Physics

Computational thinking is a way of reasoning about the world in terms of data. This mindset channels number crunching toward an ambition to discover knowledge through logic, models and simulations. Here we show how computational cognitive science can be used to reconstruct and analyse the structure of computational thinking mindsets (forma mentis in Latin) through complex networks. As a case study, we investigate cognitive networks tied to key concepts of computational thinking provided by: (i) 159 high school students enrolled in a science curriculum and (ii) 59 researchers in complex systems and simulations. Researchers' reconstructed forma mentis highlighted a positive mindset about scientific modelling, semantically framing data and simulations as ways of discovering nature. Students correctly identified different aspects of logic reasoning but perceived "computation" as a distressing, anxiety-eliciting task, framed with math jargon and lacking links to real-world discovery. Students' mindsets around "data", "model" and "simulations" critically revealed no awareness of numerical modelling as a way for understanding the world. Our findings provide evidence of a crippled computational thinking mindset in students, who acquire mathematical skills that are not channelled toward real-world discovery through coding. This unlinked knowledge ends up being perceived as distressing number-crunching expertise with no relevant outcome. The virtuous mindset of researchers reported here indicates that computational thinking can be restored by training students specifically in coding, modelling and simulations in relation to discovering nature. Our approach opens innovative ways for quantifying computational thinking and enhancing its development through mindset reconstruction.

Hypergraphs offer an explicit formalism to describe multibody interactions in complex systems. To connect dynamics and function in systems with these higher-order interactions, network scientists have generalised random-walk models to hypergraphs and studied the multibody effects on flow-based centrality measures. But mapping the large-scale structure of those flows requires effective community detection methods. We derive unipartite, bipartite, and multilayer network representations of hypergraph flows and explore how they and the underlying random-walk model change the number, size, depth, and overlap of identified multilevel communities. These results help researchers choose the appropriate modelling approach when mapping flows on hypergraphs.

An increasing abstraction has marked some recent investigations in network science. Examples include the development of algorithms that map time series data into networks whose vertices and edges can have different interpretations, beyond the classical idea of parts and interactions of a complex system. These approaches have proven useful for dealing with the growing complexity and volume of diverse data sets. However, the use of such algorithms is mostly limited to one-dimension data, and there has been little effort towards extending these methods to higher-dimensional data such as images. Here we propose a generalization for the ordinal network algorithm for mapping images into networks. We investigate the emergence of connectivity constraints inherited from the symbolization process used for defining the network nodes and links, which in turn allows us to derive the exact structure of ordinal networks obtained from random images. We illustrate the use of this new algorithm in a series of applications involving randomization of periodic ornaments, images generated by two-dimensional fractional Brownian motion and the Ising model, and a data set of natural textures. These examples show that measures obtained from ordinal networks (such as average shortest path and global node entropy) extract important image properties related to roughness and symmetry, are robust against noise, and can achieve higher accuracy than traditional texture descriptors extracted from gray-level co-occurrence matrices in simple image classification tasks.

The present article studies the extension of two deterministic models for describing the novel coronavirus pandemic crisis, the SIR model and the SEIR model. The models were studied and compared to real data in order to support the validity of each description and extract important information regarding the pandemic, such as the basic reproductive number R0, which might provide useful information concerning the rate of increase of the pandemic predicted by each model. We next proceed to making predictions and comparing more complex models derived from the SEIR model with the SIRD model, in order to find the most suitable one for describing and predicting the pandemic crisis. Aiming to answer the question if the simple SIRD model is able to make reliable predictions and deliver suitable information compared to more complex models.

The construction of deprivation indices is complicated by the inherent ambiguity in defining deprivation as well as the potential for partisan manipulation. Nevertheless, deprivation indices provide an essential tool for mitigating the effects of deprivation and reducing it through policy interventions. Here we demonstrate the construction of a deprivation index using diffusion maps, a manifold learning technique capable of finding the variables that optimally describe the variations in a dataset in the sense of preserving pairwise relationships among the data points. The method is applied to the 2010 US decennial census. In contrast to other methods the proposed procedure does not select particular columns from the census, but rather constructs an indicator of deprivation from the complete dataset. Due to its construction the proposed index does not introduce biases except those already present in the source data, does not require normative judgment regarding the desirability of certain life styles, and is highly resilient against attempts of partisan manipulation. We demonstrate that the new index aligns well with established income-based deprivation indices but deviates in aspects that are perceived as problematic in some of the existing indices. The proposed procedure provides an efficient way for constructing accurate, high resolution indices. These indices can thus have the potential to become powerful tools for the academic study of social structure as well as political decision making.

