Participatory Modeling Updates Expectations for
Individuals and Groups, Catalyzing Behavior
Change and Collective Action in
Water‐Energy‐Food Nexus
Governance
C. Kimmich1
, L. Gallagher2
, B. Kopainsky3
, M. Dubois4
, C. Sovann5
, C. Buth6
, and
C. Bréthaut2
1
Faculty of Social StudiesDepartment of Environmental Studies, Masaryk University, Brno, Czechia, 2
Institute for
Environmental Sciences, University of Geneva, Geneva, Switzerland, 3
System Dynamics Group, Department of
Geography, University of Bergen, Bergen, Norway, 4
WorldFish Myanmar – Fish CGIAR Research Program, 5
Department
of Environmental Science, Royal University of Phnom Penh, Phnom Penh, Cambodia, 6
Mekong Flooded Forest
Landscape Team, World Wide Fund for Nature, Kratie Province, Cambodia
Abstract Participatory modeling is a potentially high‐impact approach for catalyzing fundamental
sustainability transformations. We test if participation in a group system dynamics modeling exercise
increases participants' agency through a novel method to evaluate potential behavioral change using
expectation measures. A water‐energy‐food nexus—a functionally interdependent but underconceptualized
system with low consensus and high scientific uncertainty—was mapped, and its evolution simulated by 46
participants in three interventions in a region undergoing hydropower infrastructure development in
Northeastern Cambodia. Participants' system‐related expectations were measured before and after the
interventions. Our results suggest that participants became significantly more optimistic about their
individual agency to increase agricultural and fishing income and, interestingly, less likely to participate in
local government development planning procedures. Findings also reveal how some uncertainties for
multiple variables were reduced within and across the groups. Such converging expectations suggest that
participatory modeling could contribute to making collective solutions and institutionalized agreements
more likely. This research contributes to innovation in sustainability because it unpacks some underlying
mechanics of how participatory processes can lead to new adaptive capacities, shared perspectives, and
collective actions.
Plain Language Summary Our research contributes to understanding actionable knowledge for
sustainability using a before‐after intervention with fishing and farming community representatives in a
situation of conflicting water, energy, food, and livelihoods priorities in rural Cambodia. We explain why
reducing uncertainty and building consensus on action through participatory research could potentially
catalyze new behavior that promotes sustainability and test how this happens in our intervention. The result
is a new and much needed evaluation framework and method for behavioral change outcomes in
sustainability interventions.
1. Introduction
Sustainability transformations necessarily happen under huge uncertainty about future pathways and
actions to take (Abson et al., 2017). Empowerment is a critical component of adaptive capacity, agency,
and behavior change needed to navigate such uncertainty. Collaborating with stakeholders in research
across and beyond disciplines (Pahl‐Wostl et al., 2013; Pohl et al., 2010) produces new knowledge that can
address some uncertainties (Addor et al., 2015; Yung et al., 2019) and empower communities and individuals
to develop new pathways (Gerritsen et al., 2013; Miller & Wyborn, 2018). Progress has been made on better
understanding knowledge coproduction (Dubois et al., 2016) and its outcomes (DeLorme et al., 2016; Miller
& Wyborn, 2018). However, we know much less about specific mechanisms that may induce participants in
these processes to change behavior (Carr et al., 2012; Smajgl & Ward, 2015) and why this new behavior might
catalyze sustainability outcomes.
©2019. The Authors.
This is an open access article under the
terms of the Creative Commons
Attribution License, which permits use,
distribution and reproduction in any
medium, provided the original work is
properly cited.
RESEARCH ARTICLE
10.1029/2019EF001311
Key Points:
• Participatory modeling could be
fundamental to understanding
complex social‐ecological systems
with high uncertainty and
conflicting interests
• Engagement in participatory
modeling can change expectations
and thereby increase individual and
collective agency in facing
sustainability challenges
• Our method provides a framework
to evaluate and compare
participatory modeling approaches
for their behavior change potential
Supporting Information:
• Supporting Information S1
• Data Set S1
Correspondence to:
L. Gallagher,
louise.gallagher@unige.ch
Citation:
Kimmich, C., Gallagher, L., Kopainsky,
B., Dubois, M., Sovann, C., Buth, C., &
Bréthaut, C. (2019). Participatory
modeling updates expectations for
individuals and groups, catalyzing
behavior change and collective action
in water‐energy‐food nexus
governance.Earth's Future,7
Received 12 JUL 2019
Accepted 25 OCT 2019
Accepted article online 6 NOV 2019
Author Contributions:
Conceptualization: C. Kimmich, L.
Gallagher, B. Kopainsky, M. Dubois, C.
Bréthaut
Data curation: C. Kimmich, B.
Kopainsky, M. Dubois, C. Sovann
Formal analysis: C. Kimmich, L.
Gallagher
Funding acquisition: L. Gallagher, C.
Bréthaut
(continued)
KIMMICH ET AL. 1337
Published online 17 DEC 2019
1337 1352.–
https://doi.org/10.1029/2019EF001311
,
Though participatory research is framed differently across sustainability domains, two core assumptions
seem to dominate. First, that knowledge produced through participatory processes is more likely to identify
transformational leverage points (Star & Griesemer, 1989) and feasible responses for tackling complex sustainability
challenges because the knowledge is coproduced from multiple perspectives (Cash et al., 2003).
Second, such participatory research can directly and indirectly influence behavior change required for realizing
sustainability pathways (Gerritsen et al., 2013). However, epistemological, ontological, and methodological
issues for integrating knowledge and engaging with values, power, and politics remain so significant
(Jordan et al., 2018; Miller & Wyborn, 2018; Wesselink et al., 2013) that such assumptions need to be constructively
challenged if we are to innovate a scalable, deliberate, and thoughtful practice for participatory
research in sustainability.
Participatory modeling is suited to address less structured problems where consensus is low, uncertainty is
high, and collaboration, co‐decision, and joint action are needed (Basco‐Carrera et al., 2017; Hurlbert &
Gupta, 2015). Compliance‐based thinking dominates in rationalizing uses of participatory modeling
(Basco‐Carrera et al., 2017), suggesting the primary reason for stakeholder participation in model building
is to increase acceptability, ownership, and legitimacy of modeling results. In sustainability science, participatory
modeling is celebrated for social learning about systems and root causes of problems, providing multiple
perspectives in decision support for policy design, increasing adaptive capacity, and enabling coordinated
action (Hedelin et al., 2017; Voinov et al., 2016). Participation in model development has received far less
attention as a field of practice and research, compared to focus groups, citizen panels, or participatory rural
appraisals, however (Scott et al., 2016b). We argue this overlooks the potential contribution to behavior
change of the process of participatory modeling itself.
