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Complexity theory and public management: a ‘becoming’ field

Since the special edition of Public Management Review on ‘Complexity Theory and Public Management’

in 2008 (Volume 10 (3)), co-edited by Geert Teisman and Erik-Hans Klijn, academic interest in complexity

theory, and how it might be used to understand the world and inform design and intervention in the

public policy/public management field, has grown and matured. The inspiration for this special issue

arose out of intensive interactions among interested scholars in conference panels (at American Society

for Public Administration, International Research Society for Public Management, and the Challenges of

Making Public Administration and Complexity Theory group) over the past few years and the realization

that a ‘stock-taking’ was required. While many public management scholars knew a little bit about

complexity – and some knew a lot – there was still no consensus about the contribution complexity

theory could or could not make to theory and practice. While we did not achieve consensus this time

around, the papers selected for this edition provide a picture of where we are and where scholars in this

field think we should go, and some examples of the most promising routes to get there. Before

summarizing these findings, we provide a brief overview of where we have come from and why we are

still a ‘becoming’ field.

Challenging fundamental assumptions

Nineteenth- and twentieth-century sciences which developed beneath the umbra of Newtonian

theories, embedded some pervasive assumptions which might be crudely summarized as (1)

relationships between individual components of any system can be understood by isolating the

interacting parts, (2) there is a predictability to the relationship among the parts, and (3) the result of

interactions and the working whole might eventually be understood by simply summing the parts. So in

much the same way as the expert clockmaker might be able to design, build, disassemble, and modify a

clock, understanding the individual parts and how they fit together leads to understanding the

functioning whole and the capability to replicate it precisely as required. This paradigm is dominated by

mechanical metaphors and leads to an assumption that the sum of the parts equals the whole.

Dissatisfaction with the limitations of mechanical explanations led to more sophisticated models which

were better at explaining the observed behaviour, initially of the physical world, and then increasingly

the biological, ecological, and social worlds (e.g. Byrne 1998; Cilliers 1998; Holland 1995; Kauffman

1993; Prigogine 1978; Prigogine and Stengers 1984; Stacey 1993; Waldrop 1992). Such modelling offered

new ontological insights about the nature of our world and the way it behaves. This is summed up

briefly by saying that there are recursive, ongoing non-linear interactions between the elements that

make up the whole and these elements adapt to each other in non-linear ways. Their interactions create

contingency and uncertainty about what the future will become. As a result, the whole lacks the

predictability of the machine model. Boulton (2010) refers to a complex world view as ‘becoming’

because individual components in these worlds are interdependent and in processes of ongoing

interaction with each other with the result that the world is not static and fixed, but dynamic, ever-

changing, and becoming something different from what it was in the past. Recognition of such inherent

uncertainty leads to a conclusion that Newtonian-like mechanical models are inadequate for these types

of systems because the sum of the parts does not equal the whole. Understanding of the whole cannot

be based only on an understanding of the disaggregated parts because of the ongoing non-linear change

caused by the interactions between the parts. This shift in understanding brings us to a complexity

world view: ‘sandwiched between a view that the world works like a machine and a view that the world

is chaotic, unpredictable and without structure’ (Boulton, Allen, and Bowman 2015, 29).

In this complexity-informed world view, ongoing non-linear interactions result in macro patterns

becoming established. Complexity theory explains the way many, repeated non-linear interactions

among elements within a whole result in macro forms and patterns which emerge without design or

direction. Further, an initial pattern might be disrupted by external events or internal processes and

reform into some new pattern. Boulton and colleagues sum up what they call the ‘central tenet of

complexity theory’ and its contribution to understanding change as ‘the detail and the variation’ of each

action – the effect of a regulation on various actors for example – ‘coupled with the interconnection’ of

action and environment that ‘provide the fuel for innovation, evolution and learning’ (Boulton, Allen,

and Bowman 2015, 29). That is, the future is a contingent, emergent, systemic, and potentially path-

dependent product of reflexive non-linear interactions between existing patterns and events. Its variety,

diversity, variation, and fluctuations can give rise to resilience and adaptability; is path dependent,

contingent on local context and on the sequence of what happens; subject to episodic changes that can

tip into new regimes; has more than one future; can self-organize, self-regulate; and have new features

emerge.

