Opinion Article

Illuminating divergence in perceptions in natural
resource management: A case for the investigation
of the heterogeneity in mental models
Karlijn van den Broek
Heidelberg University

Much research has been dedicated to map mental mod-
els of natural resources to aid effective management of
the natural resource. The variety of approaches result in
a variety of outputs, but most research in this domain
reports mental models that have been aggregated across
participants. This results in a misrepresentation of mental
models as it overlooks valuable variance in understanding
between individuals that could be key in effective decision-
making. This paper illustrates such variance in mental
models through a case study that explored mental mod-
els of the Nile perch fisheries at Lake Victoria. This case
study suggests that divergence in mental model present a
barrier to effective management of the fisheries. Hence,
this paper proposes avenues to further investigate and
report the heterogeneity of mental models between and
within individuals. Such research uncovers divergence in
understanding, which can be addressed to aid decision-
making in natural resource management.

Keywords: cognitive maps, decision-making, divergent percep-
tions, fisheries, Lake Victoria, method, mental models, natural re-
source management, stakeholders, system understanding

Investigating mental models of natural
resources

Mental models are internal constructs that structure anexternal environment, facilitate interpretation and
function as an important factor in individual decision mak-
ing (Denzau & North, 1994). These cognitive representa-
tions can reflect complex dynamic systems and its function-
ing, the components of the system (the driving forces) and
dynamics. Mental models allow a person to describe, ex-
plain and predict system states and allow decision-makers
to adopt strategies for interaction with that system (Rouse
& Morris, 1986; Veldhuyzen & Stassen, 1977). A compre-
hensive literature is available on mental models in relation
to natural resources. These include people’s mental mod-
els of climate change and vulnerability to natural hazards
(Amelung, Fischer, Kruse, & Sauerborn, 2016; Bostrom,
2016; Dutt & Gonzalez, 2012; Gigerenzer & Gaissmaier,
2011; Halbrendt et al., 2014; Henly-Shepard, Gray, & Cox,
2015; Kumar & Dutt, 2018; Leiserowitz, Smith, & Mar-
lon, 2010; Otto-Banaszak, Matczak, Wesseler, & Wech-
sung, 2011; Sterman, 2008; Tschakert & Sagoe, 2009; We-
ber, 2006), agricultural dynamics (Gray et al., 2015; Hal-

brendt et al., 2014; Hoffman, Lubell, & Hillis, 2014; Van-
windekens, Baret, & Stilmant, 2014), water management
(Jones, Ross, Lynam, & Perez, 2014; Kolkman, Kok, & van
der Veen, 2005; Lynam et al., 2012), forest management
(Kearney, Bradley, Kaplan, R., & Kaplan, S., 1999; Tikka-
nen, Isokääntä, Pykäläinen, & Leskinen, 2006), eutrophica-
tion (Cloern, 2001; Janssen, 2001), lake ecosystems (Down-
ing et al., 2014; Hobbs et al., 2016), and fisheries (Garavito-
Bermúdez, Lundholm, & Crona, 2016; Gray, Chan, Clark,
& Jordan, 2012; Gray, Hilsberg, McFall, & Arlinghaus,
2015; Henly-Shepard et al., 2015; Li, Gray, & Sutton, 2016;
Radomski & Goeman, 1996).

Mental models are assessed through a range of methods
including (semi-structured) interviews with open-ended
questions (Abel, Ross, & Walker, 1998; Findlater, Don-
ner, Satterfield, & Kandlikar, 2018; Garavito-Bermudez,
2018; Jones, Ross, Lynam, & Perez, 2014; Otto-Banaszak,
Matczak, Wesseler, & Wechsung, 2011); the Fuzzy Cogni-
tive Mapping approach in which participants draw a cogni-
tive map reflecting the dynamic processes of the subject at
hand (Gray et al., 2015; Henly-Shepard et al., 2015; Özesmi
& Özesmi, 2003; Tschakert & Sagoe, 2009); the Conceptual
Content Cognitive Map, in which concepts are identified
and organised among certain dimensions (Kearney, & Ka-
plan, 1999) and the ARDI method in which participants
identify the actors, resources, dynamics and their interac-
tions (Etienne, du Toit, & Pollard, 2011; Mathevet, Eti-
enne, Lynam, & Calvet, 2011). Most of these approaches
are employed with groups of participants co-constructing
the representation of the dynamic system of the natural re-
source, while few researchers have applied these methods
on an individual level (Findlater et al., 2018; Gray et al.,
2015; Jones et al., 2014; Otto-Banaszak et al., 2011).

