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In this study, we examine the perceived influence of others, across both strong and weak social ties, on COVID-19 vaccination decisions in the United States. We add context to social influence by measuring related concepts, such as perceived agreement of others and perceived danger of COVID-19 to others. We find that vaccinated populations perceived more influence from their social circles than unvaccinated populations. This finding holds true across various social groups, including family, close friends, and neighbors. Vaccinated participants perceived that others agreed with their decision to get vaccinated more than unvaccinated participants perceived others to agree with their decision to not get vaccinated. Despite the clear differences in perceived social influence and agreement across the groups, the majority of participants across both vaccinated and unvaccinated populations perceived no social influence from all social group in their decisions. Aligning with this result, we find through open-ended responses that both vaccinated and unvaccinated participants frequently cited fear as a motivating factor in their decision, rather than social influence: vaccinated participants feared COVID-19, while unvaccinated participants feared the vaccine itself., Comment: Preprint of paper currently under review

A recent highly-publicized study by Park et al. (Nature 613: 138-144, 2023), claiming that science has become less disruptive over recent decades, represents an extraordinary achievement but with deceptive results. The measure of disruption, CD-5, in this study does not account for differences in citation amid decades of exponential growth in publication rate. In order to account for both the exponential growth as well as the differential impact of research works over time, here we apply a weighted disruption index to the same dataset. We find that, among research papers in the dataset, this weighted disruption index has been close to its expected neutral value over the last fifty years and has even increased modestly since 2000. We also show how the proportional decrease in unique words (highlighted by Park et al. (2023) is expected in an exponentially growing corpus. Finding little evidence for recent decrease in disruption, we suggest that it is actually increasing., Comment: 6 pages, 3 figures, submitted to Advances in Complex Systems on 11 April 2023

Here we test Neutral models against the evolution of English word frequency and vocabulary at the population scale, as recorded in annual word frequencies from three centuries of English language books. Against these data, we test both static and dynamic predictions of two neutral models, including the relation between corpus size and vocabulary size, frequency distributions, and turnover within those frequency distributions. Although a commonly used Neutral model fails to replicate all these emergent properties at once, we find that modified two-stage Neutral model does replicate the static and dynamic properties of the corpus data. This two-stage model is meant to represent a relatively small corpus (population) of English books, analogous to a `canon', sampled by an exponentially increasing corpus of books in the wider population of authors. More broadly, this mode -- a smaller neutral model within a larger neutral model -- could represent more broadly those situations where mass attention is focused on a small subset of the cultural variants., Comment: 12 pages, 5 figures, 1 table

Obtaining an accurate picture of the current state of the economy is particularly important to central banks and finance ministries, and of epidemics to health ministries. There is increasing interest in the use of search engine data to provide such 'nowcasts' of social and economic indicators. However, people may search for a phrase because they independently want the information, or they may search simply because many others are searching for it. We consider the effect of the motivation for searching on the accuracy of forecasts made using search engine data of contemporaneous social and economic indicators. We illustrate the implications for forecasting accuracy using four episodes in which Google Flu Trends data gave accurate predictions of actual flu cases, and four in which the search data over-predicted considerably. Using a standard statistical methodology, the Bass diffusion model, we show that the independent search for information motive was much stronger in the cases of accurate prediction than in the inaccurate ones. Social influence, the fact that people may search for a phrase simply because many others are, was much stronger in the inaccurate compared to the accurate cases. Search engine data may therefore be an unreliable predictor of contemporaneous indicators when social influence on the decision to search is strong., Comment: 5 pages

