4,714 research outputs found
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 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 āherding behaviorā among traders. We use concepts
from earthquake physics to quantify the decay of volatility shocks after the āmain
shock.ā 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
āanticipated newsā 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 āzealotsā, 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
Shift in house price estimates during COVID-19 reveals effect of crisis on collective speculation
We exploit a city-level panel comprised of individual house price estimates
to estimate the impact of COVID-19 on both small and big real-estate markets in
California USA. Descriptive analysis of spot house price estimates, including
contemporaneous price uncertainty and 30-day price change for individual
properties listed on the online real-estate platform Zillow.com, together
facilitate quantifying both the excess valuation and valuation confidence
attributable to this global socio-economic shock. Our quasi-experimental
pre-/post-COVID-19 design spans several years around 2020 and leverages
contemporaneous price estimates of rental properties - i.e., real estate
entering the habitation market, just not for purchase (off-market) and hence
free of speculation - as an appropriate counterfactual to properties listed for
sale, which are subject to on-market speculation. Combining unit-level matching
and multivariate difference-in-difference regression approaches, we obtain
consistent estimates regarding the sign and magnitude of excess price growth
observed after the pandemic onset. Specifically, our results indicate that
properties listed for sale appreciated an additional 1% per month above what
would be expected in the absence of the pandemic. This corresponds to an excess
annual price growth of roughly 12.7 percentage points, which accounts for more
than half of the actual annual price growth in 2021 observed across the studied
regions. Simultaneously, uncertainty in price estimates decreased, signaling
the irrational confidence characteristic of prior asset bubbles. We explore how
these two trends are related to market size, local market supply and borrowing
costs, which altogether lend support for the counterintuitive roles of
uncertainty and interruptions in decision-making.Comment: Main manuscript: 16 pages (5 figures); Supplementary Information: 11
pages (11 figures, 3 Tables
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 scientistsā 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
- ā¦