Mental models: an interdisciplinary synthesis of theory and methods

By Natalie A. Jones, H. Ross, Tim Lynam, P. Perez, A. Leitch. 2011

913 citations recorded by the source corpus.

Mental models are personal, internal representations of external reality that people use to interact with the world around them. They are constructed by individuals based on their unique life experiences, perceptions, and understandings of the world. Mental models are used to reason and make decisions and can be the basis of individual behaviors. They provide the mechanism through which new information is filtered and stored. Recognizing and dealing with the plurality of stakeholder's perceptions, values, and goals is currently considered a key aspect of effective natural resource management (NRM) practice. Therefore, gaining a better understanding of how mental models internally represent complex, dynamic systems and how these representations change over time will allow us to develop mechanisms to enhance effective management and use of natural resources. Realizing this potential, however, relies on developing and testing adequate tools and techniques to elicit these internal representations of the world effectively. This paper provides an interdisciplinary synthesis of the literature that has contributed to the theoretical development and practical application of the mental model construct. It explores the utility and applicability of the construct in the context of NRM and includes a review of elicitation techniques used within the field. The major theoretical and practical challenges that arise in drawing on the construct to provide a cognitive dimension to NRM are also addressed.

Subfields: Artificial Intelligence, Environmental Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/c65d53bbe1b165f63d55290e7fdd95c6fd20973c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Michel E. Etienne
  2. Tim Lynam
  3. Raphael Mathevet
  4. Coralie Calvet
  5. Deborah Blackman
  6. Helen Ross
  7. A. Guerrero
  8. Darren Gergle

In Environmental Engineering

  1. Helen Ross
  2. C. Pahl‐Wostl

In Management Science and Operations Research

  1. Michel E. Etienne
  2. C. Pahl‐Wostl
  3. Art Dewulf
  4. Geeske Scholz
  5. Tim Lynam
  6. Raphael Mathevet
  7. Coralie Calvet
  8. Emilee J. Rader

Citations in the corpus