Planning With Complexity: An introduction to collaborative rationality for public policy
In an increasingly complex world, planners and public policy makers are finding that traditional approaches to confronting complicated challenges are proving inadequate. As such, resolution of complex problems has increasingly been sought from alternative theories and practices. Innes and Booher (2010) argue that collaborative decision-making is an effective way of overcoming division and conflict to craft sustainable policy solutions. To make sense of the emerging alternative theories and practices the authors outline a new theory of collaborative rationality. The new theory is intended to benefit planning and policy makers who seek to understand how, why, and where collaborative approaches can be used effectively.
To understand the need for a unifying theory of collaboration, it is necessary to understand three trends in the evolution of planning and policy making. First, traditional linear models that rely primarily on formal expertise are being discarded in favor of nonlinear socially constructed processes that engage not only experts, but interested stakeholders as well. Second, ideas for what constitutes appropriate knowledge for consideration by planners and policymakers are changing. Traditionally, only “scientific” knowledge was appropriate for consideration. Increasingly; however, people are recognizing the limitations of science to confront complex social problems. Finally, new forms of reasoning have begun to gain legitimacy. Instead of processes that rely on reasoning from means to ends using only logical steps and objective evidence, collaborative processes are increasingly drawing upon multiple experiences and a broad knowledge base to assemble solutions that transcend traditional reasoning.
In these trends the authors see the emergence of a new form of planning and policy making. They call it collaborative rationality; the basics of which have to do with the process of deliberation. A process is collaboratively rational when all the affected interests engage in a fully informed dialogue using techniques that ensure the legitimacy, comprehensibility, sincerity, and accuracy of the affected parties views regardless of their relative power. The authors stress that these are conditions to be aimed at, though they can never be fully achieved. However, even though consensus between the parties may not be achieved, so long as substantial agreement is reached among a supermajority, the results can be considered collaboratively rational. Furthermore, the authors emphasize that this process is parallel to the scientific method in that the processes are legitimate and rational by being well informed by collective knowledge and decision-making. At this point – having established a working definition – it is necessary to delve into the theoretical bases from which collaborative rationality is constructed.
To begin, the authors’ establish the foundation of collaborative rationality on the theory of phenomenology. Phenomenology is a way of knowing that seeks to remedy problems inherent in complex systems that logical positivism struggles to contend. Phenomenologists argue that knowledge is about phenomena as wholes, rather than divided into components. The goal of knowing is understanding, rather than explanation. To a phenomenologist, the uniqueness of each situation rather than its similarity to others must be emphasized. The goal is to develop narratives that make sense of the complex dynamics of particular situations rather than laws to apply across multiple cases. Thus, on the basis of interpreting phenomena, knowing can be more reflective, inventive, and sophisticated as each new situation is confronted with greater understanding and insight. But for the purpose of collaborative rationality, this perspective on knowing does not help because all knowledge is relative. The theory of communicative rationality (Habermas 1981) brought rationality to relative knowledge.
The theory of communicative rationality identifies conditions under which the results of deliberations can be viewed as rational. The theory holds that the conditions can never be perfectly achieved any more than a pure scientific experiment can be achieved in the social sciences. The first condition is that dialogue must be face to face between all of the differing interests comprising a deliberative body. Second, four speech conditions must be met: a) all utterances must be comprehensible among participants; b) utterances must be true – meaning they must be supportable by logic and evidence; c) the speaker must be sincere; and d) the speaker must have legitimacy. Finally, participants in the deliberation must question assumptions taking nothing for granted – persuaded only by the force of a better argument and not by power, ignorance, or peer pressure. Should these conditions be met, then the results are rational. Yet, while communicative rationality forms the foundation of collaborative rationality, the authors point to two other important theories that further inform, and confirm, the utility of collaborative rationality.
The first theory is a constellation of tried and true practices developed by negotiation and alternative dispute resolution (ADR) scholars. The groundbreaking book Getting to Yes (Fisher and Ury, 1981) provides an influential guide for the practice of collaborative rationality. The principles developed in the book have shaped the language that suffuses collaborative dialogues. The language includes phrases such as: separate the people from the problem; focus on interests not positions; invent options for mutual gain; insist on using objective criteria; and, develop the best alternative to negotiated agreement (BATNA). For example, if participants in a collaboration can avoid taking recalcitrant positions, and instead reveal to one another their underlying interests, they can potentially find common ground. Alternatively, to maximize negotiating power Fisher and Ury implore participants in a negotiation to constantly bear in mind their best alternative if there is no agreement. Thus, for a collaborative dialogue to create durable solutions, every participant must know what their interests are and be able to communicate and stand up for them.
