Approaches to decision-making under uncertainty in the belief function framework are reviewed. Most methods are shown to blend criteria for decision under ignorance with the maximum expected utility principle of Bayesian decision theory. A distinction is made between methods that construct a complete preference relation among acts, and those that allow incomparability of some acts due to lack of information. Methods developed in the imprecise probability framework are applicable in the Dempster-Shafer context and are also reviewed. Shafer’s constructive decision theory, which substitutes the notion of goal for that of utility, is described and contrasted with other approaches. The paper ends by pointing out the need to carry out deeper investigation of fundamental issues related to decision-making with belief functions and to assess the descriptive, normative and prescriptive values of the different approaches. Decision-Making with Belief Functions: a Review
Help! I can’t reproduce a machine learning project!
Three Mighty Good Reasons to Learn R for Data Science
Ahoy, mateys! Happy International Talk Like A Pirate Day
New Engen improves customer acquisition marketing campaigns using Amazon Rekognition
New Engen is a cross-channel performance marketing technology company that uses its proprietary software products and creative solutions to help their clients acquire new customers. New Engen integrates marketing, AI, and creative expertise to provide a one-stop solution that helps their customers optimize their digital marketing budget across Facebook, Google, Instagram, Snap, and more.
R Packages worth a look
Create Interactive Graphs with ‘Echarts JavaScript’ Version 4 (echarts4r)Easily create interactive charts by leveraging the ‘Echarts Javascript’ library which includes 33 chart types, themes, ‘Shiny’ proxies and animations.
Whats new on arXiv
Solving for multi-class: a survey and synthesis
A couple more papers on genetic diversity as an explanation for why Africa and remote Andean countries are so poor while Europe and North America are so wealthy
Back in 2013, I wrote a post regarding a controversial claim that high genetic diversity, or low genetic diversity, is bad for the economy:
The hot hand—in darts!
Roland Langrock writes:
R Packages worth a look
Open System Files, ‘URLs’, Anything (xopen)Cross platform solution to open files, directories or ‘URLs’ with their associated programs.
Training models with unequal economic error costs using Amazon SageMaker
Many companies are turning to machine learning (ML) to improve customer and business outcomes. They use the power of ML models built over “big data” to identify patterns and find correlations. Then they can identify appropriate approaches or predict likely outcomes based on data about new instances. However, as ML models are approximations of the real world, some of these predictions will likely be in error.