Participatory modeling has been called a cultural process (Crane, 2010) that can reveal key uncertainties in
play and resolve some of these through facilitating information sharing, group problem and action analysis,
deliberation, and negotiation (Rouwette et al., 2011; Rouwette & Vennix, 2006). Comparative analyses of
participatory modeling techniques attest to effects on social capital (Davies et al., 2015) and on participant
mental models and attitudes (Scott et al., 2016b), which are theorized as key determinants of behavior
(Denzau & North, 1994; Rodrik, 2014). However, there have been no explorations of their impact on participants'
expectations even though expectation changes are strong predictors of behavior change (Delavande
et al., 2011b; Jensen, 2010; Kimmich & Fischbacher, 2016; McKenzie et al., 2013) and are likely influenced by
changes in both individual and shared mental models (Glynn et al., 2017). Expectations have been shown to
predict behavior in a number of different areas and field settings, including expected income and education
(Manski, 2004), income and migration (McKenzie et al., 2013), coffee prices and labor allocation (Hill, 2010),
wood prices and industrial organization (Kimmich & Fischbacher, 2016), or monsoon onset and planting
decisions (Giné et al., 2009). See Delavande et al. (2011b) for a review.
This research contributes to understanding the actionable dimension of “actionable knowledge” production
by testing behavioral change efficacy in participatory systems modeling using a before‐after intervention
design in field conditions. We tested behavior change in a participatory systems thinking and
modeling procedure called causal loop diagram (CLD) mapping and simulation (Kopainsky et al.,
2017) conducted with fishing and farming community stakeholders in Kratie Province, the site of burgeoning
water‐energy‐food (WEF) nexus tensions in Northeastern Cambodia in the Mekong River Basin.
Theoretically, we provide explanations for the mechanisms of behavioral change resulting from systems
thinking‐ and modeling‐based interventions. Empirically, our field results show support for a recent proposition
in the behavioral literature that intervening to change expectations can drive changes in behavior.
Methodologically, we contribute a novel and needed evaluation framework for behavioral outcome
evaluation in sustainability interventions. Practically, we derive lessons for participatory sustainability
research and practice. These four contributions have implications for design and evaluation of programs,
projects, networks, and partnerships in sustainability.
2. Materials and Methods
2.1. Hypotheses
We define efficacy of participatory system modeling interventions as actual and prospective behavioral
change that was intended and induced by the intervention. This includes all types of individual behaviors,
either at the household and farm scale, or as part of organizations.
10.1029/2019EF001311Earth's Future
KIMMICH ET AL.
Investigation: B. Kopainsky, M.
Dubois, C. Sovann, C. Buth
Methodology: C. Kimmich, L.
Gallagher, B. Kopainsky, M. Dubois, C.
Sovann, C. Bréthaut
Project administration: L. Gallagher,
C. Bréthaut
Resources: L. Gallagher, M. Dubois, C.
Buth
Supervision: L. Gallagher, B.
Kopainsky, M. Dubois, C. Sovann, C.
Bréthaut
Validation: C. Kimmich, L. Gallagher,
B. Kopainsky, M. Dubois, C. Buth
Visualization: C. Kimmich, B.
Kopainsky
Writing ‐ original draft: C. Kimmich,
L. Gallagher
Writing – review & editing: C.
Kimmich, L. Gallagher, B. Kopainsky,
M. Dubois, C. Sovann, C. Buth, C.
Bréthaut
1338
We formulate two hypotheses to evaluate whether these processes are likely to elicit new behavioral change
in participants. We hypothesize that participant expectations concerning (1) the likelihood of certain future
events and (2) their own individual agency to respond to these events will change because of the intervention.
We assume that optimism or pessimism regarding future outcomes will be affected by these expectations and,
in turn, influence future choices in behavior. This activation hypothesis relates to the theory on self‐efficacy
in bringing about specific expected outcomes (Gittelsohn et al., 2012) and empowerment outcomes from participatory
modeling (Basco‐Carrera et al., 2017). Self‐efficacy is a key component of empowerment (Diener &
Biswas‐Diener, 2005). The hypothesis suggests that participants become more optimistic about their individual
opportunities under their control, rather than external outcomes that they cannot influence.
We also postulate that participatory modeling reduces uncertainty and enables convergence in expectations
across members of the group. Shared expectations are an important prerequisite for collective action (Runge,
1986) and therefore fundamental to institutional innovation and making choices about shared resources.
Once participants converge on the likelihood of an event or outcome, support for an institutional solution
becomes common knowledge, which facilitates collective choice. We assume that joint exploration of system
properties in a participatory modeling protocol leads to shared mental models (Scott et al., 2016b) and
hypothesize that convergence dominates divergence in expectations‐forming across participants. We focus
on uncertainty at the group level rather than changes in individuals' uncertainty, which would require
expectation elicitation of multiple intervals, exceeding survey time constraints when multiple expectation
variables are measured.
2.2. Case Selection: Why Focus on Participatory Processes in WEF Nexus Governance?
We chose to test our hypotheses in the nexus context using participatory CLD mapping and simulation.
Three reasons led us to choose this method and context:
First, with increasing pressures on natural resources, and externalities and trade‐offs between sectors
becoming more visible (Al‐Saidi & Elagib, 2017), the WEF nexus has emerged as an important phenomenon
and theme in sustainability (Gallagher et al., 2016; Liu et al., 2018). While a vast number of WEF nexus frameworks
have emerged within less than a decade, agreement on key ingredients of this socioecological system
has not been achieved (Albrecht et al., 2018; Allan et al., 2015; Allouche et al., 2015; Villamayor‐Tomas
et al., 2015). In this respect, low consensus on methods (Kaddoura & El Khatib, 2017), conflicting interests
(Fox & Sneddon, 2019; Lebel & Lebel, 2018), and scientific uncertainty make the WEF nexus a paradigmatic
case for sustainability science (Boyd et al., 2015; Cash et al., 2003; Hurlbert & Gupta, 2015).
Second, model‐based decision support is an area ripe for innovation in sustainability research (Basco‐
Carrera et al., 2017; Gerritsen et al., 2013; van Voorn et al., 2016). New insights on how this form of knowledge
production and use could translate to behavior change have potential to scale given its common usage.
The WEF nexus makes a good case precisely because its computational modeling frameworks and methodologies
(Albrecht et al., 2018) are currently debated (Kaddoura & El Khatib, 2017) and evolving to operationalize
the WEF nexus usefully in governance and decision making (Shannak et al., 2018; Weitz
et al., 2017).