Introducing a complexity frame to public management

As an alternative to Newtonian mechanics, this last observation about the contribution of complexity

theory for understanding unpredictability and change in human systems leads us to its relevance for the

study of public policy and public management. Scholars and practitioners of public policy and public

management are concerned with how to create or change particular patterns of interaction between

actors to get a particular result: for example, how might governments design a set of institutions to

bring about certain behaviours; or given a set of institutions, how might the interactions between actors

and the institutions be governed to achieve a particular outcome; and how might unintended negative

effects be avoided or positive ones enhanced? Furthermore, complexity theory facilitates a focus on

multiple levels of scale simultaneously. Thus the individual actors, and multiple layers of institutions of

varying complexity which interact, can all be brought into view through the multi-scalar complexity lens.

We note, within the diverse scientific traditions of public policy and public management theories,

attempts to explain dynamism and non-linear contingency in how change takes place have become an

increasingly pertinent concern (Eppel 2017). In the last 20 years – and rising sharply from around 2008

(Gerrits and Marks 2015) – we see increasingly explicit use of complexity theory concepts for explaining

the way the public policy/management worlds behave and how we might better design and manage

change in these worlds. David Byrne has also deepened our understanding of the methodological

implications of complexity for the social sciences generally (Byrne, 1011, Byrne and Callaghan 2014).

Scholars such as Sanderson (2009), Room (2011), and Morcol (2012) have all argued for complexity

theory for understanding of how the social world of policy processes work. Cairney (2012, 2013; Cairney

and Geyer, 2017) caution us that the looseness with which complexity concepts are sometimes applied

could be an impediment but they also see a place for complexity theory as a bridge between academic

and policymaker perspectives in support of pragmatism and insights about how to influence emergent

behaviour. Sanderson (2009) advocates that the ambiguity and uncertainty arising from a complex

adaptive world can be mitigated through the use of an epistemology based on pragmatism and

complexity theory. Room (2011) suggests a blending of extant theories such as institutionalism with

complexity theory for better understanding the micro/macro dynamics of public policy. He suggests that

there is a complementarity in which complexity theory supplies the micro mechanisms lacking in

institutional theory and institutional theory supplies a macro framing specific to public policy which

complexity theory lacks. Morcol (2012) argues further that complexity theory provides mechanisms and

concepts for understanding the macro/micro problems at the heart of public policy process. That is,

complexity theory provides a micro mechanism for explaining the macro patterns of interest to public

policy scholars. Growing interest in complexity and policy is evidenced in the establishment of a new

Journal on Policy and Complex Systems in 2014.

In a parallel and consistent vein, Teisman and colleagues in the Netherlands (Teisman, van Buuren, and

Gerrits 2009), Rhodes and colleagues in Ireland (Rhodes et al. 2011), Koliba and colleagues in the United

States (Koliba, Meek, and Zia 2011), and Eppel and colleagues in New Zealand (Eppel, Turner, and Wolf

2011) have each employed complexity theory concepts to better understand the core processes of

public management such as agenda setting, policy formation, decision-making, and implementation.

These authors have more or less independently come to the conclusion that complexity theory and

network theory are required and should be linked together to provide an adequate basis on which to

develop governance theory and practice guidelines in modern public management contexts. The extent

of complementarity between complexity theory and network governance (Klijn and Koppenjan 2014;

Koppenjan and Klijn 2014) and new public management theories is reflected in the establishment of the

journal Complexity, Governance and Networks in 2014.

