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■ Research Paper
Managing Complex Issues throughEvolutionary Learning
Laboratories
Ockie J. H. Bosch1*, Nam C. Nguyen1, Takashi Maeno2
and Toshiyuki Yasui21 Systems Design and Complexity Management
Alliance, Business School, The University of Adelaide, Adelaide,SA
Australia2Graduate School of Systems Design and Management, Keio
University, Hiyoshi, Japan
Policy makers, managers and leaders in organizations,
governments and business institu-tions are under increasing
pressure to make the right management decisions in the face ofa
continually changing political and socio-economic landscape. To
make matters morechallenging, the complex environmental,
socio-economic, business-financial issues thatdecision makers need
to deal with tend to transcend the jurisdictions and capacities of
anysingle organization. There is a multitude of difficult,
long-term global challenges ahead,almost all of which are coupled
with the most pressing concerns of different countries atnational
and local levels. Despite many efforts to deal with these complex
issues facingour society, the solutions so far have seldom been
long lasting, because ‘treating the symp-toms’ and ‘quick fixes’,
using traditional linear thinking, are the easiest way out, but
donot deliver the solutions. This paper describes the processes for
unravelling complexitythrough participatory systems analysis and
the interpretation of systems structures to iden-tify leverage
points for systemic interventions. It further demonstrates the
promotion ofeffective change and the enhancement of cross-sectoral
communication and collaborativelearning. This learning focuses on
finding solutions to complex issues by applying an itera-tive,
systems-based approach, both locally—Evolutionary Learning
Laboratory (ELLab)—and globally—Global Evolutionary Learning
Laboratory (GELL). A generic frameworkand processes for
implementing and institutionalizing ELLabs are described, and how
thesebecome part of the GELL for managing complex issues is
explained. Four case studies areused to demonstrate diverse
examples of the application and implementation of the
ELLabapproach. Copyright © 2013 John Wiley & Sons, Ltd.
Keywords management; policy making; investment decisions;
complexity; systems thinking;participatory systems analysis; Global
Evolutionary Learning Laboratory (GELL)
*Correspondence to: Ockie J. H. Bosch, Systems Design and
Complexity Management Alliance, Business School, The University of
Adelaide,Adelaide, SA 5005 Australia.Email:
[email protected]
Received 24 October 2012Accepted 14 January 2013Copyright © 2013
John Wiley & Sons, Ltd.
Systems Research and Behavioral ScienceSyst. Res (2013)Published
online in Wiley Online Library(wileyonlinelibrary.com) DOI:
10.1002/sres.2171
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INTRODUCTION
Complexity characterizes theworld and all humanendeavours
today—in business, government,social, natural, scientific and
political spheres.Local and global problems and challenges
facingour world today are highly complex in nature,involving
decision makers, scientists, NGOs andvarious other stakeholders.
These problems andchallenges cannot be addressed and solved in
iso-lation and with the single dimensional mindsetsand tools of the
past. Collaborative, systemicand integrated approaches are
essential to deliverthe sustainable outcomes desired. It has
becomecrucially important for decision makers andmanagers involved
in the management of anysystem to be equipped with the necessary
capabil-ities and skills to make good policy and manage-ment
decisions.
In recent years, there has been a growingrecognition of human
capacity development as akey lever for sustainable economic, social
andecological development. However, recent litera-ture on the
success of external actors and agenciesin implementing effective
change in developingcountries or regions shows poor outcomes
acrossthe board (Umaña 2002; Land et al., 2009; Thomasand Amadei
2010). One of the key barriers toprogress is the lack of common
understandingand shared vision of how to address the complexissues
facing our world. The lack of cross-functional collaboration leads
to fragmenteddecision-making and uncoordinated actions.This is
further exacerbated by cross-purposenegotiations, the wasting of
public and naturalresources, and a loss of confidence in
leadershipand governance. Over time, these all escalate intoa
vicious cycle of mediocre performance and pooroutcomes for all
concerned. A further importantcontributor to poor outcomes is the
fact thatmany of the ways in which problems are beingaddressed are
simply ‘quick fixes’ or ‘treatingthe symptoms’. The establishment
of a systems-based Learning Laboratory (LLab) has proven tobe an
innovative and effective approach (Boschand Nguyen 2011; Nguyen et
al., 2011) for dealingwith highly complex and multi-dimensional
pro-blems and ensuring that solutions will be foundat the level of
the root causes.
In addition, we manage the systems we are partof in a highly
compartmentalized structure—organizations, divisions within
organizations,business institutions, government
departments,university schools, disciplines and so on.
Thesestructures help our society to operate in an orderlyway.
However, without an understanding thatall these different sectors
in life are highlyinterconnected and that there is a strong needfor
interdisciplinary, cross-sectoral communicationand collaboration,
solutions that effectively addressthemulti-dimensional
andmultidisciplinary natureof complexity will remain elusive.
This paper presents the methodology and appli-cation of a ‘new
way of thinking’ and radicalapproach to enhancing cross-sectoral
and organi-zational communication and collaboration, to dealwith
increasing complexity and to promote effect-ive change at local and
global levels.
SYSTEMS THINKING
Although systems thinking is an ‘old’ concept(Midgley 2003), it
is increasingly being regardedas a new way of thinking to
understand and man-age complex problems at both local and
globallevels (Bosch et al., 2007b; Cabrera et al., 2008).Maani and
Cavana (2007) used the analogy of aniceberg to illustrate the
conceptual model knownas the Four Levels of Thinking (Figure 1) as
aframework for systemic interventions.
In this model, events or symptoms (those issuesthat are easily
identifiable) represent only the visi-ble part of the iceberg above
the waterline. Mostdecisions and interventions currently take
placeat this level, because quick fixes (treating thesymptoms)
appear to be the easiest way out,although they do not provide
long-lasting solu-tions. However, at the deeper (fourth) level
ofthinking that hardly ever comes to the surface arethe ‘mental
models of individuals and organisa-tions that influence why things
work the way theydo. Mental models reflect the beliefs, values
andassumptions that we personally hold, and theyunderlie our
reasons for doing things the way wedo’ (Maani and Cavana 2007,
p.15).
Moving to the third level of thinking is a criticalstep towards
understanding how these mental
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models can be integrated in a systems structurethat reveals how
the different components areinterconnected and affect one another.
Thus,systemic structures unravel the intricate lace ofrelationships
in complex systems.The second level of thinking is to explore
and
identify the patterns that become apparent whena larger set of
events (or data points) becomelinked to create a ‘history’ of past
behaviors or out-comes and to quantify or qualify the
relationshipsbetween the components of the system as a whole.The
systems thinking paradigm and method-
ology embrace these four levels of thinking bymoving decision
makers and stakeholders fromthe event level to deeper levels of
thinking andproviding a systemic framework to deal with com-plex
problems (Maani and Cavana 2007).The application of systems
thinking has grown
extensively and encompasses work in many di-verse fields and
disciplines such as, to mentionbut a few, management (Jackson,
2003), business(Sterman 2000; Walker et al., 2009), decision
mak-ing and consensus building (Maani and Maharraj2004), human
resource management (Quatroet al., 2007), organizational learning
(Galanakis2006), health (Newell 2003; Lee 2009), commoditysystems
(Sawin et al., 2003), agricultural productionsystems (Wilson 2004),
natural resource manage-ment (Allison and Hobbs 2006),
environmentalconflict management (Elias 2008), education (Hung
2008), social theory and management (Mingers2006), and food
security and population policy(Keegan and Nguyen 2011). This paper
is the firstto demonstrate how a comprehensive systemsthinking
approach, embedded in a cyclic Evolu-tionary Learning Laboratory
(ELLab) framework,can be used to deal effectively with complex
issuesin a variety of contexts.