Finally, WEF nexus governance research is increasingly concerned with questions of participation. WEF
nexus governance requires complex adaptation among a large set of stakeholders with all the attendant
uncertainties, politics, and power dynamics (Allouche et al., 2015; Foran, 2015; Gallagher et al., 2019). We
now understand that tracing causes of problems and potential responses in WEF nexus governance requires
transdisciplinary systems thinking (Hagemann & Kirschke, 2017; Howarth & Monasterolo, 2017). Public
participation has been extensively addressed by research on challenges of inclusion of different stakeholder
groups in decision making and the varying degrees of legitimacy and success implied by such processes (see,
among others, Fung, 2006; Irvin & Stansbury, 2004; Reed, 2008; Heijden & Heuvelhof, 2012). A particular
subset of WEF nexus governance literature has shown that participatory modeling holds some potential
for anticipatory governance (Boyd et al., 2015; Guston, 2014) in river basin WEF nexus situations
(Sendzimir et al., 2007; Smajgl et al., 2015; Smajgl & Ward, 2013). It can be useful to address the complexity
of stakes in WEF nexus governance (Halbe et al., 2015; Howarth & Monasterolo, 2017; Smajgl & Ward,
2013). It has been shown to increase understanding of empirical reality and intricacy by integrating different
perspectives and narratives related to WEF nexus intersectoral challenges (Gallagher et al., 2019). It
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KIMMICH ET AL. 1339
facilitates knowledge sharing and integration (Hagemann & Kirschke, 2017) and has been used to open new
formal or informal deliberations between WEF nexus actors, as well as reinforcing existing science‐policy
interfaces (Smajgl et al., 2015). Yet the link between participation and anticipatory governance outcomes
is underdeveloped (Smajgl & Ward, 2015). Exploring the effect of participatory modeling may have on actor
behavior in WEF nexus governance systems is a critical next step in this strand of research.
2.3. Place‐Based Case Selection, Sampling Procedure, and Participant Identification
The Mekong River Basin is the site of large‐scale and largely uncoordinated hydropower development in a
situation of rapid climate and socioeconomic changes, where the Mekong River Commission is the primary
intergovernmental body for cooperation between the lower basin riparian countries (Viet Nam, Thailand,
Lao PDR, and Cambodia) and upstream partners (China and Myanmar). Hydropower is viewed as a poverty
reduction measure in this region, and yet these infrastructures are also expected to impact significantly on
ecosystem integrity, fisheries and agricultural systems, and, subsequently, nature‐based livelihoods and food
production (Mekong River Commission, 2017; Molle et al., 2012).
Mekong fishers and farmers are and will continue to operate in a complex situation, making decisions under
a large degree of future uncertainty. Navigating this WEF nexus has been an ongoing challenge for the transboundary
governance of this globally significant river basin. The CGIAR WLE (CGIAR's Research Program
on Water, Land and Ecosystems) Challenge program on water and food (2002–2013) and subsequent
research investments on various dimensions of the Mekong WEF nexus (Gallagher et al., 2019; Foran,
2015; Grafton et al., 2016; Lebel & Lebel, 2018; Molle, 2009; Middleton et al., 2015; Orr et al., 2012;
Pittock et al., 2016; Smajgl & Ward, 2013; Smajgl et al., 2015; Villamayor‐Tomas et al., 2016) mean this is
one of the best researched WEF nexus cases globally. Our Mekong WEF nexus case study is a WEF situation
in Kratie Province, Cambodia (Liu et al., 2018). Local communities in this region will be impacted by two
major hydropower projects in Cambodia: Stung Treng dam in Stung Treng Province, a proposed mainstem
(central river channel) gravity dam being built for energy export to Thailand; and Sambor dam in Kratie
Province, the largest proposed dam in the entire river basin (Wild et al., 2019; Wu et al., 2010). Kratie and
Stung Treng Provinces are also experiencing other forms of development that impact the agricultural and
fishing activities that currently support local populations' subsistence and livelihoods in different ways.
Rubber plantation expansion, river bed sand mining, and road network infrastructure are all in progress,
alongside a burgeoning ecosystem‐based tourism sector driven by the presence of rare and endangered
Irrawaddy freshwater dolphins (Kratie Provincial Department of Planning, 2014).
With natural resource exploitation closely linked to fraught domestic politics (Milne & Mahanty, 2015; Un &
Sokbunthoeun, 2009) and poor local participation in large dam planning (Siciliano et al., 2015; Sithirith,
2016), local government development planning processes are the major formal planning mechanisms open
to people in places like Kratie Province. Local‐level priority and concerns are identified at commune level
(the third level of local government administration in Cambodia, below district and provincial administrations)
by Commune Councils in a 3‐year rolling Commune Development Plan (CDP), which is then
reviewed annually to produce a Commune Investment Plan (CIP) for the year. The CDP/CIP dominates
local formal development planning (Plummer & Tritt, 2012) as an institution dating back to French colonial
times and the first level of government administration to be reestablished following the civil war (Öjendal &
Lilja, 2009). In principle, it is a good process that relies on and encourages citizen participation to build legitimacy.
There are many concerns about how it works in practice, however (Milne & Mahanty, 2015; Plummer
& Tritt, 2012; Vuković & Babović, 2018).
Underfunded, and conducted in the context of specific cultural and sociopolitical dynamics of Cambodia's
rural areas, the annual CIP rarely challenges the CDP or other plans generated higher up in the political hierarchy
(Öjendal & Sedara, 2006; Sedara, 2012), and infrastructure (often roads and local irrigation projects) is
identified repeatedly as the main priority without any evidence as to its effectiveness in poverty reduction.
A process of decentralization and deconcentration of government functions (hereafter “D&D reforms”) is
shifting government toward more transversal service delivery goals with new local planning procedures to
encompass district and commune plans (Sedara, 2012; Royal Decree No. NS/RKM/1208/1429 2008; Royal
Decree no. NS/RKM/1014/1174 2014). However, the D&D reforms face their own implementation challenges
(Cambodia Development Resource Institute, 2004; National Committee for Sub‐National Democratic
Development, 2010, 2017), and with no current alternatives, local people engage in CIP meetings.
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KIMMICH ET AL. 1340
We chose to select participants from villages in communes within Kratie Province that are in proximate distance
to the Mekong River channel to assure that a majority of our participants were involved in the local
manifestation of complex intersectoral issues and involved at the scale of which response planning will likely
take place. Drawing on the Cambodian Commune Council Database (National Committee for Sub‐National
Democratic Development, 2015), we instrumentally selected (Gerring, 2007) two villages along the Mekong
in Cambodia where some of the WEF nexus dynamics are paramount (Allouche et al., 2015; Lebel & Lebel,
2018; Pittock et al., 2016) based on criteria that made it more likely we would capture WEF nexus dimensions
in the models. Dependence on natural resources and proximity to water bodies had to be high. We
wanted some recent technology adoption to be present, to capture potential emerging adaptive dynamics
as a region that has had limited electricity connections but seeks to increase energy access. Furthermore,
alternative livelihoods, including resource‐dependent and resource‐independent incomes, had to be present.
Therefore, we chose one village with solar photovoltaic system (PV) use (Damrae, G1), while the other has
ecotourism (Koh Phdao, G2).
