Concept: Wealth condensation
Many studies show that open access (OA) articles-articles from scholarly journals made freely available to readers without requiring subscription fees-are downloaded, and presumably read, more often than closed access/subscription-only articles. Assertions that OA articles are also cited more often generate more controversy. Confounding factors (authors may self-select only the best articles to make OA; absence of an appropriate control group of non-OA articles with which to compare citation figures; conflation of pre-publication vs. published/publisher versions of articles, etc.) make demonstrating a real citation difference difficult. This study addresses those factors and shows that an open access citation advantage as high as 19% exists, even when articles are embargoed during some or all of their prime citation years. Not surprisingly, better (defined as above median) articles gain more when made OA.
Accurate and timely estimates of population characteristics are a critical input to social and economic research and policy. In industrialized economies, novel sources of data are enabling new approaches to demographic profiling, but in developing countries, fewer sources of big data exist. We show that an individual’s past history of mobile phone use can be used to infer his or her socioeconomic status. Furthermore, we demonstrate that the predicted attributes of millions of individuals can, in turn, accurately reconstruct the distribution of wealth of an entire nation or to infer the asset distribution of microregions composed of just a few households. In resource-constrained environments where censuses and household surveys are rare, this approach creates an option for gathering localized and timely information at a fraction of the cost of traditional methods.
Humans prefer relatively equal distributions of resources, yet societies have varying degrees of economic inequality. To investigate some of the possible determinants and consequences of inequality, here we perform experiments involving a networked public goods game in which subjects interact and gain or lose wealth. Subjects (n = 1,462) were randomly assigned to have higher or lower initial endowments, and were embedded within social networks with three levels of economic inequality (Gini coefficient = 0.0, 0.2, and 0.4). In addition, we manipulated the visibility of the wealth of network neighbours. We show that wealth visibility facilitates the downstream consequences of initial inequality-in initially more unequal situations, wealth visibility leads to greater inequality than when wealth is invisible. This result reflects a heterogeneous response to visibility in richer versus poorer subjects. We also find that making wealth visible has adverse welfare consequences, yielding lower levels of overall cooperation, inter-connectedness, and wealth. High initial levels of economic inequality alone, however, have relatively few deleterious welfare effects.
We propose a simple agent-based model on a network to conceptualize the allocation of limited wealth among more abundant expectations at the interplay of power, frustration, and initiative. Concepts imported from the statistical physics of frustrated systems in and out of equilibrium allow us to compare subjective measures of frustration and satisfaction to collective measures of fairness in wealth distribution, such as the Lorenz curve and the Gini index. We find that a completely libertarian, law-of-the-jungle setting, where every agent can acquire wealth from or lose wealth to anybody else invariably leads to a complete polarization of the distribution of wealth vs. opportunity. This picture is however dramatically ameliorated when hard constraints are imposed over agents in the form of a limiting network of transactions. There, an out of equilibrium dynamics of the networks, based on a competition between power and frustration in the decision-making of agents, leads to network coevolution. The ratio of power and frustration controls different dynamical regimes separated by kinetic transitions and characterized by drastically different values of equality. It also leads, for proper values of social initiative, to the emergence of three self-organized social classes, lower, middle, and upper class. Their dynamics, which appears mostly controlled by the middle class, drives a cyclical regime of dramatic social changes.
The possibility to analyze everyday monetary transactions is limited by the scarcity of available data, as this kind of information is usually considered highly sensitive. Present econophysics models are usually employed on presumed random networks of interacting agents, and only some macroscopic properties (e.g. the resulting wealth distribution) are compared to real-world data. In this paper, we analyze Bitcoin, which is a novel digital currency system, where the complete list of transactions is publicly available. Using this dataset, we reconstruct the network of transactions and extract the time and amount of each payment. We analyze the structure of the transaction network by measuring network characteristics over time, such as the degree distribution, degree correlations and clustering. We find that linear preferential attachment drives the growth of the network. We also study the dynamics taking place on the transaction network, i.e. the flow of money. We measure temporal patterns and the wealth accumulation. Investigating the microscopic statistics of money movement, we find that sublinear preferential attachment governs the evolution of the wealth distribution. We report a scaling law between the degree and wealth associated to individual nodes.
- Proceedings of the National Academy of Sciences of the United States of America
- Published about 4 years ago
The distribution of wealth in the United States and countries around the world is highly skewed. How does visible economic inequality affect well-off individuals' support for redistribution? Using a placebo-controlled field experiment, I randomize the presence of poverty-stricken people in public spaces frequented by the affluent. Passersby were asked to sign a petition calling for greater redistribution through a “millionaire’s tax.” Results from 2,591 solicitations show that in a real-world-setting exposure to inequality decreases affluent individuals' willingness to redistribute. The finding that exposure to inequality begets inequality has fundamental implications for policymakers and informs our understanding of the effects of poverty, inequality, and economic segregation. Confederate race and socioeconomic status, both of which were randomized, are shown to interact such that treatment effects vary according to the race, as well as gender, of the subject.
