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

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 decisionoutcome feedback, to assist in the development of expertise in mineral exploration targeting.

Full text

International Journal of
Environmental Research
and Public Health
Review
Learning and Expertise in Mineral Exploration
Decision-Making: An Ecological Dynamics Perspective
Rhys Samuel Davies 1, * , Marianne Julia Davies 2, David Groves 3, Keith Davids 2, Eric Brymer 4,
Allan Trench 1,3, John Paul Sykes 1,3 and Michael Dentith 5


Citation: Davies, R.S.; Davies, M.J.;
Groves, D.; Davids, K.; Brymer, E.;
Trench, A.; Sykes, J.P.; Dentith, M.
Learning and Expertise in Mineral
Exploration Decision-Making: An
Ecological Dynamics Perspective. Int.
J. Environ. Res. Public Health 2021,18,
9752. https://doi.org/10.3390/
ijerph18189752
Academic Editors: Martin Burtscher
and Paul B. Tchounwou
Received: 13 August 2021
Accepted: 14 September 2021
Published: 16 September 2021
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Copyright: © 2021 by the authors.
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This article is an open access article
distributed under the terms and
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Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1Business School, The University of Western Australia, Perth, WA 6009, Australia;
Allan.Trench@uwa.edu.au (A.T.); john.sykes@greenfieldsresearch.com (J.P.S.)
2Centre for Sport & Exercise Science, Sheffield Hallam University, Sheffield S10 2BP, UK;
marianne.j.davies@student.shu.ac.uk (M.J.D.); k.davids@shu.ac.uk (K.D.)
3Centre for Exploration Targeting, School of Earth Sciences, The University of Western Australia,
Perth, WA 6009, Australia; di_groves@hotmail.com
4Faculty of Health, Gold Coast Campus, Southern Cross University, Gold Coast, QLD 4225, Australia;
eric.brymer@scu.edu.au
5School of Earth Sciences, The University of Western Australia, Perth, WA 6009, Australia;
michael.dentith@uwa.edu.au
*Correspondence: rhyssamuel.davies@research.uwa.edu.au
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:
mineral exploration; ecological dynamics; expertise; needs-supportive environment;
representative learning design
1. Introduction
Despite increased expenditure, global greenfield discovery rates have stalled for over a
decade (Figure 1[
1
]). Reversal of the current trend is deemed critical for the desired global
transition to renewable energy sources, particularly wind and solar, as well as the evolution
of currently energy intensive industries, such as the automotive industry moving from
fossil fuels to predominantly battery-driven vehicles [
2
]. Without access to raw materials,
especially those defined as critical metals, society is unlikely to realise long-term goals
towards achieving sustainability [
3
–
5
]. Additionally, well-targeted exploration will help
reduce the environmental footprint of conducting exploration activities by decreasing the
average number of holes drilled to make each discovery.
Int. J. Environ. Res. Public Health 2021,18, 9752. https://doi.org/10.3390/ijerph18189752 https://www.mdpi.com/journal/ijerph

Int. J. Environ. Res. Public Health 2021,18, 9752 2 of 17
Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 2 of 18
will help reduce the environmental footprint of conducting exploration activities by de-
creasing the average number of holes drilled to make each discovery.
Traditionally, mineral exploration has followed a predominantly empirical ap-
proach, which initially entails searching for evidence of mineralisation upon the surface
of the planet, then, with consideration of known deposit types, targeting drilling to define
the extent of an ore body below the surface [6]. As outcropping deposits in well-explored
areas are progressively depleted, there is a need for exploration to extend to less well-
explored search spaces, typically in more remote locations, and for undefined deposit
types to be considered [7]. These points suggest that exploration needs to include regions
where surficial evidence for deposits is absent or unclear, requiring a conceptual approach
to targeting, guided by an understanding of underlying mineralising processes shared
across multiple deposit types (Mineral Systems Concept [8]). As the industry experiences
this transition, Davies and Davies [9] argue that creativity in the application of the Mineral
Systems Concept is key to realising long-term exploration success.
This paper presents the Dynamics model [9], incorporating the Dynamics challenge-
performance curve [10] as a principled framework for understanding and supporting cre-
ativity and the development of expertise in predictive exploration targeting. These models
provide guidance to the minerals industry in identifying and realising current decision
constraints and adapting learning and working environments to promote greater degrees
of creativity and on-going development of exploration targeting expertise.
Figure 1. Declining rate of exploration success during last decade, despite increased exploration expenditure since mid-
2000’s. Adapted with permission from Schodde, R.C [1].
1.1. Exploration Targeting
Exploration targeting involves defining and exploring areas that have potential to
host economic mineralisation. This process, outlined in Figure 2, is recognised as a series
of decisions [11–13]. In this decision-making process, both explicit and tacit (or implicit)
knowledge is employed to assess the validity of various options available to the decision-
maker [14]. At the time of the decision, the outcome of each option cannot be known for
certain but is inferred through assessment of the information available [15]. To improve
the quality of an assessment, and therefore the likelihood of a positive decision-outcome,
an individual or team is required to call upon relevant experience from a host of disci-
plines. Specifically, exploration decision-making encompasses a range of geoscience-re-
lated disciplines [16], as well as general disciplines such as economics, business strategy,
management and socio-political implications that may influence license to operate [17].
Exploration targeting thus represents a highly complex and dynamic task.
Figure 1.
Declining rate of exploration success during last decade, despite increased exploration expenditure since mid-
2000’s. Adapted with permission from Schodde, R.C [1].
Traditionally, mineral exploration has followed a predominantly empirical approach,
which initially entails searching for evidence of mineralisation upon the surface of the
planet, then, with consideration of known deposit types, targeting drilling to define the
extent of an ore body below the surface [
6
]. As outcropping deposits in well-explored areas
are progressively depleted, there is a need for exploration to extend to less well-explored
search spaces, typically in more remote locations, and for undefined deposit types to be
considered [
7
]. These points suggest that exploration needs to include regions where
surficial evidence for deposits is absent or unclear, requiring a conceptual approach to
targeting, guided by an understanding of underlying mineralising processes shared across
multiple deposit types (Mineral Systems Concept [
8
]). As the industry experiences this
transition, Davies and Davies [
9
] argue that creativity in the application of the Mineral
Systems Concept is key to realising long-term exploration success.
This paper presents the Dynamics model [
9
], incorporating the Dynamics challenge-
performance curve [
10
] as a principled framework for understanding and supporting
creativity and the development of expertise in predictive exploration targeting. These
models provide guidance to the minerals industry in identifying and realising current
decision constraints and adapting learning and working environments to promote greater
degrees of creativity and on-going development of exploration targeting expertise.
1.1. Exploration Targeting
Exploration targeting involves defining and exploring areas that have potential to
host economic mineralisation. This process, outlined in Figure 2, is recognised as a series
of decisions [
11
–
13
]. In this decision-making process, both explicit and tacit (or implicit)
knowledge is employed to assess the validity of various options available to the decision-
maker [
14
]. At the time of the decision, the outcome of each option cannot be known for
certain but is inferred through assessment of the information available [
15
]. To improve the
quality of an assessment, and therefore the likelihood of a positive decision-outcome, an
individual or team is required to call upon relevant experience from a host of disciplines.
Specifically, exploration decision-making encompasses a range of geoscience-related disci-
plines [
16
], as well as general disciplines such as economics, business strategy, management
and socio-political implications that may influence license to operate [
17
]. Exploration
targeting thus represents a highly complex and dynamic task.