Representative sampling within each community is hard to achieve. Although community‐level data are
available to identify numbers of people of age cohorts, employment, and so on, there is no name list that
could be used for randomized sampling. In addition, we needed a subsample of farmers and fishers. Given
that real‐world group sampling procedures are frequently nonrandom by design and can yield valid inferences
if carefully analyzed (Harrison & List, 2004; Smith, 1983), we decided to target heterogeneity on the
basis of age, gender, and education, including village chiefs' subjective judgments about participants' knowledge
about farm and fisher livelihoods. Among the villagers, we selected participants from the fishing and
farming communities, with some of them officially employed and involved in political processes at the commune
level. We aimed to sample a maximum heterogeneity along the dimensions of gender, age, education,
and wealth. The objective was to integrate a variety of perspectives into the procedure with the underlying
assumption being that demographic heterogeneity is related to variation in expectations and therefore
strengthens the hypothesized effects of the intervention. We did not test for the effects of diversity but rather
controlled for some of the potential effects of demographics through related survey items (see the supporting
information section S3 for an analysis of group and gender effects).
A purposefully heterogeneous composition was achieved by stratified sampling to guide selection to include
members of farming and fishing households of both genders and different ages. The village chief's local
knowledge, together with local field expert interviews, and review of demographic information and existing
infrastructure in the Cambodia Commune Database (National Committee for Sub‐National Democratic
Development, 2015) and Commune Investment Plan 2014 (Kratie Provincial Department of Planning,
2014) supported the selection. The data sources are formal government data and are not always freely available
or up to date. We accessed them through personal contacts in the Ministry of Interior and the Provincial
Administration in Kratie.
2.4. The Participatory Modeling Intervention
Our intervention protocol describes and simulates the current livelihood and environmental condition situation
in the region through explicit scenario modeling, using participants' knowledge and facilitating dialogue
about future risks and potential actions to mitigate or adapt to these.
The type of participatory modeling matters (Davies et al., 2015). We employed a CLD procedure that involves
stakeholders selecting essential variables, developing causal effects between them, and then simulating
development of these relationships into the future through scenario analysis (Kopainsky et al., 2017).
Though simulation is rarer, CLD mapping has been applied in many cases (Hovmand et al., 2012). A combination
of socio‐ecological system indicator selection and participatory simulation has been recently
applied to coastal management in the Dutch Wadden Sea (Vugteveen et al., 2015). A matrix approach to
facilitating causal link development has been applied to coastal management in Egypt (Sanò et al., 2014),
and individual model aggregation has been addressed in a participatory CLD mapping and simulation process
on the Volta River Basin (Kotir et al., 2017).
The optimal group size has been reported at around 15 participants in the literature (Phillips & Phillips,
1993). Accordingly, two CLD mapping and simulation exercises were conducted with two groups of 15 participants
from the two different villages sampled for the intervention. The third intervention was a mixed
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KIMMICH ET AL. 1341
group of 16 participants consisting of inhabitants from both villages, without including any participant from
the other two groups. With this mixed group, we tested for the effects of integration between the two village
perspectives (PV use and ecotourism) on the CLD intervention (see supporting information for details of
group comparison).
The CLD intervention design was grounded in the participatory system dynamics literature but further
adapted to groups at the community level with low formal educational background (Kopainsky et al.,
2017). We avoided computer simulation and mapped with physical materials (Hovmand et al., 2012) that
included a hands‐on simulation exercise (Kopainsky et al., 2017). Participants engaged in identifying key
variables, mapping relationships between them. The current situation and related problems were then simulated
using glasses of water to depict stocks and flows to represent time‐dependent change in stock levels to
develop and explore future scenarios collectively. Afterward, solutions were identified and grouped according
to actions by individual and commune‐level actors and then simulated for future outcomes.
Using scripts that serve as replicable protocols (Hovmand et al., 2012), the intervention was led by one group
facilitator and one modeler/technical advisor and guided by several research assistants acting as process coaches
(Hovmand, 2014). The CLD procedure lasted between 3 and 4 hr. Figure 1 provides an example of a
CLD produced by Group 3 (G3). The protocol, the underlying scripts, and the Group 1 (G1) and Group 2
(G2) outcomes are included in the supporting information.
3. Data
3.1. Intervention Design for Hypothesis Testing and Data Collection
We developed a novel evaluation procedure based on an empirically supported assumption that peoples'
expectations about the future drive their behavior (Delavande et al., 2011b; Jensen, 2010; Kimmich &
Fischbacher, 2016; McKenzie et al., 2013), hypothesizing that the learning enabled by participatory modeling
(Hedelin et al., 2017) reduces uncertainty by updating participant expectations and increasing their
agency to respond to future change. Our design involves a within‐subjects treatment with before and after
surveys. We measured expectations before and after the intervention, building on behavioral economic theory
(Manski, 2004) and recent empirical evidence (Delavande et al., 2011b; Jensen, 2010; Kimmich &
Fischbacher, 2016; McKenzie et al., 2013). The intervention design and survey were pretested in February
and conducted in March 2018. All participants received the same treatment, which took approximately 6
hr in total.
We tested general treatment efficacy with the difference between a pretreatment and posttreatment measurement
of expectations as behavioral proxies, provided by the surveys. Differential efficacy was measured
as relative efficacy between the three groups, comparing the two villages and the mixed group. Note that we
did not measure differential efficacy between the CLD intervention and any other form of intervention,
which would require respective control groups.
We elicited subjective expectations to measure prospective behavioral change. The method of eliciting expectations
with subjective probabilities is preferable over Likert scales or other elicitation methods (Manski,
2004). In the developing economy context, even with illiterate subjects, subjective probability measures have
been shown to be understood, internally consistent, and frequently highly accurate when compared to
actual realizations and past experiences (Delavande et al., 2011a).
Visual aids help improve accuracy of returns, especially in mixed cultural and linguistic contexts (Delavande
et al., 2011b). We used predefined binary classes and used coffee beans as a measure to support elicitation.
An elicitation pretest suggested that the tactile dimension of using coffee beans leads to a more reflected
weighing than percentage number elicitations. A test for potential biases is still missing, however. We did
not incentivize questions with payments to expectations answers, which is in line with common practice
and supported by empirical findings (Delavande et al., 2011b).
We tested whether participants understood the concept. The first three questions and related expectations
items tested the two extremes and equal probabilities. Table 2 provides descriptive statistics for the expectations
items. Figures 1 and 2 provide box plots of the distributions before and after the treatment and the
changes, respectively. The results were supportive. The median corresponded to the expected value in all
three questions (see Table 2). The mean indicates that there was nevertheless a share of farmers who did
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not expect the sun to rise with certainty and did expect the river to run dry. There was also a considerable
share of participants (42.86%) who expected the next child in their household to be a boy more or less
likely than did 50%. Overall, however, there has been a learning effect, as the mean moved toward the
median and the standard deviation (SD) decreased for each of the three questions.
We also tested whether participants understood nested probabilities using an adapted set of questions concerning
the occurrence of a drought in Cambodia (like the one in 2015/2016) within 5 and 10 years, respectively.