The outcomes of such methods are also presented in dif-
ferent ways. While only few studies measure mental mod-
els on an individual level, even fewer report mental models
at this level. The latter type of research presents selected
individual cognitive maps (Findlater et al., 2018) or de-
scribes the interviews and concepts generated per person
(Otto-Banaszak et al., 2011). However, the majority of
research on mental models, including most of the research
that measured mental models on an individual level, aggre-
gate the mental models across their participants. Research
that presents mental models for different groups (e.g. dif-
ferent stakeholders) present separate cognitive maps (Abel,
Ross, & Walker, 1998), statistics of the mental models

Corresponding author: Karlijn van den Broek, Alfred-Weber-Institute for
Economics, Heidelberg University, Bergheimer Straße 58, DE - 69115 Hei-
delberg, e-mail: karlijn.vandenbroek@awi.uni-heidelberg.de

10.11588/jddm.2018.1.51316 JDDM | 2018 | Volume 4 | Article 2 | 1

mailto:karlijn.vandenbroek@awi.uni-heidelberg.de
https://doi.org/10.11588/jddm.2018.1.51316


van den Broek: Heterogeneity in mental models.

(Findlater et al., 2018; Gray et al., 2015; Mathevet et al.,
2011) or a textual description of the patterns in the mental
models (Garavito-Bermudez, 2018). In such studies, quali-
tative interview data is coded (through word-search or con-
sensus analysis) into existing categories (Findlater, Don-
ner, Satterfield, & Kandlikar, 2018) or into a coding sys-
tem derived from the data (Mathevet et al., 2011), which
in turn allows for a statistical description of the models.

Another popular approach is to combine the responses
from all participants (and thus all groups) into one all-
compassing model. Such an aggregated model includes
all the variables that were initiated by each participant
(Gray et al., 2015; Gray et al., 2012; Mathevet et al., 2011;
Tschakert & Sagoe, 2009). Individual cognitive maps of en-
vironmental issues have been found to include 23 variables
on average and combining just 20 individual mental models
may result in a collective mental model that includes 120
variables (Özesmi & Özesmi, 2004). Such complex models
are hardly helpful for decision-makers that want to iden-
tify opportunities to better manage the natural resource.
Therefore, in this paper we propose that mental model
data can be better exploited by considering the variance in
mental models across individuals.

Exploring mental models of Nile perch fish
stock at Lake Victoria
This paper presents a case study of an exploration of the
heterogeneity of mental models of the Nile perch fish stock
among different stakeholders at Lake Victoria. A thor-
ough stakeholder analysis resulted in a sample consisting
of 76 participants from 33 different institutions, in Uganda,
Kenya and Tanzania. These included 9 governmental or-
ganisations, 9 NGO’s, 5 business organisations, 3 research
institutions and 7 community groups. To ensure a wide
variety of approaches, matching the exploratory approach,
mental models were assessed though a combination of in-
terviews and cognitive mapping, as well as on an individual
as group level.

The interactions with the stakeholders during field trips
at Lake Victoria showed great heterogeneity in their men-
tal models in terms of 1) the state of the Nile perch stock,
and 2) the causes of changes to the stock. The issue in rela-
tion to the Nile perch fishery was characterised differently
and at different degrees of specificity among stakeholders.
While most participants reported that the Nile perch stock
had decreased, others thought that the Nile perch stock had
increased. Some stakeholders reported that the Nile perch
fish stock had declined whilst others mentioned a reduc-
tion in fish catch. Still others reported that the reduction
in catch was specific to mature Nile perch fit for export.

Not only did stakeholders provide different accounts of
this problem, heterogeneity was also apparent in percep-
tions of the drivers of changes in fish catch. Examples
of the drivers discussed include: fishing pressure, illegal
fishing, climate change, fishing in breeding grounds, pres-
ence of water hyacinth, floods, growing populations, local
demand for immature Nile perch, corruption, the open ac-
cess nature of the lake, commercialization of the fishing
industry in the region, a lack of enforcement, and a lack of
ownership or responsibility to conserve. See Figure 1 for
an example of a cognitive map drawn by a group of fishers.

Similarly, discussions with stakeholders about the fu-
ture of the Nile perch stock demonstrated diverse views.
Some stakeholders were convinced the Nile perch stock was
steadily increasing, some perceived the stock to be highly

volatile while others assumed a stable stock flow, and some
experienced the stock to be decreasing rapidly. Stakehold-
ers who believed that the Nile perch would decrease rapidly
also strongly differed in the envisioned period until a tip-
ping point. Whilst some expected this to happen as soon
as in the next five years, others expected a 50-year period.

