Symbolic time series analyses are used in economics and other social sciences as a way of reducing the impact of noise on data and to exhibit more clearly the evolution of time series. We show that causality tests applied to symbolic series may fail to detect actual relations or generate statistical artifacts. Well-known causality detection methods, like transfer entropy, Granger's test, or Peter-Clark Momentary Conditional Independence (PCMCI), may miss some existing causal relationships or, more frequently, yield non-existent ones. The performance of these methods may differ, depending on the specific choices of lag structures and alphabet sizes, as well as on the characteristics of the underlying dynamic process.

This paper presents a model of the interplay between a policymaker and various interest groups, viewing the incumbent as a strategic lobbyist seeking to maximize re-election chances. We assume a political context where the government has discretionary power over tax extraction and public expenditure allocation. The model predicts that the incumbent’s optimal strategy is to form a minimum winning coalition, generating systematic asymmetry by favoring specific groups while exploiting others. To test this hypothesis, we conducted a repeated-interaction experiment involving one subject as the incumbent and two others representing interest groups. The results indicate that incumbents learn to strategically differentiate between groups to secure electoral support, a behavior that contradicts the predictions of inequality aversion models which favor equal resource distribution. Furthermore, we find that this adoption of "divide-and-rule" strategies correlates with Machiavellianism, suggesting that the mechanics of political survival in this context are driven by instrumental rationality rather than social preferences.

We ask whether COVID-19 lockdown stringency altered national Olympic performance between Rio 2016 and Tokyo 2020, using the Oxford Stringency Index and the 99 countries that won a medal in either edition. As in , mean performance is unaffected: stringency is insignificant in every OLS and ANOVA specification. The distribution is not. Among the 84 non-traditionally dominant nations, medal changes are three to six times more dispersed in high-stringency countries; the difference is absent among dominant nations and concentrated in men's events. A common shock left the competitive hierarchy intact while sharply raising outcome uncertainty for smaller Olympic teams.

In this paper, we analyze how global optima of an agent's preferences can be reconstructed from the solutions found for local problems. A sheaf-theoretic analysis provides an abstract characterization of the global solution, and polynomial approximations are obtained when only a few local instances are available.

We study the existence of a Regional Differential in rugby sevens: whether, in tournaments where no competing team enjoys formal home status, some national sides systematically over- or under-perform depending on where the event is staged. Using the universe of 2672 men's and women's matches from international rugby sevens tournaments played between 2016 and 2025, principally the World Rugby Sevens Series, but also the World Cup Sevens and the Olympic Games, we replace the binary “locality” indicator of the classic home advantage literature with continuous measures of geographic, temporal, and cultural proximity between each team and the host country. We show that aggregate associations between proximity and performance (which appear large and highly significant in naive specifications) are almost entirely an artifact of team composition and scheduling: once team and opponent quality are absorbed through fixed effects in a symmetric team-match panel, no general regional advantage survives. However, this null aggregate masks substantial heterogeneity. For a well-defined subset of teams (most notably France, but also New Zealand, the United States, Canada, Spain, Ireland, Kenya, Samoa, and Brazil), performance declines significantly and monotonically with the distance separating the host city from home. Some of the established southern-hemisphere powers (Fiji, South Africa, Australia, Argentina) are essentially distance-neutral. We further show that for some teams the gradient operates through east-west jet lag rather than pure displacement. The Regional Differential in sevens is therefore real but team-specific rather than universal, a distinction obscured by pooled estimation.

Standard CATE estimators become inadequate under strong treatment-effect heterogeneity: confidence intervals for conditional means need not cover individual counterfactual effects. We propose an Individualized Causal Prediction (ICP) framework that constructs finite-sample valid conformal prediction intervals for the individual causal effect of a specific query unit. The method localizes calibration to a causally relevant neighborhood using cosine similarity weighted by Causal Forest variable importance, augments small local samples synthetically, and calibrates intervals with doubly robust AIPW conformity scores satisfying Neyman orthogonality. Under standard identifying assumptions (SUTVA and strong ignorability) and an outcome-independent calibration-set selection condition, the resulting intervals attain marginal coverage at the nominal level. The local design also supports approximately conditional coverage by making calibration scores more representative of the query unit. Experiments on a high-heterogeneity synthetic dataset and the IHDP benchmark demonstrate that local strategies improve point accuracy over global baselines while maintaining nominal or above-nominal coverage.

We present here a novel approach to the analysis of common knowledge based on Category Theory. We formalize knowledge hierarchies as presheaves over a category of agent sequences. The category of these presheaves constitutes a topos. We define an unfolding monad on the resulting topos, and use a Knaster-Tarski theorem to obtain common knowledge as a greatest fixed point under natural uniformity and exchangeability conditions on agent sequences.

