Chapter 2: Literature Review
Positivism, complexity, and communicative action form the three branches of theory relevant to using games in planning. Each corresponds to a particular planning framework: positivism to rational planning; complexity to incremental planning; and communicative action to collaborative planning. This literature review describes the tenets and brief criticisms of each theoretical branch, including how each larger theory applies to its corresponding planning framework; analyzes how planners can implement games in each framework (see table 2.1); and describes benefits and roadblocks to using planning games. I describe participatory action research (PAR) as a lens for public engagement and games as a type of PAR. This review does not focus on virtual games, computer games, or computer simulations.
Positivism and Rational Planning: Using Games for Testing
The University of Chicago developed rational planning as part of the systemic efforts to apply the scientific method to the social sciences (Brooks 2003). The scientific method (or hypothetical-deductive approach) follows four basic steps: (1) hypothesis, (2) deduction, (3) test, and (4) confirmation. The scientific method, which relies on gathering data through observations, emerged as the legitimized methodology for discovering scientific knowledge in the positivist framework. This method of inquiry is based on three tenets. One, legitimate knowledge consists only of observational statements and deductive links between such statements. Two, science’s ultimate goal is a unified system of statements and axioms logically connected to reality with objective observations. Three, statements are certain only if they are grounded in observation and belong to the logically unified, axiomatic system. Positivism recognizes only one realm of knowledge, and the goal of scientific inquiry is to discover that unified body of knowledge. Because there is only one universal and objective body of knowledge, only one legitimate and unified methodology exists to discover that knowledge: the hypothetical-deductive method (Polkinghorne 1983).
Ideal scientific theory exhibits six characteristics: it is explicit, universal, abstract, discrete, systematic, and complete and predictive (Flyvbjerg 2001). The scientific method aims to develop observable singularities in support of the ideal, unified theory. In classical science, the hypothetical-deductive model works in two directions, with the ability to explain past phenomena and predict future phenomena. Moreover, scientific explanations must meet two requirements: explanatory relevance and testability. Explanations can be either deductive-nomological (deductively related to universal laws) or probabilistic (inductively related to probabilistic laws) (Hempel 1966). Testability is accomplished by following the scientific method, which provides a unified methodology that can both falsify hypotheses and provide a replicable process to follow. If results can be replicated (the hypothesis is or is not repeatedly falsified), then the knowledge is more likely to be universal and, thus, true knowledge.
Rational planning, in the positivist framework, conforms to the scientific method by following a series of steps: (1) identify goals; (2) identify alternatives; (3) describe consequences of each alternative; (4) select an alternative; (5) implement the selected alternative; and (6) evaluate the results. This framework remains a dominant paradigm in planning practice today (Brooks 2003).
Critiques of Rational Planning
Critics of rational planning find this framework unrealistic because it presumes rational behavior (Brooks 2003). This requirement of normative rationality is infeasible in planning practice (Alexander 1984). Rational planning fails to account for politics or power (Brooks 2003) by ignoring the political context in which planning occurs (Alexander 1984). Rationalism accentuates utility as the goal, ignoring distributive effects of policies (Brooks 2003). Davidoff (1965) argues that in order to solve the social equity problems of society, planning must take an approach beyond mere technical problem-solving. The rational paradigm thus fails to address the political and ethical considerations inherent to planning (Klosterman 1985). Rational planning also downplays public participation (Brooks 2003), which is desirable in a field that purports to serve the public interest. In addition, as a social science, planning has limited potential for developing predictive theory. The ability to make predictive theory is a primary distinction between the social and natural sciences (Flyvbjerg 2001); attempts to develop predictive theory in planning, therefore, are misguided.
Games in Rational Planning
Early game theory discussed strategic games as determined, such that all possible outcomes are predictable. Strategic games are those whose outcomes depend on the strategies and skills of individual players involved, in contrast to probabilistic games (games of chance). Early game theorists of the mid-twentieth century focused on discovering solutions to specific games and replicating the solutions of other theorists (Dimand and Dimand 1992), much in the paradigm of the scientific method. Especially in the 1920s through early 1940s, game theorists (usually mathematicians) studied two-person, noncooperative games (Leonard 1992). These zero-sum, or win/lose, games involved two players who each strategize independently to maximize her own best outcome at the expense of the other player. Von Neumann and Morgenstern, the founding fathers of game theory, demonstrated finite solutions for all zero-sum games (Raiffa 1992). In other words, under certain conditions, all possible outcomes of a game were finite and predictable.