Int. J. Environ. Res. Public Health 2021,18, 9752 3 of 17
Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 3 of 18
Figure 2. The stages of exploration targeting. Within each stage there is a period of data collection, integration, and anal-
ysis. Moving between stages requires a reduction in real options, such that a decision is made to focus on a specific area,
decreasing the overall exploration search space. Although a single feedback loop is presented, it is noted that the true
process of exploration is far less rigid. Feedback occurs at all stages, potentially leading to a reverse step in the process.
For example, newly acquired data may present a case for defining a different, or second, target commodity within a project
area.
1.2. The Exploration Search Space
Mining activities constitute sampling without replacement, from a fixed but un-
known number of deposits, within any given search space [16]. Once resources are ex-
tracted from a location, they are permanently depleted. Ongoing exploration of locations
containing high data density, significant known mineralisation and previous mining ac-
tivities thus presents a long-term declining rate of return [18,19].
The current decline in exploration success is likely due to known search spaces reach-
ing maturity [20,21]. In order to reverse the declining rate in exploration success, there is
a need for sustained greenfield exploration in immature or newly discovered search
spaces, often in remote or extreme environments [22]. For this to occur, explorers must
move away from locations of high data density to areas of greater uncertainty with limited
information, but also greater opportunity for significant new mineral discoveries [23].
This approach to the process of exploration targeting carries inherent risks and challenges.
One popular new frontier is exploration for buried deposits beneath transported,
post-mineral cover. When buried under recently transported material, mineralisation is
often impossible to detect by surficial exploration methods. Exploration for deposits in
areas with considerable cover, including much of Australia, requires a conceptual target-
ing approach. Conceptual targeting requires exploration geologists to utilise available in-
formation so as to predict the likelihood that an economic ore body exists within a given
search space. Rather than looking for empirical evidence of mineralisation, such an ap-
proach is underpinned by a detailed understanding of the processes leading to ore deposit
formation and associated physical and chemical footprints [8]. In conducting conceptual
targeting, the exploration geologist requires datasets that map mineralising processes and
knowledge of a wide range of related deposit types [24].
1.3. The Mineral Systems Concept
Ore deposit formation is recognised to be a focused mineralisation event. These
events represent “self-organising” critical systems and are underpinned by the interaction
of complex, non-linear deposit forming processes [25]. Based on an understanding of these
processes, the Mineral Systems Concept provides a framework for conceptual exploration
targeting [26]. However, the application of an improved understanding of mineralisation
systems is yet to provide a new wave of exploration discoveries [11].
Figure 2.
The stages of exploration targeting. Within each stage there is a period of data collection, integration, and
analysis. Moving between stages requires a reduction in real options, such that a decision is made to focus on a specific
area, decreasing the overall exploration search space. Although a single feedback loop is presented, it is noted that the true
process of exploration is far less rigid. Feedback occurs at all stages, potentially leading to a reverse step in the process. For
example, newly acquired data may present a case for defining a different, or second, target commodity within a project area.
1.2. The Exploration Search Space
Mining activities constitute sampling without replacement, from a fixed but unknown
number of deposits, within any given search space [
16
]. Once resources are extracted from
a location, they are permanently depleted. Ongoing exploration of locations containing
high data density, significant known mineralisation and previous mining activities thus
presents a long-term declining rate of return [18,19].
The current decline in exploration success is likely due to known search spaces reach-
ing maturity [
20
,
21
]. In order to reverse the declining rate in exploration success, there is a
need for sustained greenfield exploration in immature or newly discovered search spaces,
often in remote or extreme environments [
22
]. For this to occur, explorers must move away
from locations of high data density to areas of greater uncertainty with limited information,
but also greater opportunity for significant new mineral discoveries [
23
]. This approach to
the process of exploration targeting carries inherent risks and challenges.
One popular new frontier is exploration for buried deposits beneath transported, post-
mineral cover. When buried under recently transported material, mineralisation is often
impossible to detect by surficial exploration methods. Exploration for deposits in areas with
considerable cover, including much of Australia, requires a conceptual targeting approach.
Conceptual targeting requires exploration geologists to utilise available information so as to
predict the likelihood that an economic ore body exists within a given search space. Rather
than looking for empirical evidence of mineralisation, such an approach is underpinned by
a detailed understanding of the processes leading to ore deposit formation and associated
physical and chemical footprints [
8
]. In conducting conceptual targeting, the exploration
geologist requires datasets that map mineralising processes and knowledge of a wide range
of related deposit types [24].
1.3. The Mineral Systems Concept
Ore deposit formation is recognised to be a focused mineralisation event. These events
represent “self-organising” critical systems and are underpinned by the interaction of
complex, non-linear deposit forming processes [
25
]. Based on an understanding of these
processes, the Mineral Systems Concept provides a framework for conceptual exploration
targeting [
26
]. However, the application of an improved understanding of mineralisation
systems is yet to provide a new wave of exploration discoveries [11].
Application of the Mineral Systems Concept to define new search spaces or predict
new deposit types is conceptually challenging. This approach requires the development

