Comparing supervised learning algorithms and artificial neural networks for conflict prediction: performance and applicability of deep learning in the field

Quality & Quantity - Machine learning algorithms and artificial neural networks promise a new and powerful approach for making better and more transferable predictions in global conflict...     

Spatial social networks

We introduce a spatial cost topology in the network formation model analyzed by Jackson and Wolinsky, Journal of Economic Theory (1996), 71: 44–74. This cost topology might represent geographical, social, or individual differences. It describes variable costs of establishing social network connections. Participants form links based on a cost-benefit analysis. We examine the pairwise stable networks within this spatial environment. Incentives vary enough to show a rich pattern of emerging behavior. We also investigate the subgame perfect implementation of pairwise stable and efficient networks. We construct a multistage extensive form game that describes the formation of links in our spatial environment. Finally, we identify the conditions under which the subgame perfect Nash equilibria of these network formation games are stable. We are very grateful for the constructive comments of Matt Jackson and an anonymous referee. We also like to thank Vince Crawford, Marco Slikker, Edward Droste, Hans Haller, Dimitrios Diamantaras, and Sudipta Sarangi for comments on previous drafts of this paper.We acknowledge Jay Hogan for his programming support. Part of this research was done while visiting the CentER for Economic Research, Tilburg University, Tilburg, The Netherlands.Financial support from the Netherlands Organization for Scientific Resrarch (NWO), grant B46-390, is gratefully acknowledged.-->,     

The uncertainty track: Machine learning,statistical modeling,synthesis

This note provides an evaluation of the contributions of the M5 Competition to the construction of prediction intervals. We consider the choice of criteria used in the evaluations, the relative performance of designed and benchmark methods and the take-home lessons both for statistical forecasters and for those interested in forecasting retail sales.     

The structural,social, and strategic factors associated with successful business networks

Business networks, formal arrangements between independent businesses established to enhance member success, are generally accepted as an important strategy to help small businesses survive and prosper, and to promote regional economic development. However, knowledge about what contributes to the success of business networks themselves is less extensive and based primarily on case studies or reports of network directors. The purpose of this paper is to partially address this shortcoming. We consider the structural and social features identified in previous studies as likely correlates of business network success. Using a social constructionist definition of network success, we distinguish successful from less successful networks from among a sample of 29 industry and community business networks in the USA. Findings from interviews with 1122 members and 29 network leaders suggest that trust is central to understanding network success. Structural features have complex positive and negative indirect effects on success through trust and resource exchanges. The findings highlight the fact that business networks, while offering great potential as a way to enhance economic vitality of regions and industries, cannot be viewed as a simple remedy.     

Remaining life estimation of used components in consumer products: Life cycle data analysis by Weibull and artificial neural networks

Environmental awareness and legislative pressures have made manufacturers responsible for the take-back and end-of-life treatment of their products. To competitively exploit these products, one option is to incorporate used components in “new” or remanufactured products. However, this option is partly limited by a firm's ability to assess the reliability of used components. A comprehensive two-step approach is proposed. The first stage phase statistically analyzes the behavior of components for reuse. A well-known reliability assessment method, the Weibull analysis, is applied to the time-to-failure data to assess the mean life of components. In the second phase, the degradation and condition monitoring data are analyzed by developing an artificial neural network (ANN) model. The advantages of this approach over traditional approaches employing multiple regression analysis are highlighted with empirical data from a consumer product. Finally, the Weibull analysis and the ANN model are then integrated to assess the remaining useful life of components for reuse. This is a critical advance in sustainable management of supply chains since it allows for a better understanding of not only service requirements of product, but the remaining life in a product and hence its suitability for reuse or remanufacture. Future work should assess: (1) reduction in downtime of process equipment through the implementation of this technique as a means to better manage preventative maintenance; (2) reduce field failure of remanufactured product; (3) selling-service strategy through implementation of the proposed methodology.     

Predicting criminal recidivism using neural networks

Prediction of criminal recidivism has been extensively studied in criminology with a variety of statistical models. This article proposes the use of neural network (NN) models to address the problem of splitting the population into two groups — non-recidivists and eventual recidivists — based on a set of predictor variables. The results from an empirical study of the classification capabilities of NN on a well-known recidivism data set are presented and discussed in comparison with logistic regression. Analysis indicates that NN models are competitive with, and may offer some advantages over, traditional statistical models in this domain.     

Information flows and crashes in dynamic social networks

We develop a dynamic model of information transmission and aggregation in social networks in which continued membership in the network is contingent on the accuracy of opinions. Agents have opinions about a state of the world and form links to others in a directed fashion probabilistically. Agents update their opinions by averaging those of their connections, weighted by how long their connections have been in the system. Agents survive or die based on how far their opinions are from the true state. In contrast to the results in the extant literature on DeGroot learning, we show through simulations that for some parameterizations the model cycles stochastically between periods of high connectivity, in which agents arrive at a consensus opinion close to the state, and periods of low connectivity, in which agents’ opinions are widely dispersed.

     

Forecasting exchange rates using feedforward and recurrent neural networks

In this paper we investigate the out-of-sample forecasting ability of feedforward and recurrent neural networks based on empirical foreign exchange rate data. A two-step procedure is proposed to construct suitable networks, in which networks are selected based on the predictive stochastic complexity (PSC) criterion, and the selected networks are estimated using both recursive Newton algorithms and the method of nonlinear least squares. Our results show that PSC is a sensible criterion for selecting networks and for certain exchange rate series, some selected network models have significant market timing ability and/or significantly lower out-of-sample mean squared prediction error relative to the random walk model.     