This paper develops a noncooperative model of directed network formation in which agents create links to access valuable information while sharing the costs generated along the paths through which information is obtained. Each agent is endowed with a positive amount of information and chooses, simultaneously, which other agents to contact. A directed link initiated by one agent allows her to access the information of the contacted agent and of the latter's reachable network, but each link in the resulting information path entails a unit cost. Payoffs therefore depend on the total value of accessible information net of the accumulated connection costs required to obtain it. The paper characterizes the relationship between strategy profiles and directed graphs, defines accessibility, paths, components, and minimal connectedness, and studies the Nash architectures induced by individual best responses. The central result is that strict Nash equilibria must take the form of circular directed networks. Moreover, circular networks are exactly the Nash networks that use the minimum number of links while allowing every agent to access all available information. Although noncircular weak Nash networks may exist, they are structurally redundant and do not satisfy the same minimality property. The model also shows that strict Nash networks are both Pareto optimal and efficient in terms of aggregate welfare. Finally, the paper compares this framework with Bala and Goyal's model, emphasizing that shared path costs and heterogeneous information values generate different equilibrium implications. The analysis supports the equivalence between strict stability and minimal connectivity in directed information networks.

In this paper, we investigate the performance of five causality‐detection methods and the aggregation of their results when considering multiple units in a panel data setting. We employ voting rules as an aggregation procedure to determine which causal paths are identified for the sample population. Using both simulated and real‐world panel data, we show the performance of these methods in detecting the correct causal paths by comparing them to a benchmark that represents a standard growth model as the ground truth .

In this paper, we present a methodology to classify dataset entries in datasets, based on their relevance for answering different specific queries. It employs a repeated individualized inference approach to identify entries with significant Shapley values, contributing with accurate answers to queries about other entries in the dataset. This information is captured in three matrices: a general relevance matrix, a Shapley value matrix, and a significant Shapley value matrix. Since usually the information in datasets is non-homogeneously distributed, relevance is often concentrated in a few entries. This is in particular observed in a representative case study.

ObjectiveThis study investigates the distinct impacts of home and away fans on home advantage (HA) in football, using a natural experiment in Argentinean football. Since 2013, away fans have been banned due to violence, and during the 2020 COVID-19 lockdown, all fans were excluded.MethodWe analyzed 7261 top-tier Argentinean matches from 2003 to 2022, covering periods with full attendance, home-only fans, and no fans. Using linear and logistic regressions, we assess the effects of different crowd conditions on goal differentials and win probabilities, controlling for team and season. Subgroup analyses compare traditionally strong teams to others.ResultsThe absence of away fans had a minor, statistically insignificant effect on HA overall, though the top five teams saw a brief increase after the ban. In contrast, the absence of home fans during COVID-19 significantly reduced HA for all teams. Not-top five teams often faced a home disadvantage. HA rebounded sharply for the top five teams once home fans returned.ConclusionHome fans significantly influence HA, while away fans have negligible impact. Local support is particularly crucial for less dominant teams, with implications for crowd management, match planning, and broadcasting in professional football.

Combinators, as defined originally by Moses Schönfinkel, give rise to a Turing-complete model of computation. This paper presents a diagrammatic representation of combinators as presheaves defined over a category of generic figures. We adopt Sergeyev’s grossone numeral system, which, together with our categorical representation of combinators, ensures a sharper characterization of non-halting combinators. As a result of our analysis, we show how certain “infinite” combinators can be recast in the grossone formalism using the notion of observability, which captures the general concept of tractable properties of sequences with length less than or equal to grossone.

This paper explores the heterogeneity of causal structures of economic growth among countries by proposing a two-step procedure. First, we apply a causal discovery technique to uncover the underlying causal structure for each country. Second, we employ hierarchical clustering over these estimates to identify groups of similar countries in terms of their causal relationships. We obtain five 'causality clubs', each one associated with a different structure of causal determinations of the growth process. We find that the usual associations between income or geographical location and the nature of economic growth processes may not always hold true.

Cyber-physical systems (CPSs) are fundamental components of Industry 4.0 production environments. Their interconnection is crucial for the successful implementation of distributed and autonomous production plans. A particularly relevant challenge is the optimal scheduling of tasks that require the collaboration of multiple CPSs. To ensure the feasibility of optimal schedules, two primary issues must be addressed: (1) The design of global systems emerging from the interconnection of CPSs; (2) The development of a scheduling formalism tailored to interconnected Industry 4.0 settings. Our approach is based on a Category Theory formalization of interconnections as compositions. This framework aims to guarantee that the emergent behaviors align with the intended outcomes. Building upon this foundation, we introduce a formalism that captures the assignment of operations to cyber-physical systems.

