Using Complexity to Design Intelligent Social Systems

 


‘Complexity’

“Complexity thinkers define ‘intelligence’ in terms of exploring a range of possible actions and selecting ones that are well-suited to the immediate outcome…” – Davis and Sumara

“There’s more than one way to skin a cat.” – American aphorism.

 

‘Intelligence’ is not necessarily about a rational or comprehensive consideration of immediate circumstances. Instead, in complexity terms, the intelligent unity is the one that generates a diversity of possibilities and that has a mechanism for critically debating the merits of those possibilities. So understood, intelligence is a sort of ‘scouting’ of possible responses.

This paper, then, is a ‘scouting report;’ a guide for evaluating possible choices for managers and policy-makers in choosing when, where and how to engage the complex social challenges that we face today, not only in the proposed South Side St. Petersburg CRA, but in greater St. Petersburg, Pinellas County and Florida as well. This report can be a useful resource in developing revitalization initiatives for growing people, businesses and institutions in a society capable of engaging the serious social challenges that we face today.

Introduction

It has become abundantly clear that the current ideas and initiatives that have been advanced as solutions for the social problems we are facing today in the South Side St. Petersburg CRA, (e.g. substandard living conditions, poor health and education systems, diminished or absent political voice; lack of physical security and environmental degradation), have been ineffective. Moreover, there is a growing understanding that the causes of the problematic social conditions do not reside within one physical area, one segment of society, one institution or even within one ideology. The origins of the social problems are complex and interventions must deal with interdependent causes and navigate nonlinear and often unpredictable change processes involving a diverse range of stakeholders.

Unfortunately, when tackling complex social problems, people’s tendency is always to employ convenient ways of intervening; to break problems into discrete, understandable, component effects and causes, while at the same time realizing it is necessary to find ways to motivate society as a whole – but not understanding how to make that happen.

In the light of the challenges complex problems pose, people could be forgiven for becoming cynical about chances of being able to tackle issues or achieve economic or social goals. Many policies fail to address the problems they are designed to combat. In particular, it might seem that “knowledge” is less useful to the policy process than was initially supposed, as the “rational” model of policy-making, which is centered around intentionally guiding institutions towards achieving common goals, begins to look like not just an unrealistic description of the policy process but also in fact an irrelevant ideal.

Fortunately, in recent years the complexity sciences have improved our understanding of complex problems, and have provided concepts and ideas which incorporate both old and new insights to present alternative theories for change, greater understandings of underlying processes and crucially, better approaches for tackling them in a strategic and direct manner.

The central contention of this paper is that the main problem is not intractable problems, or poor application of the right tools, but rather use of the wrong tool for the job. The complexity sciences are beginning to give us alternative theories for change, greater understandings of underlying processes and, crucially, better approaches for tackling them.

It is vital that actors charged with implementing policies and programs in the face of complexity take responsibility for choosing an appropriate approach.

 

Complex Problems and the Challenges They Pose

Why do complex problems present big challenges for traditional approaches to implementation?

  1. The capacities to tackle complex problems are often distributed among actors. Problems manifest themselves in different ways and at different levels, and rather than one organization or hierarchy being fully in control of meeting a particular objective, action may rely on differing degrees of collaboration from a variety of actors.
  2. Complex problems are difficult to predict. Many social, political and economic problems are not amenable to detailed forecasting. Where causality is not well understood success may rely on adaptation and flexibility to emerging insights, rather than trying to completely fix the shape of policy responses in advance.
  3. Complex problems often involve conflicting goals. There may be many divergent but equally plausible interpretations of a policy issue, with different groups approaching it from different starting points or assumptions. Implementation cannot be technocratic, but requires a negotiated understanding and synthesis through communicative processes.

Consequently, the ways in which policy draws on available knowledge becomes one of the central determinants of its success. The difference is that, rather than working in a linear fashion, policymakers must be mindful of constraints and opportunities as to where, when and how knowledge and decision-making can best be linked.

Where

Implementing agencies need to work in a collaborative mold, facilitating decentralized action and self-organization:

  • Decentralization and autonomy: One key priority is decentralizing policy-making and implementation, distributing power in decision-making and allowing increased autonomy for units lower down the hierarchy.
  • Engaging local institutions and anchoring interventions: Implementing agencies may need to work with and through local organizations addressing an issue at different scales; this may be best done through co-management and power sharing.
  • Convening and boundary management: Agencies may be able to play a unique role in facilitating processes that build trust and collaboration between key stakeholders. They must act as trustworthy stewards of these processes, including the provision of transparent mechanisms for conflict resolution.
  • Building adaptive capacity: Capacity building is likely to be central to efforts to enable actors to capitalize on any autonomy for addressing problems. Supporting adaptive capacity networks is shown to be a central priority for stimulating emergent responses.
  • Remove the barriers to self-organization: There may be different types of barriers and systemic issues which are preventing actors from adapting to emerging problems: these could be related to national legislation or political systems, or issues of power, discourse and social capital.
  • Supporting networked governance: Agencies must approach the delivery of their mandate with a networked approach to policy and governance. Accountability structures can usefully focus on holding units accountable for their mission or role description. Relationship management concern and participatory processes should be central focuses.
  • Leadership and facilitation: Even where the capacity to act is distributed, leadership emerges as a critical variable in the success of collaborative responses. However, in the face of complex problems this leadership must be facilitative and enabling, working through attraction rather than coercion.
  • Incremental intervention: Where a central agency does need to intervene, it should be approached in an incremental manner, starting from existing networks and taking an evolutionary approach to support, looking to ‘seed’ decentralized action and support emerging responses rather than implementing idealistic blueprints.