Two farmers had inconsistent expectations, stating that a drought within 5 years was more likely than
was a drought within 10 years. This deviation of 4.35% is slightly higher than what has been found in another
consistency test (Delavande et al., 2011b).
We elicited the following variables to control for demographic and household characteristics: A set of questions
concerning gender, age, and education controls for basic demographics. We also included a set of questions
concerning household size and position of the surveyed person in the household. Sources of household
income were differentiated to capture income from farming, fishing, tourism, and public and private
employment. We also include items on food self‐provision and expenditure for food to account for households'
semi‐subsistence. Households' vulnerability to food insecurity and food quality was covered to control
for potential effects on expectation measures. For example, Indian farmers' monsoon predictions have been
shown to be influenced by their dependence on rain‐fed irrigation (Giné et al., 2009).
Finally, we also tested whether the CLD intervention could influence more qualitative expectations dimensions
unlikely to appear in the model. This allowed us to separate the effects of the CLD intervention from
communication effects or information sharing that happens naturally while building the CLD.
3.2. Data Analysis Strategy
Employing nonparametric tests instead of t tests is recommended with small samples and when t‐test distributional
assumptions are likely to be violated. A Shapiro‐Wilk test for normality (see Table S2 in the supporting
material) showed that there is a statistically significant deviation from normality for the tests not only for
extremes (sunrise and river) but also for expectations related to fish income, organic fertilizer effects on
income and food quality, others' and own decision to invest into a pump, and participation in the annual
CIP. We therefore chose nonparametric tests, using the Wilcoxon signed‐rank test for paired samples, which
tests for a zero difference of the median before and after the treatment. We compare the results with the exact
Figure 1. A causal loop diagram produced by Group 3 in our intervention in approximately 45 min of discussion and
deliberation (see Script S4 in the supporting information). It depicts key variables and the cause‐and‐effect relationships
linking them, as identified by the group participants with the support of facilitators.
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Wilcoxon signed‐rank test (Harris & Hardin, 2013), which supported previous
findings. Note that due to the small sample size, the power of the
tests is relatively low. The statistical test results provide only preliminary
support for the hypotheses.
One assumption of the Wilcoxon test is independent sampling within and
between groups. This assumption is violated with clustered data of the
type we produced in our three intervention groups. Newson's method to
calculate confidence intervals and Somers' D can account for clustered
data, however (Newson, 2002). We therefore compared our results with
the results from Newson's method, comparing them with both clustered
and nonclustered results. Table S3 in the supporting information reports
Somers' D without clustering, which can be compared with the
Wilcoxon signed‐rank test results in the main text.
4. Results and Discussion
Instrumental sample selection provided an equal distribution along gender
and heads of households and a large variation in terms of age and education
(Table S1). Standard consistence tests used in expectation
elicitation procedures, testing extremes, equal, and nested probabilities
assured that participants understood the concept (see consistence items in
Table 1 and section 2).
4.1. Individual Empowerment Results
Recalling that our activation hypothesis proposes that affecting self‐
efficacy in participatory modeling brings about specific empowerment
outcomes (Basco‐Carrera et al., 2017; Diener & Biswas‐Diener, 2005;
Gittelsohn et al., 2012), we test if participants become more optimistic
about the opportunities directly under their control.
We asked the participants: “How likely do you think it will be that your
income from agricultural production (and fishing, respectively) within
the next five years will increase?” Using the nonparametric Wilcoxon
signed‐rank test, we found statistical support for the general income
expectation variables over which participants have control. Participants
were significantly more optimistic concerning their farm‐ and fishing‐
related incomes after the intervention; that is, we can reject the null hypothesis for agricultural income (P
> 0.008) as well as income from fishing (P > 0.002).
We also tested whether such increased optimism could derive from specific agricultural techniques, rather
than from a more general empowerment effect. Neither fishpond income (P > 0.92) nor effects of the CIP
on income (P > 0.75) contributed to this optimism. With the help of Somers' D, we can also interpret the
effect size. Concerning agricultural income, for example, participants are between 11% and 42% more likely
to expect their income from agriculture to increase than to decrease after the CLD intervention (see Table 2).
The Wilcoxon signed‐rank test findings are supported by Newson's method (see section 2), accounting for
clustered data across the three intervention groups (see Table 2). While agricultural and income expectations
changed significantly, the source of this optimism is neither directly related to fishpond nor related to CIP
income expectations, where no significant changes could be observed. Increasing optimism about children
taking over farming or fishing activities is in line with more optimistic income expectations.
We also found statistically significant changes in other expectation variables modeled by the participants.
We found a decrease in expected likelihood that somebody in the community will invest in a pump and that
the commune will invest in a fishpond. The findings concerning raised expectations for earnings from corn
planting during droughts are not robust when compared with other model specifications (see nonclustered
results in Table S3).
Figure 2. Box plots of the changes in expectations for our key variables.
“Drought”: likelihood of a drought like in 2015/2016 reoccurring; “Take
over by child”: likelihood that the family farm or fishing activity will be
taken over by someone from the family; “Agr. Income”: likelihood that
household income earned through agricultural activities will increase; “Fish
income”: likelihood that household income earned through fishing activities
will increase; “Food expenditure”: likelihood that the share of food bought
rather than produced by the household will increase in the next 5 years;
“Corn”: likelihood the household would earn more from planting corn than
from rice in the next season, and then under drought conditions; “Organic
fert.”: likelihood that yields would be higher with organic fertilizers than
with conventional (chemical) fertilizers, and expected effects on food quality;
“Fishpond”: likelihood that a new fishpond will be constructed in the
commune within the next 5 years, by the respondent or someone else, and
the anticipated implications for food quality and household income;
“Irrigation pump”: likelihood that someone else or the respondent would
purchase a water pump in the next 5 years; “Dam/dike”: small dikes or dams
will be constructed by respondent's commune within the next 5 years and
anticipated implications for agricultural production; “CIP”: likelihood that
the respondent will participate in the CIP process within the next 5 years,
that CIP decisions will have a positive effect on household income of the
respondent, and that the respondent could influence a CIP decision.
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KIMMICH ET AL. 1344
We also tested for variables that were unlikely to be modeled explicitly in a CLD framework to identify the
potential effects of communication during the modeling process. We found a significant decrease in expected
food quality from the application of organic fertilizers and suppressed expectations about food quality from
fishpond investments (although less statistically robust, see Table S3). Given that food quality was not modeled
by any of the groups (see section 2), this result shows that the CLD mapping and simulation was not the
only instrument shaping expectations during the intervention. Participants went beyond the boundary
object of the CLD intervention to form new expectations independently for variables not discussed by
the group.
In brief, we found support for the activation hypothesis. The optimism concerning agricultural and fishing
income seems to be general and cannot be traced to any specific agricultural method, such as pump or fishpond
investments, or earnings from corn. Therefore, we cannot trace this optimism to any specific production.
