830 research outputs found
Recommended from our members
Essays on Probabilistic Machine Learning for Economics
This thesis consists of three essays that explore the use of probabilistic machine learning techniques in combination with information-theoretic concepts to answer economic questions. Over the past years, economists have started applying machine learning methods to a wide range of topics. Probabilistic methods in the context of unsupervised learning represent one particular modelling approach at the intersection of computer science and statistics. While widely used in applied statistics, these models, however, do not necessarily provide relevant and interpretable outputs from an economist's perspective. In this thesis, I appeal to information-theoretic methods to summarise the probabilistic information inferred from such models and construct economically meaningful measures.Nikolas Kuhlen gratefully acknowledges the financial support of The Alan Turing Institute under research award No. TU/C/000030
Process study of a complex technology transfer and integration : the case of digital interactive broadcast media
Imperial Users onl
Compression-based inference of network motif sets
Physical and functional constraints on biological networks lead to complex
topological patterns across multiple scales in their organization. A particular
type of higher-order network feature that has received considerable interest is
network motifs, defined as statistically regular subgraphs. These may implement
fundamental logical and computational circuits and are referred as ``building
blocks of complex networks''. Their well-defined structures and small sizes
also enables the testing of their functions in synthetic and natural biological
experiments. The statistical inference of network motifs is however fraught
with difficulties, from defining and sampling the right null model to
accounting for the large number of possible motifs and their potential
correlations in statistical testing. Here we develop a framework for motif
mining based on lossless network compression using subgraph contractions. The
minimum description length principle allows us to select the most significant
set of motifs as well as other prominent network features in terms of their
combined compression of the network. The approach inherently accounts for
multiple testing and correlations between subgraphs and does not rely on a
priori specification of an appropriate null model. This provides an alternative
definition of motif significance which guarantees more robust statistical
inference. Our approach overcomes the common problems in classic testing-based
motif analysis. We apply our methodology to perform comparative connectomics by
evaluating the compressibility and the circuit motifs of a range of
synaptic-resolution neural connectomes
A Connected World: Social Networks and Organizations
This Element synthesizes the current state of research on organizational social networks from its early foundations to contemporary debates. It highlights the characteristics that make the social network perspective distinctive in the organizational research landscape, including its emphasis on structure and outcomes. It covers the main theoretical developments and summarizes the research design questions that organizational researchers face when collecting and analyzing network data. Then, it discusses current debates ranging from agency and structure to network volatility and personality. Finally, the Element envisages future research directions on the role of brokerage for individuals and communities, network cognition, and the importance of past ties. Overall, the Element provides an innovative angle for understanding organizational social networks, engaging in empirical network research, and nurturing further theoretical development on the role of social interactions and connectedness in modern organizations
How can innovation economics benefit from complex network analysis?
There is a deficit in economics of theories and empirical data on complex networks, though mathematicians, physicists, biologists, computer scientists, and sociologists are actively engaged in their study. This paper offers a focused review of prominent concepts in contemporary thinking in network research that may motivate further theoretical research and stimulate interest of economists. Possible avenues for modelling innovation, considered the driving force behind economic change, have been explored. A transition is needed from the analysis in economics of the transaction to the explicit examination of market structure and how it processes, or is processed by, innovation.Network; statistics; economy; innovation; modelling
- …