1,135 research outputs found
Capturing Values at the Boundaries
Novelty is not a sufficient condition for innovation. For new ideas and
products to succeed, they must be integrated into the collective understanding
and existing infrastructure, illustrating how the past determines the future.
Here, we develop a comprehensive framework to understand how the structure of
accumulated past successes curves the adjacent possible trajectory of future
innovations. We observe that certain technological building blocks, upon
frequent combination, coalesce into noticeable clusters manifested as
well-defined domains within the exploration landscape. These clusters compress
the space around them, thus bending the trajectory of exploration towards them
as if exerting a gravitational pull on new ideas and actions. Our methodology
quantifies this effect, mapping out the curvatures within the adjacent possible
space of actions and identifying significant curvatures that define the
boundaries of consensus domains. These domains, serving as knowledge
repertoire, guide inventors towards proven solutions and past successes,
explaining why the most commercially successful inventions often emerge at the
fringes of established domains. Through a case study of Edison's patents, we
demonstrate his well-known design strategy of leveraging institutionalized
domains, manifested as high curvature in this space. In contrast, Tesla's
inventions are predominantly located in low-curvature areas. Our further
analysis reveals that innovations in areas of high curvature are indeed more
likely to capture market values, supporting our observations. Our framework
provides insights into how new ideas interact with and evolve alongside
established structures in institutional frameworks and collective
understanding, illustrating the complex dialogue between innovation and
convention.Comment: 59 pages, 5 main figures, 13 supplementary figure
Technological novelty profile and invention's future impact
We consider inventions as novel combinations of existing technological
capabilities. Patent data allow us to explicitly identify such combinatorial
processes in invention activities. Unconsidered in the previous research, not
every new combination is novel to the same extent. Some combinations are
naturally anticipated based on patent activities in the past or mere random
choices, and some appear to deviate exceptionally from existing invention
pathways. We calculate a relative likelihood that each pair of classification
codes is put together at random, and a deviation from the empirical observation
so as to assess the overall novelty (or conventionality) that the patent brings
forth at each year. An invention is considered as unconventional if a pair of
codes therein is unlikely to be used together given the statistics in the past.
Temporal evolution of the distribution indicates that the patenting activities
become more conventional with occasional cross-over combinations. Our analyses
show that patents introducing novelty on top of the conventional units would
receive higher citations, and hence have higher impact.Comment: 20 pages, 7 figure
Small cities face greater impact from automation
The city has proven to be the most successful form of human agglomeration and
provides wide employment opportunities for its dwellers. As advances in
robotics and artificial intelligence revive concerns about the impact of
automation on jobs, a question looms: How will automation affect employment in
cities? Here, we provide a comparative picture of the impact of automation
across U.S. urban areas. Small cities will undertake greater adjustments, such
as worker displacement and job content substitutions. We demonstrate that large
cities exhibit increased occupational and skill specialization due to increased
abundance of managerial and technical professions. These occupations are not
easily automatable, and, thus, reduce the potential impact of automation in
large cities. Our results pass several robustness checks including potential
errors in the estimation of occupational automation and sub-sampling of
occupations. Our study provides the first empirical law connecting two societal
forces: urban agglomeration and automation's impact on employment
Constructing cities, deconstructing scaling laws
Cities can be characterised and modelled through different urban measures.
Consistency within these observables is crucial in order to advance towards a
science of cities. Bettencourt et al have proposed that many of these urban
measures can be predicted through universal scaling laws. We develop a
framework to consistently define cities, using commuting to work and population
density thresholds, and construct thousands of realisations of systems of
cities with different boundaries for England and Wales. These serve as a
laboratory for the scaling analysis of a large set of urban indicators. The
analysis shows that population size alone does not provide enough information
to describe or predict the state of a city as previously proposed, indicating
that the expected scaling laws are not corroborated. We found that most urban
indicators scale linearly with city size regardless of the definition of the
urban boundaries. However, when non-linear correlations are present, the
exponent fluctuates considerably.Comment: Accepted for publication, Journal of the Royal Society Interfac
A common trajectory recapitulated by urban economies
Is there a general economic pathway recapitulated by individual cities over
and over? Identifying such evolution structure, if any, would inform models for
the assessment, maintenance, and forecasting of urban sustainability and
economic success as a quantitative baseline. This premise seems to contradict
the existing body of empirical evidences for path-dependent growth shaping the
unique history of individual cities. And yet, recent empirical evidences and
theoretical models have amounted to the universal patterns, mostly
size-dependent, thereby expressing many of urban quantities as a set of simple
scaling laws. Here, we provide a mathematical framework to integrate repeated
cross-sectional data, each of which freezes in time dimension, into a frame of
reference for longitudinal evolution of individual cities in time. Using data
of over 100 millions employment in thousand business categories between 1998
and 2013, we decompose each city's evolution into a pre-factor and relative
changes to eliminate national and global effects. In this way, we show the
longitudinal dynamics of individual cities recapitulate the observed
cross-sectional regularity. Larger cities are not only scaled-up versions of
their smaller peers but also of their past. In addition, our model shows that
both specialization and diversification are attributed to the distribution of
industry's scaling exponents, resulting a critical population of 1.2 million at
which a city makes an industrial transition into innovative economies
Deconstructing Human Capital to Construct Hierarchical Nestedness
Modern economies generate immensely diverse complex goods and services by
coordinating efforts and know-how of people in vast networks that span across
the globe. This increasing complexity puts us under the pressure of acquiring
an ever-increasing specialized and yet diverse skill portfolio in order to stay
effective members of a complex economy. Here, we analyze the skill portfolios
of workers in an effort to understand the latent structure and evolution of
these portfolios. Analyzing the U.S. survey data (2003-2019) and 20 million
resumes, we uncover a tree structure of vertical skill dependencies such that
skills that only a few jobs need (specialized) are located at the leaves under
the broadly demanded (general skills). The resulting structure exhibits an
unbalanced tree shape. The unbalanced shape allows the further categorization
of specialized skills: nested branching out of a deeply rooted sturdy trunk
reflecting a dense web of common prerequisites, and un-nested lacking such
support. Our longitudinal analyses show individuals indeed become more
specialized, going down the nested paths as moving up the career ladder to
enjoy higher wage premiums. The specialization, however, is most likely
accompanied by demands for a higher level of general skills, and furthermore,
specialization without the strengthening of general skills is deprived of wage
premiums. We examine the geographic and demographic distribution of skills to
explain disparities in wealth. Finally, historical changes in occupation skill
requirements show these branches have become more fragmented over the decade,
suggesting the increasing labor gap.Comment: 26 pages, 7 figure
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