Importantly, this increase in optimism is not in line with other findings on agricultural income
expectations in Cambodia (Grafton et al., 2016; Scheidel et al., 2014). Rather, this optimism appears to be
a more general phenomenon resulting from having participated in the CLD procedure.
4.2. Shared Expectations and Collective Action Results
Shared expectations are an important prerequisite for collective action (Runge, 1986) and therefore fundamental
for institutional innovation. Once participants converge on the likelihood of an event or outcome,
support for an institutional solution becomes common knowledge that facilitates collective choice. We
assume that joint exploration of system properties in a participatory modeling protocol leads to shared mental
models (Scott et al., 2016a) and hypothesize that convergence dominates divergence in expectations‐
forming across participants. We focus on uncertainty at the group level rather than changes in individuals'
uncertainty, which would require expectation elicitation of multiple intervals, exceeding survey time constraints
when multiple expectation variables are measured.
The results show that the CLD intervention has contributed to some convergence in expectations and a
related reduction in uncertainty in our groups. We compared the SD of each variable before and after the
Table 1
Summary Statistics of the Expectation Variables Before and After the Intervention for Three Consistence Tests and 21 Key Variables
Variable N
Before After
Mean SD Med Min Max Mean SD Med Min Max
consistence: sunrise 42 7.17 4.15 10 0 10 8.19 3.12 10 0 10
consistence: river 42 2.10 3.47 0 0 10 0.98 2.40 0 0 10
consistence: boy 42 4.45 1.98 5 0 10 4.48 1.71 5 0 9
drought (5y) 46 4.98 2.13 5 0 10 5.20 1.68 5 1 10
drought (10y) 46 6.02 2.71 6 0 10 6.24 1.99 6 3 10
takeover by child 46 5.09 2.78 5 0 10 5.76 2.46 5 1 10
agr. income (5y) 46 5.15 2.25 5 0 10 6.13 1.68 6 3 10
fish income (5y) 46 2.70 2.18 3 0 8 4.02 2.15 4 0 8
food expenditure (5y) 46 5.04 2.39 5 0 10 4.91 1.56 5 1 7
corn: earnings 46 3.70 2.23 4 0 8 4.33 2.12 4 0 9
corn: earn. with drought 46 3.80 2.62 3.5 0 10 3.89 2.49 4 0 8
organic fert.: yield 46 7.35 2.51 8 0 10 7.00 2.42 8 0 10
organic fert.: food quality 46 8.76 1.90 10 0 10 8.30 1.58 8 4 10
fishpond: commune 46 5.50 2.18 5 0 10 5.02 2.18 5 0 10
fishpond: own 46 5.17 3.10 5 0 10 5.17 2.60 5 0 10
fishpond: food quality 46 6.98 1.89 7 2 10 6.57 1.60 7 2 9
fishpond: income 46 6.61 1.89 7 2 10 6.57 2.12 7 1 10
irrigation pump: others 46 6.78 2.70 7 0 10 6.15 2.61 7 0 10
irrigation pump: own 46 6.74 3.30 7 0 10 7.17 2.96 8 0 10
dam/dike commune 46 3.96 2.81 5 0 10 4.24 2.58 5 0 10
dam/dike yield increase 46 6.30 2.05 7 1 10 6.43 2.21 7 0 10
CIP: participation 46 7.46 2.33 8 0 10 7.13 2.17 7.5 2 10
CIP: income effects 46 6.20 2.48 6.5 1 10 6.30 2.00 7 2 10
CIP: own influence 46 5.33 2.39 5 0 10 5.48 2.27 5 1 10
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intervention to analyze expectation convergence. Overall, we found support for the shared expectations
hypothesis. Table 2 indicates that the SD was reduced in almost all cases, except for expected income
increase from fishponds. We used Levene's test to identify whether the variances changed after the
intervention, including the median modifications of the Brown‐Forsythe test. The null hypothesis was
moderately rejected for food expenditure (PW0 > 0.056; PW50 > 0.055; PW10 > 0.066), but not for any of the
other items.
Another proxy variable supports the argument that collective action at the commune level was affected by
the CLD mapping and simulation intervention. Participants judged it less likely that they would participate
in the CIP again in their posttest survey (see Table 3). It may be that an empowerment effect related to self‐
organization possibilities creates pessimism about their individual and collective agency in the CIP. This
argument could be supported by recent findings concerning the CIP in Cambodia (Plummer & Tritt,
2012), but the political‐economic dimension of this needs further theorization in WEF nexus research
(Foran, 2015). This is particularly relevant for local perceptions of provincial administration capacities for
natural resources management, which has been recently devolved to provinces under D&D reforms
(Vuković & Babović, 2018).
There is limited application of risk‐based management in Cambodia with low availability and sharing of
local risk information (Mochizuki et al., 2015), suggesting that it was our intervention that reduced uncertainty
about sources of potential risk for our groups, though not uniformly, across variables.
4.3. Discussion—The Implications of Changes and Convergence in Expectations for Nexus
Governance in Mekong
We set out to test the idea that reducing uncertainty and building consensus on action through participatory
modeling could potentially catalyze new behaviors with this research. We hypothesized that participant
expectations for (1) the likelihood of certain future events and (2) their own individual agency to respond
to these events will change because of the CLD intervention. We also postulate that participatory modeling
reduces uncertainty and enables convergence in expectations across members of the group.
Our expectations‐testing results suggest that these ideas may be true in some interesting ways. The research
team achieved a new understanding of WEF nexus risk anticipation in local stakeholders. We entered into
Table 2
Within‐Cluster Somers' D With Standard Errors Adjusted for the Three Groups
CLD_treatment Coefficient Jknife SEs z P > z 95% confidence interval
drought (5y) 0.0623229 0.0935694 0.67 0.505 −0.1210698 0.2457156
drought (10y) −0.0028329 0.0790037 −0.04 0.971 −0.1576772 0.1520115
takeover by child 0.1331445 0.0738423 1.80 0.071 −0.0115837 0.2778727
agr. income (5y) 0.2677054 0.0790918 3.38 0.001 0.1126882 0.4227225
fish income (5y) 0.3541076 0.1022549 3.46 0.001 0.1536918 0.5545235
food expenditure (5y) −0.0524079 0.1248944 −0.42 0.675 −0.2971965 0.1923806
corn: earnings 0.1657224 0.1362876 1.22 0.224 −0.1013964 0.4328411
corn: earn. with drought 0.0410765 0.0233324 1.76 0.078 −0.0046541 0.0868071
organic fert.: yield −0.0963173 0.1450005 −0.66 0.507 −0.3805131 0.1878785
organic fert.: food quality −0.2294618 0.0467748 −4.91 0.000 −0.3211387 −0.1377848
fishpond: commune −0.0991501 0.0470523 −2.11 0.035 −0.191371 −0.0069293
fishpond: own −0.0212465 0.0159911 −1.33 0.184 −0.0525885 0.0100955
fishpond: food quality −0.1458924 0.0834862 −1.75 0.081 −0.3095223 0.0177376
fishpond: income 0.0254958 0.0967985 0.26 0.792 −0.1642259 0.2152174
irrigation pump: others −0.1203966 0.0479123 −2.51 0.012 −0.2143031 −0.0264901
irrigation pump: own 0.0538244 0.0553522 0.97 0.331 −0.0546641 0.1623128
dam/dike: commune 0.0694051 0.0799335 0.87 0.385 −0.0872616 0.2260718
dam/dike: yield increase 0.0609065 0.0625659 0.97 0.330 −0.0617204 0.1835335
CIP: participation −0.1033994 0.0360144 −2.87 0.004 −0.1739864 −0.0328125
CIP: income effects 0.0354108 0.0355212 1.00 0.319 −0.0342095 0.1050311
CIP: own influence −0.0042493 0.0297723 −0.14 0.887 −0.0626019 0.0541033
Note. The table shows the results of testing the hypothesis that participants become more optimistic about variables on which they have direct influence, as compared
to those variables that are subject to external forces (activation hypothesis).
