Heuristics in Decision Making

Heuristics are simple rules of thumbs for problem solving that follow a logic that is quite different from consequential logic. They have long been regarded, as an inferior technique for decision making that is the source of irrational decision behavior. Recently, decision making researchers have demonstrated that some heuristics are highly efficient and can compete with complex decision models in some application domains. This paper explores the different streams of research, summarizes the state of the art decision making model, and discusses its implications for complex decisions in engineering and technology management

An Investigation on Fast and Frugal Model for New Project Screening

Research in psychology is increasingly interested in decision-makers\u27 use of heuristics or rules of thumb because they have accuracies close to more complex decision rules and seem particularly useful in difficult decision-making contexts when uncertainty is high and speed is of the essence. One particularly difficult decision setting is the fuzzy front-end of new product development because a large number of product ideas need to be screened to identify the few that should be developed further. This process is currently poorly supported through decision tools and mainly occurs on the basis of managerial “gut-feel”. This study explores managerial “gut-feel” by investigating the performance of simple project screening heuristics: two so-called Fast and Frugal (F&F) heuristics, Take-the-Best and Tallying, and three logistic regression models with 3, 5, and 7 decision variables are used to screen a simulated dataset of 52 projects. Each model\u27s ability to recognize successful projects and correctly reject poor projects is compared against the predictions of the other decision models. The results how that the logistic regression models outperform the F&F models in overall prediction quality and in the ability to predict project failure. However, the Tallying model has an overall performance that is close to the logistic regression and both F&F models are better at predicting success than the logistic regression model. Furthermore, the regression model that only takes 3 decision variables into consideration performs better than the regression models with 5 and all 7 decision variables. This indicates that a simple “less is more” decision approach, which is the basis of managerial “gut-feel”, can be a successful strategy for front-end screening

Fast and Frugal Heuristics for New Product Screening - Is Managerial Judgment Good Enough?

Project screening in the fuzzy front-end of product development is dominantly based on managerial judgment, yet little is known about the quality of heuristic screening decisions. This research models three commonly discussed fast and frugal (F&F)heuristics for project screening (take-the-best, tallying and elimination-by-aspect) and explores their performance. An illustrative dataset of 52 new product development projects is used to compare the performance of F&F heuristics against that of regression models, which reflect compensatory judgment behaviour. The findings uncover a \u27less is more\u27 effect that justifies the use of simple heuristics in early stage product screening; two out of the three F&F heuristics reach accuracies of over 80% for project selection and 70% for project rejection and the best F&F model, tallying, performs similarly to the best regression model. The findings warrant a fresh look at managerial screening heuristics as \u27good enough\u27 decision making approach