Complexity Theory and Tourism Policy Research

Introduction 

In the study of tourism phenomena, chaos and complexity theories have been used, particularly events, and aspects considered to be chaotic.  They are also used to question models and methods which simplify and linearly conceptualise phenomena by saying that both the complexity and dynamism of these phenomena and the environments in which they operate are ignored.

This subject will discuss the possible contribution of debate on complexity theory to a broader understanding of tourism policy making in management, policy and organisational literature.  It is a social conceptualization of tourism politics that acknowledges its social context and the role that negotiating and communicating people play in developing and implementing policies.  Five concepts of complexity are identified as a basis for developing tourism policy research that takes into account the complex social and human interactions that influence politics.

In order to enable policy analysers to deepen understanding on the social dimensions of policy by developing studies that recognise both complexity and social context, the implications of the complexity are taken into account in the social arena.  The purpose of this study is to identify the complexity of complexity science and to explore debates on how its concepts can be used to understand tourism policy, taking its context, its dynamics and its relationships into account.  It aims to add complexity to current literature and contribute to the discussions on using complexity theory to develop a tourism policy understanding.

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Five key concepts and their relevance to understanding tourism policy 

1. Complex adaptive systems

In complex adaptive systems, theory of complexity is intended to understand changes. The term complex is used to describe an interaction process in which agents (people) choose their individual actions.  A complex system is adaptive because of its environmental impact and control (Brian Arthur et al., 1997; Harvey, 2001). A large number of people work in organisations, each working on a set of laws or standards, allowing them to communicate and compromise with others within the policy environment, consist of the dynamic adaptive process.  Conventions and laws and access to resources are limited by their actions. It is generated by official hierarchical relations and informal working ties that are affected by a variety of intangible and human factors.  In this context, it is possible to use the idea of a complex adaptive system to emphasise how people offer opportunities within systems and are bound up by connexions between themselves. 

The concept of a complex adaptive system is useful to understand the specific context of policies.  Policy will be interpreted and expressed within an economic context, for instance in a destination where tourism policy is specifically described as part of economic politics.  This will not only constrain the community, but also create opportunities and restrictions as tourism policy intersects with other policy sectors such as transport, the environment, culture and leisure. The relationship also relies on tourism's place on the political hierarchy of any given destination, its access to capital and its ability to influence a large number of individuals within partnership organisations.   These several and cross-cutting interactions contribute to actions both "patterned" and "unpredictable" within the policy process (Battram, 1998; Stacey, 2003).  The relationships between these areas and the policies within them change constantly, although the main connexions between the different policy areas can be established (e.g., tourism, finance, transport, the environment).

In an environment composed of other complex systems, tourism policy systems are "nested."  The boundaries between these processes are not distinct, and Byrne (2001) describes'' nested inter-penetration structures'' which converge, connect, and control through hierarchies and institutional frontiers as policy is developed and implemented.  The relationships between people in those different systems continue to evolve, and change is strongly triggered by changes in the broader political climate, such as the transition from state to ' governance ' with new structures and strategies for executing ' joined-up ' organisational policies across boundaries (Sanderson, 2000; Healey, 2007).  Policy is developed in a situation characterised by change and instability, so conceptualising policy systems in a stable context or developing models that are based on the concept of ' equilibrium ' makes little sense.

2. Emergence 

Emergence is characteristic of a dynamic evolutionary system which emerges as the internal structure of systems evolve and evolves through development and learning (Battram, 1998; Manson, 2001).  It is a' bottom-up' process, which results in a system or part of a system adapting and creating an emerging order through the collective behaviour of the interacting individuals (Stacey, 2003).In a policy process, attention is drawn to the manner in which allegiances and groupings arise within alliance and political groupings, how individuals outside the formal system lobby and influence powerful policymakers.

Emergence draws attention to the fact that experience of what each part of a system does isolatedly cannot predict a system's behaviour.  This causes a complex system to have capacity larger than the number of components (Battram, 1998; Manson, 2001; Stacey, 2003; Waldrop, 1992;) and' differs in any way from its components ' (Urrry, 2003:24). Emergence of political development in a dynamic, partnership-based world is affected by changes that may be too difficult for managers to monitor in complex adaptive structures. Emergency encourages people to recognise that behaviour in a complex environment is more important than regulated (Tosey, 2002). This emphasises the need for exploratory strategies based not only on a comprehension of the system and its relationship with other processes, but on the interactions of individuals and groups of policymakers to explore the complexities that are complex and contradictory.