When

Implementing agencies need to deliver adaptive responses to problems, building space for interventions to be flexible to emerging lessons. This can be done in the following ways:

  • Appropriate planning: Systems around ex ante analysis should be light and flexible, and focus on providing utility, for example by enhancing awareness of the key risks or lessons. Accountability can be tied to clear principles for action rather than to unpredictable results or inflexible activity plans, and rules for the adjustment of plans can be established in advance.
  • Iterative impact-oriented monitoring: Continual monitoring of the effects an intervention is having will be critical to its success – and this should be done in order to check and revise understandings of how change can be achieved, rather than simply recording progress. It is therefore imperative to make any evaluation as utilization-focused as possible, to ensure the requisite feedback is received to allow for timely adaptation.
  • Stimulating autonomous learning: In the face of complex problems, evidence shows that actors are more likely to be responsive to emerging evidence where it emerges in the context of trust and ownership. Monitoring and evaluation functions must be embedded throughout implementation chains, and the autonomy to shape M&E frameworks should be devolved.
  • Implementation as an evolutionary learning process: Experimentation through intervention may need to become the central driver of learning. This could be put center-stage in an evolutionary implementation process, revolving around variation, where a number of different options are pursued, and also through selection, where based on feedback from the environment, some are deemed a greater success and replicated.
  • Creating short, cost-effective feedback loops: Judicious use of participatory M&E and transparency may be important because who carries out the monitoring has proven a crucial determinant of effective adaptation. There are a number of local-level methods for citizen involvement in the governance of implementation available, including emerging innovation in systems for beneficiary feedback, and transparency and accountability initiatives.
  • Accountability for learning: Measures may need to be taken to ensure policies place explicit value on learning as well as delivery: intervention must be seen as an expression of hypotheses and complex tasks may require learning objectives rather than performance goals. Promoting innovation in service delivery may require valuing redundancy and variety.

How

Implementation systems and processes must draw on an eclectic mix of sources of knowledge at many different levels and junctures. Of particular importance are tools, which allow for the negotiation between and synthesis of multiple perspectives, for example:

 

  • Decisions from deliberation: Carefully managed and structured processes of deliberation have proven to have wide benefits on both decisions made and their subsequent implementation. These must be embedded in inclusive, face-to-face fora, focusing on eliciting reasoned and legitimate inputs to action.
  • Focusing on how change happens: Implementation processes must tie together analytical and management efforts with explicit questions as to how change happens in their context. Ideas and assumptions underlying implementation must be made explicit in order to allow them to be purposefully tested; planning tools such as ‘theory of change’ and theory-based evaluation may assist.
  • Realistic foresight: Foresight and futures techniques can be used to provide broad and realistic forward-looking analysis and fix shared structures for ongoing implementation. Tools such as scenario planning have proven invaluable in enabling organizations to be both resilient and nimble, so long as a broad range of perspectives are taken into account.
  • Peer-to-peer learning: Rather than focusing on technocratic knowledge-transfer processes, adaptation and learning may often work more effectively through peer networks, such as through study tours or ‘peer review’. Research on communities of practice has shown how the informal dynamics of linkages can be the driver of creativity and reflection.
  • Broadening dialogues: Processes of contestation and argument may be important for informing and improving the foundations of policy and action, and implementation should look to build and work with critical voices, rather than avoiding them. Promoting reflexive research is important as is building the capacity of disadvantaged stakeholders to fully articulate their position.
  • Sense making for common ground: A shared vision of the problem at hand is often a prerequisite for progress on complex issues. Key stakeholders must jointly negotiate concepts and models, and boundary objects such as shared models or standards can play a key role in anchoring collective action.
  • Facilitation and mediation: Efforts to combine different sources of knowledge must tread carefully, and policy-makers must become adept in managing power in the knowledge-policy interface. Power should be shared in both analytical and decision-making processes, with space made for critical reflection and the consensual resolution of impasses and conflicts.

 

So where are the approaches most relevant? In some sectors, ‘complex’ models of implementation are well-established and proven effective; in other areas, persistent and well-recognized issues with implementation seem to bear the hallmarks of the negative side-effects of traditional tools applied to complex problems. This research has not attempted to specify what problems should be considered ‘complex’, but to give readers the tools to decide for themselves whether an issue faced is complex, and to provide guidance on what to do if it is. The extent to which any one challenge exhibits the characteristics of these three dimensions is likely to be a matter of degrees, and the relevance of the principles and priorities set out above will vary accordingly. Implementation will likely require a mixture of these principles with more traditional approaches and similarly the tools presented above have a domain of appropriate application, and need to be applied well and with sensitivity to context.