Others have taken aim at how public sector change might be better managed generally by enlisting

complexity thinking and concepts to inform processes of designing and generating change (Boulton,

Allen, and Bowman 2015; Geyer and Rihani 2010; Innes and Booher 2010). These authors identify

common themes such as the impossibility of prediction and therefore the need to adopt more

experimental approaches to intervention based on the assumption that there will be new phenomena

(unknown unknowns) likely to emerge endogenously. What has occurred previously will continue to

affect the present (and the future). As a result, any externally applied change will have uncertain effects,

some of which will lead to a helpful change and some not so. Doing public policy and public

management in such a world requires cognisance of the above characteristics – and particularly the

dynamics of self-organization, path-dependency, adaptation, and emergence – in how we approach

policy and change (Rhodes et al. 2011). We also need complexity’s lens to see the whole while taking

into account the relationships between the elements at different levels of scale. Koliba and Zia (2012)

talk about the need for complexity friendly methods for modelling the complex governance system.

Innes and Booher (2010) built their theory of collaborative rationality for public policy on analysis of the

ongoing dialectic interaction between collaboration and praxis as a means for understanding complex

change. Cairney and Geyer (2015) have made a substantial contribution to thinking about the

contribution of complexity theory to policy studies and how it might add to understanding of particular

policy fields, such as health (Tenbensel 2013) or concepts such as power (Room, 2015) as well as

complexity friendly methods for research and practice.

Overview of papers in this edition

This plethora of contributions and theoretical explorations cries out for framing and assessment to help

guide scholars engaging with complexity in the public management/policy domains. To that end, our call

for contributions asked authors to consider how complexity contributes to public management theory

and practice using one (or more) of three lenses: (1) complexity theory-informed alternative

perspectives on the framing of problems and design of processes of public administration to be

considered, (2) insights into alternative institutions that are shaping public administration and

management processes, and (3) alternative practices to match the complexity of the environment and

the challenges faced by public management scholars administrators.

Furthermore, we note the need for a distinction to be made between the use of complexity theory to

create and test concepts and theories to describe the world as it is (which is often the domain of the

natural sciences), and the use of these concepts and theories to design and bring about change (this

latter often the domain of social sciences). While these perspectives inform each other, they often rely

on different ontological and epistemological foundations, and this is apparent in the papers in this

special edition where we see both describe and design features in the way authors have used

complexity theory.

Alternative perspectives

Alternative perspectives provided by complexity theory have evolved markedly in the intervening years

between this issue and the last special issue of PMR addressing complexity. We have already mentioned

the application of complexity concepts to understanding multi-actor decision-making and institutional

change for instance. The authors in this issue further explore models which attempt to incorporate the

specific use of complexity concepts such as feedback loops, adaptation, attractors, and emergence to

reframe understanding of common phenomena experienced in public administration such as policy

processes, implementation, natural resources management, and public-sector reform.

In all of the papers in this issue, there is the explicit recognition that a complexity perspective entails the

rejection of assumptions of predictability and control in public management, and the adoption of

assumptions of multiple, interacting self-organizing entities that learn and change over time. While

there are periods of stable behaviour and features of the system that function as constraints on

elements of the system, the diversity and adaptation of entities creates the possibility for both

evolutionary and unpredictable, sudden change.

An example of two inter-country independent decision-making processes that became coupled over

time is used by Marks and Gerrits to illustrate the contribution of game theoretic models to

understanding complex public administration processes. Their game theory model is tested through an

experiment aimed at explaining how representatives of the two governments involved who met each

other in two presumed independent decision-making arenas took the history of their interactions from

one to the other, thereby influencing the overall outcome. Thus they demonstrate the interdependency

and connectedness between systems that otherwise might be assumed independent. Further, the

authors provide a testable formalized model that describes the interaction and co-evolution of

independent agents over time for future scholars to build upon.