3. The edge of chaos 

Chaos ' boundary (Battram, 1998; Tosey, 2002), or minimal instability (Stacey et al, 2000), is described as the transition phase in a complex system where organised behaviour co-exists with disordered and chaotic behaviour (Battram, 1998; Mitleton Kelly, 1998). The transition phase in a complete system is described by Stakey et al., 2000. This is a place for intense learning, innovation, and creativity (Battram, 1998; Tosey, 2002), the point of maximum fitness or evolvability (Lewin, 1993) and is a place for intense learning and creativity.  The process is breaking with the past (Stacey et al, 2000; Battram, 1998) and new systems of order emerge from and alongside the confusion (Mitleton-Kelly 1998) at the border of the chaos can quickly and spontaneously alter.  Forces like creation, structures or chaos-free systems (Battram 1998 and Manson 2001).

The paradoxical phenomenon at a chaos ' edge was defined by Battram (1998) Mitleton-Kelly (1998) and Stacey (2003), which seem to include contradictory elements at the same time.  In terms of understanding the complexities of political affairs, the edge of uncertainty questions many conventional policy concepts such as the belief that the inconsistencies and paradoxes of progress need to be overcome and the problems generated.  While this traditional approach is tantamount to success, stability and predictability dynamics, the border of chaos opens up the possibility of unresolved contradictions and paradoxes.  It highlights political dynamics and measures with regard to ongoing tension that generate irregular, unstable and unpredictable patterns.

Various authors have considered the type of decisions at the end of the chaos and have drawn attention to the intuitive, aggressive, agenda building and brain storming and dialectical research Zimmerman (2001) calls the "waste can decisions.'  It contrasts with traditional management approaches in a relatively stable and consensual environment. Darwin (2001) identifies the traditional toolkit of stable decision-making methodologies including SWOT, PEST(EL), 5-strength analysis as well as stakeholder analysis.  He claims these methods are underpinned by' backstage activity.' Darwin's (2001) research has no specific link with the development of tourism policy, but in political texts (including Gunn 2002 and Veal 2002) the methods associated with linear thinking and stability are generally recommended.

The definition of uncertainty helps to develop an appreciation of policy making in the context of governance-related changes.  The new role of governments and the development of new structures, agencies and processes characterises governance. Governance.  More collaboration and partnership work has been emphasised in particular (Richards and Smith 2002; Stoker 2004; Stoker and Wilson 2004). These changes led to a period of intensive policymaking, a number of sometimes inconsistent initiatives and a turbulent and complex context (Healey, 2007).  These also prompted politicians to step away from traditional policy approaches and develop new research and innovative methods requiring more collaboration.

4. Positive and negative feedback 

Mitleton-Kelly (1998) relies on negative feedback as to policy and planning processes built within balance structures.  "The process necessary for the production of stability dynamics" is a negative feedback (Stacey, 2003:33) assuming that there is clear links between causes and effects. For instance, Gunn (2002) and Veal (2002) discussed models for policy processes based on assumptions of negative feedback.  They indicate an explicit follow-up process in which the role of the policymaker is to take action to reduce the gap between the outcome intended and the actual outcome.

The term positive feedbacks is used to gradually widen the gap between the necessary and actual outcomes (Mitleton-Kelly, 1998; Sanderson, 2000; Stacey, 2003; Urry, 2003).  The analysis of negative and positive feedback in social systems highlights how policy solutions to a multifaceted issue in a complex environment are successful on one level and ineffective on the other. Positive feedback bikes "that intensify the first pressure on the system and prevent shocks from absorbing to restore the original equilibrium" (urry. 2003:11).  For example, a destination with low standards of quality may develop a service training programme for the improvement of their employees ' skills by adopting policy.  This could lead to a training of 100 people and a successful perception at one level.  But if these 100 people use training in other destinations or in other sectors to gain better jobs, the overall effect of such policy intervention may contribute to further reduction of service standards. In this scenario, the idea of positive feedback focuses on the impacts of a number of dynamic relations, trends and experiences within a system and within its context where a political intervention exacerbates the issues to be addressed.

