What makes organizations so similar? We contend that the engine of rationalization and bureaucratization has moved from the competitive marketplace to the state and the professions. Once a set of organizations emerges as a field, a paradox arises: rational actors make their organizations increasingly similar as they try to change them. We describe three isomorphic processes-coercive, mimetic, and normative—leading to this outcome. We then specify hypotheses about the impact of resource centralization and dependency, goal ambiguity and technical uncertainty, and professionalization and structuration on isomorphic change. Finally, we suggest implications for theories of organizations and social change.

The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (Chinese Translation)

This paper presents a new map representing the structure of all of science, based on journal articles, including both the natural and social sciences. Similar to cartographic maps of our world, the map of science provides a bird's eye view of today's scientific landscape. It can be used to visually identify major areas of science, their size, similarity, and interconnectedness. In order to be useful, the map needs to be accurate on a local and on a global scale. While our recent work has focused on the former aspect, this paper summarizes results on how to achieve structural accuracy. Eight alternative measures of journal similarity were applied to a data set of 7,121 journals covering over 1 million documents in the combined Science Citation and Social Science Citation Indexes. For each journal similarity measure we generated two-dimensional spatial layouts using the force-directed graph layout tool, VxOrd. Next, mutual information values were calculated for each graph at different clustering levels to give a measure of structural accuracy for each map. The best co-citation and inter-citation maps according to local and structural accuracy were selected and are presented and characterized. These two maps are compared to establish robustness. The inter-citation map is more » then used to examine linkages between disciplines. Biochemistry appears as the most interdisciplinary discipline in science. « less

Mapping the backbone of science.

A comprehensive survey on support vector machine classification: Applications, challenges and trends

The search for models to predict the prices of financial markets is still a highly researched topic, despite major related challenges. The prices of financial assets are non-linear, dynamic, and chaotic; thus, they are financial time series that are difficult to predict. Among the latest techniques, machine learning models are some of the most researched, given their capabilities for recognizing complex patterns in various applications. With the high productivity in the machine learning area applied to the prediction of financial market prices, objective methods are required for a consistent analysis of the most relevant bibliography on the subject. This article proposes the use of bibliographic survey techniques that highlight the most important texts for an area of research. Specifically, these techniques are applied to the literature about machine learning for predicting financial market values, resulting in a bibliographical review of the most important studies about this topic. Fifty-seven texts were reviewed, and a classification was proposed for markets, assets, methods, and variables. Among the main results, of particular note is the greater number of studies that use data from the North American market. The most commonly used models for prediction involve support vector machines (SVMs) and neural networks. It was concluded that the research theme is still relevant and that the use of data from developing markets is a research opportunity.

Literature review: Machine learning techniques applied to financial market prediction

https://repositorio.fei.edu.br/bitstream/FEI/3436/1/Mattos_pdf

The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions

Stock price prediction using support vector regression on daily and up to the minute prices

Can artificial intelligence enhance the Bitcoin bonanza

Citation networks are the basis for main path analysis (MPA), which has become an important tool in bibliometric studies. MPA can be used to map the main body of work of a scientific field, highlighting its most important literature and chronological evolution. Its uses goes from surveying the state of the art of a given subject to selecting study material for new research. MPA is conducted on a citation network and there is a well established literature accounting for methods of finding the most relevant paths. However, the details of how the citation network is actually built are not richly described in the specialized literature. Manually relating the available references of a given field would prove to be a difficult task. Given this context, we propose an automatic method, providing a simple algorithm for building citation networks with computer implementations and preventing cyclic paths. The algorithm is built quantitatively and is applicable to studies on the mechanisms of any science field. As an example, we go through every proposed step to select the papers which constitute the main path of the literature on forecasting stock prices using machine learning techniques.

Bruno Miranda Henrique

Papers

Literature review: Machine learning techniques applied to financial market prediction

Stock price prediction using support vector regression on daily and up to the minute prices

Can artificial intelligence enhance the Bitcoin bonanza

Building direct citation networks

Contracting in Brazilian public administration: A machine learning approach