Haynes makes use of complexity theory to focus on multiple levels of public administration systems. He

extends the conceptualization of the public administration complex system to include the behaviour

disposition of the individual in relation to their public and personal values, to conclude that the multi-

level capacity in complexity theory is, in part, bounded by public service values. Further, he uses the

complexity concept of attractors to explain how public service values at different levels (individual,

family/community, professional, and political) can play a role in constraining (or indeed enabling) system

change over time. Both Haynes and Marks & Gerrits extend the understanding of complex adaptive

systems (CAS) theory and public management by taking their analysis of participating actors below the

level of description of the organization and the institutions. They consider the largely unconscious

psychological dispositions of individual actors and their history with other actors and its influence on

patterns of institutional and organizational decision-making which are relevant to the design.

Rather than develop new models, Rhodes and Dowling assess to what extent fitness landscape models

(Wright 1932; Kaufmann and Levin 1987) have been used effectively by public management scholars to

date through a systematic review. Fitness landscapes are evolutionary models that capture how the

behaviour and characteristics of independent agents operating in a shared context result in individual

and system-wide outcomes. The authors remark on their frequent use at the level of metaphor and the

limited attention paid to mapping the concepts of the model to the features of the empirical

phenomenon being described. This conclusion might easily be applied to a number of other complexity

concepts (Cairney and Geyer 2017), which, after several decades of scholarly effort, raises concerns

about the translation of these concepts into the public management domain. Nevertheless, Rhodes and

Dowling conclude that in combination with network theory, fitness landscape models are ‘more aligned

with the actual features of complex governance systems than game theory models which rely on highly

stylized assumptions about how agents behave and equally fuzzy definitions of performance’ (Rhodes

and Dowling, this issue). We return to these ‘fuzzy definitions of performance’ in our conclusion.

Alternative institutions

Alternative institutions are those that can influence the actions of interdependent, autonomous agents

as they iteratively explore alternative solutions to wicked problems, such as distributed authority

arrangements, multi-sector for a for decision-making and multi-channel feedback arising from new

communication technologies. For example, in Haynes’ contribution, the notions of public service values

and public value are explored through the lens of CAS theory. The paper offers a concrete and practical

example for understanding the dynamic influence of values on complex policy systems. Haynes argues

for recognition of ‘soft’ patterns of values such as belief systems and their dynamic influence on

organizational behaviours as well as ‘hard’ patterns such as rules and structures and shows how the CAS

lens enables this.

Castlenovo and colleagues attend to the issues raised by the federal–state–local governance structures

and how these might be re-imagined/understood using complexity theory. For them, their complexity-

based lens acts as a heuristic device to understand the misalignment of locally implemented outcomes

with the centrally defined objectives of a nationwide public programme in Italy where the ‘Napoleonic’

administrative traditions dominate – arguing for a rethinking of these traditions.

Tenbensel, rather than arguing for a particular type of institutional change, builds on the approach taken

by Room (2011) and advocated by Cairney and Geyer (2017) in bringing institutional theory together

with complexity theory using Crouch’s concept of recombinant governance. Through an examination of

the fitness of various governance hybrids in the health sector in New Zealand he demonstrates the

usefulness of being able to distinguish among various versions of hybridity and to argue for a more

evolutionary perspective on institutional design and change.

Alternative practices

Complexity offers alternative ways of framing intervention and bringing about successful change that

navigates the traps of unexpected changes and opens up different ways of achieving innovation. Gear

and colleagues take us into the conceptual framing and research methodology needed to examine the

complex problem of intimate partner violence (IPV). They identify the limitations faced in developing

healthcare interventions in the absence of a complex adaptive systems view. Existing efforts to

understand sustainable approaches in primary healthcare settings have been dominated by the direct

cause–effect thinking reflected in randomized control trials and like methodologies that have been so

prevalent in health research. Reframing the person entrapped by IPV and their world, and the world of a

primary healthcare setting as two interacting complex adaptive systems, shifts the research focus to the

reflexive interactions that occur between the person experiencing IPV and the primary healthcare

setting. According to CAS theory, we would expect these interactions to lead to mutual adaptations

within each of these complex systems, and therefore intervention sustainability will occur when the

interaction and mutual adaptation generate outcomes that stimulate ongoing engagement by both

systems. Without the CAS perspective, the self-organization, coevolution, and emergence that leads to

sustainability cannot be studied. The conceptualization and research design developed to study

healthcare responses to IPV might also be more widely applicable to other complex social interventions.

