The Z-index: A geometric representation of productivity and impact which accounts for information in the entire rank-citation profile

We present a simple generalization of Hirsch's h-index, Z = \sqrt{h^{2}+C}/\sqrt{5}, where C is the total number of citations. Z is aimed at correcting the potentially excessive penalty made by h on a scientist's highly cited papers, because for the majority of scientists analyzed, we find the excess citation fraction (C-h^{2})/C to be distributed closely around the value 0.75, meaning that 75 percent of the author's impact is neglected. Additionally, Z is less sensitive to local changes in a scientist's citation profile, namely perturbations which increase h while only marginally affecting C. Using real career data for 476 physicists careers and 488 biologist careers, we analyze both the distribution of $Z$ and the rank stability of Z with respect to the Hirsch index h and the Egghe index g. We analyze careers distributed across a wide range of total impact, including top-cited physicists and biologists for benchmark comparison. In practice, the Z-index requires the same information needed to calculate h and could be effortlessly incorporated within career profile databases, such as Google Scholar and ResearcherID. Because Z incorporates information from the entire publication profile while being more robust than h and g to local perturbations, we argue that Z is better suited for ranking comparisons in academic decision-making scenarios comprising a large number of scientists.Comment: 9 pages, 5 figure

A quantitative perspective on ethics in large team science

The gradual crowding out of singleton and small team science by large team endeavors is challenging key features of research culture. It is therefore important for the future of scientific practice to reflect upon the individual scientist's ethical responsibilities within teams. To facilitate this reflection we show labor force trends in the US revealing a skewed growth in academic ranks and increased levels of competition for promotion within the system; we analyze teaming trends across disciplines and national borders demonstrating why it is becoming difficult to distribute credit and to avoid conflicts of interest; and we use more than a century of Nobel prize data to show how science is outgrowing its old institutions of singleton awards. Of particular concern within the large team environment is the weakening of the mentor-mentee relation, which undermines the cultivation of virtue ethics across scientific generations. These trends and emerging organizational complexities call for a universal set of behavioral norms that transcend team heterogeneity and hierarchy. To this end, our expository analysis provides a survey of ethical issues in team settings to inform science ethics education and science policy.Comment: 13 pages, 5 figures, 1 table. Keywords: team ethics; team management; team evaluation; science of scienc

Applications of Statistical Physics to the Social and Economic Sciences

This thesis applies statistical physics concepts and methods to quantitatively analyze socioeconomic systems. For each system we combine theoretical models and empirical data analysis in order to better understand the real-world system in relation to the complex interactions between the underlying human agents. This thesis is separated into three parts: (i) response dynamics in financial markets, (ii) dynamics of career trajectories, and (iii) a stochastic opinion model with quenched disorder. In Part I we quantify the response of U.S. markets to financial shocks, which perturb markets and trigger “herding behavior” among traders. We use concepts from earthquake physics to quantify the decay of volatility shocks after the “main shock.” We also find, surprisingly, that we can make quantitative statements even before the main shock. In order to analyze market behavior before as well as after “anticipated news” we use Federal Reserve interest-rate announcements, which are regular events that are also scheduled in advance. In Part II we analyze the statistical physics of career longevity. We construct a stochastic model for career progress which has two main ingredients: (a) random forward progress in the career and (b) random termination of the career. We incorporate the rich-get-richer (Matthew) effect into ingredient (a), meaning that it is easier to move forward in the career the farther along one is in the career. We verify the model predictions analyzing data on 400,000 scientific careers and 20,000 professional sports careers. Our model highlights the importance of early career development, showing that many careers are stunted by the relative disadvantage associated with inexperience. In Part III we analyze a stochastic two-state spin model which represents a system of voters embedded on a network. We investigate the role in consensus formation of “zealots”, which are agents with time-independent opinion. Our main result is the unexpected finding that it is the number and not the density of zealots which determines the steady-state opinion polarization. We compare our findings with results for United States Presidential elections

Scale invariant properties of public debt growth

Public debt is one of the important economic variables that quantitatively describes a nation's economy. Because bankruptcy is a risk faced even by institutions as large as governments (e.g. Iceland), national debt should be strictly controlled with respect to national wealth. Also, the problem of eliminating extreme poverty in the world is closely connected to the study of extremely poor debtor nations. We analyze the time evolution of national public debt and find "convergence": initially less-indebted countries increase their debt more quickly than initially more-indebted countries. We also analyze the public debt-to-GDP ratio R, a proxy for default risk, and approximate the probability density function P(R) with a Gamma distribution, which can be used to establish thresholds for sustainable debt. We also observe "convergence" in R: countries with initially small R increase their R more quickly than countries with initially large R. The scaling relationships for debt and R have practical applications, e.g. the Maastricht Treaty requires members of the European Monetary Union to maintain R < 0.6.Comment: 9 pages, 8 figure