ESTABLISHING A SYSTEMS-BASEDEVOLUTIONARY LEARNING LABORATORY
The LLab is a process, as well as a setting, inwhich a diverse
group of participants engage ina cyclical process of thinking,
planning, actionand reflection for collective learning towards
acommon good. It is an environment where policymakers, managers,
local facilitators and research-ers collaborate and learn together
to understandand address complex problems of common inter-est in a
systemic way (Maani and Cavana 2007).The ultimate goal is to
achieve coherent actionsdirected towards sustainable outcomes.
The ELLab is a seven-step iterative process(Figure 2) of group
thinking and acting in whichthe participants engage in well-defined
activitiesand thus learn together in an ‘experimenting
lab’environment about how best to deal with the com-plex
multi-dimensional and multi-stakeholder
The Iceberg Approach$$$ for addressing symptoms or events (Quick
Fixes)
Symptoms/Events
?
Systems ApproachAddressing fundamental problems to achieve
sustainable systems
Mental Models/Mind Maps – People’s Understanding
$$$ for mitigating unintended consequences
$$$ for root causes of issue
Patterns –interactions between
components
Systemic Structures What does system look
like
Figure 1 The iceberg approach versus a systems approach
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Managing Complex Issues through ELLabs
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problems they are facing. Although it builds onevolutionary
design principles as described in thework of Banathy (1996) and the
concept of evolu-tionary leadership developed by Laszlo (2001),the
process of establishing an ELLab (Figure 2)could be regarded as a
unique ‘methodology’ tocollaboratively integrate and use existing
and fu-ture knowledge to help manage complex issues.It starts at
the ‘fourth level of thinking’ with anissues workshop (step 1) and
a series of forumswith specialist groups to gather the mental
modelsof all stakeholders involved in the issue underconsideration,
their perceptions of how the systemworks, what they regard as
barriers to successand drivers of the system, and possible
strategies(solutions) to overcome these problems.
This is followed by implementing the ‘thirdlevel of thinking’
through follow-up capacity-building (step 2) sessions during which
the partici-pants (all stakeholders) learn how to integrate
thevarious mental models into a systems structure(step 3). The
Vensim software program (Systems2011) is a valuable tool for the
development of asystems model (causal loop diagram) of the
issueunder consideration. This learning step is ofparticular
importance in order for all involved totake ‘ownership’ of the
systems model.
Once completed, the participantsmove to the ‘sec-ond level of
thinking’ by interpreting and exploring
the model for patterns, how different componentsof the model are
interconnected and what feedbackloops, reinforcing loops and
balancing loops exist.This step aims to assist relevant
stakeholders to de-velop an understanding of their
interdependenciesand the role and responsibility of each
stakeholdergroup in the entire system. The main barriers anddrivers
of the system are discussed in more detail,which provides the
stakeholders with an oppor-tunity to develop a deeper understanding
of theimplications of coordinated actions, strategiesand policies.
Overall, this process provides allstakeholders with a better
understanding of eachother’s mental models and the development of
ashared understanding of the issue(s) underconsideration.
The interpretation leads to the identification of le-verage
points for systemic intervention (step 4). Le-verage points are
places within a complex system(e.g. an economy, a living body, a
city and an eco-system) ‘where a small shift in one thing can
pro-duce big changes in everything . . . leverage pointsare points
of power’ (Meadows 1999, p.1). Senge(2006, p.64) also refers to
leverage points as the‘right places in a system where small,
well-focusedactions can sometimes produce significant, endur-ing
improvements’. Identification of leveragepoints greatly assists the
devising of systemic inter-ventions (finding systems-based
solutions) that willcontribute to the achievement of goals or
solvingproblems in the system under consideration.
The outcomes are used to develop a refinedsystems model, which
forms at the same time anintegrated master plan (step 5) with
systemicallydefined goals and strategies (systemic interven-tions).
In order to operationalize the master plan,Bayesian belief network
(BBN) modelling (Cainet al., 1999; Smith et al., 2007) is used to
determinethe requirements for implementation of themanagement
strategies; the factors that couldaffect the expected outcomes; and
the order inwhich activities should be carried out to ensurecost
effectiveness and to maximize impact.
The process of developing good policies and in-vestment
decisions is based on the best knowledge(scientific data and
information, experientialknowledge, expert opinions) that is
available atany point in time. The systems model can be usedto test
the possible outcomes of different systemic
Figure 2 Evolutionary Learning Laboratory for managingcomplex
issues
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interventions by observing what will happen tothe system as a
whole when a particular strategyor combination of strategies is
implemented, thatis, before any time or money is invested in
actualimplementation.Of particular value is the ability through
BBN
modelling to also ‘back-cast’. That is, the goal isset at a 100%
probability that it will be achievedand the model back-casts and
points out which ofthe components, actions or conditions have
themost influence on the achievement of the goal.This is a powerful
way of determiningwhere to in-vest time and resources, instead of
having only alist of recommendations, without an understand-ing of
how they are interconnected, which onesare the most important to
invest in and in whatorder the strategies should be implemented to
en-sure an efficient and cost-effective plan of action.Once the
systemic interventions have been
identified and an operational plan has beendeveloped, the next
step for the people who areresponsible for the different areas of
managementis to implement the strategies and/or policies (step6)
that will create the biggest impact. Targets aredetermined, and
monitoring programs are imple-mented to measure and/or observe the
outcomesof the strategies and policies. Inmany cases, it
onlyrequires an adjustment of existing monitoringprograms to comply
with the targets set withinthe ELLab process (e.g. to include
factors to bemeasured that were used in the construction ofthe
Bayesian management model).Because no systems model can ever be
com-
pletely ‘correct’ in a complex and uncertain worldand unintended
consequences always occur, theonly way to manage complexity is by
reflecting(step 7) at regular intervals on the outcomes of
theactions and decisions that have been taken to deter-mine how
successful or unsuccessful the interven-tions are and to identify
unintended consequencesand new barriers that were previously
unforeseen.The iterative process serves as a valuable informal
co-learning experience and leads to new levels ofcapability and
performance. Working in this wayas a coalition is the most
effective way to deal withcomplex issues; because themethodologies
and pro-cesses acknowledge that complex problems
aremulti-dimensional and have to involve all stake-holders, they
require cross-sectoral communication
and collaborative approaches to resolve, and dealwith many
uncertainties that need adaptive man-agement approaches as more
knowledge becomesavailable through the iterative process of
learningby doing.