Sustainability of the collaborative governance network is also the focus of Scott and colleagues.

Complexity theory concepts are used to both describe how sustainability is linked to the adaptability

and flexibility of the collaborative project but also to offer insights into how the collaborative process

might be designed to encourage the development of sustainability. Like many other papers in this

edition, their use of complexity theory is combined with other theories – collaborative governance, in

this instance.

Meek and Marshall use a CAS lens to understand how the multi-actor institutional governance of a

complex Southern Californian metropolitan water system contributes to an adaptive resilience able to

respond effectively to the external stressors of severe and sustained drought. Ongoing self-organization

and adaptation within and among the governance actors and other stakeholders are characteristics of

the governance system which lead to emergent features which help maintain resilience.

In the Castelnovo paper referred to already, we encounter the empirical descriptions needed to

interpret the complexity factors that shaped an implementation trajectory. They offer self-organization,

co-evolution, and emergence as mechanisms for understanding the peculiar implementation path which

might otherwise be assumed to be the cumulative effect of a series of legislative interventions not

always coherent in and among themselves. In so doing they pave the way for the design of alternative

implementation practices.

Finally, scholar-teachers have also begun to incorporate complexity theory into teaching practice. It has

proved useful for both integrating theories and for helping students and practitioners to better frame

and understand the challenges of public management. In schools of government, planning, and business

we are starting to see individual modules, components of programmes, and indeed entire master’s

degrees being developed to introduce students to a complexity ‘perspective’ and to be exposed to the

tools and techniques to understand and intervene in complex systems. Due to constraints of space, this

issue does not include any articles on this topic, but instead the editors are working on a separate

special issue in ‘Complexity, Governance & Networks’ dedicated to the ways complexity is being taught

to public management/policy students around the world.

Whither complexity in public management?

The relevance of complexity theory for circumventing the weaknesses of a mechanistic approach to

understanding public policy and management has been well-trodden ground for decades. That this

continues to be pursued as complexity theory spreads across policy domains suggests that it is this

fundamental capacity that is at the core of the attraction for many scholars and practitioners. As

highlighted above, the use of complexity theory in public management has developed both in relation to

the description of phenomena and design of institutions and interventions to effect change.

From a theoretical perspective, the scholarship of the last decade and the papers in this volume

demonstrate that complexity theory sits alongside, and in many cases augments existing theories of

public policy and public management. Public policy and public management draw on a variety of parent

disciplines such as politics, organization science, economics, management, sociology, and psychology

(Raadschelders 2011) and bridging or integrating this plurality continues to be an implicit – and in some

cases explicit – objective of scholars applying complexity theory to this domain. A complexity

perspective can describe how interdependent agents interact over time – within the constraints of

history, institutional forms, and/or values – to increase or decrease overall (or individual) fitness,

sustainability, or resilience. It does this without the need to fall back on predictable cause and effect

relationships among agents or contexts while still leaving room for the identification of patterns and

likely pathways.

Furthermore, the ‘positive role for complexity theory as a way to bridge academic and policy maker

discussions’ (Cairney and Geyer 2017, 1) – and we would add ‘practitioners’ – is evident in many of the

papers. Complexity acts as a challenge to the quest for certainty in policymaking and also prompts

discussion about the role of pragmatism in policymaking. In this issue, authors have argued for linking

complexity frameworks with institutional theory, network theory, public value theory, and game theory

to better understand the dynamics of processes, outcomes, and change in public policy/management

systems over time. Its strengths lie in its facilitation of a focus on multiple levels of scale and its

provision of micro-level mechanisms for macro-level theories such as institutional theory and

punctuated equilibrium theory (Eppel 2017). The key mechanisms explored in this issue are based on

game theoretic interactions, search processes on fitness landscapes, evolution arising from recombinant

novelty, and information exchange in networks – building on the core complexity dynamics of self-

organization, adaptability, and emergence. In respect of institutions, the conclusion one may draw from

these papers is that it is unlikely that current institutional forms – whether they be hierarchical, market,

network, or values based – exhaust the range of potential institutional forms that could be designed or

evolve in the public policy and administration space. Experimenting with new forms would appear to be

an important complexity-friendly policymaking practice that would lead to more sustainable public

systems.