Commentary: The case for caution in predicting scientists’ future impact

We stress-test the career predictability model proposed by Acuna et al. [Nature 489, 201-202 2012] by applying their model to a longitudinal career data set of 100 Assistant professors in physics, two from each of the top 50 physics departments in the US. The Acuna model claims to predict h(t+\Delta t), a scientist's h-index \Delta t years into the future, using a linear combination of 5 cumulative career measures taken at career age t. Here we investigate how the "predictability" depends on the aggregation of career data across multiple age cohorts. We confirm that the Acuna model does a respectable job of predicting h(t+\Delta t) up to roughly 6 years into the future when aggregating all age cohorts together. However, when calculated using subsets of specific age cohorts (e.g. using data for only t=3), we find that the model's predictive power significantly decreases, especially when applied to early career years. For young careers, the model does a much worse job of predicting future impact, and hence, exposes a serious limitation. The limitation is particularly concerning as early career decisions make up a significant portion, if not the majority, of cases where quantitative approaches are likely to be applied.Comment: 2 pages, 1 figur

The evolution of networks of innovators within and across borders: Evidence from patent data

Recent studies on the geography of knowledge networks have documented a negative impact of physical distance and institutional borders upon research and development (R&D) collaborations. Though it is widely recognized that geographic constraints and national borders impede the diffusion of knowledge, less attention has been devoted to the temporal evolution of these constraints. In this study we use data on patents filed with the European Patent Office (EPO) for OECD countries to analyze the impact of physical distance and country borders on inter-regional links in four different networks over the period 1988-2009: (1) co-inventorship, (2) patent citations, (3) inventor mobility and (4) the location of R&D laboratories. We find the constraint imposed by country borders and distance decreased until mid-1990s then started to grow, particularly for distance. We further investigate the role of large innovation "hubs" as attractors of new collaboration opportunities and the impact of region size and locality on the evolution of cross-border patenting activities. The intensity of European cross-country inventor collaborations increased at a higher pace than their non-European counterparts until 2004, with no significant relative progress thereafter. Moreover, when analyzing networks of geographical mobility, multinational R&D activities and patent citations we cannot detect any substantial progress in European research integration above and beyond the common global trend

Exploiting citation networks for large-scale author name disambiguation

We present a novel algorithm and validation method for disambiguating author names in very large bibliographic data sets and apply it to the full Web of Science (WoS) citation index. Our algorithm relies only upon the author and citation graphs available for the whole period covered by the WoS. A pair-wise publication similarity metric, which is based on common co-authors, self-citations, shared references and citations, is established to perform a two-step agglomerative clustering that first connects individual papers and then merges similar clusters. This parameterized model is optimized using an h-index based recall measure, favoring the correct assignment of well-cited publications, and a name-initials-based precision using WoS metadata and cross-referenced Google Scholar profiles. Despite the use of limited metadata, we reach a recall of 87% and a precision of 88% with a preference for researchers with high h-index values. 47 million articles of WoS can be disambiguated on a single machine in less than a day. We develop an h-index distribution model, confirming that the prediction is in excellent agreement with the empirical data, and yielding insight into the utility of the h-index in real academic ranking scenarios.Comment: 14 pages, 5 figure

Networks of innovators within and across borders. Evidence from patent data

Recent studies on the geography of knowledge networks have documented a negative impact of physical distance and institutional borders upon research and development (R&D) collaborations. Though it is widely recognized that geographic constraints hamper the diffusion of knowledge, less attention has been devoted to the temporal evolution of these constraints. In this study we use data on patents filed with the European Patent Office (EPO) for 50 countries to analyze the impact of physical distance and country borders on inter-regional links in four different networks over the period 1988-2009: (1) co-inventorship, (2) patent citations, (3) inventor mobility and (4) the location of R&D laboratories. We find the constraint imposed by country borders and distance decreased until mid-1990s then started to grow, particularly for distance. The intensity of European cross-country inventor collaborations increased at a higher pace than their non-European counterparts until 2004, with no significant relative progress afterwards. Moreover, when analyzing networks of geographical mobility, multinational R&D activities and patent citations we do not depict any substantial progress in European research integration aside from the influence of common global trends