Description: This article is about research studies that use the Delphi technique. It describes what a Delphi study is and gives a thumbnail sketch of two Delphi studies the Society of Actuaries has sponsored in the past 10 years, as well as a recently initiated Delphi study.

Description: This article discusses steps to follow to ensure a more rational and productive approach to data analysis.

Description: Spreadsheets have become ubiquitous in business and forecasting. However, they do not usually have the same level of control that any other complex program would require. As a result, we often get erroneous results which can have far reaching, negative consequences.

Description: Feature article discussing the difficult situation of actuarial forecasting with little or no date.

Description: Alternative sources of learning available for free or at low cost if you wish to round out your F&F education about machine learning, artificial neural networks, genetic algorithms and the millennia of biological advances we draw from when we use these techniques.

Description: Continuation of a tutorial on Fuzzy (vs. Boolean, or 'crisp') logic and how you can use it to create actuarial models that better fit the real world. The article provides basic theory in non-technical terms and shows applications using the R programming language.

Description: Fuzzy logic is form of multi-valued logic that allows the creation of fuzzy rules. This article introduces the "sets" package for the R programming language for fuzzy logic.

Description: Summary of the issue, with emphasis on common sense and accuracy rather than blind precision. Also describes two F&F contests.

Description: A Delphi Research Study of Long-Term Care Financing Solutions." Two members of the Project Oversight Group discuss the reasons for the study and the Delphi method and summarize the results of the study.

Description: Unstructured data is often synonymous with Big Data; and the term is used and misused a lot. Richard Xu and his colleague, Dihui Lai, dispel some of the confusion about Big Data in their article “Big Data In Life Insurance—Does It Exist? If So, How Should We Handle It?” They address some of the ways to deal with the mounting challenges of capacity and speed as data scales up rapidly in size. Hadoop was once just the name of a toy elephant; but there is nothing toy-like about how it has been employed to handle very large datasets. Five exabytes supposedly represents all the words ever spoken by human beings; but according to IBM, the new SKA telescope initiative will generate over an exabyte of data every day.1 How will we cope with big data? Read Richard and Dihui’s article for some hints.