Int. J. Environ. Res. Public Health 2021,18, 9752 4 of 17
and testing of hypotheses, based on an understanding of mineralisation processes, to
predict the potential for existing and/or undefined deposit types to exist within a search
space [
17
]. For cases where exploration is conducted in newly defined search spaces,
there are often limited geoscientific datasets, creating a significant barrier to entry in that
new data acquisition is an obstacle. Where data representative of system elements are
available, it is difficult to integrate multiple geoscientific datasets and model complex
interactions between mineralisation processes. Most importantly, the development of
predictive targeting hypotheses requires significant creative input, based on a rich and
diverse background of knowledge and understanding. Due to the risks and low success
rates associated with these search challenges, there is a heuristic tendency for explorers to
be biased towards preferential focus on advanced, data-dense ‘brownfields’ projects, over
conceptual early stage ‘greenfields’ projects.
1.4. Technology as a Solution
Hronsky and Groves [
26
] recognise the propensity for mineral discoveries to follow the
emergence of new concepts or technologies. As such, technological development has been
proposed as part of the solution to the current decline in exploration success [
27
]. Mineral
exploration is becoming increasingly data rich and knowledge poor, such that advanced
algorithms and increasing computational power may provide opportunity to recognise
patterns in larger, more complex datasets [
27
]. However, a critique of solely technology-
orientated solutions is that they may fail to resolve scenario or context-related issues that
need to be considered in the search process [
14
]. For example, although development
of new geophysical survey methods may present improved mapping of components of
mineral system processes [
28
], this fails to provide optimal survey locations, prior to data
collection commencing, or methods for integrating newly acquired data into the broader,
highly complex exploration decision-making process.
Greenfield exploration requires explorers to operate in data-poor environments [
23
].
They are required to fill significant gaps in existing geoscientific datasets, to predict poten-
tial decision outcomes and integrate with ongoing exploration activities [
29
]. By relying
solely on technological development, we may fail to consider the requirement for human
innovation and creative problem-solving in the generation and critique of multiple pre-
dictions, based on disparate datasets. Techniques such as ‘deep learning’ have provided
significant progress in the ability of Artificial Intelligence (AI) to begin exploring a creative
frontier [
30
]. Unfortunately, this creativity has been critiqued as merely mimicking existing
data rather than producing genuinely novel outcomes, especially for challenging, high-
dimensional problems [
31
]. With regard to the critique of predicted hypotheses, current
AI is limited in its ability to interrogate correlations and avoid biases since current deep
learning methods are poorly correlated with prior knowledge [31,32].
By providing comprehensive training data, AI appears capable of predicting locations
of well-understood deposits in data-rich environments [
33
]. Nevertheless, AI remains
far from being able to generate new conceptual deposit types or search spaces, based
on a holistic understanding of Earth sciences. In an article on the subject of learning,
Gopnik [
34
] states that “Despite enormous strides in machine intelligence, even the most
powerful computers still cannot learn as well as a five-year-old does.” She identifies the
care, nurturing and support provided to a child as key ingredient in learning and creativity.
In summary, mineral exploration is recognised as a complex, non-linear process [
26
] and
although advances in AI and technology present significant opportunities to improve the
quality of exploration targeting, the combination of technology and human creativity are
considered key to realising long-term, recurring success [10].
1.5. Creative Problem Solving
Creative thinking is key to the development of novel solutions to overcome complex,
high-dimensional problems. Within mineral exploration, previous explorers conducted
targeting to test specific hypotheses. Since mining activities involve sampling without re-

Int. J. Environ. Res. Public Health 2021,18, 9752 5 of 17
placement, economic discoveries realised by previous explorers no longer remain within the
search space. As such, it is important that future exploration efforts test newly developed
hypotheses regarding new search spaces and subtly different deposit types.
Through consideration of underlying system processes, often shared between multiple
deposit types, the Mineral Systems Concept presents a robust, scientific framework for
creative problem solving. By reviewing existing empirical information, it is possible to
separate features that are likely to be representative of key ore forming processes that define
a mineral system, from those that are only locally relevant to a deposit [
24
]. Primed with
this knowledge, an explorer can generate innovative, science-based hypotheses regarding
new search spaces in which the same mineralisation processes have occurred or differing
local features that may result in new deposit types. This approach allows the explorer
to recognise fundamental patterns across geological settings and styles of mineralisation,
thus generating hypotheses to test new exploration targets. However, creative problem
solving and innovation are not simply academic endeavours. They require a high level of
individual confidence and a perceived supportive environment [35].
1.6. Subjectivity in Geoscience
Subjectivity is the norm; a surprisingly small number of geoscience-specific studies
have delved into the process of predictive decision-making and subjective assessment.
Polson and Curtis [
36
] and Bond, Lunn, Shipton and Lunn [
37
] discuss the role of heuristics
in generating geological hypotheses, or interpretations, based on uncertain data. They
highlighted the potential for experts to reach contradictory conclusions when analysing
the same data, advising that group workshops can help to reduce bias through sharing
of knowledge and expertise [
38
]. Although these papers provide important insights into
the elicitation process and adequacy of group assessment workflows, they fail to discuss
the importance of developing expertise and the role it has in influencing the quality of
interpretations. This omission is possibly due to an assumption that participants were
already experts. Only Bond et al. [
37
] make comment that having a Masters or Ph.D.
qualification significantly improved expert performance in interpreting a seismic dataset.
Davies et al. [
39
] conducted a group workshop to evaluate the orogenic gold endowment
of a greenstone belt in Western Australia, noting a significant degree of variation in expert
estimates, but failing to find a relationship between estimates and participant experience.
This issue presents an important question regarding the role of training and expertise in
generating accurate or realistic hypotheses during creative problem-solving tasks.
1.7. Ecological Dynamics
Future exploration success relies on relevant skills of structured and creative percep-
tion, decision-making, and the planning of exploration targeting activities in challenging,
uncertain and often remote environments. Targeting new conceptual deposit types or
search spaces, based on a holistic understanding of Earth sciences, requires an explorer to
acquire relevant knowledge and perceptual skills to undertake creative problem solving
and decision making. For this to be successful, an explorer must develop new hypotheses
to underpin novel exploration actions. This presents an important question, regarding
the role of both domain-based and broader expertise in enabling individual capacity for
creative thinking, problem solving, decision making, and hypothesis generation. To gain
insights into this issue, it is worthwhile examining the contemporary literature on expertise
and skill acquisition.
Contemporary perspectives on skill acquisition, heavily influenced by the conceptuali-
sation of Ecological Dynamics (ED), have resulted in the development of an understanding
of expertise in decision-making at the person–environment scale of analysis [
40
–
43
]. Eco-
logical dynamics is a multi-dimensional framework shaped by several scientific disciplines,
integrated to explain human behaviours such as performance, learning and expertise, in
diverse and challenging performance environments such as sport, education, and work.
Major theoretical influences are provided by key concepts from ecological psychology [
44
],