USING ELLABS TO DEALWITH COMPLEXISSUES IN AVARIETY OF
CONTEXTS
As mentioned earlier, the ELLab approach isgeneric and can be
used in dealing with anycomplex issue, regardless of its context
(e.g.organizational, natural or social systems) or dis-cipline area
under consideration (e.g. business,health, engineering, education
and marketing).In the following sections, four case studies areused
to demonstrate four diverse examples ofthe application and
implementation of the ELLabapproach.
Sustainable Development of a UNESCOBiosphere Reserve in
Vietnam
Biosphere reserves (BRs) are sites recognizedunder the UNESCO
Man and the Biosphere(MAB) program to demonstrate
innovativestate-of-the-art approaches to conservation
andsustainable development. A comprehensivedescription of the
origin and the evolution of theBR concept is presented in a paper
(Ishwaranet al., 2008). There are currently 580 BRs in 114countries
(UNESCO 2012). UNESCO has recom-mended the launch of pilot projects
to use BRsas learning laboratories to address the gap be-tween BR
knowledge systems (scientific, experi-ential and indigenous) and
the imperative forwider sustainable development. In this regard,the
first pilot project, the Cat Ba Biosphere Re-serve (CBBR)
sustainability project in HaiphongCity, Vietnam, has been initiated
(Nguyen et al.,2011). The project focuses on the
interconnected-ness of environment, tourism, livelihood ofpeople
and economic benefits, and the adoptionof policies and processes by
government andmanagement bodies to ensure that long-termsustainable
management will become institutio-nalized and ongoing.
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Identify IssuesTwo workshops were conducted in March andOctober
2007 (Bosch et al., 2007a) with a rangeof stakeholders to gather
their mental modelson the key issues and challenges that Cat
BaIsland is facing. These include waste treatment,pollution, the
high number of floating farms,overuse of underground water, strong
growth intourism, lack of fresh water and electricity(especially in
the summer—tourist season), lackof skilled labour for the tourism
industry,uncontrolled tourism development,
insufficientinfrastructure, lack of access to suitable marketsfor
locally produced products, encroachment onconservation areas, lack
of integrated planning,lack of capacity, environmental degradation
andpoverty.
Build CapacityA 2-month systems thinking and
associatedcapacity-building program was subsequently con-ducted in
Australia (October and November 2008)for a group of 10 policy
makers, managers andtechnical officers from different levels of
govern-ment, across sections of agencies and an NGO,engaged in
different capacities in the managementof the CBBR. The process and
outcomes of thiscapacity-building program have been reported ina
recent paper (Nguyen et al., 2012).
Develop a Systems ModelDuring the capacity-building program,
partici-pants worked with the research team to integratethe various
issues identified in the issueworkshops into a preliminary systems
model.Subsequently, the model (Figure 3) was refinedand validated
by various relevant stakeholders(managers and rangers of Cat Ba
NationalPark, hotel owners, farmers, local people andofficials from
different government depart-ments) in a series of workshops, focus
groupdiscussions and in-depth interviews conductedin Haiphong City
and on Cat Ba Island atthe end of 2008 and early 2009. This
involvementin the evaluation of the model was criticalbecause it
led to taking ownership of the modeland enhanced the ability of
stakeholders to
understand and carry out future interventionstrategies and
actions aimed at improving thesystem for sustainable outcomes.
Figure 3 illustrates the identified interrela-tionships and
interdependencies amongst thekey components of the system. The
systemsmodel represents a ‘big picture’ of the CBBRsystem and
provides a useful platform forlearning, collaboration and decision
makingfor relevant stakeholders including policymakers,
researchers, managers, practitionersand local people.
Identify Leverage Points and Systemic InterventionsA follow-up
workshop was conducted inHaiphong City in May 2009 with the main
objec-tive to identify key leverage points and areasfor systemic
interventions for sustainability—onthe basis of the systems model
of the CBBRand its associated systems archetypes. Systemsarchetypes
‘reveal an incredibly elegantsimplicity underlying the complexity
of manage-ment issues. . . [they allow us] to see more placeswhere
there is leverage in facing difficultchallenges, and to explain
these opportunities toothers’ (Senge 2006, p. 93). Four systems
arche-types were identified in the systems model ofthe CBBR—‘limits
to growth’, ‘fixes that fail’,‘tragedy of the commons’ and
‘shifting theburden’. These archetypes are discussed in detailby
Nguyen and Bosch (2012) and not repeated inthis paper.
The leverage areas require systemic interven-tions that are
deemed critical for the long-termsustainability of the CBBR. Those
identifiedincluded cross-sectoral collaboration; develop-ment and
implementation of government plans;capacity building for decision
makers, managersand local people; waste management and treat-ment;
people’s awareness; conservation ofendangered species; investment
for agriculture;improving the livelihood of commoners; andtourism
development. These leverage areas formthe basis for integrated
projects and policiescovering multiple aspects of the
sustainability ofthe CBBR, including social, economic, culturaland
environmental well-being.
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Develop Action PlansA series of Bayesian models were constructed
todevelop action plans for the identified leverageareas and
systemic interventions. An example ofthese models is illustrated in
Figure 4.The Bayesian model developed in this study
(Phan 2011) is designed as a decision support toolto assist the
management board of the CBBR andCat Ba National Park in developing
feasible man-agement and action plans for the conservationand
protection of the population of an endangeredspecies
(serow—mountain goat) in the CBBR.Short-term and long-term measures
for this
endangered species are needed. In the short term,stronger
engagement of local people, especiallythe potential poachers, to
participate in serowprotection is necessary. Intensifying patrol
activ-ities in prioritized conservation areas is needed
to avoid any further loss of individual animals.Simultaneously,
more stringent law enforcementby authorities and adopting more
severe punish-ment measures for illegal hunting are required.
In the long term, providing opportunities toimprove the
financial position of the poorthrough technical support and
education is oneof the most important and sustainable solutionsto
improve the livelihoods of people on theisland. This would avoid
the increasing impactof local people on the resources of the
NationalPark. Raising the conservation awareness of localresidents
and improving the knowledge andmanagement capacity on biodiversity
conserva-tion and conservation planning of managers ofthe Cat Ba
National Park are vital to ensure aneffective conservation outcome
in the CBBR(Phan 2011).