Roth (1995, 22) categorizes three uses of games that fall under the positivist framework: “Speaking to Theorists,” “Searching for Facts,” and “Whispering in the Ears of Princes.” The first category includes experimental games intended to provide further evidence in support of theory. The second category refers to experimental games intended to discover and isolate the effects of variables. The third category involves experimental games intended to provide empirical evidence for policy choices (Roth 1995). Ostrom, Gardner, and Walker’s (1994) work reflects Roth’s third category of game uses, where games provide a method of testing rule changes in order to identify policy changes that will result in optimum outcomes. Additionally, games can be used experimentally to understand the functioning of a system by altering rules and comparing outcomes that result from different rule sets (Mayer 2009), reflecting Roth’s (1995) second category of game uses. Games in the positivist framework are rigid-form, rather than free-form, and rely on tightly prescribed, limited rules for action (Mayer 2009). These strict rule sets enable games to be replicable and conform to the scientific method. Games can also be used for testing models, as demonstrated by Gourmelon et al. (2013).
Complexity and Incrementalism: Using Games for Prediction
Incrementalism developed in response to the failings of rational planning (Brooks 2003). Lindblom (1959) describes the impossibility of using the rational framework to address complex problems; instead, decision-makers engage in a series of smaller decisions, using previous results and experience to dictate current decisions. In this model, a planner makes small, incremental steps, iteratively evaluating results, instead of attempting a comprehensive solution (Lindblom 1959). While incrementalism developed before complexity theory was first described, it represents an application of this theoretical framework to planning practice.
Complexity theory describes dynamic, complex systems, which are not the simple, linear systems of classical science, and identifies a number of defining characteristics. First, any phenomenon studied is part of a system that is more than simply the sum of its parts. Classical science is reductionist in nature, seeking the smallest individual agent or variable that can be isolated and related to other variables. Complexity theory argues that the characteristics and behaviors of the entire system cannot be explained simply by the characteristics and behaviors of the system’s components. The interactions, and results of interactions, between these components form a whole, complex system (Allen and Holling 2010) such that the sum of a system’s components is greater than the individual components. Juarrero (1999) calls this correlation and coordination between parts a defining characteristic of a system (versus an aggregate). These interactions form an underlying order and structure (Alberti 2008), which consists of both organization and internal and external structure (Juarrero 1999). These emergent properties are characteristic only of the whole system, not of the individual components of a system.
Complex systems exhibit nonlinearity, including positive feedback loops (Arthur 1994). Nonlinearity occurs when the results (outputs) of a system’s interactions are not proportional or predictable based on the inputs (Alberti 2008). Complex systems are open systems that interact with their environment, rather than the closed systems of classical science. Most systems are open systems; “[o]nly the entire universe is closed and isolated” (Juarrero 1999, 110).
Complex systems are hierarchical, such that the upper-level structures constrain the behavior of those structures nested below (Alberti 2008). Systems also do not have a single, stable point of equilibrium; multiple equilibria are possible (Arthur 1994). Moreover, path dependency shows that current driving forces only partially explain the current state of a complex system. As chance events occur, the system develops along a different set of pathways, and the interaction rules evolve as the system evolves. Small changes at the beginning can result in quite dramatically different outcomes (Arthur, Ermoliev, and Kaniovski 1994). Gould (1990) calls this contingency; Arthur (1994) refers to this as historical path dependency; and Smith and Jenks (2006) describe this as the current state of the system relating to its previous states.
Social scientists have applied complexity theory in a variety of ways, including the search for underlying structures and their effect on human behavior. Byrne and Callaghan (2014) describe participatory processes as a way to glean information from actors in a system whose actions have effects on that system. Indeed, they argue that society’s complex systems “can only be understood and changed if the role of reflexive human agents is recognized and incorporated within the whole process of understanding as a basis for change” (Byrne and Callaghan 2014, 249). The emergent characteristics of societal systems depend on the individual interaction and behavior of individuals. These characteristics cannot be understood without looking at the entire system, the actors within it, and the interactions between actors and between actors and their larger environment. The emphasis here is on the structure of society (the system) and how human agents influence that system (emergence). By understanding this, one can discover how to change the system’s structure in order to effect change on human behavior — in other words, how to change the hierarchical structure in order to constrain the behavior of individual agents.