In this paper we introduce the concept of multicombinators as an alternative Turing-complete model of computation, modally extending the formalism of Combinatory Logic. We present a diagrammatic representation of multicombinators as presheaves defined over a category of generic figures. By adopting Sergeyev’s grossone numeral system, we obtain a sharper characterization of non-halting multicombinators. As a result of our analysis, we show how results for “infinite” combinators using the grossone formalism involving the notion of observability may be extended to the setting of multicombinators. These results formalize the general concept of tractable properties of multicombinators involving sequences of length less than or equal to grossone.

Social media platforms like Twitter (now X) provide a global forum for discussing ideas. In this work, we propose a novel methodology for detecting causal relationships in online discourse. Our approach integrates multiple causal inference techniques to analyze how public sentiment and discourse evolve in response to key events and influential figures, using five causal detection methods: Direct-LiNGAM, PC, PCMCI, VAR, and stochastic causality. The datasets contain variables, such as different topics, sentiments, and real-world events, among which we seek to detect causal relationships at different frequencies. The proposed methodology is applied to climate change opinions and data, offering insights into the causal relationships among public sentiment, specific topics, and natural disasters. This approach provides a framework for analyzing various causal questions. In the specific case of climate change, we can hypothesize that a surge in discussions on a specific topic consistently precedes a change in overall sentiment, level of aggressiveness, or the proportion of users expressing certain stances. We can also conjecture that real-world events, like natural disasters and the rise to power of politicians leaning towards climate change denial, may have a noticeable impact on the discussion on social media. We illustrate how the proposed methodology can be applied to examine these questions by combining datasets on tweets and climate disasters.

The academic evaluation of the publication record of researchers is relevant for identifying both relevant topics and talented candidates for promotion and funding. A key tool for this is the use of the indexes provided by Web of Science and Scopus, costly databases that sometimes exceed the possibilities of academic institutions in many parts of the world. We develop a methodology that uses data in one of the databases to infer the most commonly used index of the other one. In this way, access to just one database allows recovering the information contained in both. Using machine learning methods, we select just a few of the hundreds of variables in one database, which are used in a panel regression to infer the main index in the other database. Since the information of Scopus can be freely scraped from the web, this approach allows the inference of the impact factor of publications (the main index in Web of Science), a key index used to assess the quality of academic research around the globe.

The successes of Machine Learning, and in particular of Deep Learning systems, have led to a reformulation of the Artificial Intelligence agenda. One of the pressing issues in the field is the extraction of knowledge out of the behavior of those systems. In this paper we propose a semiotic analysis of that behavior, based on the formal model of learners . We analyze the topos-theoretic properties that ensure the logical expressivity of the knowledge embodied by learners. Furthermore, we show that there exists an ideal universal learner , able to interpret the knowledge gained about any possible function as well as about itself, which can be monotonically approximated by networks of increasing size.

In the last decade, significant advancements in digital technologies have revolutionized production systems, leading to the development of Cyber-Physical Systems (CPS) and Digital Twins (DT). This paper proposes a design of a production management system that leverages a Digital Twin associated with the shop floor. The objective is to enhance the resilience of production systems by providing real-time monitoring and enabling cloud-based outsourcing during production line failures. The methodology involves creating a Digital Twin model of the CPS, which is used to monitor the production process and visualize key performance indicators through a business intelligence tool. The contribution of this study lies in addressing the growing need for resilient production systems capable of withstanding disruptions and unexpected events. The DT provides accurate information useful for making informed decisions during disruptions. This research contributes to improving management practices by integrating production and enterprise data, facilitating decision-making processes aligned with company objectives, and enhancing the overall resilience of production systems. These findings illustrate the potential of cloud production services to maintain service levels. Among the academic contributions of this paper, is the use of System Dynamics as a suitable approach to modeling the behavior of a digital twin.

This paper investigates the performance of a two-stage multi-criteria decision-making procedure for order scheduling problems. These problems are represented by a novel nonlinear mixed integer program. Hybridizations of three Multi-Objective Evolutionary Algorithms (MOEAs) based on dominance relations are studied and compared to solve small, medium, and large instances of the joint order batching and picking problem in storage systems with multiple blocks of two and three dimensions. The performance of these methods is compared using a set of well-known metrics and running an extensive battery of simulations based on a methodology widely used in the literature. The main contributions of this paper are (1) the hybridization of MOEAs to deal efficiently with the combination of orders in one or several picking tours, scheduling them for each picker, and (2) a multi-criteria approach to scheduling multiple picking teams for each wave of orders. Based on the experimental results obtained, it can be stated that, in environments with a large number of different items and orders with high variability in volume, the proposed approach can significantly reduce operating costs while allowing the decision-maker to anticipate the positioning of orders in the dispatch area.