Number of tourists
Tourism revenue
Hotels andRestaurants
Waste
Tourism pollution
Attraction of CBisland
Use ofunderground water
Availableunderground water
Biodiversity
S
S
S
S
S
O S
S
O
S
S
R_T1
B_T1
Livelihood ofCommoner
Misuse of NRNR conservation
GDP per capita
Agriculturerevenue
Investment inagriculture
Information andcommunication
Access to market
Poverty
Health
PopulationEducated
population
S
S
O
O
S
SS
S
S
S
S
O
OS
R_Env
R_Eco1
REco2
REco3
R_S1
B_T3
Life expectancy
S
S
S
B_T2
Governancestructure
Other incomesources
Social evils/crimeS
O
R_S2NGOs
Infrastructure
S
S
S
R_T2
R_T3
ServicesO
S
B_T4
The system is influenced by
Policies
Student populationSS
Cultural valuesS
S
R_S3
Agriculturepollution
S
BEco
Tourismdevelopment People's
awareness
Immigration
S O
S
Food safety
New construction
O S
O
Other pollution sources
S
O
Figure 3 Systems model of CBBR—a platform for collaboration
(adapted from Nguyen et al., 2011). Legend: S, same direction;O,
opposite direction; R, reinforcing; B, balancing; T, tourism; Eco,
economic; Env, environment; S, social. 1, 2, 3 refer to loop
number; for example, R_T1, reinforcing loop no. 1 of tourism
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ImplementationA series of strategies are currently being
imple-mented to improve the livelihood of the com-moner. A
comprehensive model has also beendeveloped for sustainable tourism
developmentas a mechanism for improving the livelihoods ofpeople on
the island (Mai 2012), whereas modelsfor improvingwastemanagement
and agriculturalmarket access are currently being completed.Several
small projects and actions have also beenundertaken to address the
various leverage pointsand systemic intervention strategies that
had beenidentified from the systems model and its asso-ciated
systems archetypes. These include buildingthe capacity of the
rangers to systemically managethe National Park; conducting a
social welfarestudy relating to community development in theCBBR;
producing an annual Cat Ba EcosystemHealth Report Card;
establishing communitypartnerships in natural resource
managementand environmental protection; and relocating thefloating
farms away from main tourism areas andout of the national marine
protected areas.
ReflectionThe early and consistent involvement of keydecision
makers and stakeholders (nearly 200participants to date) has been
of paramountimportance for the successful formation
andimplementation of an ELLab for sustainabilityin the CBBR. This
involvement will be of signifi-cant importance for the seamless
continuity andsustainability of the project.
Frequent reflection on the successes and failuresof implemented
strategies (systemic interventions)has led to new knowledge and
ideas. For example,to enhance awareness of sustainable practicesand
increasing employment of locals, a CBBRbrand system has been
introduced that is awardedto products (e.g. fish sauce and honey)
and busi-nesses (e.g. tourist boat services, recreation
parks,hotels, guest houses and restaurants) that complieswith a set
of relevant criteria such as businessregistration, water
savingmechanisms, employinglocal people, fire safety standards,
food safetyand hygiene standards. The collaborative learningprocess
has also led to a strong realization that theCBBR management
regulations need revision,especially to improve integrated planning
andactions across different sectors of society.
Policy Design for Child Safety in Japan
In OECD member countries, more than 125 300children died from
injuries from 1991 to 1995,which amounts to 39% of all deaths.
Japan wasranked as a medium-risk performer in deaths bydrowning,
fire, falls and intentional harm,whereasdeaths due to car accidents
were significantlylower than in other countries (UNICEF.
2001).Japanese society often regards parents as the onlypeople
responsible for child safety. Japaneseparents tend to feel isolated
and frustrated,because there is a clear lack of a
coordinatedapproach with other stakeholders in the societyto help
prevent injury to their children (Kakefuda
Distance_village0 to 25122512 to 3576>= 3576
33.132.634.3
2820 ± 1300
Distance_station0 to 12941294 to 1998>= 1998
33.132.634.3
1560 ± 750
Total_forest0 to 4848 to 62>= 62
31.735.932.4
49.7 ± 20
Steepness0 to 2828 to 41>= 41
30.932.037.0
33 ± 15
PresencePresentAbsent
41.558.5
0.415 ± 0.49
Elevation0 to 7979 to 125>= 125
33.132.634.3
97 ± 48
Figure 4 Bayesian model of serow occurrence in the CBBR (adapted
from Phan, 2011)
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et al., 2008). The complexity of this issue warranteda
participatory systems analysis approach to createpossible solutions
by embedding the systemsmodel in an ELLab context in order to
‘experiment’with potential solutions that could lead to
betterpolicies for a safe and secure society.
Identify the IssuesThe mental models of a wide variety of
rele-vant stakeholders about the issue were obtainedfrom a focus
group meeting (conducted inSeptember 2011) to identify and
visualize allfactors related to child injuries.
Build Capacity and Develop a Causal Loop ModelA workshop was
held in September 2011 duringwhich various stakeholders
collaboratively con-structed a causal loop diagram to identify
thecomponents of the system and to explore theinteractions and
relationships between them.The facilitator of the group had
undergone inten-sive training in systems methodologies, whichmade
it possible to structure the mental modelsof the various
participants into a model.
Identify Leverage Points and Systemic InterventionsSpecial
attention was given to the identificationof reinforcing and
balancing loops in order to assistin the identification of possible
leverage pointsfor systemic interventions. This was carried
outthrough visual observation and discussionsbetween participants
on the potential degree ofchange that could be caused by changes to
particu-lar components of the system. Seven systemicintervention
points were identified (in bold,Figure 5): safer product designs,
caring volunteersto support frustrated parents, closer
involvementof social workers, more integrated approach
bygovernment, more paediatricians, shortening ofthe time between an
accident and hospitalization,and better care of students in
schools.
Develop Action PlansThe participating stakeholders used the
sevensystemic intervention points to structure a BBNmodel for
designing policies on child safety(Figure 6). The model was
populated by variousstakeholders who jointly used their
experientialknowledge to decide on the probabilities of howthe
parent nodes would affect the child nodes.For example, what are the
probabilities that morescholarships and better insurance policies
will
Figure 5 Causal loop diagram and identified systemic
interventions points for child safety in Japan
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(a)
(b)
Figure 6 Populated Bayesian model for child safety: (a) current
conditions and (b) indicating the main leverage points andsystemic
interventions that were identified
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lead to an increase in the number of paediatri-cians? How would
designer training and agovernment that could test the designs
changethe probability that the design of products willbe safe? and
What is the probability that therewill be less school accidents if
there are moreschool volunteers and smaller classes?Through this
co-designing process, the stake-
holders recognized that the Bayesian model,populated with
information about the currentconditions, indicated that there is
only a 19.0%probability that the rate of child injuries will
bereduced (Figure 6(a)).A sensitivity analysis of the model
indicated
that the most effective parameter to reduce childinjuries was to
increase the number of volunteernursing councillors (Figure 6(b)).
The model indi-cated that if the number of volunteer
nursingcouncillors is set at 100%, the probability thatless child
injuries will occur will rise to 46.7%.However, also providing
designer training in childsafety, establishing a government board
forproduct evaluation, reducing the size of classes inschools and
having sufficient numbers of paedia-tricians will increase the
probability to have lesschild injuries to about 72%. Therefore,
although apolicy to increase the number of volunteer
nursingcouncillors would make a big difference, theseadditional
four systemic interventions were alsoidentified as important to
significantly reducechild injuries (step 5).
ImplementationA change in the policy to increase
voluntarynursing staff and implementation of the additionalsystemic
interventions have been proposed inorder to experiment how these
interventions willaffect child injuries.
ReflectionThe models have been constructed with the
bestexperiential knowledge available at the time.These models are
therefore embedded in thecyclical process of ‘experimenting’ and
reflectingthrough which new knowledge will be created.Strategies
will be refined in a co-learning
environment to find the best solutions for thiscomplex problem
over time—forming the ELLab.