What is clear, however, is that complexity can no longer be swept under the carpet. While there is not yet one comprehensive framework, there is a growing collection of models, tools, and approaches to effectively develop interventions in the face of these multifaceted problems. These will allow those charged with implementing policies and programs to be able to more explicitly, systematically and rationally deal with the challenges that are presented. However, taking responsibility for complexity is a double-edged sword. On the one hand, there are a new set of tools to use, and/or more legitimacy given to approaches not previously seen as ‘scientific’ or ‘rigorous’. But on the other hand, this will make areas of practice previously hidden from sight more visible, and actors will find themselves held accountable for aspects of their work which may have previously slipped under the radar. This shift may therefore represent an uncomfortable or unattractive transition. However, what is clear is that it is an essential transition in order to achieve results in the face of complexity.

 

 

 

 

Appendix I

Key concepts of complexity theory

These first three concepts relate to the features of systems that can be described as complex:

1. Systems characterized by interconnected and interdependent elements and dimensions are a key starting point for understanding complexity science.

2. Feedback processes crucially shape how change happens within a complex system.

3. Emergence describes how the behavior of systems emerges – often unpredictably – from the interaction of the parts, such that the whole is different to the sum of the parts.

Complexity and change

The next four concepts relate to phenomena through which complexity manifests itself:

4. Within complex systems, relationships between dimensions are frequently nonlinear, i.e., when change happens, it is frequently disproportionate and unpredictable.

5. Sensitivity to initial conditions highlights how small differences in the initial state of a system can lead to massive differences later; butterfly effects and bifurcations are two ways in which complex systems can change drastically over time.

6. Phase space helps to build a picture of the dimensions of a system, and how they change over time. This enables understanding of how systems move and evolve over time.

7. Chaos and edge of chaos describe the order underlying the seemingly random behaviors exhibited by certain complex systems.

Complexity and agency

The final three concepts relate to the notion of adaptive agents, and how their behaviors are manifested in complex systems:

8. Adaptive agents react to the system and to each other, leading to a number of phenomena.

9. Self-organization characterizes a particular form of emergent property that can occur in systems of adaptive agents.

10. Co-evolution describes how, within a system of adaptive agents, co-evolution occurs, such that the overall system and the agents within it evolve together, or co-evolve, over time.

Source: Ramalingam and Jones (2008).

 

Complex systems have features that are also complicated and may act in patterned ways, but whose interactions are constantly changing. An air traffic control system is complex because its functioning depends on many variables that keep varying, such as weather, aircraft downtime, peak loading, etc.

With complicated systems, one can usually predict outcomes by knowing the design (or having a detailed engineering manual at hand). In contrast, depending on the interplay of the elements in the system, complex systems may produce highly divergent outcomes.

It is impossible to predict the way a complex system will respond with sufficient accuracy. One is limited to establishing the conditions characteristic of a complex system that lead to emergence, then working creatively at leverage points to change the configuration of the system until the desired ends are produced.

 

Appendix II

Technical vs Adaptive or ‘Wicked’ Social Problems

Examples of adaptive or ‘wicked’ problems include reforming public education, restoring wet land environments, and improving community health. In these cases, reaching an effective solution requires learning by the stakeholders involved in the problem, who must then change their own behavior in order to create a solution.

Technical vs Adaptive problems:

  • The problem is well defined vs complex;
  • The answer is known in advance vs the answer is not known;
  • One or a few organizations have the ability to implement the solutions vs no single entity has the resources or authority to bring about the necessary change.

Characteristics of wicked problems:

  • There is no definitive formulation of an wicked problem that provides the problem solver with all the information needed to formulate the problem, break it into manageable chunks and solve it;
  • Wicked problems have no stopping rule. You can’t say you have solved an adaptive problem;
  • Solutions to wicked problems are not true-or-false, but good-or-bad;
  • There is no immediate and no ultimate test of a solution to an wicked problem;
  • Every solution to an wicked problem is a ‘one-shot operation’ because there is no opportunity to learn by trial and error. The consequences of intervention cannot be undone. History matters and provides the context for the next intervention;
  • Wicked problems do not have an exhaustively describable set of potential solutions, nor is there a well-documented set of permissible operations that may be incorporated into the plan;
  • Every wicked problem is essentially unique; solutions are not transferable;
  • Every wicked problem can be considered to be a symptom of another problem;
  • The existence of a discrepancy representing an wicked problem can be explained in numerous ways; each stakeholder will have their own perspective on the nature of the problem and the solution;
  • No single entity has the resources or authority to bring about the necessary change.

 

 

 


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About Freedom to...

Consultant helping people and employers in health care, education and government to see and understand how their organization works and how to change them to improve performance.
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