Doing Good Today and Better Tomorrow: A Roadmap to High Impact Philanthropy Through Outcome-Focused Grantmaking

Describes Hewlett's experience with implementing the outcome-focused grantmaking (OFG) process in its environment program as a guide for identifying a portfolio of grants with maximum impact. Outlines trials and errors, recent innovations, and challenges

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

Algorithm Portfolio for Individual-based Surrogate-Assisted Evolutionary Algorithms

Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation tools for computationally expensive problems (CEPs). However, a randomly selected algorithm may fail in solving unknown problems due to no free lunch theorems, and it will cause more computational resource if we re-run the algorithm or try other algorithms to get a much solution, which is more serious in CEPs. In this paper, we consider an algorithm portfolio for SAEAs to reduce the risk of choosing an inappropriate algorithm for CEPs. We propose two portfolio frameworks for very expensive problems in which the maximal number of fitness evaluations is only 5 times of the problem's dimension. One framework named Par-IBSAEA runs all algorithm candidates in parallel and a more sophisticated framework named UCB-IBSAEA employs the Upper Confidence Bound (UCB) policy from reinforcement learning to help select the most appropriate algorithm at each iteration. An effective reward definition is proposed for the UCB policy. We consider three state-of-the-art individual-based SAEAs on different problems and compare them to the portfolios built from their instances on several benchmark problems given limited computation budgets. Our experimental studies demonstrate that our proposed portfolio frameworks significantly outperform any single algorithm on the set of benchmark problems

Optimization of factorial portfolio of trade enterprises in the conditions of the non-payment crisis

The economic mechanism for factoring management of trade enterprises was improved by applying a tool for refinancing receivables involving third parties, which will contribute to the effective management of fundraising processes from the standpoint of the income approach. The instruments for the implementation of the economic mechanism of factoring management of commercial enterprises, consisting of five blocks were improved (analysis of commercial enterprise debtors’ solvency in order to transfer them to factoring services; analysis of accounts receivable and assessment of its real value; planning of cash flows from factoring operations; factoring implementation assessment; monitoring and control of the repayment of receivables in the process of factoring services), that allows substantiating practical recommendations for improving the level of factoring management. Based on the concept of a portfolio of investments, a factoring model was built to optimize the debtors of the enterprise