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this work understanding that dams were at the heart of the WEF nexus in this region. Yet hydroelectric dam
construction was not the primary risk identified for our participants. Local villagers consider themselves to
be facing more immediate challenges to food security, like illegal fishing and locally degraded fish stocks,
while dam developments are far‐off concerns. Moreover, participants became significantly more
optimistic concerning their individual agency to cope (i.e., increase agricultural and fishing income) in
the face of such risks and, conversely, less enthusiastic about the likelihood that they will participate in
their local upcoming CIP processes. The interpretation we offer is that the CIP results reflect the ongoing
issue of the state's inability to provide communities' access to key resources for their development that the
D&D reforms have been established to remedy. Local citizens participate in the CDP/CIP processes
because there is no alternative. But if, as our results suggest, the sense of individual and collective agency
is heightened, people will perhaps decide not to participate in the CIP. The convergence in expectations
for multiple variables across the groups suggests that while collective solutions and institutionalized
agreements are more likely to emerge, they are less likely to be realized through the CIP given
expectations about reduced participation. Other community planning activities, both formal (e.g.,
Department of Agricultural Extension (DAE), 2012) and informal, may hold more promise for local WEF
nexus governance.
Our evidence regarding the activation and convergence hypotheses is in line with previous studies from a
wide range of application domains that evaluate the effectiveness of participatory system dynamics (e.g.,
Rouwette, 2003; Rouwette et al., 2011; Scott et al., 2016b). Those studies focused on more traditional settings
for participatory modeling such as corporate or public policy applications in board room environments. The
observed alignment of mental models seems to hold also in the context of groups at the community level
with low formal educational background (convergence hypothesis). Similar to those of Rouwette (2003)
and Rouwette et al. (2011), our participants became significantly more optimistic about individual opportunities
under their control. While existing studies found no change in perceived behavioral control, our
Table 3
Mean, Median, and SD Differences Across Groups With Regard to Changes in Expectations
Variable
Mean Median SD_d
G1 G2 G3 G1 G2 G3 G1 G2 G3
consistence: sunrise −0.27 2.92 −0.10 0.00 0.00 0.00 −0.60 −2.02 0.01
consistence: river −0.36 −2.17 0.27 0.00 0.00 0.00 −0.68 −1.60 −1.03
consistence: boy 0.00 0.00 0.09 0.00 0.00 0.00 −0.07 −0.20 −0.50
drought (5y) 0.20 −0.25 0.73 0.00 0.00 1.00 −0.39 −0.29 −0.43
drought (10y) 0.00 0.12 0.53 0.00 0.50 1.00 −0.31 −0.78 −1.16
takeover by child 0.40 0.19 1.47 0.00 0.00 1.00 −0.69 −0.04 −0.07
agr. income (5y) 1.07 0.25 1.67 1.00 0.00 1.00 −0.72 −0.71 −0.41
fish income (5y) 0.73 1.94 1.27 0.00 2.00 2.00 0.21 −0.07 −0.04
food expenditure (5y) −0.67 −0.44 0.73 0.00 −0.50 0.00 −0.65 −1.00 −0.59
corn: earnings 1.60 0.38 −0.07 1.00 0.00 0.00 0.17 −0.53 0.02
corn: earn. with drought −0.07 0.06 0.27 0.00 0.00 0.00 −0.34 0.29 −0.38
organic fert.: yield 0.20 −1.06 −0.13 1.00 0.00 −1.00 −0.20 −0.60 0.55
organic fert.: food quality −0.40 −0.19 −0.80 0.00 0.00 −1.00 0.16 −1.24 0.22
fishpond: commune −0.60 −0.31 −0.53 0.00 0.00 0.00 0.16 −0.57 0.63
fishpond: own 0.20 −0.12 −0.07 0.00 0.00 0.00 −0.49 −0.49 −0.56
fishpond: food quality −0.67 −0.75 0.20 −1.00 −1.00 0.00 −0.62 −0.34 −0.05
fishpond: income 0.13 −0.44 0.20 0.00 −0.50 0.00 0.37 −0.02 0.29
irrigation pump: others −0.53 −0.25 −1.13 0.00 0.00 0.00 0.71 −0.37 −0.36
irrigation pump: own −0.20 0.00 1.53 0.00 0.00 0.00 −1.48 0.10 0.31
dam/dike commune −0.80 0.69 0.93 0.00 0.00 1.00 0.63 −0.17 −0.90
dam/dike yield increase 0.60 0.19 −0.40 0.00 0.00 0.00 0.72 0.11 −0.58
CIP: participation −0.33 −0.06 −0.60 0.00 0.00 0.00 0.24 −0.44 −0.15
CIP: income effects −0.13 0.25 0.20 0.00 0.00 0.00 −0.33 −0.50 −0.63
CIP: own influence 0.27 0.12 0.07 0.00 0.00 0.00 −0.31 0.29 −0.31
Note. Mean and median values of the change are reported, while SD_d reports the difference in standard deviations before and after the intervention. N = 15 for
G1 and G2 and N = 16 for G3. The table summarizes the results of differences in expectation changes across the groups (shared expectations hypothesis), with
changes in standard deviations provided in the last three columns.
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KIMMICH ET AL. 1347
results point at an overall increase in the likelihood of individual as well as collective action. Future research
will have to address the question whether these differences in outcomes are an artifact of somewhat different
evaluation criteria used (perceived behavioral control versus expectations regarding future individual and
collective action) or whether they are a function of either our participatory modeling protocol or the specific
WEF nexus context in which we were operating.
Some caveats are important to interpreting our findings. In our experience, the CLD process generated discussion
about other things than what were modeled, and so there is some leakage into issues and topics that
were not controlled for in the process. For example, some expectation effects were observed for food quality
from organic fertilizer use and related to aquaculture, which were not actually modeled in the CLD procedures.