These differences in mental models may be highly prob-
lematic in terms of collaboration between stakeholders to-
ward the management of the lake’s resources. Indeed,
in the discussions with the stakeholders, different types
of stakeholders (including fishing communities, businesses,
and governments) emphasized that there was insufficient
collaboration between the stakeholders to manage the
lake’s resources.

Misrepresentations of mental models
From this exploratory work, it is clear that mental mod-
els of dynamic natural resource systems may differ widely
across stakeholders. Conservation issues may be inter-
preted differently, including the driving forces and pro-
cesses that lead to the issue. Since it is likely that these dif-
ferences in perceptions prohibit effective decision-making
between stakeholders to manage the natural resource, it is
this difference between individuals that is of interest. That
is, differences in mental models may underpin challenges in
natural resource management.

Nevertheless, it is this variance in mental models be-
tween individuals that is often overlooked in mental model
research. Many approaches in the mental model litera-
ture report aggregated models, including the elicitation of
mental models in group settings and the aggregation of
individual models. However, it is unlikely that such an ag-
gregated model can be found in a single participant. This
assumption of homogenous models therefore results in a
misrepresentation of mental models in the natural resource
literature.

Mental models are often elicited to demonstrate how a
certain dynamic system works and to directly infer man-
agement solutions from the mental models. For exam-
ple, the fuzzy cognitive mapping approach is often used
to conduct a scenario analysis, which is to inform decision-
making to address the conservation issue (Gray, Gray, et
al., 2015; Gray, Hilsberg, et al., 2015). Such approaches
assume that the participants will (jointly) produce a men-
tal model that reflects the processes accurately. However,
the divergence in mental models suggests that it is unlikely
that all participants participants (individually or jointly)
will have an accurate understanding of the system. The
mental model approach could, alternatively, provide an op-
portunity to map out differences in understanding between
individuals, thereby illuminating the divergence of the per-
ception of the environmental problem.

Investigating heterogeneity of mental
models to address dynamic natural
resource issues
Many of the current approaches in mental model research
disregard valuable information by not inspecting the vari-
ance in mental models that can underpin challenges in
decision-making and addressing the conservation issues ef-
fectively. Investigating this heterogeneity in mental models
may therefore be key to improve decision-making processes.
That is, divergence in mental models has been found to

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van den Broek: Heterogeneity in mental models.

Overpopula
tion

Fishing 
regulations

Corruption

High 
Demand

Poverty

Use of 
illegal 

fishing gear

Availability 
of illegal 

gear

Overfishing

Water 
pollution

Reduced 
water level

Migration 
of fishers

Use of 
poison for 

fishing

Nile perch 
stock

Large fish 
industry

Use of 
artificial 
fish feed

Figure 1. Cognitive map created by a group 6 Ugandan fishers.

affect communication processes between decision-makers
(Blickensderfer, Cannon-Bowers, & Salas, 1997; Marks,
Zaccaro, & Mathieu, 2000; Waller, Gupta, & Giambat-
ista, 2004), coordination among decision-makers (Marks,
Sabella, Burke, & Zaccaro, 2002), collective efficacy (the
belief among group members that the required action can
be organised and executed; Mathieu, Rapp, Maynard,
& Mangos, 2010) and strategy implementation (Gurtner,
Tschan, Semmer, & Nägele, 2007).

Convergence between mental models of individuals
within a group can be even more important than the accu-
racy of their mental models for group performance. For
example, in a study where basketball players rated the
effectiveness of strategic actions for basketball scenario’s
(which had been rated by subject matter experts), the ac-
curacy of the team members (their agreement with the
subject matter experts) could not predict the team’s last
season’s performance while the agreement between partic-
ipants on the actions did (Webber, Chen, Payne, Marsh,
& Zaccaro, 2000). Mapping out the heterogeneity in un-
derstanding can therefore provide a first step to enhance
convergence in mental models between individuals to aid
decision-making. Furthermore, the identification of the di-
vergence of mental models facilitates tailoring conservation
campaigns to the stakeholder’s mental model, since mes-
sages tailored to recipient’s characteristics are most effec-
tive (van den Broek, Bolderdijk & Steg, 2017). The field
of natural resource management would particularly benefit
from this, as conservation of natural resources requires the
collaboration of diverse stakeholders.