The concepts of ‘sustainability’ and ‘resilience’ make an appearance in several of the articles in this issue

as objectives of research and practice that are facilitated by a complexity approach. However, there is

little agreement or indeed clear definition about what either of these outcomes represent in the context

of public administration. Survival – or the ongoing existence of agents, institutions, or systems if not of

the individual humans that make these up – is, of course, one option, but this is not clarified or

challenged either in the papers in this issue or in the wider academic community. It is incumbent upon

those scholars working in this area and using these concepts to clearly define and debate what they

mean if the policy or practice recommendations arising from their research are to be seriously

considered.

In addition to this definitional lacuna around sustainability and resilience, the incorporation of

performance management research, theory and practice, has been largely absent in the public

administration complexity literature. The fitness landscape literature would appear to provide an

obvious link to performance, as evidenced by the use of the phrase ‘performance landscapes’ to

describe this approach in organizational theory (Siggelkow and Levinthal 2003; Rhodes and Donnelly-Cox

2008). This leads us to speculate about the compatibility of complexity theory with our basic

understanding of the nature of performance management. The issue may partially be due to the

multidimensionality of performance management (Bouckaert and Halligan 2008) and the limitations of

how performance management has been conceived and practised in the new public management

environment (Moynihan et al. 2011). Moynihan and colleagues (2011) point to the limitations of current

research on performance management to take adequate cognisance of governance complexity. So there

appears to be some room for each scholarly trajectory to learn from the other.

But perhaps more important is that fact that we are still quite far from developing complexity-based

models of agent interactions, behaviour, and change over time that demonstrably produce/predict real-

world outcomes of any kind, not just performance. However, the kind of direct cause and effect theories

we have come to believe represent the pinnacle of scholarly achievement and the reliance on

experiments or random control trials to prove same are unlikely to address the sorts of ‘wicked’

problems (Rittel and Webber 1973) that lie at the heart of public policy and management. The need to

continue to adopt and refine complexity-informed theory, institutions, and practice in a domain of

human endeavour as rich and varied as public administration is as vital now as it was a decade ago.

Additional information

Notes on contributors

Elizabeth Anne Eppel

Elizabeth Anne Eppel is a Senior Research and Teaching Fellow in the School of Government, Victoria

University of Wellington, New Zealand. Her research interests are complexity in public policy processes,

governance networks, and collaborative governance.

Mary Lee Rhodes

Mary Lee Rhodes (B.A., M.Sc., MBA, Ph.D.) is an Associate Professor of Public Management at Trinity

College, Dublin. Her research is focused on complex public service systems and the dynamics of

performance. Prof. Rhodes has published numerous articles on housing as a complex adaptive system

and her most recent book on Complexity and Public Management was published by Routledge in 2011.

Her current research is on the nature and dynamics of social impact and she is developing research on

social innovation, social finance, and well-being.

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References

Bouckaert, G., and J. Halligan. 2008. Managing Performance: International Comparisons. Abingdon:

Routledge. [Google Scholar]

Boulton, J. G. 2010. “Complexity Theory and Implications for Policy Development.” Emergence:

Complexity and Organisation 12 (2): 31–40. [Google Scholar]

Boulton, J. G., P. M. Allen, and C. Bowman. 2015. Embracing Complexity: Strategic Perspectives for an

Age of Turbulence. Oxford: Oxford University Press. [Crossref], [Google Scholar]

Byrne, D. S. 1998. Complexity Theory and the Social Sciences: An Introduction. London: Routledge.