Enhancing the Reputation of a UniversitySchool in Japan
The Graduate School of Systems Design andManagement (SDM) at
Keio University in Japanwas established in 2008. This school is
rapidlybecoming a focus point in the Asia-Pacific regionfor its
mission to educate students who can solvecomplex and large-scale
problems in any systemranging from social (human dimensions)
tohighly technological issues. The school is build-ing its
foundation on systems and design think-ing and has a strong focus
on industry andcommunity needs, while taking into account thatall
problems are embedded in a complex web inwhich environment,
security and safety, healthand welfare, economics, politics and
culture areall highly interconnected. What makes the
schoolparticularly unusual is the fact that it attracts stu-dents
for masters and PhD programs from all dif-ferent disciplinary
backgrounds (Figure 7), whichcreates a collaborative learning
environment forthe evolution of creative and innovative thinkingand
systems design.
In April 2011, SDM decided to revisit its initialvision and
strategies in order to develop a‘clearer and more committed
operation’ and tobe recognized as a world-class institution in
thearea of systems design. Because of the complexityof this task
and the intention of the school to findlong-lasting solutions,
rather than quick fixes,SDM decided, as part of this process, to
establishthe school as an ELLab.
Identify the Issues and Build CapacityThe first step was to hold
a workshoprepresented by a number of students and staffmembers who
were all trained (step 2) in thedevelopment and interpretation of
systemsmodels. The participants’ mental models onhow they believe
the school can improve its repu-tation, the drivers and barriers in
achievingthis and possible solutions to overcome thebarriers were
collected.
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Develop and Interpret a Bayesian BeliefNetwork ModelIn this
particular case, the mental models wereintegrated by directly
structuring them into aconceptual inference diagram, which formed
thebasis for a BBN model (Figure 8).
The probability tables were populated with theexperiential
knowledge (mental models) of the par-ticipants to form a first
draft model that describedthe main components of the system and how
theyare related to each other (step 3). Testing of
differentscenarios by changing different components of themodel and
combinations of components facilitatedan evaluation of how well the
model reflects thereal situation.With this information, the
probabilitytables were revisited and refined until the
modelprovided a realistic description of the current schooland the
system in which it operates.
Identifying Leverage Points and SystemicIntervention
StrategiesPatterns and relationships were exploredby changing each
of the components in the ‘whatcan we do’ or ‘action’ nodes of the
model indivi-dually to observe how such a change affects theend
goal of SDM to be recognized as a world-renowned school with a
reputation of excellence.
Appointing or consulting a competitive intelli-gence
professional that can provide appropriate
intelligence for different audiences (e.g. industryand potential
students) formore effective promotionand marketing of SDM [the
probability for this tooccur changed from 54% to
90%—comparingFigure 8(a, b)] will have the largest single effect
onachieving the goal to become a world-class school,increasing the
probability from 64% to 72%. Otheroutcomes that will improve the
probabilities forachieving the end goal include an increase in
thenumber of applications (from 58% to 86%) and theprobability that
more high-quality professors willbe attracted to SDM (from 57% to
85%). A furtherimprovement of the relationships that SDM alreadyhas
with industry will have the second largest effecton the goal. This
will lead to the probability toincrease the budget of SDM from 56%
to 80%; forstudents to have access to better research
facilitiesfrom 63% to 90%; and the ability to fund languagetraining
from 52% to 72%.
Implementing both the aforementioned actionswill lead to an
increase in the probability toachieve the end goal from 64% to 76%.
This prob-ability can further be increased to 80% by review-ing the
criteria for entry to SDM. More stringentcriteria will lead to a
higher probability of high-quality students; and if they have good
commu-nication skills (through language training) andwork under the
supervision of high-quality pro-fessors (who are attracted by good
promotion),the probability for high-quality research willincrease
from the current 61% to almost 80%.
Science and Technology, 25
Engineering, 21
Law, 7Environment, 5
Economics, 4
Politics, 3
Literature, 3
Pedogogy, 2
Commerce, 2
Agriculture, 2
Others, 21
Figure 7 Diverse backgrounds of students in the 2012
postgraduate class
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ImplementationIn summary, to achieve SDM’s goal of being
recog-nized as a world-class institution, investmentshould first be
in appointing or consulting a com-petitive intelligence
professional and in furtherenhancing its relationships with
industry. A com-bination of these two actions will have the
biggesteffect on the end goal. Other actions that could
beimplemented, but would not significantly
contribute to achieving the end goal, include theprovision of
language training and more stringentselection criteria to ensure
high-quality studentswith good communication skills.
The school is consulting an expert in the area ofcompetitive
intelligence (one of its staff members)to develop effective
marketing and promotionmaterial and mechanisms for different types
ofaudiences (e.g. large companies, potential students
(a)
(b)
Figure 8 First draft BBN model to enhance the reputation of the
school: current situation (a) and with systemic
interventionsimplemented (b)
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and government departments). Stronger collabor-ation with
industry is being established throughthe selection of real issues
in different companiesand government agencies for student
assignmentsand masters projects (e.g. Toshiba, NEC and Yoko-hama
City).
ReflectionThe effects of changes in those parameters ofthe model
that were identified during model de-velopment as being affected by
the actions under-taken are being monitored (e.g. increase in
theschool budget, language competency, number ofstudent enrolments
and availability of quality re-search facilities in industry). The
outcomes of thesewill be used to refine the first draft
model—start-ing the cyclic process of experimenting and adapt-ing
of the SDM ELLab.
Managing Tree Density in the Rangelands ofNorthern Queensland,
Australia
Much of Australia’s grazing land is composed ofwoodland. Trees
and native pastures coexist inthese ecosystems, where they compete
for water,nutrients and sunlight. However, there is also amutually
beneficial relationship between treesand pastures, provided that
the balance is right.When a favourable tree–grass balance
exists,trees provide shade and shelter for livestockand support
biodiversity. They also carry outkey ecosystem functions, such as
water and soilnutrient cycling, and contribute to healthy
landcondition by preventing erosion and salinity,storing carbon and
enhancing soil condition(Liedloff and Smith 2010).
There is an increasing recognition of the rolethat trees play in
grazing systems, which hasled to a demand for sustainable
woodlandmanagement. Of particular importance is theman-agement of
tree cover thickening in the tropicalsavannas, which has the
potential to change catch-ment hydrology (Krull et al., 2007),
carbon stocks(Burrows et al., 2002; Henry et al., 2002),
pasturebiomass available for grazing animals (van Lange-velde et
al., 2003) and wildlife habitat (Tassickeret al., 2006). Tree
thickening is therefore an
important issue to many stakeholders, includingpastoralists,
conservationists, land managers andthose interested in carbon
markets, each with awide range of opinions and vested interests in
theprocess (Bosch et al., 2007b).
The demand for better management of the com-plex interactions
between different factors andcomponents of the tree thickening
system has ledto the establishment of an ELLab for
sustainablewoodland management.
Identify IssuesSeveral workshops were held during 2005
indifferent localities in the rangelands of NorthernQueensland.
Graziers, researchers and extensionofficers discussed the tree
thickening problemand identified the factors that they
believedwould influence tree density. Possible manage-ment actions
and non-manageable factors thatmight influence density were also
identified anddiscussed.
