scholarly journals How Behavioural Economics Relates to Psychology - Some Bibliographic Evidence

2019 ◽  
Author(s):  
Fabian Braesemann
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Behavioural Economics ◽  
Academic Background ◽  
Disciplinary Boundary ◽  
The Core ◽  
Bibliographic Data ◽  
Statistical Results

Whether behavioural economics has a fundamental influence on economics is debated by behavioural and heterodox economists as well as by methodologists and historians of economics. At the core of this debate is the question whether behavioural economics is shaped by large-scale content imports from psychology, or whether these transfers have been too selective to challenge dominant approaches in economics. This study contributes to the debate in analysing a variety of bibliographic data from the disciplinary boundary between economics and psychology. Two datasets from the boundary of behavioural economics and psychology are compared to sets of economic and psychology publications in quantifying the use of mathematics, the share of empirical contributions, the authors’ academic background, and their cross-citations via network analysis. In contrast to proposals made by some methodologists and behavioural economists, the statistical results confirm content transfers from psychology via behavioural economics only to a limited extend. The observed level of interaction provides evidence for a selective import of specific psychological findings by a small number of established investigators in behavioural economics. These findings were then intensively debated as divergences from rationality within the growing, but econ-centered community of behavioural economists.

scholarly journals Reputation, Status Networks, and the Art Market

Arts ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 81 ◽  
Author(s):  
L.E.A. Braden ◽  
Thomas Teekens
Keyword(s):  
Network Analysis ◽  
Market Research ◽  
Large Scale ◽  
Art Market ◽  
High Status ◽  
Exhibition History ◽  
Bibliographic Data ◽  
History Of ◽  
Influence Process

The effect of an artist’s prestige on the price of artwork is a well-known, central tenant in art market research. In considering how an artist’s prestige proliferates, much research examines networks, where certain artistic groupings and associations promote individual member’s artistic standing (i.e., “associative status networks”). When considering the role of associative status networks, there are two models by which status may increase. First, the confirmation model suggests that actors of similar status are associated with each other. Second, the increase model suggests that a halo effect occurs, whereby an individual’s status increases by association with higher-status artists. In this research, we examine the association of artists through museum exhibition to test confirmation versus increase models, ascertaining whether prestige acquisition is a selection or influence process. This research capitalizes on the retrospective digitization of exhibition catalogues, allowing for large-scale longitudinal analysis heretofore unviable for researchers. We use the exhibition history of 1148 artists from the digitized archives of three major Dutch museums (Stedelijk, Boijmans-Van Beuningen, Van Abbe) from 1930 to 1989, as well as data on artists’ market performance from artprice.com and bibliographic data from the WorldCat database. We then employ network analysis to examine the 60-year interplay of associative status networks and determine how different networks predict subsequent auction performance. We find that status connections may have a point of diminishing returns by which comparison to high prestige peers increases one’s own prestige to a point, after which a high-status comparison network becomes a liability.

scholarly journals Analysis on the Co-authoring in the Field of Management in China: Based on Social Network Analysis

2017 ◽  
Vol 12 (06) ◽  
pp. 149 ◽  
Author(s):  
Chuanyi Wang ◽  
Zhe Cheng ◽  
Zhiwei Huang
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Small Groups ◽  
Information Exchange ◽  
Research Output ◽  
Collaboration Network ◽  
The Core ◽  
Bibliographic Data ◽  
Academic Information

Using bibliographic data extracted from CNKI database, social network analysis is used to generate and analyze the network of co-authors of China in the field of management. This article suggests that: the density of the network is low, which means the collaboration between authors in China is not tight; the relations between the degree centrality and research output are weak. The author who published more papers may not have more co-authors. Through the lens of betweenness centrality, several authors in key positions of network are always dominating the academic information exchange and the small groups of authors have changed from 2006 to 2015. The result of core-periphery analysis reflects that only a very small proportion of scholars are in the core of the network while most are relatively independent. The similarity of working experience, academic authority and geographical closeness are helpful to form and enhance the collaboration network.