Secondly, differences across our groups were considerable. Table 3 provides differences across groups
concerning median and mean changes in expectations. Although SD measures decreased across groups in
most cases, there were differences across groups, and SD of some groups actually increased for several variables.
This suggests that the development of shared expectations is by no means an unconditional result of
the CLD intervention but is more likely contingent upon participants' contributions that are in turn influenced
by internal group dynamics, or major external events, rather than intended intervention effects within
the scope of the intervention protocol itself. Our cases were all treated on the same day in the same location
with the same team, so we controlled for some influences; but participants did come from different communities,
and group dynamics changed in each group with personalities. Finally, we can only say something
about the uncertainties that we actually measured and cannot infer results for reducing uncertainties concerning
variable coverage and model structure.
5. Conclusions
Our starting point in this paper was that two assumptions drive much participatory research in sustainability:
that knowledge produced through participatory processes is more likely to identify transformational
leverage points (Cash et al., 2003; Star & Griesemer, 1989) and that such knowledge and processes can
directly and indirectly influence behavior change for sustainability (Gerritsen et al., 2013). Given the various
issues for knowledge integration while engaging with values, power, and politics (Jordan et al., 2018; Miller
& Wyborn, 2018; Wesselink et al., 2013), we argued that such assumptions need testing.
Our analysis contributes to explaining one possible mechanism of behavioral change resulting from systems
thinking‐ and modeling‐based interventions. Specifically, sustainability research suggests that participatory
modeling can trigger self‐efficacy outcomes and reduce perceived uncertainties in addressing collective
action challenges. Our empirical results provide preliminary support for this claim and for a recent proposition
in behavioral sciences that intervening to change expectations can drive changes in behavior.
Methodologically, we tested a novel evaluation framework for behavioral outcome evaluation in sustainability
interventions. We measured positive and negative changes in expectations of participants in our procedure
in line with the hypothesis that the participatory CLD mapping and simulation could affect their
perceived self‐efficacy (activation hypothesis). Our participants also demonstrated convergence in some
key expectations about likelihood of future events and capacities to respond to these (shared expectations
hypothesis). This is significant because reducing uncertainties at the group level removes some barriers
impeding the complex adaptation among a large set of stakeholders with all the attendant uncertainties, politics,
and power dynamics that WEF nexus governance demands. Our intervention effectively coproduced a
new awareness of future individual and local shared risks that may follow global environmental change,
regional and national energy strategies, and technical decisions on hydropower dam siting and design and
ideas for potential responses to manage these in our case study participants, despite driver factors for the
coming changes lying largely outside of local control. These results suggest three implications for participatory,
model‐based sustainability research and practice in general, and WEF nexus governance in specific.
The first significant point for designing participatory processes that are intended to generate governance outcomes
is how updating participants' expectations does not rely on outputs produced and published after
interventions. It appears to be at least as much about the process as the end product. The conceptual system
interpretation and dynamic simulation in our CLD procedure changes some conditions (risk awareness and
income expectations) that can affect future behavior of those participating during the 6‐hr workshop process.
This suggests a second insight on the importance of paying close attention to how expectations are being
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KIMMICH ET AL. 1348
updated during the group activity in case these are wrong. For example, the individual increases in optimism
for income in our groups are not in line with other findings on agricultural income expectations in
Cambodia (Grafton et al., 2016; Scheidel et al., 2014). Not only did our intervention change expectations
for the future, but it changed them in a way that could be risky for participants if they are wrong. The implication
of this is that mechanisms to evaluate and navigate expectations updating, convergence, and divergence
are needed during participatory modeling procedures, not afterward. The method we used could
reduce the risk of unknowingly changing expectations against contradicting evidence while also supporting
sustainability researchers to link their intervention and future potential behavior change impacts.
A third and final key insight for WEF nexus governance is that convergence and divergence in expectations
point to clear areas of certainty and uncertainty in groups, which is critical to catalyzing opportunities and
conditions for collective action. Group interpretation is an epistemic nudge, entailing deliberative processes
of empowerment and so potentially strengthening agency and enabling collective action (John, 2018). This
information can identify individuals primed for interventions, for example, technical training or further
negotiations, on issues where uncertainty is a barrier. It also means that while heterogeneity in group modeling
is advised to stimulate discourse, any participatory modeling exercise should carefully weigh advantages
and disadvantages where collective action is the goal. In too homogenous groups, convergence may
be achieved without surfacing and challenging assumptions. Conversely, groups too heterogeneous by design
can produce unwarranted divergence in expectations, increasing uncertainty and reducing agreement.
One important caveat in our results is permanence of change and convergence in expectations. Structural
power issues will undermine empowerment gains made in our process without further interventions. In this
regard, future research could explore if different forms of research designs, participatory approaches, and
group compositions have different effects on expectations, paying attention to power dynamics. Note that
without a systematic experimental comparison of group compositions and intervention methods, the external
validity of our findings remains limited and open to further analyses. Testing the effectiveness of different
group selection procedures, including a random selection baseline, and their relation to participatory
approaches, could increase external validity. For example, we chose an optimal demographic composition
and group size, but future studies could experimentally test influence of group heterogeneity, leadership,
group sizes, and many other variables on behavioral effects of CLD interventions, or any other workshop
technique or epistemic approach.
We conclude that participatory CLD mapping and simulation is likely to be one effective implementation
method for WEF nexus governance processes. We also conclude that expectations measures can be used,
with caution, to evaluate the likelihood of future behavioral change following participatory processes. Our
contribution lies in developing and testing one mechanism for measuring changes in expectations and showing
that it can be done in challenging field conditions.
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Acknowledgments
The authors declare no competing
interests that could directly undermine
or be perceived to undermine the
objectivity, integrity, and value of this
primary research article. Author
contributions: C. K. co‐lead on
conception and design of this research,
data analysis, interpretation of data and
drafting the manuscript and supporting
information; L. G. co‐lead on
conception and design of this research,
interpretation of data, drafting the
manuscript and supporting information
and is the corresponding author; B. K.,
M. D., C. S., and C. B. contributed to
research design, data acquisition,
interpretation of data, and drafting the
manuscript; and C.B. contributed to
conceptualization of the research and to
substantial review of the manuscript.
The authors gratefully acknowledge the
Nomis Foundation and the Mava
Foundation as funding partners in this
research and in‐kind support from
CGIAR Research Program on Fish
Agri‐Food Systems (FISH) led by
WorldFish. We thank the participants
from Kratie Province and the students
from the Royal University of Phnom
Penh who conducted the survey work
during the field interventions. We also
thank Andrea Betancourt for her
project management support and
comments on an earlier version of the
manuscript. The data supporting the
results and conclusions in the paper can
be found in Tables 1, in Figure 2, and
the supporting information Table S4.
Key references supporting conclusions
include Delavande et al. (2011b) and
Runge (1986).
1349
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