Heterogeneity in mental models can be measured by ex-
amining the variance (standard deviations, ranges etc.) in
the complexity of the mental models (number of variables
included, number of links included, the ratio of these two,
the density) and concepts in the mental models (variance
in central, forcing and receiving variables across partici-
pants; Gray, Gray, et al., 2015). For example, variables
from all mental models can be listed, and it can be indi-
cated how frequently they were included in an individual
mental model. Furthermore, individual mental models that
together represent the heterogeneity of the mental models
can be reported. Reporting such findings in addition to
communalities across mental models (e.g. mean number of
variables, most common links) will ensure the presentation
of a complete picture of the heterogeneity of the mental
models.

An aggregated mental model of the perceptions of the
Nile perch stock at Lake Victoria would have disregarded
key nuances. Such an aggregated model would only in-
clude 4.4 concepts, with 4.5 links. The typical mental
model would show that the stakeholders think the Nile
perch stock has declined, and that this is due to corruption,
which is linked to the use of illegal fishing gear, climate
change and water pollution. However, when we consider
the full range of mental models, we see that stakeholders
have diverse perceptions of the causes for the decline of the
Nile perch. The number of concepts included ranges from
1 to 16, with a standard deviation of 3.4, and the number
of links ranges from 1 to 19, with a standard deviation of
4.0. Inspecting the variety of the concepts and links, we
now see that some stakeholders focus on the responsibility
of the fisher (attributing the use of illegal gear to a lack of
awareness, or a lack of ownership of the lake’s resources), or
the consumer (high demand for Nile perch leading to over-
fishing), or the government (lack of monitoring, effective
policy), still others focus on demographic factors ( over-
population leading to overfishing, poverty causing fishers
to use illegal fishing gear).

Such divergence in perceptions may be explained by
a number of individual differences between stakehold-
ers. Research has shown that differences in the num-
ber of target species of fishers, and their dependency on
the species, influences fishers’ perception of the ecosys-
tem structure, and the complexity of mental model of the
ecosystem (Garavito-Bermúdez, 2018; Garavito-Bermúdez
et al., 2016; Gray, Hilsberg, McFall, & Arlinghaus, 2015).
Moreover, many stakeholders expressed that they expect
significant differences in mental models between migra-
tory fishers and indigenous fishers because of differences in
perceived ownership of the natural resources between the
two groups. Furthermore, research has demonstrated that
eliciting a mental model near the natural resource results
in more specific mental models with lower density com-
pared to elicitation practices that are conducted at people’s
homes, which were more generic and more dense (Jones et
al., 2014). Similarly, the interaction with the lake (type
of interaction, frequency), is likely to affect mental models
and may cause systematic difference between stakeholder
groups. Knowing which factors underpin such variance
may provide an indication on how to harmonize mental
models to aid decision making.

Mental models of complex systems inevitably leave room

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van den Broek: Heterogeneity in mental models.

for disagreement, but few studies report the variance in
mental models of their sample, and the heterogeneity of
mental models has therefore not yet received sufficient re-
search attention. Such research would demonstrate the
divergence in understanding, which can then be addressed
to aid decision-making among individuals. Besides this
heterogeneity in mental models between individuals, it is
also important to investigate the variance in mental mod-
els within individuals. That is, future research should also
consider the development of the mental models. Little
research on natural resource mental models has investi-
gated if these mental models are static, or change over
time. Since the latter is more likely due to changing en-
vironments and the updating of mental models with new
information, it is important to understand how these men-
tal models change and how this affects decision-making.
Through repeated measures of mental models, the stable
components of mental models can be distinguished from
the dynamic components. Such research would further our
understanding of the heterogeneity of mental models that
inform decision-making processes.

Acknowledgements: The author would like to thank
the MultiTip team for their contributions to the devel-
opment of this project. In particular, the author would
like to thank Prof. Funke for inspiring this paper and
his contributions to the conceptualization of this paper.
This project was funded by the Bundesministerium für
Bildung und Forschung.

Declaration of conflicting interests: The author de-
clares that the research was conducted in the absence of
any commercial or financial relationships that could be
constructed as a potential conflict of interest.

Handling editor: Andreas Fischer

Copyright: This work is licensed under a Creative Com-
mons Attribution-NonCommercial-NoDerivatives 4.0 In-
ternational License.

Citation: van den Broek, K. (2018). Illuminating di-
vergence in perceptions in natural resource management:
A case for the investigation of the heterogeneity in men-
tal models. Journal of Dynamic Decision Making, 4, 2.
doi:10.11588/10.11588/jddm.2018.1.51316

Received: 23 Aug 2018
Accepted: 28 Nov 2018
Published: 07 Dec 2018

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