Large-Scale Network Analysis for Online Social Brand Advertising

MIS Quarterly ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 849-868 ◽  
Author(s):  
Kunpeng Zhang ◽  
◽  
Siddhartha Bhattacharyya ◽  
Sudha Ram ◽  
◽  
...  
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Large Scale Network ◽  
Brand Advertising ◽  
Scale Network

scholarly journals Social Network Analysis and Mining to Monitor and Identify Problems with Large-Scale Information and Communication Technology Interventions

PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0146220 ◽  
Author(s):  
Aleksandra do Socorro da Silva ◽  
Silvana Rossy de Brito ◽  
Nandamudi Lankalapalli Vijaykumar ◽  
Cláudio Alex Jorge da Rocha ◽  
Maurílio de Abreu Monteiro ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information And Communication Technology ◽  
Communication Technology ◽  
Large Scale ◽  
Information And Communication

scholarly journals Degree Adjusted Large-Scale Network Analysis Reveals Novel Putative Metabolic Disease Genes

Biology ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 107
Author(s):  
Apurva Badkas ◽  
Thanh-Phuong Nguyen ◽  
Laura Caberlotto ◽  
Jochen G. Schneider ◽  
Sébastien De Landtsheer ◽  
...  
Keyword(s):  
Network Analysis ◽  
Network Topology ◽  
Large Scale ◽  
Metabolic Diseases ◽  
Critical Factor ◽  
Disease Genes ◽  
Alcoholic Fatty Liver ◽  
Large Scale Network ◽  
Depth Study ◽  
Public Datasets

A large percentage of the global population is currently afflicted by metabolic diseases (MD), and the incidence is likely to double in the next decades. MD associated co-morbidities such as non-alcoholic fatty liver disease (NAFLD) and cardiomyopathy contribute significantly to impaired health. MD are complex, polygenic, with many genes involved in its aetiology. A popular approach to investigate genetic contributions to disease aetiology is biological network analysis. However, data dependence introduces a bias (noise, false positives, over-publication) in the outcome. While several approaches have been proposed to overcome these biases, many of them have constraints, including data integration issues, dependence on arbitrary parameters, database dependent outcomes, and computational complexity. Network topology is also a critical factor affecting the outcomes. Here, we propose a simple, parameter-free method, that takes into account database dependence and network topology, to identify central genes in the MD network. Among them, we infer novel candidates that have not yet been annotated as MD genes and show their relevance by highlighting their differential expression in public datasets and carefully examining the literature. The method contributes to uncovering connections in the MD mechanisms and highlights several candidates for in-depth study of their contribution to MD and its co-morbidities.

A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Feng Shi ◽  
Liuqing Chen ◽  
Ji Han ◽  
Peter Childs
Keyword(s):  
Information Retrieval ◽  
Network Analysis ◽  
Text Mining ◽  
Engineering Design ◽  
Large Scale ◽  
Semantic Network ◽  
Document Retrieval ◽  
Design Information ◽  
Improve Design ◽  
Correlation Degree

With the advent of the big-data era, massive information stored in electronic and digital forms on the internet become valuable resources for knowledge discovery in engineering design. Traditional document retrieval method based on document indexing focuses on retrieving individual documents related to the query, but is incapable of discovering the various associations between individual knowledge concepts. Ontology-based technologies, which can extract the inherent relationships between concepts by using advanced text mining tools, can be applied to improve design information retrieval in the large-scale unstructured textual data environment. However, few of the public available ontology database stands on a design and engineering perspective to establish the relations between knowledge concepts. This paper develops a “WordNet” focusing on design and engineering associations by integrating the text mining approaches to construct an unsupervised learning ontology network. Subsequent probability and velocity network analysis are applied with different statistical behaviors to evaluate the correlation degree between concepts for design information retrieval. The validation results show that the probability and velocity analysis on our constructed ontology network can help recognize the high related complex design and engineering associations between elements. Finally, an engineering design case study demonstrates the use of our constructed semantic network in real-world project for design relations retrieval.

Sign in / Sign up

Export Citation Format

Share Document