Int. J. Environ. Res. Public Health 2021,18, 9752 6 of 17
nonlinear dynamics [
45
] and the complexity sciences approach in neurobiology [
46
]. In
ecological psychology, it is recognised that human behaviour is continuously regulated
by information, shaping performance during activities such as the exploration of an envi-
ronment [
44
]. Information use is based upon individual perception of affordances, which
are opportunities or invitations acting to solicit or constrain behaviours within a specific
performance environment [
44
]. The ecological approach has been enriched through the
integration of tools and concepts from nonlinear dynamics, explaining how information
is related to the dynamics of tasks and individuals within the performance environment.
Dynamical systems theorising on human behaviour [
45
] propose the emergence of be-
havioural tendencies in perceptual, cognitive, and action sub-systems. Ecological dynamics
emphasises the performer-task-environment system as the appropriate scale of analysis
to explain behaviours, eschewing cognitive- or environment-biased conceptualisations of
skill and expertise [47].
Much existing ED-related research has focused on developing an understanding of
expertise, talent, and skill acquisition in sport, described by some as the most appropri-
ate context for studying expert decision-making [
48
]. Ecological Dynamics takes into
account the multiple dimensions of skill performance and learning, including perceptual,
psychological, emotional, social, and physical aspects of the individual performer, while
interacting with a specific task and environmental constraints [
49
]. These ideas signify
the importance of the person–environment interactions at the heart of skilled behaviours,
founded on the deeply integrated relationship between perception, cognitions, and actions
of a performer. Based on these fundamental ideas of Ecological Dynamics, it is suggested
that creative behaviours and solutions emerge during performance from continuous inter-
actions with the environment [
50
,
51
]. This key idea implies that the emergence of creative
behaviours and performance solutions is not solely a re-call of existing internalised repre-
sentations or models but requires adaptation and iteration through continuous interactions
with the environment, during processes of searching, exploration and discovery.
1.8. Perception-Action Coupling
Goal directed behaviours, such as creative problem solving, are viewed as func-
tional coordination patterns, emerging under interacting personal, task and environmental
constraints, which result in actions becoming tightly coupled to perceptual information,
shaping intrinsic self-organisation tendencies in people [
52
,
53
]. Expressions of skill and
expertise are continuously regulated by information. Learning is defined as the process
of gradual attunement to real-time information that is meaningful, affords or supports
goal-directed behavioural outcomes, and harnesses inherent system degeneracy (i.e., the
same task outcomes can be achieved with different system components) [
54
]. Constraints,
recognised as boundaries influencing behaviour, are classified into three broad categories
related to the organism (the individual), task and environment [49].
Examples of mineral exploration constraints are presented in Table 1. Task constraints
are aspects related to a particular goal or challenge. Although not an exhaustive list, in
mineral exploration these include activity and specific goal parameters, corporate and ex-
ploration strategy statements, Key Performance Indicators (KPIs), finances and equipment.
Individual constraints include the experience, attitudes and skill of individual people or
teams. For example, education, values, beliefs, confidence, motivation, and risk-aversion.
Environmental constraints are both physical and socio-cultural. Physical environmental
constraints in mineral exploration include geology, mineralisation processes and accessibil-
ity of search areas. Company and national culture, management, reward and punishment
systems, infrastructure, social networks, values, and social licence represent socio-cultural
constraints that may impact the quality of decision-making. The values, attitudes and
beliefs that give rise to organisation and industry culture are defined as a ‘form of life’ [
55
]
and can significantly influence the behaviours of individuals within a system.

Int. J. Environ. Res. Public Health 2021,18, 9752 7 of 17
Table 1. Examples of constraints in mineral exploration targeting.
Group Constraint Description
Environmental
Geology
Geology of an exploration project (much of this remains unknown, as only
the geoscientific datasets outlined under task constraints are available to
the explorer)
Company/culture Organisational structure, explicit and implicit rules or values
Management/leadership Methods and styles applied to guiding individuals and teams within
an organisation
Government/mining law Political landscape and specific laws governing mineral
exploration/extraction
Social perception Social landscape and social licence to operate
Land access
Access to exploration ground, defined by stakeholders, law and availability
Market Factors influencing commodity price and availability of
investment funding
Academia Academic institutions conducting research and training students
Economic geology theory Current level of geoscientific theory and knowledge relevant to
mineral exploration
Exploration and mineral
processing technology Current technology available to the minerals industry
Individual/team
Individual attributes/skills Knowledge and expertise of individual geoscientist
Individual attitudes/motivation
Individual psychology, including motivation, attitude, values
Team structure Selection of individuals with complementary skills, working in a
collaborative manner
Team capabilities Capabilities of team due to individual skills, psychology, teamwork, and
corporate resources
Task
Geoscientific datasets Geoscientific datasets that relate to the location of undiscovered ore
deposits (geological, geochemical, geophysical, geochronological, etc.)
Corporate/exploration strategy High-level strategic plan and goals (company vision)
Key Performance
Indicators (KPIs) Measurable performance outcome specific to a particular activity
Finances Availability of finances to conduct exploration activities
Service providers/equipment Consultants, research institutes, technology providers, and in-house
company equipment
Training/CPD Training and professional development
Those highlighted in grey are typically recognised as key constraints in mineral exploration, although the remaining constraints outlined
here also play a significant role in influencing decision-making.
Within a sporting context, an example of emergent and creative decision-making might
be a mid-field soccer player identifying an opportunity (affordance) to make a pass, such
that an attacking player can move into a position to potentially score. Here, the mid-field
player perceives unfolding opportunities to exploit space, time, and movement (constraints)
as a result of representative practice experience. During mineral exploration, geoscientists
are similarly required to become attuned to meaningful specifying information, as outlined
in Table 1(constraints), to predict the likely location of an undiscovered economic ore
deposit that can be subjected to exploration activities (affordances) such as drill-testing or
additional data collection. Hypotheses regarding the location and quality of undiscovered
ore deposits are generated, tested, and adapted based on perceived affordances available
to an individual, within the constraints of a specific time and situation [
56
]. Differences
in individual knowledge, perception, motivation, and meaning shape the influence of
constraints and account for diversity of perceived affordances and resultant decisions, even
when identical constraints are presented to separate explorers [
54
]. Finally, the results of