Metropolitan patenting, inventor agglomeration and social networks: A tale of two effects

We investigate the separate effects on metropolitan patenting of inventor agglomeration and the structure of social networks linking inventors within and across metropolitan areas in the United States between 1977 and 2002. Using patent data we have been able to assign a metropolitan location to individual inventors, link inventors who have co-authored patents, and characterize the structural features of the networks linking inventors. Our main question concerns the relative importance of salient features of the social networks linking inventors on metropolitan patenting productivity. We find that agglomerative features of metropolitan areas are more important determinants of metropolitan patenting productivity than structural feature of the inventive networks. While the aggregation of isolated inventors correlates positively with patenting productivity, density of connections correlates negatively.     

Identification of peer effects through social networks

We provide new results regarding the identification of peer effects. We consider an extended version of the linear-in-means model where interactions are structured through a social network. We assume that correlated unobservables are either absent, or treated as network fixed effects. We provide easy-to-check necessary and sufficient conditions for identification. We show that endogenous and exogenous effects are generally identified under network interaction, although identification may fail for some particular structures. We use data from the Add Health survey to provide an empirical application of our results on the consumption of recreational services (e.g., participation in artistic, sports and social activities) by secondary school students. Monte Carlo simulations calibrated on this application provide an analysis of the effects of some crucial characteristics of a network (i.e., density, intransitivity) on the estimates of peer effects. Our approach generalizes a number of previous results due to Manski [Manski, C., 1993. Identification of endogenous social effects: The reflection problem. Review of Economic Studies 60 (3), 531–542], Moffitt [Moffitt, R., 2001. Policy interventions low-level equilibria, and social interactions. In: Durlauf, Steven, Young, Peyton (Eds.), Social Dynamics. MIT Press] and Lee [Lee, L.F., 2007. Identification and estimation of econometric models with group interactions, contextual factors and fixed effects. Journal of Econometrics 140 (2), 333–374].     

Neighborhood social networks and female self-employment earnings in Taiwan

Using the sample of Taiwanese female self-employed workers from the Taiwan Women and Family Survey (TWFS) conducted in 1989, this article explores neighborhood social networks and female self-employment earnings. The evidence from linear regression and endogenous treatment effect models suggests that an individual’s past provision of personalized assistance to adjacent neighbors significantly increases current self-employment earnings, with corrections for self-selection into network participation. An individual’s past voluntary contributions to community organizational activities also significantly increase current earnings, both with and without corrections for self-selection. However, there are negative self-selections into both cooperative neighborhood networks and community organizational activities in the sense that female self-employed workers with high earnings capacity are less likely to choose to participate in these types of social networks.     

Machine learning advances for time series forecasting

In this paper, we survey the most recent advances in supervised machine learning (ML) and high-dimensional models for time-series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods, we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feedforward and recurrent versions, and tree-based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of ML in economics and finance and provide an illustration with high-frequency financial data.     

Research collaboration model in academic social networks

ABSTRACT

This paper proposes a hybrid collaboration recommendation method that accounts for research similarities and the previous research cooperation network. Research cooperation is measured by combining the collaboration time and the number of co-authors who already collaborated with at least one scientist. Research similarity is based on authors’ previous publications and academic events they attended. A weighted directed graph is built to discover new collaborators by using direct and indirect connections between scientists. Moreover, a consensus-based system is built to integrate bibliography data from different sources. The experimental results show that our method improves the recommendation performances over other methods.     

Influences on expatriate social networks in China

This study applied social network theory to investigate the factors influencing expatriate social networks and the consequences of expatriate social networks in China. Based on analysis of 171 Taiwanese expatriates in China, this study found that core self-evaluations and extraversion are significant for expatriates in developing expressive and instrumental ties with host country nationals. Moreover, job autonomy assists expatriates in developing instrumental ties with host country nationals. The expressive and instrumental ties of expatriates with host country nationals are significant for overseas adaptation. Finally, instrumental ties with host country nationals are significant for expatriate job performance.     

Factual and modal notions in social research

The article discusses a distinction between factual and modal notions, and corresponding generalizations, in social research. The discussion starts from the suggestion, made by Charles Ragin, that theoretical statements in social research most often can be formulated as statements about sets of cases and relations between such sets. In contrast to this view, it is argued that theoretical statements in social research often require modal notions referring to possibilities and probabilities which cannot be formulated as statements about sets of cases. In order to show this, the article reformulates Ragin’s set-theoretic approach in the conceptual framework of statistical variables. It is shown that this can be done for both crisp and fuzzy set versions of Ragin’s approach. The article then goes on to argue that social research is often interested in modal generalizations (probabilistic and deterministic rules) which require a fundamentally different conceptual framework. The article shows how such a framework can be defined, and finally indicates its usage for causal interpretations.     

Digital organizing for social impact: Current insights and future research avenues on collective action,social movements,and digital technologies

Digital technologies such as social media have drastically transformed the contexts and processes associated with collective action. New actors have gotten involved in efforts at changing the societal status quo, existing actors have adjusted to new ways of organizing digitally, and original forms of social movements have emerged and grown. All these developments do not, however, hold the potential for unequivocally positive societal impact. New forms of abuse and many unintended consequences have also multiplied. It is high time to take stock of the insights from current scholarship on these important issues.Here we bring together major findings from existing scholarship and introduce the papers of this special issue. We also propose an agenda for future research by highlighting ongoing debates and issues that require further consideration. Questions associated with the increasingly intricated participation of human and technological agents deserve attention, for instance. Careful considerations of the actual impact of digital organizing at different levels (e.g., individual, group, organization, society) also require further scholarship. We finally caution researchers to be ethical and to remain careful of the potential for use and misuse of their scholarship on these important and often polemical topics.     

力学反问题研究中的人工神经网络方法

随着力学学科的不断发展,力学反问题的神经网络方法研究越来越引起工程人员的重视。力学反问题的神经网络方法在工程实践中也得到了一定的应用。