[Google Scholar]

Byrne, D. S. 2011. Applying Social Science: The Role of Social Science Research in Politics, Policy and

Practice. Bristol: Policy Press. [Crossref], [Google Scholar]

Byrne, D. S., and G. Callaghan. 2014. Complexity Theory and the Social Sciences: The State of the Art.

Abingdon: Routledge. [Google Scholar]

Cairney, P. 2012. “Complexity Theory in Political Science and Public Policy.” Political Studies Review 10

(3): 346–358. doi:10.1111/j.1478-9302.2012.00270.x. [Crossref], [Web of Science ®], [Google Scholar]

Cairney, P. 2013. “Standing on the Shoulders of Giants: How Do We Combine the Insights of Multiple

Theories in Public Policy Studies.” Policy Studies Journal 41 (1): 1–21. doi:10.1111/psj.12000. [Crossref],

[Web of Science ®], [Google Scholar]

Cairney, P., and R. Geyer, eds. 2015. Handbook on Complexity and Public Policy. Cheltenham: Edward

Elgar. [Google Scholar]

Cairney, P., and R. Geyer. 2017. “A Critical Discussion of Complexity Theory: How Does ‘Complexity

Thinking’ Improve Our Understanding of Politics and Policy Making?” Complexity, Governance and

Networks 3 (2): 1–11. doi:10.20377/cgn-56. [Crossref], [Google Scholar]

Cilliers, P. 1998. Complexity and Postmodernism: Understanding Complex Systems. London: Routledge.

[Google Scholar]

Eppel, E. 2017. “Complexity Thinking in Public Administration’s Theories-in-Use.” Public Management

Review 19 (6): 845–861. doi:10.1080/14719037.2016.1235721. [Taylor & Francis Online], [Web of

Science ®], [Google Scholar]

Eppel, E., D. Turner, and A. Wolf. 2011. “Complex Policy Implementation: The Role of Experimentation

and Learning.” In Future State: Directions for Public Management in New Zealand, edited by B. Ryan and

D. Gill, 182–212. Wellington: Victoria University Press. [Google Scholar]

Gerrits, L., and P. Marks. 2015. “How the Complexity Sciences Can Inform Public Administration: An

Assessment.” Public Administration 93 (2): 539–546. doi:10.1111/padm.12168. [Crossref], [Google

Scholar]

Geyer, R., and S. Rihani. 2010. Complexity and Public Policy: A New Approach to 21st Century Politics,

Policy and Society. Abingdon: Routledge. [Google Scholar]

Holland, J. H. 1995. Hidden Order: How Adaptation Builds Complexity. Reading, MA: Addison-Wesley.

[Google Scholar]

Innes, J. E., and D. E. Booher. 2010. Planning with Complexity. Abingdon: Routledge. [Crossref], [Google

Scholar]

Kauffman, S. A. 1993. The Origins of Order. Self Organisation and Selection in Evolution. New York:

Oxford University Press. [Google Scholar]

Kauffman, S. A., and S. Levin. 1987. “Towards a General Theory of Adaptive Walks on Rugged

Landscapes.” Journal of Theoretical Biology 128 (1): 11-45. [Crossref], [PubMed], [Web of Science ®],

[Google Scholar]

Klijn, E.-H., and J. F. M. Koppenjan. 2014. “Complexity in Governance Network Theory.” Complexity,

Governance and Networks 1: 61–70. doi:10.7564/14-CGN8. [Crossref], [Google Scholar]

Koliba, C., and A. Zia. 2012. “'Complexity Friendly' Meso-level Frameworks for Modelling Complex

Governance Systems.” In COMPACT 1: Public Administration in Complexity, edited by L. Gerrits and P.

Marks, 119–139. Litchfield Park, AZ: Emergent. [Google Scholar]

Koliba, C., J. W. Meek, and A. Zia. 2011. Governance Networks in Public Administration and Public Policy.