Int. J. Environ. Res. Public Health 2021,18, 9752 8 of 17
hypothesis-testing actions are observed, and the perception of constraints and affordances
are subsequently adapted, both explicitly and implicitly [
57
]. The learning outcome is
influenced by perception of action outcomes, meaning that the provision of objective
and subjective feedback plays a significant role in the learning process and on-going
development of expertise.
1.9. Learning and Expertise
Critical to improving exploration decision-making is understanding the differences
between novice and expert explorers, and how one might transition from one to the
other. Expertise can be loosely defined as being more attuned to specifying information,
along with the creativity to recognise appropriate affordances for actions that lead to the
discovery of new ore deposits. This requires the explorer to be perceptive of the constraints,
including those outlined in Table 1, and able to recognise geoscientific patterns and features
representative of key ore-forming processes and other specifying information within any
set of dynamic and complex constraints. To do this, the explorer needs to gain experience
that is representative, valid, and diverse.
For learning to be effective, it must take place in an authentic environment, containing
perceptual information and affordances that are representative of the environments into
which the skill will later be transferred [
58
,
59
]. If affordances are representative, then
the learner can develop accurate declarative and tacit knowledge and an attunement
to key information in the environment. With a broad base of relevant experiences, the
learner has the opportunity to develop attunement to perceptual information in varied
contexts, allowing them to recognise invariant cues and distinguish them from incidental
information. This supports learners in the transfer and adaption of expertise into contexts
containing novel constraints. Exploration targeting represents a complex domain, in which
professionals are regularly presented with situations and tasks that are novel, requiring
utilisation of a broad base of knowledge, sometimes developed for other purposes. A
risk faced by mineral explorers is that their skill sets are too narrow, often focused on a
single discipline (e.g., core logging, structural geology, geochemistry or geophysics), or a
single deposit style or jurisdiction (e.g., orogenic gold in Western Australia, or sediment-
hosted copper in the Central African Copperbelt). This represents fractionated expertise,
meaning explorers are therefore not able to apply the Mineral Systems Concept to varying
contexts or environments, such as the transition to new search spaces or different styles
of mineralisation.
2. The Dynamics Model
Ericsson et al. [
47
] proposed that variations in individual expertise are predominantly
a result of experience (e.g., “The best geologist is he [sic] who has seen the most rocks:” [
60
]),
as opposed to factors such as innate talent. However, there are examples of experts reaching
similar levels of ability within considerably different timeframes (e.g., Chess master level
status reached in both 3200 and 23,000 hours of practice [
61
]), raising an important question
regarding the influence that quality of experience has on developing expertise.
Ecological Dynamics and other non-linear pedagogy-based research has resulted in a
growing number of studies examining key factors that influence the rate at which expertise
is achieved: (a) autonomy-supportive learning environments [
62
–
64
]; (b) motivation [
65
,
66
];
(c) effect of anxiety on performance [
67
]; (d) perception-action coupling [
68
]; (e) embodied
cognition [
69
]; (f) affective learning design [
58
]; (g) development of coordinative struc-
tures [
70
]; (h) judgment and decision making [
71
], (i) the need for adaptive expertise [
72
];
and (j) that expertise is only gained from experience in an environment with valid cues
and opportunity for feedback [73].
Based on this research, Davies and Davies [
9
,
10
] presented the Dynamics model of
decision-making and learning, shown in Figure 3, to more clearly define the influence
that human psychological needs and experiences have on the development of adaptive
expertise. The Dynamics model is based on an Ecological Dynamics framework, specifically

Int. J. Environ. Res. Public Health 2021,18, 9752 9 of 17
Newell’s [
49
] constraints model. The ‘energetic’ organismic (individual) constraints are
separate in the Dynamics model, to highlight their importance in the design and manage-
ment of learning environments. The energetic constraints combine motivation, using Self
Determination Theory (SDT [
34
]), arousal and focus of attention. The model also includes
iterative feedback loops to the energetic and more stable individual constraints, to define
an autopoietic (i.e., self-organising and self-regulating) system. Key elements recognised in
the model are that learning is a non-linear process [
74
] (Ennis, 1992) and that motivation,
intention, effort and focus of attention are the initial start points, defining the quality of
individual input into learning and decision-making [
10
,
75
]. Focus shifts from acquiring
specific knowledge or skills toward learning-to-learn and creatively solve problems.
Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 9 of 18
master level status reached in both 3200 and 23,000 hours of practice [61]), raising an im-
portant question regarding the influence that quality of experience has on developing ex-
pertise.
Ecological Dynamics and other non-linear pedagogy-based research has resulted in
a growing number of studies examining key factors that influence the rate at which ex-
pertise is achieved: (a) autonomy-supportive learning environments [62–64]; (b) motiva-
tion [65,66]; (c) effect of anxiety on performance [67]; (d) perception-action coupling [68];
(e) embodied cognition [69]; (f) affective learning design [58]; (g) development of coordi-
native structures [70]; (h) judgment and decision making [71], (i) the need for adaptive
expertise [72]; and (j) that expertise is only gained from experience in an environment with
valid cues and opportunity for feedback [73].
Based on this research, Davies and Davies [9,10] presented the Dynamics model of
decision-making and learning, shown in Figure 3, to more clearly define the influence that
human psychological needs and experiences have on the development of adaptive exper-
tise. The Dynamics model is based on an Ecological Dynamics framework, specifically
Newell’s [49] constraints model. The ‘energetic’ organismic (individual) constraints are
separate in the Dynamics model, to highlight their importance in the design and manage-
ment of learning environments. The energetic constraints combine motivation, using Self
Determination Theory (SDT [34]), arousal and focus of attention. The model also includes
iterative feedback loops to the energetic and more stable individual constraints, to define
an autopoietic (i.e., self-organising and self-regulating) system. Key elements recognised
in the model are that learning is a non-linear process [74] (Ennis, 1992) and that motiva-
tion, intention, effort and focus of attention are the initial start points, defining the quality
of individual input into learning and decision-making [10,75]. Focus shifts from acquiring
specific knowledge or skills toward learning-to-learn and creatively solve problems.
Figure 3. Dynamics model for decision-making and learning, adapted with permission from Davies,
M.J. and Davies, R.S. [9]. Motivation is the initial start point, defining the quality of individual input
into learning and decision-making tasks. Decision-making behaviours are shaped by intentions and
perception of available affordances, within situational constraints. These constraints are broadly
categorised into individual, environment, and task. Differences in individual perception, motivation
and meaning shape the influence of constraints and account for diversity of decisions. Within min-
eral exploration, these decisions can include activities such as collecting additional geoscientific data
Figure 3.
Dynamics model for decision-making and learning, adapted with permission from Davies,
M.J. and Davies, R.S. [
9
]. Motivation is the initial start point, defining the quality of individual input
into learning and decision-making tasks. Decision-making behaviours are shaped by intentions and
perception of available affordances, within situational constraints. These constraints are broadly
categorised into individual, environment, and task. Differences in individual perception, motivation
and meaning shape the influence of constraints and account for diversity of decisions. Within mineral
exploration, these decisions can include activities such as collecting additional geoscientific data
(e.g., airborne geophysical surveys, surficial geochemical sampling), direct drill-testing of targets, or
even choosing to relinquish ground. The results of hypothesis-testing actions are observed, changes
in perception and decision-making may be implicit or explicit making them more difficult to identify,
the influence of constraints may be redefined, and hypotheses subsequently adapted. This process
of active learning results in perception-action coupling, where feedback or results have an impact
on individual learning and motivation, through a learning feedback loop, defining an autopoietic
(i.e., self-organising and self-regulating) system.
The model guides investigation of constraints and their complex inter-relationships.
Over time, different constraints will have a greater or lesser influence in defining affor-
dances. For example, changes in commodity prices will have a greater or lesser impact
when combined with other constraints such as changes in technology, social perception,
or legislation. From a skill development, creativity and risk-taking perspective, changes
in constraints such as management culture, power relationships, training programs, and
reward systems are all likely to have mutable and complex influences.

