Maximizing benefits in project selection: a hybrid approach

Charitable foundations and government programmes should endeavour to allocate their limited resources to best serve their constituents. Yet, mathematical programming techniques are rarely used despite overwhelming evidence of their superiority in selecting projects that yield higher levels of total benefits. We present a novel ‘hybrid selection model’ that combines binary linear programming and heuristic rank-based models applied to two case studies. The first case focuses on providing services to women and shows a hybrid model would have selected the top three ‘signature’ projects and maintained an above-average overall project benefit while securing a 180% improvement in the number of projects funded, a 66% improvement in the number of women served and a 132% improvement in the total benefit achieved. In the second case, we apply the hybrid approach to data from the US government’s largest forest preservation programme and demonstrate that the hybrid approach could allow the programme to select up to 11 top-scoring projects while still achieving a 97% gain in the total overall benefit compared to their traditional method. These case studies show that the hybrid approach has the potential to be applied in a variety of settings and improve how foundations and programmes achieve their goals.     

Internal Migration of Blacks in South Africa: An Application of the Roy Model

This study investigates the internal migration of black males in South Africa over the period after the formal end of Apartheid using the 1996 census data. The two issues of our primary interests are the following: (i) whether migration patterns of black individuals are consistent with the income‐maximising hypothesis as related to the destination choice; and (ii) whether the redistribution of human capital is detected in internal migration. The results from conditional logit regressions on choices among individuals in 318 districts show that individuals prefer districts with higher expected wages, conditional on other regional characteristics. In addition, there exist differing preferences on the share of population with post‐secondary education by individuals with commensurate educational attainments. Black individuals with post‐secondary education tend to migrate into areas with a higher share of population with post‐secondary education and vice versa, which confirms the divergence of human capital levels across districts.     

Impossibilities for strategy-proof committee selection mechanisms with vetoers

When a group of voters selects a committee out of a set of candidates, it is common and often desirable to endow these voters with some veto power. I present impossibility results showing that even limited veto power makes many mechanisms of interest manipulable. This applies in particular (i) to mechanisms the range of which contains a degenerate lottery in which a committee is chosen for sure and (ii) to mechanisms that are constructed from extensive game forms with a finite number of strategies. These impossibilities hold on a large set of domains including the domain of additive preferences, and even when probabilistic mechanisms are allowed and voters can report cardinal preferences.     

Supplier selection and partnerships: Effects upon restaurant operational and strategic benefits and performance

This study focused on identifying strategic criteria involved in selecting suppliers of restaurant products/services specific to casual dining, full service independent restaurants to obtain operational and strategic benefits for those managers operating in this highly competitive marketplace. A total of 765 responses were obtained from managers at independently owned restaurants throughout the U.S. Results revealed that ‘product’ and ‘financial/technical’ criteria had significant and positive effects upon operational benefits, while ‘product’ and ‘service’ had positive effects upon strategic benefits. When the sample was segmented into low and high supplier partnership groups, the positive effects of ‘financial/technical’ and ‘cost/price’ were significantly greater for the ‘high’ partnership group pertaining to operational benefits. ‘Product’ and ‘service’ and their effects upon strategic benefits were greatest for the high partnership group. Based on our findings, implications, limitations, and recommendations for future studies have been provided.     

How does the effect of pre-play suggestions vary with group size? Experimental evidence from a threshold public-good game

Numerous studies have examined factors influencing the likelihood of cooperative outcomes in nonzero-sum games, but there has been little study of the interaction between two of the most important: group size and pre-play cheap talk. We report results from an experiment in which groups of size between 2 and 15 play a one-shot multi-player threshold public-good game. In our random leader treatment, all group members select a suggestion (e.g., “Everyone should choose X”), with one randomly chosen to be broadcast to the group. In a choice only treatment, subjects choose suggestions but none is sent, and in a baseline treatment, there are no suggestions at all. We find a negative interaction between group size and this kind of communication: the beneficial effect of both suggestions overall and cooperative suggestions on cooperation, cooperative outcomes, and payoffs decreases sharply as the group size increases. We find a similar negative interaction in a follow-up treatment in which all group members’ suggestions are broadcast to the group. Our results suggest that care should be taken in generalising conclusions from small-group experiments to large groups.     

试论人工智能应用于我国传统制造业的启示

现阶段,人工智能的应用和普及已经成为社会运行发展的一大趋势。与此同时,传统制造业面临着与人工智能相结合应用的升级和转型。论文基于人工智能发展与应用的背景,对人工智能带来的机遇和挑战展开分析,指出了人工智能应用于我国传统制造行业未来发展趋势方面的启示,并在此基础上提出人工智能在传统制造行业的应用办法,提高人工智能运行效率。     

Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes

This paper contributes to the nascent literature on nowcasting and forecasting GDP in emerging market economies using big data methods. This is done by analyzing the usefulness of various dimension-reduction, machine learning and shrinkage methods, including sparse principal component analysis (SPCA), the elastic net, the least absolute shrinkage operator, and least angle regression when constructing predictions using latent global macroeconomic and financial factors (diffusion indexes) in a dynamic factor model (DFM). We also utilize a judgmental dimension-reduction method called the Bloomberg Relevance Index (BRI), which is an index that assigns a measure of importance to each variable in a dataset depending on the variable’s usage by market participants. Our empirical analysis shows that, when specified using dimension-reduction methods (particularly BRI and SPCA), DFMs yield superior predictions relative to both benchmark linear econometric models and simple DFMs. Moreover, global financial and macroeconomic (business cycle) diffusion indexes constructed using targeted predictors are found to be important in four of the five emerging market economies that we study (Brazil, Mexico, South Africa, and Turkey). These findings point to the importance of spillover effects across emerging market economies, and underscore the significance of characterizing such linkages parsimoniously when utilizing high-dimensional global datasets.     

新型煤炭企业物资供应人才内涵与选用标准

介绍了新型煤炭企业物资供应人才的内涵,构建了安全效益、经济效益和管理效益3个维度;分析了选用新型煤炭企业物资供应人才的3大标准,提出了从道德品质、素质能力、态度观念等方面着手的观点,对实践具有重要指导和参照意义。     

An information-theoretic approach for forecasting interval-valued SP500 daily returns

We develop an iterative and efficient information-theoretic estimator for forecasting interval-valued data, and use our estimator to forecast the SP500 returns up to five days ahead using moving windows. Our forecasts are based on 13 years of data. We show that our estimator is superior to its competitors under all of the common criteria that are used to evaluate forecasts of interval data. Our approach differs from other methods that are used to forecast interval data in two major ways. First, rather than applying the more traditional methods that use only certain moments of the intervals in the estimation process, our estimator uses the complete sample information. Second, our method simultaneously selects the model (or models) and infers the model’s parameters. It is an iterative approach that imposes minimal structure and statistical assumptions.