Churchman's “process of unfolding”—Its significance for policy analysis and evaluation

Churchman's process of unfolding is one of his lesser known concepts, yet it is essential for understanding his dialectical systems approach. This paper gives a brief introduction to the concept and presents a simple framework for applying it to policy analysis and program evaluation.     

Study on the Measurement of Industrial Structure “Sophistication,Rationalization and Ecologicalization” Based on the Dynamic Analysis of Grey Relations——A Case Study of Beijing-Tianjin-Hebei

The Beijing-Tianjin-Hebei region is one of the regions with the most dynamic economies,highest degrees of openness, strongest innovation capacities and largest populations, as well as an important engine driving China's economic development. Currently, the development of the BeijingTianjin-Hebei region faces many difficulties, including an inefficient distribution of regional functions,imbalance in industrial structure, low sophistication, and serious overburdening of resources and the environment. Promoting the overall coordinated development of the region has become an urgent challenge. In this paper, according to the conventions of industrial structure optimization, the three core indicators of regional industrial structure rationalization, sophistication and ecologicalization are constructed. With the help of the grey dynamic correlation model, the three indicators are coupled with the economic growth rate, and the industrial structure optimization index of the Beijing-Tianjin-Hebei region is comprehensively measured. The study shows that rationalization of the industrial structure in the Beijing-Tianjin-Hebei region is increasing but that this trend is not obvious; sophistication of the industrial structure is relatively high and has also been operating at a high level; however, the overall ecological level of the industrial structure in the Beijing-Tianjin-Hebei region is not high and presents the characteristics of high in the middle and low at both ends. In terms of the relationship between industrial structure and economic growth in the Beijing-Tianjin-Hebei region, ecologicalization of the industrial structure has the greatest influence on economic growth, followed by sophistication of the industrial structure, and rationalization of the industrial structure has the weakest influence.     

Turning Hindsight into Foresight—Our Search for the “Philosopher's Stone” of Failure

This paper tells the story of work using Systems thinking to study failure. It describes the methodological developments that have taken place in this area at the Open University and looks at how the emphasis has shifted from the examination of past failures to the prediction and prevention of future failures and the facilitation of organizational learning through the analysis of failures. It ends with an outline of some of the work on systems failures that is currently under way.     

“Towards Re-Inventing Psychohistory”:Predicting the Popularity of Tomorrow’s News from Yesterday’s Twitter and News Feeds

Rapid advances in machine learning combined with wide availability of online social media have created considerable research activity in predicting what might be the news of tomorrow based on an analysis of the past.In this work,we present a deep learning forecasting framework which is capable to predict tomorrow’s news topics on Twitter and news feeds based on yesterday’s content and topic-interaction features.The proposed framework starts by generating topics from words using word embeddings and K-means clustering.Then temporal topic-networks are constructed where two topics are linked if the same user has worked on both topics.Structural and dynamic metrics calculated from networks along with content features and past activity,are used as input of a long short-term memory(LSTM)model,which predicts the number of mentions of a specific topic on the subsequent day.Utilizing dependencies among topics,our experiments on two Twitter datasets and the HuffPost news dataset demonstrate that selecting a topic’s historical local neighbors in the topic-network as extra features greatly improves the prediction accuracy and outperforms existing baselines.