Incrementalism fits with complexity theory in its recognition of path dependence. Rather than attempting a comprehensive, overarching solution, incrementalism allows for continual evaluation of current conditions, with smaller actions aimed at addressing current conditions. Incrementalism also recognizes the limits of bounded rationality in the face of complex, open, nonlinear systems, proposing a series of small, reactive steps rather than a large effort aimed at wholesale change of the system. Hopkins (2001) echoes these ideas with his characterization of major planning decisions: they are interdependent, indivisible, irreversible, and are made with imperfect foresight. Planning is therefore the setting of a path (Hopkins 2001) and the continual adaptation to the results and anticipated outcomes of that path.
Critiques of Incrementalism
Critics of incrementalism argue that this framework discounts situations where comprehensive planning is appropriate or necessary. By its nature, incremental planning addresses problems in a piecemeal fashion, which is not appropriate for new problems or for addressing public dissatisfaction with current policies. Incrementalism is politically conservative, favoring the status quo and leaving current institutions unchallenged. Similarly, this framework favors powerful members of society in that it encourages planners to make decisions that are feasible, that is, supported by those already in power. Incrementalism suggests only small solutions for large problems with its emphasis on small steps. The potential for disaster and the difficulty in addressing a wrong course of action both increase under this framework as well (Brooks 2003).
Games in Incrementalism
Game theory revolutionized economics; instead of the individual, rational man of neoclassical economics, who makes decisions in isolation, the rational man of experimental economics makes decisions in the context of the decisions made by other players (Schotter 1992). Game theory also spread to other social sciences in the mid-twentieth century, particularly sociology and political science. Researchers applied strategic game theory to operations research, management science, and world politics in the post-World War II era, accelerating social science’s acceptance of this model. Researchers in other disciplines applied von Neumann and Morgenstern’s 1940s-era work to account for other sociopolitical behaviors related to cooperation and competition. Game theory aimed to discover the principles behind rational choice (decision) behavior; social scientists formulated problems as problems of decision, using strategic games as explanations for social phenomena (O’Rand 1992). Games thus became a way to understand and predict the behaviors underlying social problems.
Game theory rose to prominence well before the development of complexity theory, but both share common concerns: path dependency of outcomes, multiple possible equilibria, and emergence of behaviors based on interactions between components (players). Morgenstern saw games as a way of predicting mutually exclusive institutional arrangements that could arise out of a given set of conditions: what complexity theory terms path dependency and multiple equilibria. Game theory modeled multiple equilibria with games that had more than one possible solution (Schotter 1992). Morgenstern emphasized unplanned social structures rather than planned institutions (Schotter 1992), and other game theory analyzes outcomes in terms of interactions between players (Riker 1992): what complexity theory describes as emergence.
Games in the incrementalist framework are more free-form than in the rational planning framework. Free-form games have been used for predicting actions and responses within specific contexts, and for contingency or scenario planning (Mayer 2009). Games are particularly suited to address environmental dilemmas, such as common pool resource problems, as demonstrated by the rich literature on this application (e.g., Ostrom, Gardner, and Walker 1994; Kartez 1991; Gourmelon et al. 2013; Krolikowska et al. 2007; Rivera, Sheer, and Miller 2013; Simpson 2001). In particular, games can be used empirically to predict or model the real-world outcomes of resource management policy changes, including individual behavior, as demonstrated by Ostrom, Gardner, and Walker (1994). While the initial proponents of game theory presented it as a normative theory, it quickly developed as a descriptive theory, such that games are used to describe behavior, particularly strategic choice behavior (Riker 1992).