Boca Raton, FL: CRC Press. [Google Scholar]

Koppenjan, J. F. M., and E.-H. Klijn. 2014. “What Can Governance Network Theory Learn from

Complexity Theory? Mirroring Two Perspectives on Complexity.” In Network Theory in the Public Sector:

Building New Theoretical Frameworks, edited by R. Keast, M. Mandell, and R. Agranoff, 157–173.

London: Routledge. [Google Scholar]

Morcol, G. 2012. A Complexity Theory for Public Policy. New York: Routledge. [Google Scholar]

Moynihan, D. P., S. Fernandez, S. Kim, K. M. LeRoux, S. J. Piotrowski, B. E. Wright, and K. Yang. 2011.

“Performance Regimes Amidst Governance Complexity.” Journal of Public Administration Research and

Theory 41 (Supplement 1): i114–i155. doi:10.1093/jopart/muq059. [Crossref], [Web of Science ®],

[Google Scholar]

Prigogine, I. 1978. “Time, Structure and Fluctuations.” Science 201 (4358): 777–785.

doi:10.1126/science.201.4358.777. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]

Prigogine, I., and I. Stengers. 1984. Order Out of Chaos: Man’s New Dialogue with Nature. Toronto:

Bantam Books. [Crossref], [Google Scholar]

Raadschelders, J. C. N. 2011. Public Administration: The Interdisciplinary Study of Government. New

York: Oxford University Press. [Crossref], [Google Scholar]

Rhodes, M. L., and G. Donnelly-Cox. 2008. “Social Entrepreneurship as a Performance Landscape.”

Emergence: Complexity and Organizaton 10 (3): 35–50. [Google Scholar]

Rhodes, M. L., J. Murphy, J. Muir, and J. A. Murray. 2011. Public Management and Complexity Theory:

Richer Decision-Making in Public Services. New York: Routledge. [Google Scholar]

Rittel, H., and M. Webber. 1973. “Dilemmas in a General Theory of Planning.” Policy Sciences 4: 155–

169. doi:10.1007/BF01405730. [Crossref], [Web of Science ®], [Google Scholar]

Room, D. G. 2015. “Complexity, Power and Policy.” In Elgar Handbook on Complexity and Public Policy,

edited by R. Geyer and P. Cairney, 19–31. Cheltenham: Edward Elgar. [Google Scholar]

Room, G. 2011. Complexity, Institutions and Public Policy. Cheltenham: Edward Elgar. [Crossref], [Google

Scholar]

Sanderson, I. 2009. “Intelligent Policy Making for a Complex World: Pragmatism, Evidence and

Learning.” Political Studies 57: 699–719. doi:10.1111/j.1467-9248.2009.00791.x. [Crossref], [Web of

Science ®], [Google Scholar]

Siggelkow, N., and D. Levinthal. 2003. “Temporarily Divide to Conquer: Centralized, Decentralized, and

Reintegrated Organizational Approaches to Exploration and Adaptation.” Organizational Science 14 (6):

650–669. doi:10.1287/orsc.14.6.650.24840. [Crossref], [Web of Science ®], [Google Scholar]

Stacey, R. D. 1993. Strategic Management and Organisational Dynamics: The Challenge of Complexity.

London: Prentice Hall. [Google Scholar]

Teisman, G., A. van Buuren, and L. Gerrits, eds. 2009. Managing Complex Governance Systems:

Dynamics, Self-Organization, and Coevolution in Public Investments. New York: Routledge. [Crossref],

[Google Scholar]

Tenbensel, T. 2013. “Complexity in Health and Health Care Systems.” Social Science and Medicine 91:

181–184. doi:10.1016/j.socscimed.2013.06.017. [Crossref], [Web of Science ®], [Google Scholar]

Waldrop, M. M. 1992. The Emerging Science at the Edge of Order and Chaos. New York: Simon and

Schuster. [Google Scholar]

Wright, S. 1932. “The Roles of Mutation, Inbreeding, Crossbreeding and Selection in Evolution”.

Proceedings of the Sixth International Congress of Genetics (1): 356-366. [Google Scholar]