In social network markets, the act of consumer choice in these industries is governed not just by the set of incentives described by conventional consumer demand theory, but by the choices of others in which an individual's payoff is an explicit function of the actions of others. We observe two key empirical features of outcomes in social networked markets. First, a highly right-skewed, non-Gaussian distribution of the number of times competing alternatives are selected at a point in time. Second, there is turnover in the rankings of popularity over time. We show here that such outcomes can arise either when there is no alternative which exhibits inherent superiority in its attributes, or when agents find it very difficult to discern any differences in quality amongst the alternatives which are available so that it is as if no superiority exists. These features appear to obtain, as a reasonable approximation, in many social network markets. We examine the impact of network structure on both the rank-size distribution of choices at a point in time, and on the life spans of the most popular choices. We show that a key influence on outcomes is the extent to which the network follows a hierarchical structure. It is the social network properties of the markets, the meso-level structure, which determine outcomes rather than the objective attributes of the products., Comment: 14 pages, 5 figures

Two general patterns have been identified for the adoption and subsequent abandonment of ideas or products within a population. One is symmetric, so that concepts or products which are adopted rapidly then decline rapidly from their peak, and those which are slower to move to their peak decline more slowly. The other is asymmetric, where the decline from the peak is considerably slower than is the rise to the peak, and vice versa. We posit that these contrasting patterns arise from two fundamentally different modes of behaviour which are used by humans in making choices in different contexts. Namely, choice based on the imitation of the choices of others versus purposeful selection based upon the inherent attributes of the concept or product. We illustrate the proposition with the example of internet searches for the phrase 'swine flu' in a wide range of countries across the world. The methodology offers a general heuristic for distinguishing between these two general and contrasting modes of behavioural choice., Comment: 8 pages, 3 figures, 1 table

Studies of collective human behavior in the social sciences, often grounded in details of actions by individuals, have much to offer `social' models from the physical sciences concerning elegant statistical regularities. Drawing on behavioral studies of social influence, we present a parsimonious, stochastic model, which generates an entire family of real-world right-skew socio-economic distributions, including exponential, winner-take-all, power law tails of varying exponents and power laws across the whole data. The widely used Albert-Barabasi model of preferential attachment is simply a special case of this much more general model. In addition, the model produces the continuous turnover observed empirically within those distributions. Previous preferential attachment models have generated specific distributions with turnover using arbitrary add-on rules, but turnover is an inherent feature of our model. The model also replicates an intriguing new relationship, observed across a range of empirical studies, between the power law exponent and the proportion of data represented., Comment: 8 pages, 4 figures, 1 table

Evidence is growing that in many markets consumers select not simply on the basis of the perceived attributes of products, but their preferences are modified by the behaviour of others. Economists have paid relatively little attention to such markets. The classic Bass diffusion model incorporates the imitation of others as a part of the behavioural rules used by consumers in making choice. We extend the Bass model to be able to apply it to long-term case studies where substantial changes over time in the population making choices at any given point have to be taken into account. We consider evidence from the activity of hill-walking. We use as a particular illustration the Munros, a list of Scottish hills over 3,000 feet in height which many British walkers aspire to complete. The extension to the Bass model can of course be used in other situations where such population changes are important., Comment: 7 pages, 2 figures, 1 table

The Internet is known to have had a powerful impact on on-line retailer strategies in markets characterised by long-tail distribution of sales. Such retailers can exploit the long tail of the market, since they are effectively without physical limit on the number of choices on offer. Here we examine two extensions of this phenomenon. First, we introduce turnover into the long-tail distribution of sales. Although over any given period such as a week or a month, the distribution is right-skewed and often power law distributed, over time there is considerable turnover in the rankings of sales of individual products. Second, we establish some initial results on the implications for shelf-space strategy of physical retailers in such markets., Comment: 10 pages, 3 figures

The evolution of vocabulary in academic publishing is characterized via keyword frequencies recorded the ISI Web of Science citations database. In four distinct case-studies, evolutionary analysis of keyword frequency change through time is compared to a model of random copying used as the null hypothesis, such that selection may be identified against it. The case studies from the physical sciences indicate greater selection in keyword choice than in the social sciences. Similar evolutionary analyses can be applied to a wide range of phenomena; wherever the popularity of multiple items through time has been recorded, as with web searches, or sales of popular music and books, for example., Comment: 9 pages, 4 figures