Sleep plays a role in strengthening new words and integrating them with existing vocabulary knowledge, consistent with neural models of learning in which sleep supports hippocampal transfer to neocortical memory. Such models are based on adult research, yet neural maturation may mean that the mechanisms supporting word learning vary across development. Here, we propose a model in which children may capitalise on larger amounts of slow-wave sleep to support a greater demand on learning and neural reorganisation, whereas adults may benefit from a richer knowledge base to support consolidation. Such an argument is reinforced by the well-reported “Matthew effect”, whereby rich vocabulary knowledge is associated with better acquisition of new vocabulary. We present a meta-analysis that supports this association between children’s existing vocabulary knowledge and their integration of new words overnight. Whilst multiple mechanisms likely contribute to vocabulary consolidation and neural reorganisation across the lifespan, we propose that contributions of existing knowledge should be rigorously examined in developmental studies. Such research has potential to greatly enhance neural models of learning.
Daily distribution of dietary protein may be important in protecting against sarcopenia, specifically in terms of per meal amounts relative to a proposed threshold for maximal response. The aims of this study were to determine total and per meal protein intake in older adults, as well as identifying associations with physical activity and sedentary behavior. Three-day food diaries recorded protein intake in 38 participants. Protein distribution, coefficient of variation (CV), and per meal amounts were calculated. Accelerometry was used to collect physical activity data as well as volume and patterns of sedentary time. Average intake was 1.14 g·kg(-1)·day(-1). Distribution was uneven (CV = 0.67), and 79% of participants reported <0.4 g·kg(-1) protein content in at least 2/3 daily meals. Protein intake was significantly correlated with step count (r = 0.439, p = 0.007) and negatively correlated with sedentary time (r = -0.456, p = 0.005) and Gini index G, which describes the pattern of accumulation of sedentary time (r = -0.421, p = 0.011). Total daily protein intake was sufficient; however, distribution did not align with the current literature; increasing protein intake may help to facilitate optimization of distribution. Associations between protein and other risk factors for sarcopenia may also inform protective strategies.
The rich get richer principle, manifested by the Preferential attachment (PA) mechanism, is widely considered one of the major factors in the growth of real-world networks. PA stipulates that popular nodes are bound to be more attractive than less popular nodes; for example, highly cited papers are more likely to garner further citations. However, it overlooks the transient nature of popularity, which is often governed by trends. Here, we show that in a wide range of real-world networks the recent popularity of a node, i.e., the extent by which it accumulated links recently, significantly influences its attractiveness and ability to accumulate further links. We proceed to model this observation with a natural extension to PA, named Trending Preferential Attachment (TPA), in which edges become less influential as they age. TPA quantitatively parametrizes a fundamental network property, namely the network’s tendency to trends. Through TPA, we find that real-world networks tend to be moderately to highly trendy. Networks are characterized by different susceptibilities to trends, which determine their structure to a large extent. Trendy networks display complex structural traits, such as modular community structure and degree-assortativity, occurring regularly in real-world networks. In summary, this work addresses an inherent trait of complex networks, which greatly affects their growth and structure, and develops a unified model to address its interaction with preferential attachment.
Social influence drives human selection behaviours when numerous objects competing for limited attentions, which leads to the ‘rich get richer’ dynamics where popular objects tend to get more attentions. However, evidences have been found that, both the global information of the whole system and the local information among one’s friends have significant influence over the one’s selection. Consequently, a key question raises that, it is the local information or the global information more determinative for one’s selection? Here we compare the local-based influence and global-based influence. We show that, the selection behaviour is mainly driven by the local popularity of the objects while the global popularity plays a supplementary role driving the behaviour only when there is little local information for the user to refer to. Thereby, we propose a network model to describe the mechanism of user-object interaction evolution with social influence, where the users perform either local-driven or global-driven preferential attachments to the objects, i.e., the probability of an objects to be selected by a target user is proportional to either its local popularity or global popularity. The simulation suggests that, about 75% of the attachments should be driven by the local popularity to reproduce the empirical observations. It means that, at least in the studied context where users chose businesses on Yelp, there is a probability of 75% for a user to make a selection according to the local popularity. The proposed model and the numerical findings may shed some light on the study of social influence and evolving social systems.