Build Capacity and Develop a ModelThe knowledge of the workshop
participants wascaptured by mapping out an influence diagram.The
process allowed for the integration of thedifferent mental models
of the stakeholders(varying perspectives and divergent views).
Whiledivergent views occur, the appreciation of eachother’s views
gained through ‘mapping the sys-tem’ helped stakeholders to develop
a commonunderstanding of the management system.
The influence diagram (Figure 9) provided astructure through
which stakeholders could ex-press and discuss their understanding
of the causeand effect relationships between managementactions,
controlling factors and resource manage-ment outcomes or goals. The
diagram also assistedthe stakeholders in identifying how their
know-ledge contributed to a better understanding ofthe overall
management system and to appreciatehow other stakeholders
understand the links be-tween management actions and
outcomes(providing a mechanism for externalizing and in-ternalizing
knowledge). This co-learning process(capacity building) consists of
individual stake-holders who are socializing and externalizing
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their nowledge within a group, combiningthis knowledge, and
learning from each other(internalization) (Nonaka and Konno
1998).The influence diagram was used as a
framework for the development of a BBN model(Figure 10) through
which it was possible tointegrate experiential knowledge,
scientific dataand models to populate the BBN model. Thisprocesses
ensured that the knowledge createdby scientists became integrated
with the under-standing of systems by land managers,
conserva-tionists and other stakeholders.Figure 10 shows a
completed BBN systems
model for tree density management. Each nodehas two or more
states, and arrows represent thecausal relationships between nodes.
Conditionalprobability tables (CPTs) specify the
relationshipsbetween the nodes. Bosch et al. (2007b, Table
1)described the CPT in an example of how fuelbuild-up and fire
season influence fire intensity.The first row represents the
scenario wherefuel build up is high (>1800 kg/ha) and the
fireseason (time of fire) is ‘late_dry’ (October/November). ‘Under
this scenario there is a100% chance that fire intensity will be
hot.By completing the probability table for eachnode in the BBN,
available data, informationand experiential knowledge are
integrated in asystematic way. The result is a knowledgebase and a
dynamic systems model that can
assist stakeholders (particularly managers) indecision-making
through analysing differentscenarios’ (Bosch et al., 2007b, p.
220).
Identify Leverage Points and Systemic InterventionsAn evaluation
of the model and identification ofleverage points and systemic
interventions that willaffect the goal (avoid thickening of tree
density)was done by testing model behavior with stake-holders
through applying different managementscenarios and predicting the
possible outcomes.Back-casting was also used to identify
whichactions and factors would have the largest effecton the goal,
providing or confirming the systemicinterventions identified during
scenario analysis.
The incidence of fire and the factors that deter-mine the nature
of fires were identified as themost important leverage point for
controllingthickening of trees. This conclusion was verifiedby
scientific data and models (Liedloff andSmith 2010) and
experiential knowledge of landmanagers. It was mentioned that where
fire hasbeen a regular feature within the landscape, theremoval of
fire will often lead to woodlandthickening. Grazed woodland
ecosystems evolvedwith fire, which suppresses tree
thickening.Without a disturbance such as fire, many landtypes will
have a higher tree density.
Figure 9 Influence diagram of issues related to managing tree
density (adapted from Bosch et al., 2007b)
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Develop Management Plan and ImplementationFrom the BBN model, it
was clear that themost economic and environmentally sustainableway
to control tree cover thickening is with fire,provided conditions
such as fuel load are satisfac-tory. The BBN model served as a tool
to identifypossible management scenarios before
actualimplementation.
ReflectionThe approach of stakeholder involvement andsystems
thinking described earlier led to a modelthat represents the mutual
understanding ofstakeholders and their current knowledge basefor
decision making. However, this knowledgebase is rarely perfect
because natural systems arecomplex, and their management takes
placeagainst a background of continuous and
unpredictable change in environmental, economicand social
conditions. Because of this, theuncertainties in achieving the
desired resourcemanagement outcomes remain high. However,new
knowledge about management systemsbehavior is continuously
generated throughobservation (monitoring) and the evaluation
ofoutcomes of implemented management strategies.Embedding the BBN
model in the cyclic processof the ELLab allowed for continuous
improvementof the knowledge base, and its usefulness formanaging
natural resources under uncertain andvariable conditions.
Reflecting on management outcomes empha-sized the importance of
fire as a management tool.It became clear that tree density and
structure areconstantly changing because of climatic variationand
the use of fire. In many regions, a thickeningof trees occurred
during higher rainfall periods
Figure 10 BBN model for tree density containing alternative
scenario (adapted from Bosch et al., 2007b)
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and thinning during drought. Where fire has beena regular
feature within the landscape, its removaloften led to the
thickening of tree cover. Inextensive systems where thickening has
occurred,mustering costs have increased by up to 30% andproduction
has suffered as pastures compete forscarce resources. It has become
clear that the mosteconomic and environmentally sustainable way
tocontrol woody thickening is with fire, providedconditions such as
fuel load are satisfactory.This finding during the reflection stage
has led tothe development of a more detailed model thatfocuses on
the influence of management and non-management drivers on woody
vegetation change(Liedloff and Smith 2010).
THE GLOBAL EVOLUTIONARY LEARNINGLABORATORY
Once an ELLab has been established in eachparticular region or
country, it will operate as amanagement tool for the reform and
sustainablemanagement of complex issues in their respective
systems. As described in the above case studies,management
strategies and policies are imple-mented and the ELLab runs
‘Reflection’meetings(step 7) to discuss the outcomes (successes
andfailures) and decide how to change the manage-ment or how to
adapt a policy. These reflectionmeetings will lead to new levels of
learning andenhanced management performance in the differ-ent
sectors of the system as a whole.
Each individual ELLab will also become part ofthe Global
Evolutionary Learning Laboratory(GELL) (Figure 11) and continually
share the les-sons it has learned with ELLabs (and other
similarinnovations) in other parts of the world, throughthe lenses
of different political systems, culturesand so on. GELL is
currently being enhancedwith advanced e-technologies that will help
it toserve as a platform for continuous sharing andco-learning,
leading to new levels of learning andperformance at regional and
global levels. It willalso help individual ELLabs to learnmore and
per-form better in their own countries, organizations,businesses
and communities.