One-shot anonymous unselfishness in economic games is commonly explained by social preferences, which assume that people care about the monetary payoffs of others. However, during the last ten years, research has shown that different types of unselfish behaviour, including cooperation, altruism, truth-telling, altruistic punishment, and trustworthiness are in fact better explained by preferences for following one's own personal norms - internal standards about what is right or wrong in a given situation. Beyond better organising various forms of unselfish behaviour, this moral preference hypothesis has recently also been used to increase charitable donations, simply by means of interventions that make the morality of an action salient. Here we review experimental and theoretical work dedicated to this rapidly growing field of research, and in doing so we outline mathematical foundations for moral preferences that can be used in future models to better understand selfless human actions and to adjust policies accordingly. These foundations can also be used by artificial intelligence to better navigate the complex landscape of human morality.

Often in the setting of human-based interactions, the existence of a temporal hierarchy of information plays an important role in diffusion and opinion dynamics within communities. For example at the individual agent level, more recently acquired information may exert greater influence during decision-making processes. To facilitate further exploration of this effect, we introduce an efficient method for modelling temporally asynchronous opinion updates, where the timing of updates depends on the timing of incoming opinion states received from neighbours. The framework enables the introduction of information arrival-time lag by means of lag-vectors. These are used to weight the relevance of information received by agents, based on the delay between its receipt and the subsequent opinion update. The temporal dynamics (i.e. the times at which information is transmitted) are governed by an underlying algebraic structure called max-plus algebra. We investigate the resulting continuous opinion dynamics under the max-plus regime using a modified Hegselmann-Krause model, replacing the conventional confidence-interval based on the distance between opinions with one based instead on the recency of information received by agents.

Competitive balance model has been proposed as an extension to the balance model to address the conflict of interests in signed networks arXiv:2001.04664 . In this model two different paradigms compete with each other due to the competitive interests to dominate the system and impose their own values. Using mean-field solution method in this paper, we examine the thermal behavior of the competitive balance model. Our results show that under a certain temperature, the symmetry between two competitive interests will spontaneously break which leads to a discrete phase transition. So, starting with a heterogeneous signed network, if agents aim to ultimately decrease tension stemming from balance theory, evolution ultimately chooses only one of the existing interests and stability arises where one paradigm dominates the network. The critical temperature depends linearly on the number of nodes, which was a linear dependence in the thermal balance theory as well. Finally the results obtained through the mean-field theory are verified by a series of simulations.

The implementation of governmental Non-Pharmaceutical Interventions (NPIs) has been the primary means of controlling the spread of the COVID-19 disease. The intended effect of these NPIs has been to reduce mobility. A strong reduction in mobility is believed to have a positive effect on the reduction of COVID-19 transmission by limiting the opportunity for the virus to spread in the population. Due to the huge costs of implementing these NPIs, it is essential to have a good understanding of their efficacy. Using global mobility data, released by Apple and Google, and ACAPS NPI data, we investigate the proportional contribution of NPIs on i) size of the change (magnitude) of transition between pre- and post-lockdown mobility levels and ii) rate (gradient) of this transition. Using generalized linear models to find the best fit model we found similar results using Apple or Google data. NPIs found to impact the magnitude of the change in mobility were: Lockdown measures (Apple, Google Retail and Recreation (RAR) and Google Transit and Stations (TS)), declaring a state of emergency (Apple, Google RAR and Google TS), closure of businesses and public services (Google RAR) and school closures (Apple). Using cluster analysis and chi square tests we found that closure of businesses and public services, school closures and limiting public gatherings as well as border closures and international flight suspensions were closely related. The implementation of lockdown measures and limiting public gatherings had the greatest effect on the rate of mobility change. In conclusion, we were able to quantitatively assess the efficacy of NPIs in reducing mobility, which enables us to understand their fine grained effects in a timely manner and therefore facilitate well-informed and cost-effective interventions.

In this article, I conduct a textual and contextual analysis of the empirical literature on Zipf's law for cities. Building on previous meta-analysis material openly available, I collect full texts and bibliographies of 66 scientific articles published in English and construct similarity networks of the terms they use as well as of the references and disciplines they cite. I use these networks as explanatory variables in a model of the similarity network of the distribution of Zipf estimates reported in the 66 articles. I find that the proximity in words frequently used by authors correlates positively with their tendency to report similar values and dispersion of Zipf estimates. The reference framework of articles also plays a role, as articles which cite similar references tend to report similar average values of Zipf estimates. As a complement to previous meta-analyses, the present approach sheds light on the scientific text and context mobilized to report on city size distributions. It allows to identified gaps in the corpus and potentially overlooked articles.