The second theory – complexity science – provides a framework for understanding how a collaboratively rational process can thrive in a complex world. Complexity science focuses on large dynamic systems in which actions take place and suggests a holistic approach to the complex social issues that collaborative rationality seeks to resolve. Complexity science originated from the efforts of physical scientists to understand phenomena that could not be explained by linear mathematics. Because the systems they were attempting to study were nonlinear, system inputs failed to predict outputs. Lorenz (1993) discovered that small inputs often resulted in dramatic alterations in outputs and theorized that extreme responsiveness to small actions is one of the hallmarks of complex adaptive systems (CAS), commonly known as the “butterfly effect.” From this research, scholars have outlined features of CAS that are applicable to planning and policy (Cilliers 2005; Portugali 2006; Stacey 2001; Tsoukas 2005). In applying CAS to the social world the researchers recommend that practitioners bear five factors in mind. First, a CAS is made up of large numbers of individual agents connected by multiple networks. Second, interactions between individual agents are dynamic, meaning that the effects of interactions propagate rapidly throughout the system. Third, the interactions are nonlinear with many direct and indirect feedback loops. Fourth, the system is open to external environmental stimulus with resultant behavior determined by these interactions with the environment. Finally, with sufficient internal diversity actions will evolve as individual agents adapt to one another. From this, Innes & Booher conclude that planning and policy professionals need to operate at the systems scale rather than focusing on piecemeal individual fixes. To do so, parties to a collaboration must concentrate on the dispersal of intelligence that is linked by networks and dialogue among diverse parties that are dedicated to searching out many types of knowledge.
Drawing on the aforementioned theories, the authors developed DIAD theory as a framework for structuring the conditions of collaborative rationality. DIAD stands for: Diversity; Interdependence; and Authentic Dialogue. Thus, for a collaborative process to be “collaboratively rational” the conditions of the process must include: 1) the full diversity of interests among participants; 2) interdependence of interests among participants who cannot get their interests met independently; and 3) engagement of all in the face of an authentic dialogue that is qualified by meeting the basic speech conditions laid out by Habermas. If these conditions are met the authors predict that the dialogue can produce innovations that lead to an adaptive policy system in the context of complexity and uncertainty.
The authors also predict that an adaptive policy system will produce four further results. First, participants will discover the reciprocal nature of their interests. Instead of competition, participants will discover that meeting their own interests can come from working with the reciprocal interests of others. Second, stakeholders will develop new relationships that often survive the conclusion of the collaborative process. Colloquially, the participants will learn what it means to “walk in the shoes” of other participants. Third, both single and double loop learning will occur. This means that participants not only learn from each other about new actions and strategies, they may also rethink their initial goals and interests in the policy issue. Finally, collaborative rationality can lead to second and third order effects that the authors consider adaptations to the system because they transcend the agreements and process itself.
Like any new theory, criticism of collaborative rationality stems largely from those defending the conventional wisdom; namely, the logical positivists (Galbraith 1958). The second half of the twentieth century reserved the term rational for a particular approach to public decision-making. The conventional wisdom holds that public decisions should be based on objective data, logical deductive analysis, and the systematic comparison of alternatives. The model implies that neutral experts should gather, compile, and analyze data that decision makers can then use to make public policy (Popper 1966; Bernstein 1976). Importantly, the model also assumes the world can broken into analytically manageable components that can be disaggregated and fixed independently like the parts of a machine. Adherents to this model understandably exhibit a deep distrust of data that cannot be quantified.
In the social sciences, rational choice theory embodies the positivist ideal and is premised upon the assumption that we live in a world where people are out for themself alone. A “rational” person is one who makes deliberate, calculated choices designed to serve selfish interests and does not assume that anyone will cooperate of assist them. Early research on game theory seemed to confirm this conclusion by citing the prisoner’s dilemma. The prisoner’s dilemma purports to show how and why individuals would rationally choose to benefit themself, while fully aware of the harm they are doing to others. It is not difficult to surmise why adherents to the rational choice model are critical of collaboration. Why would an individual collaborate with others; thereby, compromising a chance to benefit fully by going it alone? The answer lies in research showing repeated prisoners dilemma games produce cooperative behavior so long as the players know the game will continue (Axelrod 1984). Removed from the laboratory, it is readily apparent that life goes on.
My experience leads me to believe that collaborative rationality can be a powerful tool for filling a void left by the positivist model’s inability to effectively contend with an increasingly complex world. While not applicable, or even necessary for addressing the bulk of day-to-day problems that the positivist model is sufficient; collaborative rationality has the ability to resolve wicked problems. One wicked problem that I am uniquely qualified to comment on involves landscape planning and policy.
In 2008, Congress passed the Collaborative Forest Landscape Restoration Act. The Act establishes a competitive program that allows the federal government to match funds with regional collaborations seeking to accomplish forest restoration projects at the landscape scale if a committee of forest resource specialists selects them. Over the past thirty years parties interested in land use decisions made by federal forest resource managers have multiplied. Concurrently, competition for dwindling resources has exacerbated the tensions between these groups. The current climate between federal forest resource managers and competing user groups perfectly exemplify the definition of a wicked problem. The question is, can collaboration overcome these acrimonious tensions to find a solution that everyone can live with? Early evidence indicates that it can. While results vary as predicted by Innes and Booher, of the ten regional collaborations approved in 2009, only one has been held up through legal challenge. It would be premature of me to comment at this point in my research on this subject why, or why not any individual collaboration has succeeded or failed. Suffice it to say that the results are an encouraging indication of the author’s theory.