5. The policy landscape  

Some theorists describe the environment as landscape (Battram, 1998; Blackman, 2001) outside a given system and claim that the environmental characteristics of the system influence the dynamic and nature of change.  Healey (2007) emphasises that environments must be "written" in order to determine their dimensions and qualities.  As far as tourist policy is concerned, reading the countryside would include understanding the wider policy and political context, the scope, nature and purpose of the development of tourism policy, and its intersections and relations with other policy areas.

Blackman (2001) describes a robust world where autonomous interventions are stifled and suggests that this robustness limits the likelihood that complex systems can transform.  The broader political climate in the UK Provides a robust landscape example and stems from the tensions between governance and the agenda of modernisation in the "Third Way" (Giddens, 1998) ideology.  Richards & Smith (2002) argues that democracy fragmentation led national governments to develop centralised control for calculating and standardising local policy. The local policy environment is therefore extremely rugged and policy makers have limited scope for autonomous action.    However …..…. (2006) indicates that in the U.K. the policy landscape for tourism might be less rugged than other policy areas as  tourism policy is discretionary, devolved to the regions and is not monitored by nationally set targets.  While tourism policy at the destination level is “embedded” in a rugged wider policy landscape, the implications of this ruggedness are indirect and apply where tourism policy intersects with other policy areas.   

The concept of landscape is interesting for tourism policy analysts not just because it draws attention to the extent that policy interventions are always embedded within their wider environment but also because it enables them to identify and investigate some of the contradictory dynamics in the wider environment.  

Using complexity theory to develop understanding of tourism policy making  

Complexity theory is informed by, and is being debated within many disciplines and is relevant to the study of tourism policy as it acknowledges its inherent intricacies .  The complexity of tourism policy is exacerbated by the lack of agreement about its nature and purpose, its multi-disciplinary roots and diverse body of theory.  Theorists interpret the policy ‘problem’ in different ways and have developed different models reflecting a variety of disciplinary perspectives.  

The literature on complexity theory present a plausible challenge to many assumptions that underpin the literature on tourism policy making.  In particular the challenge to positivism, linear thinking and the notion that policies and plans can predict, control and shape complex environments.  Complexity theory draws attention away from the relative order attributed to tourism policy making which is characterised by the development and enactment of written policies and is illustrated by process models (Gunn 2002 and Veal 2002), systems models (Gunn 2002, Hall 2000).It enables us to consider the disorder arising from  interaction, competition and human agency on the policy process.    

The five concepts outlined in this paper draw attention to some of the complexities and contradictions arising in the development and enactment of tourism policy.  Consideration of complex adaptive systems encourages reflection on the nature and interpenetration of tourism policy systems highlights the instability and turbulence of those systems.   Emergence draws attention to human agency, “bottom-up” learning and innovation and their implications on our ability to predict.  The edge of chaos  characterised by the co-existence of order and disorder, stability and instability giving rise to contradiction and uncertainty.  This questions the relevance of policy tools that are developed from an equilibrium framework.  Positive feedback draws attention to the way narrowly framed policy interventions might amplify problems and identifies the need to consider the wider and longer term implications of policy.  Policy landscape illustrates both the embeddness of policy and the contradictory dynamics that exist in the wider environment.

The concepts identified from the complexity literature questions our ability to fully understand the policy process and to forecast, regulate and control the future. It highlights the intrinsic relationship between policy research and ignorance and requires a research approach that can encourage the researcher to be both humble in recognising their own limitations and courageous in resisting the orthodoxy to reduce and simplify. 

In policy analysis, social approaches offer insights by drawing attention to the subtle intangible and contradictory aspects of the process such as the relationships and alliances that develop between people as they develop and enact policy.  They suggest more localised and deeper studies to develop a more nuanced and exploratory approach to policy analysis.  They emphasize understanding of how policies are implemented, how they generate effects over time, and how such effects are dependent on contextual circumstances, interactions and human agency

(Sanderson 2000).