Complexity theory and incrementalism recognize the interdependent relationship of hierarchical structure and emergent behavior. Policies act as hierarchical structures in planning, as do rules in games, and these structures determine possible outcomes. Planners often deal with social dilemmas like common pool resource problems, so it is important to structure these situations to encourage success (Kartez 1991). Rules constrain games and act as the hierarchical structure in complex systems. The rules of a game govern player behavior and avoid chaos (Liberman 2013). By changing the rules of a game, possible outcomes change. In fact, a small rule change can result in large effects on outcome (Ostrom, Gardner, and Walker 1994), reflecting the nonlinear dynamic of games and reinforcing Arthur’s (1994) observation that small initial changes can produce dramatically different outcomes. By examining the effects of rule changes on game outcomes, planners can analyze policies effectively (Ostrom, Gardner, and Walker 1994) and predict real-world effects.
Communicative Action and Collaborative Planning: Using Games for Problem-Framing
Collaborative planning arose as another response to the rationalist planning paradigm dominant through the mid-twentieth century. Innes and Booher (2010) describe three trends underlying this shift away from rationalism: (1) the move toward including stakeholders along with experts in the decision-making process; (2) the increasing recognition of scientific knowledge’s limits and the legitimacy of other forms of knowledge; and (3) the inclusion of forms of reason other than instrumentalism (e.g., storytelling). In this paradigm, knowledge is validated communally through dialogue, which forms the basis for action — communicative action — and planning becomes a form of collaboratively deciding how to act (Healey 1992) with the planner as facilitator of community decision-making processes (Brooks 2003).
Collaborative planning allows for multiple forms of knowledge and depends on dialogue both to develop and validate knowledge and to decide among competing knowledge claims. Participants critique knowledge claims based on Habermas’s criteria of comprehensibility, integrity, legitimacy, and truth, which allows for a rich plurality of voices (Healey 1992). Rather than reason being an individual undertaking, measured against scientific knowledge and logic, Habermas describes reason as a contextualized “mutual understanding,” developed by a particular group of people in a particular place and time (Healey 1992, 150). Collaborative planning views communication (both verbal and non-verbal) as the essence of planning, with the planner working to build consensus between conflicting parties (Brooks 2003). Collaborative planning proposed to address the false duality of rationalism and relativism, with a way to construct knowledge collectively as a basis for action.
Innes and Booher (1999) describe planning through consensus-building as a way of learning, rather than simply an arena for communication. Consensus-building in the communicative planning paradigm involves four steps: storytelling to describe the situation, task-setting, dialogue, and consensus (Innes and Booher 1999). They describe role-playing and Levi-Strauss’s (1966) bricolage (referring to a process of assembling and recombining disparate ideas in order to form new knowledge) as mental models for the planning process. Stories and storytelling are central to knowledge and action in planning, including the production of knowledge and translation from the social sciences (Sandercock 2003).
Forester (1999) describes a transformative theory of social learning that applies to the learning possible in deliberative planning exercises. The type of learning he discusses occurs when both individuals and their arguments change through the process of dialogue and negotiation. We learn from what others say and how they say it. Forester’s logic extends to the type of learning possible through role-play: we learn from what we say and how we say it, while acting the part of someone else.
Decisions and agreements are one important product of collaborative planning processes. However, collaborative processes also build community capacity, deepen policy knowledge, create contextual solutions, encourage creative problem-solving, build capital (social, political, and intellectual), and empower underrepresented groups (Innes and Booher 2010; Forester 1999).
Innes and Booher (2010) argue that collaborative processes are well suited to addressing wicked problems. Wicked problems are those that are difficult to define and separate, for which resolutions are difficult to find, and for which no optimal solution exists. Wicked problems are identified as such only through the problem-solving process. Most planning problems are wicked problems (Rittel and Webber 1973). Without an optimal solution, the planner’s task is, jointly with or on behalf of the public, to find a resolution that improves the situation. Collaborative processes build community capacity that allows for this process of discovery. As collaborative processes, games are suited to engaging the public to frame wicked problems and develop possible solutions. Indeed, for the resources invested in a collaborative process to be worthwhile, the problem should not be straightforward (Innes and Booher 2010).