Int. J. Environ. Res. Public Health 2021,18, 9752 10 of 17
Dynamics Challenge-Performance Curve
The Dynamics challenge-performance curve, shown in Figure 4, presents the complex
relationship between demands or variability of a task and expected performance output,
where individual, task and environmental demands, contained in the Dynamics model, are
combined to represent overall challenge [
76
]. An important feature in the Dynamics curve
is the ‘ugly-zone’ (a term coined by Alred [
77
]), defining the region beyond current ability
in which stable performance begins to deteriorate, converging on a transition or bifurcation
point. Within this zone, the learner explores solutions related to new implicit and explicit
problems, providing opportunity for generation of innovative ideas, coping strategies, and
step changes in understanding as new affordances. Here, the learner has an opportunity
to acquire a broad and diverse range of experience, thus increasing their relative level of
expertise. However, operating in the
'
ugly-zone
'
requires confidence and resilience and can
feel awkward or regressive. An integral risk of learning in the ugly-zone, is the potential
for a collapse in performance, arising from the learner becoming overwhelmed or over-
challenged. For this reason, it is important to recognise that, during the development of
expertise, failure will likely be the status quo. The ugly-zone must be perceived as a place of
opportunity to vary search and exploration strategies, rather than an area of risk, or failure.
As such, self-determination and energetic constraints are highlighted as fundamental to
creativity, problem-solving, and the development of expertise. The incorporation of ‘safe-
uncertainty’ into practice environments and activities is a key environmental enabling
constraint for success.
Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 11 of 18
Figure 4. Dynamics challenge-performance curve, adapted with permission from Davies, M.J. and
Davies, R.S. [10]. The complex relationship between demands of a task and expected performance
output are represented by a performance curve, where individual, task, and environmental de-
mands are combined to represent overall challenge. Limited overall demands result in a performer
remaining under-challenged, lacking the opportunity to expand their expertise. With increasing dif-
ficulty, performance trends upwards towards the ugly-zone; a point of optimal challenge, where
the opportunity to develop expertise is maximised, as new implicit and explicit affordances become
available through interaction with dynamic constraints in real-time. Within the ugly-zone, perfor-
mance becomes unstable, converging on a bifurcation point and presenting the risk of errors being
made, or a drop in performance arising from the learner becoming overwhelmed or over-chal-
lenged. The curve shows that to get back to the optimal performance, overall challenge needs to be
reduced significantly to allow reflection and learning (hysteresis). Despite this risk, it is important
that the ugly-zone be perceived as a place of opportunity for learning and development of expertise
within a well-managed, needs-supportive learning and performance environment.
3. Discussion
The Dynamics model and learning curve support the development of management
practices and training interventions, by providing a framework for understanding and,
where possible, influencing the complex interactive and adaptive constraints that shape
exploration decision-making. It is evident that the complex nature of human behaviour
and decision-making must be considered within the wider individual-task-environment
system. This includes the impact of goal directed behaviour, carried out in a landscape of
perceived affordances, within the constraints of a particular system. Training interven-
tions that focus solely on intellectual decision-making, or the role and influence of the
individual, will fail to be effective where motivational, company structure, task, socio-
cultural or other limiting factors significantly shape current behaviour.
3.1. Constraints Shaping Exploration Decision-Making
3.1.1. Goal-Directed Behaviour and Task Constraints
Goal setting is important when producing corporate and exploration strategy state-
ments, as well as defining Key Performance Indicators (KPI’s) as guiding intentions. Strat-
egy statements outline business-wide long-term aspirations and goals and KPI’s guide the
focus of attention and the affordances perceived by employees as they work towards these
goals. Well-defined goals provide clear task-related constraints for employees and teams
in decision-making positions, helping to mitigate against excessive degrees of autonomy
or risk-taking behaviour. Without this guidance, employees may struggle to effectively
orientate their actions within an organisation, leading to splintering and ‘siloing’ of teams.
Process goals are preferred over outcome goals, as they promote greater degrees of uptake
Figure 4.
Dynamics challenge-performance curve, adapted with permission from Davies, M.J. and
Davies, R.S. [
10
]. The complex relationship between demands of a task and expected performance
output are represented by a performance curve, where individual, task, and environmental de-
mands are combined to represent overall challenge. Limited overall demands result in a performer
remaining under-challenged, lacking the opportunity to expand their expertise. With increasing
difficulty, performance trends upwards towards the ugly-zone; a point of optimal challenge, where
the opportunity to develop expertise is maximised, as new implicit and explicit affordances become
available through interaction with dynamic constraints in real-time. Within the ugly-zone, perfor-
mance becomes unstable, converging on a bifurcation point and presenting the risk of errors being
made, or a drop in performance arising from the learner becoming overwhelmed or over-challenged.
The curve shows that to get back to the optimal performance, overall challenge needs to be reduced
significantly to allow reflection and learning (hysteresis). Despite this risk, it is important that the
ugly-zone be perceived as a place of opportunity for learning and development of expertise within a
well-managed, needs-supportive learning and performance environment.
The Dynamics curve and the Dynamics model, used together, can support the de-
velopment of expertise in exploration decision making. The Dynamics model supports
the identification of influencing constraints, thus providing guidance to inform the ma-
nipulation of constraints to create optimal learning experiences. Skilfully manipulating