Evolutionary Learning Laboratories for e.g.(ELLabs)
4. Identify Leverage/Systemic Interventions
3. Develop OrRefine SystemsMaps Or Models
1. IdentifyIssues
7. REFLECTION
Environmental
Eco
nom
ic Social
New Levels of Learning and Performance at LOCAL Level
5. Develop OrAdaptIntegratedStrategic &Action Plan
2. Build Capacity
6. ImplementActionsStrategies
& Policies
StakeholderMental Models
SystemsStructure
Patterns &Relationships
Cultural Values
Global Evolutionary Learning Labor(GELL)
Creating a collaborative intercultural learning
environment
GELL
New Levels of Learning and Performance at GLOBAL Level
- Sustainable Management of Cat BaBiosphere, Vietnam
- Managing Poverty Reduction
- Transforming the Graduate School forSystems Design and
Management, KeioUniversity ,Japan
- Integrated Systemic Governance,Hai PhongCity, Vietnam
- Developing Intercultural Tools forSystems Transformations,
CentralAustralia and South East China
- Managing Balance between ProductionEconomics &
Biodiversity Goals, China
- Improving Organisational performance
- Improving Child Safety, Japan
- Managing human relationships inorganisations
- Systems Education
SHARINGREFLECTIONS
Figure 11 The Global Evolutionary Learning Laboratory (GELL)
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CONCLUSION
Globally effective researchers, as well as existingand future
leaders and managers, will need tounderstand complexity and how to
deal with itin multi-stakeholder scenarios. Systems thinkingis
therefore the underlying paradigm and researchapproach. This paper
has described the applicationof systems thinking in the
establishment of ELLabsfor managing complex issues through
enhancingcross-sectoral communication and collaboration,and
promoting effective change. Each ELLabdevelops uniquely because of
the political andcultural systems of each country, organization
orbusiness. The GELL can greatly enhance ourcapacity to address
globalized issues and servesas a global knowledge hub.
The establishment of ELLabs and the GELLis an ongoing process.
The research so farhas achieved various active engagements
atspecific levels, local and global, including localcommunities,
national park staff, local andnational governments, the national
MAB com-mittees in different countries and the UNESCOMAB program.
UNESCO/MAB has alreadyacknowledged this approach as best practice
forpotential applications to more than 580 BRsglobally (Nguyen et
al., 2011).
The research has helped to build the capacityof various people
(relevant stakeholders) indifferent places where ELLabs are being
estab-lished. The stakeholders are closely involved inall the
different steps of the establishment of theirrespective ELLabs.
This close involvement hasenabled a shared vision amongst
stakeholdersand helped them to understand complexity andbe able to
identify the root causes of problems,rather than merely treating
the symptoms. It hasalso helped them to develop solutions
collabora-tively over time, experimentwith themand be ableto adapt
when required through knowledgesharing and discussions with others.
In addition,the close involvement has enabled the
relevantstakeholders to take ownership of the ELLab andto know how
to operate it.
Having a ‘champion’ is another importantlesson learned through
the research. The authorshave been fortunate to work with a
champion(a key person in a leading position, who
understands and supports the approach) in everysite where an
ELLab has been established. This isessential for the successful
implementation andoperation of the ELLab.
The key challenge in this research is securingfunding to address
the identified leverage pointsand systemic interventions. It is
common fordonors and funding agencies to provide fundingfor
treating the ‘symptoms’ with quick fixes, inorder to see (and show
to the world) immediateresults from their funding efforts. However,
it couldtake several years for a systems-based approach toachieve
long-lasting sustainable outcomes by solv-ing the root causes of
problems. Finding the fundsfor a process with often non-tangible
outcomes (asapposed to tangible outcomes such as a bridge, aschool
or a road) has proved to be a majorchallenge, especially for
developing countries.
A further important challenge is the ‘silo’structure of
ministries and organizations in everycountry, which makes
‘collaboration’ a foreignconcept. A paradigm shift is needed to
moveaway from this kind of structure. Furtherresearch to
institutionalize the ELLab concept,leading to the use of collective
intelligence indecision making across sectors and organizationsand
effective collaborative governance, hasbecome a high priority.
Computer-based modelling systems can beuseful tools to explore
and make managementaction decisions that are more systemic than
thedecisions produced by traditional approaches.Of particular
importance is their ability to beused within a participatory
process, to enableknowledge capturing, testing and refinementof
multi-stakeholders. Used in this way, acomputer-based modelling
system (such as aBBN) can (i) provide a flexible modelling
envir-onment, (ii) allow uncertainty in knowledge tobe expressed
using probabilistic relationships,(iii) allow biophysical, economic
and social vari-ables (either quantitative or qualitative) to
berelated, (iv) enable a graphical (flow chart) inter-face that is
easily understood and facilitatescommunication between stakeholders
and (v) beeasily updated as new knowledge emerges, with-out the
need for specialist computer skills (i.e.nodes added or removed,
links changed andprobabilities updated).
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In summary, a new way of thinking can changethe effectiveness of
government departments,businesses, organizations and communities
inmany ways:
• better mutual understanding of the diversemental models of
different stakeholders;
• moving away from traditional linear thinkingthat leads to
quick fixes and treatment of thesymptoms, to long-lasting systemic
solutionsthat address the root causes;
• ability to collaboratively identify leveragepoints and
systemic interventions to underpinsystems-based master and
strategic plans;
• deep understanding of the interconnectednessbetween possible
actions in order to develop effi-cient and cost-effective
management strategies;
• working knowledge of cutting edge systemstools to test the
outcomes of strategies, includingidentification of unintended
consequences—be-fore actual implementation;
• ability to use back-casting to identify thosefactors that will
have the most influence onthe achievement of goals (knowing
whereand when to invest in the system); and
• using the ELLab as an ongoing process forcontinuous
co-learning and refinement of man-agement strategies.
REFERENCES
Allison HE, Hobbs RJ. 2006. Science and Policy inNatural
Resource Management: Understanding SystemComplexity. Cambridge
University Press: UK.
Banathy BH. 1996. Designing Social Systems in aChanging World
(Contemporary Systems Thinking).Springer: New York, USA.
Bosch O, Maani K, Smith C. 2007a. Systems thinking—language of
complexity for scientists and managers.Proceedings of the
Conference on Improving theTriple Bottom Line Returns from
Small-scale Forestry.S. Harrison, A. Bosch and J. Herbohn. 18–21
June, 2007,Ormoc, the Philippines.
Bosch OJH, King CA, Herbohn JL, Russell IW, Smith CS.2007b.
Getting the big picture in natural resourceman-agement—systems
thinking as ‘method’ for scientists,policy makers and other
stakeholders. Systems Re-search and Behavioral Science 24(2):
217–232.
Bosch OJH, Nguyen NC. 2011. Establishing the GlobalLearning
Laboratories NET for managing complex
problems (Working Paper). Brisbane, Australia, Schoolof
Integrative Systems, The University of Queensland.
Burrows WH, Henry BK, Back PV, et al. 2002. Growthand carbon
stock change in eucalypt woodlands innortheast Australia:
ecological and greenhouse sinkimplications. Glob. Change Biol. 8:
769–784.
Cabrera D, Colosi L, Lobdell C. 2008. Systems think-ing.
Evaluation and Program Planning 31(3): 299–310.
Cain J, Batchelor C, Waughray D. 1999. Belief networks:a
framework for the participatory development of nat-ural resource
management strategies. Environment,Development and Sustainability
1: 123–133.
Elias AA. 2008. Towards a shared systems model ofstakeholders in
environmental conflict. InternationalTransactions in Operational
Research 15(2): 239–253.
Galanakis K. 2006. Innovation process.Make sense usingsystems
thinking. Technovation 26(11): 1222–1232.
Henry BK, Danaher T, McKeon GM, BurrowsWH. 2002.A review of the
potential role of greenhouse gasabatement in native vegetation
management inQueensland’s rangelands. Rangeland J. 24(1):
112–132.