Critiques of Collaborative Planning
Critics of the collaborative planning approach note that this framework idealizes the public’s preferences, assuming that more dialogue among empowered stakeholders will always result in better outcomes and that all views are noble. However, this approach ignores the plurality of voices, their sometimes radically divergent views, and the narrow-minded and prejudicial beliefs of some members of the populace, even as it purports to include them through the collaborative construction of knowledge. Consensus and agreement are not guaranteed, even (or especially) when including all affected stakeholders. Collaborative planning overlooks the unequal distribution of power, again emphasizing communication as the panacea for society’s ills. This framework also highlights process rather than content, assuming that any outcome is desirable as long as communication among affected stakeholders occurs. Additionally, some mundane planning activities, such as collecting information and data about a problem, may not be suited to the intensive process of collaborative planning (Brooks 2003).
Games in Collaborative Planning
While game theory has not specifically addressed communicative action or collaborative planning, some parallels exist, particularly in cooperative games. The prisoner’s dilemma is a well-known example of a situation where the outcome is better if participants can cooperate (Roth 1995). Ledyard (1995) proposes that many public goods provision problems are prisoner’s dilemma problems. Ledyard’s assertion suggests that, in such circumstances, cooperation among parties may result in better outcomes.
In a series of empirical and field experiments, Ostrom, Gardner, and Walker (1994) demonstrated that cooperation between players increased and destruction of common resources decreased with institutional measures, face-to-face communication between players, and opportunities to sanction other players. Far from Hardin’s (1968) tragedy of the commons, participants in these games collaborated to develop efficient management strategies. When the researchers provided participants with sufficient information and an arena in which they could communicate, participants collaboratively developed management and sanctioning strategies (Ostrom, Gardner, and Walker 1994). This emphasis on communication echoes the same emphasis in the collaborative planning framework.
In Innes and Booher’s (2010) collaborative process model, role-playing is a specific type of dialogue that allows trying on and acting out different ideas and scenarios. Innes and Booher (1999; 2010) focus on the actual roles participants play in their lives, however, both as individuals and as representatives of their stakeholder groups. They assume participants have at least a minimal understanding and awareness of their roles, and that these roles differ from those of other participants. In Innes and Booher’s description of consensus-building, participants role-play within their real-life roles, with the freedom to experiment outside what would be acceptable in the outside world. They cite three primary advantages of role-playing: the potential for deep learning, the development of innovative thinking, and the opportunity for play. Play is particularly important, as it allows for creative processes and full engagement without the restrictive boundaries of intractable problems (Innes and Booher 1999).
A role-playing game used as a participatory tool on Ushant Island, France, demonstrates the potential to use games for participation and for model testing. The game’s goals were to provide details for a model of environmental management, raise community awareness, and present the model to biosphere managers. Games used in this way are an effective way to incorporate local knowledge, allow common access to knowledge, legitimize the model/game approach, and promote knowledge acceptance and viewpoint sharing (Gourmelon et al. 2013). The communication and dialogue between participants, and between participants and researchers, reflects the collaborative planning approach.
Games can address the imbalance of power inherent in many public planning processes. As demonstrated in Brazil, role-playing games are effective in engaging and educating populations lacking the technical skills to otherwise participate in public processes (Camargo, Jacobi, and Ducrot 2007), thereby empowering these populations. While it is tempting to equate gameplay with the idealized consensus approach of Habermas’s communicative action, a better approach is the recognition that power is not just a destructive force, but also a generative force. This reflects Flyvbjerg’s second approach to power, after Machiavelli, Nietzsche, and Foucault: the story of power is told through real histories of conflict. While “consensus” is often a stated goal of planning, a framework that suppresses conflict may also suppress freedom in the pursuit of no-matter-what agreement. Instead, participants who collaborate are willing to work together. Collaborative frameworks allow for allocating power as a way of recognizing the potential for conflict (Shdaimah and Stahl 2012). Conflict is not suppressed, but is given voice, and the opportunity for transformative learning through argument and deliberation (Forester 1999) appears. Role-playing simulation games allow for freer expression of this conflict with less fear of real-world repercussions.
Benefits to Using Games
Simulation games are an excellent way to engage the public in the planning process because of their capacity for teaching complex subjects (Kennedy 1973). These games are particularly effective at integrating the complexity of both technical-physical and social-political aspects inherent in policy problems (Mayer 2009). Games allow the public to participate actively, gain hands-on experience, and address issues concretely (Gourmelon et al. 2013). Gameplay makes situations less formal, less tense, and more relaxed (Camargo, Jacobi, and Ducrot 2007).