Int. J. Environ. Res. Public Health 2021,18, 9752 11 of 17
constraints will promote learners to become attentive and attuned to specifying information
at appropriate levels of challenge, within representative learning environments.
3. Discussion
The Dynamics model and learning curve support the development of management
practices and training interventions, by providing a framework for understanding and,
where possible, influencing the complex interactive and adaptive constraints that shape
exploration decision-making. It is evident that the complex nature of human behaviour
and decision-making must be considered within the wider individual-task-environment
system. This includes the impact of goal directed behaviour, carried out in a landscape of
perceived affordances, within the constraints of a particular system. Training interventions
that focus solely on intellectual decision-making, or the role and influence of the individual,
will fail to be effective where motivational, company structure, task, socio-cultural or other
limiting factors significantly shape current behaviour.
3.1. Constraints Shaping Exploration Decision-Making
3.1.1. Goal-Directed Behaviour and Task Constraints
Goal setting is important when producing corporate and exploration strategy state-
ments, as well as defining Key Performance Indicators (KPI’s) as guiding intentions. Strat-
egy statements outline business-wide long-term aspirations and goals and KPI’s guide the
focus of attention and the affordances perceived by employees as they work towards these
goals. Well-defined goals provide clear task-related constraints for employees and teams
in decision-making positions, helping to mitigate against excessive degrees of autonomy
or risk-taking behaviour. Without this guidance, employees may struggle to effectively
orientate their actions within an organisation, leading to splintering and ‘siloing’ of teams.
Process goals are preferred over outcome goals, as they promote greater degrees of uptake
and motivation [
78
]. Process goals define expected quality of an activity and, given the
decision-maker typically has limited control over final outcomes in mineral exploration,
presents an achievable objective. Realistic goals should guide, but not overly constrain,
autonomous, creative decision-making within an organisation, through the provision of
well-defined strategy statements and KPI’s.
The provision of strategy statements and KPIs within mineral exploration should be
treated with care. Given the predominant industry focus on mineral extraction, there is
a risk that those in senior management positions of a non-technical background lack a
detailed understanding of the exploration process, which more closely resembles research
and development activities than the mining extraction process. The degree of uncertainty
associated with exploration activities often leads outcome orientated KPIs to promote
non-beneficial behaviours. A simple example is the introduction of a KPI that requires an
exploration team to review a large number of projects per year, leading to limited resources
being stretched across those projects, thus reducing the quality of each individual review.
3.1.2. Company Culture, Leadership, and Other Environmental Constraints
Several efforts have been made to define key aspects of company culture that influence
exploration success. Towards developing a philosophy of oil exploration, Wallace Pratt [
79
]
commented that “oil is first found
. . .
in the minds of men,” recognising the value of
vision and creative thinking in exploration. Masters [
80
] suggested that adopting the
characteristics of a small company was vital to success in oil exploration, trading control
and routine for innovation, motivation, and speed, passing power down and providing
employees with the freedom to both grow and fail.
Most notable within the minerals industry are the writings and interviews of Roy
Woodall [
6
], widely recognised as playing a significant role in the success of Western Min-
ing Corporation (WMC). Interviewed by Stanton [
81
], Roy Woodall stated that building a
successful exploration company starts with the people, and that he conducted recruitment
personally. The successful WMC team was brimming with creative intellectual energy

Int. J. Environ. Res. Public Health 2021,18, 9752 12 of 17
but could be a tough group of individuals to manage. In their report on the successful
management of minerals exploration, McKinsey Company [
82
] stated that management
and leadership play an important role in exploration success, and that “good explorers
can be made as well as born.” In an SEG Newsletter, Dan Wood [
83
] considered that
exploration is “an art informed by science.” Amongst other things, Wood suggested that
risk-taking, creativity and a ‘discovery culture’ are critical areas that govern exploration
success, sometimes preferring low-tech solutions that focus creative thinking over the latest
computational or statistical methods. Wood and Hedenquist [
84
] suggest that improve-
ments to business models and the predictive way geologists think when exploring are key
to improving exploration success rates, with technological developments playing only a
supporting role.
Although many companies within the minerals industry aspire to creating learning
organisations, there is a persistent culture of error and risk aversion, lack of feedback,
unfilled ‘near-miss’ books, and of rewarding ‘safe’ decision-making. Decision-makers
must develop the ability to conduct appropriate risk-benefit analyses, where risks are
identified up-front and considered against their potential exploitation, rather than simple
elimination or justification [
71
]. Company reward and punishment systems [
85
], as well as
an ego element of not being seen to make mistakes [
72
], further constrains creative decision-
making and learning within mineral exploration. Critical to improving culture within the
minerals industry is the degree of autonomy provided to each exploration geologist and an
acceptance of the uncertainty and technical risk associated with conducting exploration
and learning within the ugly-zone. However, providing autonomy, as part of a needs-
supportive environment, should not lead to abdication of responsibility by management.
3.1.3. Motivation, Autonomy, and Risk-Taking
In his 2012 paper, Andrew Curtis [
86
] stated that “scientists should
. . .
not be ashamed
of subjectivity, but should strive to
. . .
reduce its effects.” This statement stands contrary to
most research conducted into human intuition and decision-making, where decisions are
made within a set of highly complex constraints, with incomplete knowledge of a context
and expected outcome. Decisions, such as choice of research questions, methodologies, and
interpretation of results, are fundamental to the scientific method, but are rarely measurable
or quantifiable prior to their completion. As such, it is advised that exploration geologists
embrace subjectivity in decision-making, with recognition of the importance of experience
and creativity in making successful subjective estimates and interpretations, especially in
conducting predictive exploration targeting.
As outlined by the Dynamics challenge-performance curve (Figure 4), the ugly-zone
should be perceived as a place of opportunity. By increasing variability, there is opportu-
nity to become attuned to new affordances, ultimately driving effective learning and the
development of more adaptive expertise. Many of the constraints presented within mineral
exploration are project-specific, meaning the exploration geoscientists operating ‘on the
ground’ have access to the most detailed and up-to-date information. As such, the respon-
sibility for decision-making will typically be in collaboration with more junior employees.
To support these decision-makers, management must clearly articulate corporate strategy,
goals, and broad environmental constraints.
Additionally, attaining a culture of creativity and adaptive expertise requires the
motivational climate of a company to support the psychological needs of the employees,
providing an appropriate degree of autonomy to take risks, without undermining their
ability to learn or perform. In any context, humans are inherently driven towards personal
development and satisfaction of three basic psychological needs: autonomy, competence,
and relatedness [
87
]. If psychological needs are met, humans tend to possess greater
motivation and feelings of competence [
88
], actively seeking out meaning and cues to
support autonomous decision-making [
34
]. In contrast, when tired, under pressure, or
emotionally challenged, humans are susceptible to heuristic biases, substitution, and
risk-aversion [89].

