v, and C(u, v), the set of all cut vertices separating u and v together with u and v. Here we characterize the cut-vertex transit function of hypergraphs as the monotone transit functions satisfying (x) $R(u,v)\subseteq R(u,x)\cup R(x,v)$ for all $u,v,x\in V$. We define new hypergraph classes as restrictions and generalizations of linear hypergraphs and describe relevant properties of blocks and Berge cycles. We then show that the cut-vertex transit function coincides with the interval function exactly for linear B$^*$-hypergraphs, generalizing a similar result for graphs. Moreover, we identify a subclass of block hypergraphs and characterize it using axioms on its interval function and prove a similar characterization for block graphs.

How can so many species coexist in natural ecosystems remains a fundamental question in ecology. Classical models suggest that competition for space and resources should maintain the number of coexisting species far below the staggering diversity commonly found in nature. To overcome this paradox, theoretical studies have long highlighted a number of mechanisms that can favour species coexistence, from the distribution of interaction strengths between species to the shape of population growth functions. In particular, a family of mathematical models finds that, when sublinear population growth (SG) rates are coupled with competition between species, species diversity can stabilize community dynamics. This could suggest that SG may explain the stable coexistence of many species in natural ecosystems. Here we clarify why SG models do not solve the paradox of species coexistence. This is because, in the SG model, coexistence emerges from an unrealistic property, in which population per capita growth rates tend to infinity at low abundance, preventing species from ever going extinct due to competitive exclusion. Infinite growth at low abundance can be regularized by assuming a minimal abundance threshold, below which a species goes extinct or follows non‐infinite growth curves. When this is done, the SG model recovers the classical result: increasing the diversity of the species pool leads to competitive exclusion and species extinctions.

Market bubbles emerge when asset prices are driven unsustainably higher than asset values, and shifts in belief burst them. We demonstrate an analogous phenomenon in the case of biomedical knowledge, when promising research receives inflated attention. We introduce a diffusion index that quantifies whether research areas have been amplified within social and scientific bubbles, or have diffused and become evaluated more broadly. We illustrate the utility of our diffusion approach in tracking the trajectories of cardiac stem cell research (a bubble that collapsed) and cancer immunotherapy (which showed sustained growth). We then trace the diffusion of 28,504 subfields in biomedicine comprising nearly 1.9 M papers and more than 80 M citations to demonstrate that limited diffusion of biomedical knowledge anticipates abrupt decreases in popularity. Our analysis emphasizes that restricted diffusion, implying a socio-epistemic bubble, leads to dramatic collapses in relevance and attention accorded to scientific knowledge.

We survey archaeological evidence suggesting that among hunter-gatherers and farmers in Neolithic western Eurasia (11,700 to 5,300 years ago) elevated levels of wealth inequality occurred but were ephemeral and rare compared to the substantial enduring inequalities of the past five millennia. In response, we seek to understand not the de novo “creation of inequality” but instead the processes by which substantial wealth differences could persist over long periods and why this occurred only at the end of the Neolithic, at least four millennia after the agricultural revolution. Archaeological and anthropological evidence suggests that a culture of aggressive egalitarianism may have thwarted the emergence of enduring wealth inequality until the Late Neolithic, when new farming technologies raised the value of material wealth relative to labor and a concentration of elite power in early proto-states (and eventually the exploitation of enslaved labor) provided the political and economic conditions for heightened wealth inequalities to endure. (JEL D31, D63, N30, N50, Z13)

Dealing with uncertainty is a pivotal skill for adaptive decision-making across various real-life contexts. Cognitive models suggest that individuals continuously update their knowledge based on past choices and outcomes. Traditionally, uncertainty has been linked to negative states such as fear and anxiety. Recent evidence, however, highlights that uncertainty can also evoke positive emotions, such as surprise, interest, excitement, and enthusiasm, depending on one’s task expectations. Despite this, the interplay between mood, confidence, and learning remains underexplored. Some studies indicate that self-reported mood does not always align with confidence, as these constructs evolve on different timescales. We propose that mood influences confidence, thereby enhancing decision flexibility—defined as the ability to switch effectively between exploration and exploitation. This increased flexibility is expected to improve task performance by increasing accuracy. Our findings support this hypothesis, revealing that confidence modulates exploration/exploitation strategies and learning rates, while mood affects reward perception and confidence levels. These findings indicate that metacognition entails a dynamic balance between exploration and exploitation, integrating mood states with high-level cognitive processes.