The debates identified from the social sciences suggest that complexity theory might be used as a thinking tool to enable a more holistic approach to policy analysis.  They focus  away from understanding the detail of the policy process and towards developing an understanding of broader themes, patterns and principles that arise when developing policy in a complex and contested environment. They also focus  away from the tangible components of policy, such as a tourism strategy document and towards policy context and relationships.  

Complexity theory challenges assumptions about research and analysis raising questions about what we should try to understand and how we should try to understand.  Complexity science works on the assumption of non linearity, which implies that knowledge is local and contextual.  This raises questions about the extent to which researchers can develop models that have meaning outside the local context.   It implies the rejection of those models that claim to be universal and that are reductionist, simplifying processes and systems in order to understand them.  Research into social phenomena requires a research methodology that can encompass the inter-relationships, interactions and communications between people involved in developing and delivering policy.

Conclusion 

Complexity concepts are the subject of debate in many disciplinary areas and are subject to multiple interpretations.  In the same way that it is not possible to identify one policy theory, it is not possible to identify one complexity theory as different approaches have been developed in different disciplinary fields.   Complexity theory is negotiated and open to varied interpretation, which presents a wide range of possibilities in exploring its implications on social phenomena such as tourism policy making. 

Complexity theory does not provide a new ‘truth’ about the way the world works.  The underlying concepts of complexity can be both deterministic and reductionist, limiting learning in much the same way as traditional models about organisations and policy making. Tsoukas & Hatch (2001) claim orthodox approaches to complexity exhibit the same reductionist tendencies as the Newtonian, linear models when they identify the common principles underlying different systems.  

Social approaches to complexity highlight its role in encouraging researchers to question their assumptions,  broaden their thinking and strive towards a more holistic understanding of phenomena.  They explore the possibilities of ‘loose’ (Van Uden, 2005), reflective (Harvey, 2001)or ‘dis-connected’ (Medd, 2001b) applications of complexity theory in the social sphere, demonstrating the benefits of bringing the concepts and language of complexity into the study of social phenomena to provide a focus for debate and discussion that might underpin a deeper understanding of those issues.  

This paper intends to build on existing work in complexity theory, by drawing attention to the debates arising in the social sciences.  It identifies five concepts, emergence, complex adaptive systems, edge of chaos, positive feedback and landscape and discusses how they might be used to develop  thinking about policy making.     It suggests exploratory approaches to enable consideration of the landscapes in which policy decisions are made, the relationships, intersections and adaptations of tourism policy, the emergent nature and implications of human action, the turbulence and dynamism of change at the edge of chaos and the positive feedback arising from the complex relationships between cause and effect, as policy makers seek to resolve problems.

To date the main contribution of complexity theory to understanding social phenomena in tourism has been the challenge to linear thinking and positivism and the criticism of the emphasis of much research on the ordered, and more easily defined aspects of systems.  This has progressed thinking at a conceptual level.  It is now time to take that challenge further and to draw upon some of the wider debates about the application of complexity to social phenomena and explicitly engage in discussion about its methodological implications.  Complexity theory has important implications for policy research in cross cutting areas such as tourism.  The contribution of a social perspective is that highlights the need for more holistic understanding of policy process and  raises questions about how we should analyse policy interventions in a social world.  

Complexity theory contributes to policy analysis due to the questions it raises about the stability and equilibrium implicit in many policy process models.  It provides a basis from which to consider policy in the context of ‘real world’ phenomena, taking account of turbulence and disequilibrium, self organisation and co-evolution.  Highlighting the importance of communication, it provides new insights in conceptualising the dynamic between policy and practice.  It raises questions about the methods for researching complex social phenomena highlighting the nature of human communication and need for exploration rather than prediction.  

The debates outlined in this paper support the development of further research into the characteristics of the tourism policy process, taking into account the turbulence and complexity of its environment.  Research need to be undertaken to focus on what tourism policy is, and what happens when tourism policy makers  develop and enact policies in different policy environments, and what the implications of tourism policy are over longer time frames.  There is a need for the development of longitudinal, case based research to improve the understanding of tourism policy making in its specific context from the perspective of the people involved and to broaden understanding of policy as a social process involving collaboration and negotiation.  

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