Hung W. 2008. Enhancing systems-thinking skillswith modelling.
British Journal of Educational Technol-ogy 39(6): 1099–1120.
Ishwaran N, Persic A, Tri NH. 2008. Concept and prac-tice: the
case ofUNESCObiosphere reserves. Int. J. En-vironment and
Sustainable Development 7(2): 118–131.
Jackson MC. 2003. Systems Thinking: Creative Holism forManagers.
John Wiley & Sons: Chichester, UK.
Kakefuda I, Yamanaka T, Stallones L, Motomura Y,Nishida Y. 2008.
Child restraint seat use behaviorand attitude among Japanese
mothers. Accident Ana-lysis and Prevention 40: 1234–1243.
Keegan M, Nguyen NC. 2011. Systems thinking, ruraldevelopment
and food security: key leverage pointsfor Australia’s regional
development and popula-tion policy. Migration Australia (launch
issue) 1(1):50–64.
Krull E, Bray S, Harms B, Baxter N, Bol R, Farquher G.2007.
Development of a stable isotope index to as-sess decadal-scale
vegetation change and applica-tion to woodlands of the Burdekin
catchment,Australia. Glob. Change Biol. 13: 1455–1468.
Land T, Hauck V, Baser H. 2009. Capacity changeand performance:
capacity development: betweenplanned interventions and emergent
processesImplications FOR development cooperation (PolicyManagement
Brief No. 22). Maastricht, ECDPM.
Laszlo KC. 2001. Learning, design, and action: creatingthe
conditions for Evolutionary Learning Commu-nity. Systems Research
and Behavioral Science 18(5):379–391.
Lee, A. 2009. Health-promoting schools: evidence for aholistic
approach to promoting health and improv-ing health literacy.
Applied Health Economics andHealth Policy 7(1): 11–17.
Liedloff AC, Smith CS. 2010. Predicting a ‘tree change’in
Australia’s tropical savannas: combining different
Syst. Res RESEARCH PAPER
Copyright © 2013 John Wiley & Sons, Ltd. Syst. Res
(2013)DOI: 10.1002/sres.2171
Managing Complex Issues through ELLabs
-
types of models to understand complex ecosystembehaviour.
Ecological Modelling 221: 2565–2575.
Maani K, Maharraj V. 2004. Links between systemsthinking and
complex decision-making. System Dy-namics Review 20(1): 21–48.
Maani KE, Cavana RY. 2007. Systems Thinking, SystemDynamics:
Managing Change and Complexity. PrenticeHall: Auckland, NZ.
Mai TV. 2012. Sustainable tourism—systems thinkingand system
dynamics approaches: a case study inCat Ba Biosphere Reserve of
Vietnam. School ofAgriculture and Food Sciences. The University
ofQueensland: Australia. PhD Thesis.
Meadows D. 1999. Leverage points: Place to intervenein a System.
Hartland, VT, USA, The SustainabilityInstitute.
Midgley G, Ed. (2003). Systems Thinking (Volumes 1–4).Sage:
London, UK.
Mingers JC. 2006. Realising Systems Thinking: Know-ledge and
Action in Management Science. Springer:New York, USA.
Newell D. 2003. Concepts in the study of complexityand their
possible relation to chiropractic healthcare: a scientific
rationale for a holistic approach.Clinical Chiropractic 6(1):
15–33.
Nguyen NC, Bosch OJH 2012. A systems thinking ap-proach to
identify leverage points for sustainability:a case study in the Cat
Ba Biosphere Reserve,Vietnam. Systems Research and Behavioral
Science InPress (DOI: 10.1002/sres.2145; first published online11
October 2012).
Nguyen NC, Bosch OJH, Maani KE. 2011. Creating‘learning
laboratories’ for sustainable developmentin biospheres: a systems
thinking approach. SystemsResearch and Behavioral Science 28(1):
51–62.
Nguyen NC, Graham D, Ross H, Maani K, Bosch OJH.2012. Educating
systems thinking for sustainability:experience with a developing
country. Systems Re-search and Behavioral Science 39(1): 14–29.
Nonaka I, Konno N. 1998. The concept of ‘Ba’: buildinga
foundation for knowledge creation. CaliforniaManagement Review
40(3): 40–54.
Phan TD. 2011. Optimizing conservation effort forserow,
Capricornis milneedwardsii, in Cat Ba Archi-pelago, Hai Phong,
Vietnam. School of Geography,Planning and Environmental Management,
TheUniversity of Queensland. Master of EnvironmentalManagement.
Quatro SA, Waldman DA, Galvin BM. 2007. Develop-ing holistic
leaders: Four domains for leadershipdevelopment and practice. Human
Resource Manage-ment Review 17(4): 427–441.
Sawin B, Hamilton H, Jones A. 2003. Commodity SystemChallenges:
Moving Sustainability into the Mainstreamof Natural Resource
Economies. Sustainability Institute:Hartland, USA.
Senge PM. 2006. The Fifth Discipline: The Art andPractice of the
Learning Organization (Revised andUpdated). Random House, Inc: New
York, USA.
Smith C, Felderhof L, Bosch OJH. 2007. Adaptivemanagement:
making it happen through participatorysystems analysis. Systems
Research and Behavioral Sci-ence 24(1): 567–587.
Sterman JD. 2000. Business Dynamics: Systems Thinkingand
Modeling for a Complex World. Irwin McGraw-Hill:Boston, USA.
Systems V. 2011. Vensim program, Ventana SystemsUK. from
http://www.ventanasystems.co.uk/.
Tassicker AL, Kutt AS, Vanderduys E, Mangru S. 2006.The effects
of vegetation structure on the birds in atropical savannawoodland
in north-easternAustralia.Rangeland J. 28(2): 139–152.
Thomas E, Amadei B. 2010. Accounting for human be-havior, local
conditions and organizational constraintsin humanitarian
development models. Environment,Development and Sustainability
12(3): 313–327.
Umaña A. 2002. Generating Capacity for SustainableDevelopment:
Lessons and Challenges. UNDP,UNDP Choices Magazine.
UNESCO. 2012. Biosphere Reserves—Learning Sites forSustainable
Development. Retrieved 30th March 2012,from
http://www.unesco.org/new/en/natural-sciences/environment/ecologicalsciences/biosphere-reserves/.
UNICEF. 2001. A League Table of ChildDeaths by Injuryin Rich
Nations. Innocenti Report Card, Issue No.2.
van Langevelde, F, van de Vijver CADM, Kumar L,et al. 2003.
Effects of fire and herbivory on the stabil-ity of savanna
ecosystems. Ecology 84(2): 337–350.
Walker GH, Stanton NA, Jenkins DP, Salmon PM. 2009.From
telephones to iPhones: applying systemsthinking to networked,
interoperable products. Ap-plied Ergonomics 40(2): 206–215.
Wilson J. 2004. Changing Agriculture: An Introductionto Systems
Thinking. QLD, Australia, Print on De-mand Centre, University of
Queensland Bookshop.
RESEARCH PAPER Syst. Res
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(2013)DOI: 10.1002/sres.2171
Ockie J. H. Bosch et al.
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