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Useful Heuristics

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Making Essential Choices with Scant Information

Abstract

Imagine two managers who want to develop a real estate project. At some point during the planning phase it becomes crucial to predict the future sales prices of houses in a certain area. The first project leader, Mr O, approaches this task by searching exhaustively for all available pieces of information that he knows will influence the selling price, such as property tax, lot size, total living space, age of the house, number of bathrooms and so on. Based on his past experience, he weighs all that information according to its importance and then integrates it to predict the selling price of each house, using some statistical software. The second manager, Mr F, makes a fast decision, relying on a simple strategy based on just one single piece of information that he regards as most important, such as total living space. Which of these two managers will make a more accurate forecast? Many people, researchers and lay persons alike, suppose that the outcome of a decision can be improved by (a) an exhaustive search for information, and the integration of many pieces of information, (b) having more time to think, and calculate possible outcomes, or (c) having more computational power and the use of complex forecasting software or decision tools. This seems to indicate that Mr O will make the better decision. However, this chapter demonstrates that less can sometimes be more, and that a strategy relying on very few pieces of information, and quickly deriving a decision based on a simple algorithm, may well outperform more sophisticated, supposedly rational decision strategies.

Decision-making is one of the core tasks in project management. Traditionally, optimization methods have been developed to support managers in finding the best solutions. Alternatively, decisions can be based on a simple rule of thumb or on heuristics. Even though simple heuristics only require little in the way of time and information, they have been shown to outperform optimization methods in complex decision tasks across a wide range of situations. This chapter outlines relevant decision heuristics commonly used, demonstrates situations in which they outperform more complex decision algorithms and explains why and when simple heuristics provide powerful decision tools.

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Author information

Authors and Affiliations

  1. Cognitive Science Program, Indiana University, Bloomington, UK

    Benjamin Scheibehenne (researcher)

  2. Center for Adaptive Behavior and Cognition, Germany

    Bettina von Helversen (research scientist)

Authors
  1. Benjamin Scheibehenne
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  2. Bettina von Helversen
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Editor information

Editors and Affiliations

  1. Norwegian University of Science and Technology in Trondheim, Norway

    Knut Samset (Professor of Project Management) (Professor of Project Management)

  2. Management School, University of Southampton, UK

    Terry M. Williams (Professor of Management Science, Director) (Professor of Management Science, Director)

  3. Norwegian Competition Authority, Norway

    Kjell J. Sunnevåg (Head of Research) (Head of Research)

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© 2009 Benjamin Scheibehenne and Bettina von Helversen

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Scheibehenne, B., von Helversen, B. (2009). Useful Heuristics. In: Williams, T.M., Samset, K., Sunnevåg, K.J. (eds) Making Essential Choices with Scant Information. Palgrave Macmillan, London. https://doi.org/10.1057/9780230236837_10

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