Learning and Expertise in Mineral Exploration Decision-Making: An Ecological Dynamics Perspective

Int J Environ Res Public Health. 2021 Sep 16;18(18):9752. doi: 10.3390/ijerph18189752.

Abstract

The declining discovery rate of world-class ore deposits represents a significant obstacle to future global metal supply. To counter this trend, there is a requirement for mineral exploration to be conducted in increasingly challenging, uncertain, and remote environments. Faced with such increases in task and environmental complexity, an important concern in exploratory activities are the behavioural challenges of information perception, interpretation and decision-making by geoscientists tasked with discovering the next generation of deposits. Here, we outline the Dynamics model, as a diagnostic tool for situational analysis and a guiding framework for designing working and training environments to maximise exploration performance. The Dynamics model is based on an Ecological Dynamics framework, combining Newell's Constraints model, Self Determination Theory, and including feedback loops to define an autopoietic system. By implication of the Dynamics model, several areas are highlighted as being important for improving the quality of exploration. These include: (a) provision of needs-supportive working environments that promote appropriate degrees of effort, autonomy, creativity and technical risk-taking; (b) an understanding of the wider motivational context, particularly the influence of tradition, culture and other 'forms of life' that constrain behaviour; (c) relevant goal-setting in the design of corporate strategies to direct exploration activities; and (d) development of practical, representative scenario-based training interventions, providing effective learning environments, with digital media and technologies presenting decision-outcome feedback, to assist in the development of expertise in mineral exploration targeting.

Keywords: ecological dynamics; expertise; mineral exploration; needs-supportive environment; representative learning design.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Internet*
  • Learning*
  • Minerals
  • Systems Theory

Substances

  • Minerals