Int. J. Environ. Res. Public Health 2021,18, 9752 13 of 17
3.1.4. Practical Training Interventions
Given shareholder capital is generally used to conduct exploration activities, there is a
requirement for exploration companies to mitigate against excessive risk-taking. In this
instance, the operating constraints within the industry reduce the potential of the learner to
develop adaptive expertise, by limiting their ability to test and adapt innovative hypotheses
or attune to affordances by exposing themselves to challenging decision constraints. Davies
et al. [
39
] also recognised that certain aspects of exploration targeting are likely to have low-
validity [
89
], where noisy or highly complex situations present a lack of decision-feedback,
rendering genuine expertise unachievable through typical work-related experience and
learning. This is compounded by the limited number of projects upon which an exploration
geologist is likely to have worked during their career.
Although several studies have highlighted the ability of algorithms to outperform
professionals in low-validity environments [
90
], these studies found that forecasts made
by algorithms were generally incorrect, albeit less often than in human predictions. The
superior performance of algorithms in low-validity environments is attributed to consis-
tency [
91
], an undesirable trait in mineral exploration, where targeting activities are in
competition with previous explorers, signifying that consistent or repeated activities are
more likely to result in failure after an initial effort (sampling without replacement). To
mitigate against these limitations, a number of on-the-job training methods have been
proposed, including: observation of experts, professional discussion in communities of
practice, experimentation with differing strategies, engagement in after-action reviews,
and coaching from others with wider experience [
92
]. However, since certain aspects
of exploration targeting are recognised as challenging and potentially low-validity, it is
unlikely that sufficient expertise exists within industry to conduct wide-spread, on-the-job
training [39].
Hogarth and Soyer [
93
] and Singer [
94
] suggest that simulated experiences can be
provided to promote the development of expert intuition. As such, it is advised that practi-
cal scenario-based exploration targeting training courses should be developed, perhaps
using virtual or augmented reality environments, based on real scenarios to ensure that
contextual information is relevant and valid. Using these technologies, training environ-
ments can be designed appropriately for each individual learner, providing a representative
learning environment within which decision constraints are carefully selected to promote
positive exploration behaviours, as well as the recognition and understanding of key ex-
plicit and implicit information in a realistic exploration environment. This is achieved
through highlighting key information sources, providing appropriate feedback, and pro-
moting development of perception-action coupling, expertise, and resilience within a
needs-supportive learning environment.
Such training interventions could involve presenting the learner with information
(constraints) relating to the location of economic ore bodies. Using this information, the
learner would present their hypotheses regarding the location of ore bodies and propose
exploration programs (based on perceived affordances) to test these hypotheses. The
complexity of decision constraints would be adapted based on the level of expertise of
each participant. The design of these courses would utilize simulation programs, present-
ing the learner with realistic scenarios containing real exploration data to match future
performance environments [
10
,
46
] and include sources of both implicit and explicit knowl-
edge [
57
]. To achieve this, participants would be provided with comprehensive geoscientific
datasets, but characteristics and locations of known deposits would be withheld. Each
individual participant would work through the exercise to produce predictions for the
size, quality, and locations of undiscovered deposits within the study area, and devise an
appropriate exploration program with which to test these hypotheses. The training course
methodology would outline a broad framework, but still allow a high degree of individual
flexibility. This allows each participant to adapt individual strategy to gain maximum
benefit from their personal background and expertise, as well as devise novel affordances
for testing varied hypotheses. Although initial predictions would be conducted in isolation,

Int. J. Environ. Res. Public Health 2021,18, 9752 14 of 17
to promote engagement and divergent thinking [
78
], the exploration team could then come
together to discuss individual predictions and reasoning, allowing for discussion and a
group consensus to develop. Finally, upon completion of the exercise for a given study
area, known deposit information would be presented to the participants to simulate the
results of a comprehensive exploration program. This provides immediate feedback to
the participants, allowing them to compare their predictions with the real-world deposit
information. Opportunities for feedback, comparison and reflective learning might also be
improved through guidance from qualified trainers. By conducting such training exercises,
for a suite of study areas covering different geological environments, deposit styles, and
jurisdictions, it would be possible for an exploration team to acquire experience, typically
gained over decades in an exploration career, in a matter of days or weeks. Most impor-
tantly, this experience would be acquired in a safe environment, without risk to either
career progression or shareholder capital.
4. Conclusions
It is proposed that an increase in creative and challenging problem-solving exercises,
during application of the Mineral Systems Concept, has the potential to result in the de-
velopment of widespread search expertise and improved long-term exploration outcomes.
Through application of the Dynamics model, influential constraints can be identified and
leveraged. Constraints that are limiting creative problem solving, for example traditional
risk-adverse company culture, can be reduced, while enabling constraints can be amplified.
In combination with the Dynamics challenge-performance curve, several areas are recog-
nised as having significant influence over the quality and creative aspect of exploration
decision-making. Explorers require a degree of autonomy to be confident enough to enter
the ugly zone, take risks, and test novel search ideas. Clear goal-oriented strategies provide
a focus of attention to relevant information, and management can provide a framework
to mitigate and challenge excessive risk-taking and minimise consequences of inevitable
failures. Finally, development of appropriate scenario-based training courses is identified
as a critical suggestion, providing an opportunity for explorers to develop an accurate
perception of situational constraints, and conceive affordances for testing novel exploration
targeting hypotheses, thus significantly improving the quality of decision-making and
learning outcomes throughout the exploration industry. Since energetic individual con-
straints define the quality of individual input into learning and decision-making tasks, each
of the above suggestions must be provided within needs-supportive learning and working
environments so as to maintain motivation, effort, and focus of attention throughout the
exploration process.
Adoption of these concepts is required by the industry to present working environ-
ments that allow for effective application, and further development, of creative exploration
targeting expertise. Further research is suggested, regarding each sub-discipline and their
application to mineral exploration, as well as other industries and research organisations.
Author Contributions:
Conceptualization, R.S.D. and M.J.D.; methodology, R.S.D. and M.J.D.;
writing—original draft preparation, R.S.D., M.J.D. and D.G.; writing—review and editing, K.D.,
E.B., A.T. and J.P.S.; supervision, D.G., A.T., J.P.S. and M.D.; project administration, A.T. and M.D.;
funding acquisition, A.T. and M.D. All authors have read and agreed to the published version of
the manuscript.
Funding:
This research was funded by an Australian Government Research Training Program Schol-
arship (22056287) and Society of Economic Geologists Graduate Student Fellowship Grant (GSF_18-5).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Acknowledgments:
Thanks are due to Doug Brewster, James Bell, and Rebecca Seal for their feedback,
which greatly improved the quality of this paper.
Conflicts of Interest: The authors declare no conflict of interest.

Int. J. Environ. Res. Public Health 2021,18, 9752 15 of 17
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