Role-playing games provide opportunities for integrating three types of learning: learning of concepts, of methods and strategies, and of behaviors and customs (Camargo, Jacobi, and Ducrot 2007). Games are effective learning environments because they are responsive and reflexive: they respond to the participant’s actions and allow her to reflect on her learning experience (Kennedy 1973). In addition, games bridge social divides and improve dialogue between players, creating an environment more conducive to learning (Camargo, Jacobi, and Ducrot 2007). Players feel a sense of safety within games, further facilitating a learning environment with freedom to experiment and create (Mayer 2009). Games also teach interpersonal skills, problem structuring, and decision predicting (Krolikowska et al. 2007). The individual and social learning that occurs during gameplay is transferable to the real world (Mayer 2009). Participating in a role-play allows participants to identify with the character they are playing and more carefully consider the game’s scenario (Wheeler 2006). Games build capacity, particularly for marginalized groups (Camargo, Jacobi, and Ducrot 2007). Games provide an arena for providing participants both information about the planning problem and space for meaningful willingness to cooperate. These elements increase participants’ willingness to cooperate on a solution (Kartez 1991; Ostrom, Gardner, and Walker 1994).
Potential Roadblocks to Using Games
A number of roadblocks to effective game implementation exist, most of which the planner can overcome through thoughtful design. Participants may not trust the game facilitators if the game involves scenarios where participants role-play themselves, as this may feel too intrusive. Participants may express this distrust through disengagement or even absenteeism. Planners must pay careful attention to the purpose of the game in the context of their communities. For example, researchers in France received feedback from their participants that the game was better suited as an educational, rather than a mediation, tool, so they revised their implementation strategy in order to accommodate this context (Gourmelon et al. 2013).
Even with well-designed role-plays, the facilitator ultimately holds authority and power that the participants lack. Van Ments (1999) suggests two ways to overcome this barrier: by acknowledging explicitly that the facilitator is in control or by involving participants in the design of the role-play. While van Ments argues that the facilitator cannot overcome this power imbalance, a facilitator can approach the game as a collaborative process, where power is shared and expression of conflict is permitted (Shdaimah and Stahl 2012). When used as a public engagement tool, including members of the public in the design of the role-play may also diffuse the potential power imbalance.
Role-plays run the risk of stereotyping characters and of making participants feel shy, frustrated, or disengaged (Sandercock 2003). One way to overcome the stereotyping risk, as well as increase relevancy and saliency, is by involving participants in the design of the game. That way, planners gain a better understanding of the situation and which roles are important (Camargo, Jacobi, and Ducrot 2007). Role bias is another form of stereotyping in which participants are assigned roles that correspond to the stereotypes of their gender, race, or ethnicity. Random assignment of roles may reduce this bias. In addition, if the facilitator observes instances of stereotyping, this can become a topic of discussion in the debriefing (van Ments 1999).
Games that are too simple may have limited potential for learning and limited applicability in the real world (Camargo, Jacobi, and Ducrot 2007). Similarly, achieving the right degree of abstraction determines how salient the game is for participants. Game designers need to balance the abstraction needed to free participants from their daily life with the concrete reflection of reality needed to apply game lessons to real life (Dionnet et al. 2007). By their nature, games can only address a single situation at a time and engage a limited number of stakeholders (Camargo, Jacobi, and Ducrot 2007). Repetition of the game with a broader range of stakeholders can engage more participants.
Participatory Action Research and Public Engagement
Participatory action research (PAR, also called participatory research or action research) is a type of collaborative research as described by Shdaimah and Stahl (2012). This form of research integrates non-academic stakeholders into the planning, implementation, and interpretation of research. PAR provides a conceptual framework for public engagement in the planning process. PAR includes three emphases: research, education, and action, particularly sociopolitical action (Fals-Borda 1991). The popular participation aspect of this type of research is particularly important, as PAR researchers emphasize that “the vision and view of the world that is produced by the many will be more humane, rational and liberating than the dominating knowledge of today that is generated by the few” (Gaventa 1991, 131).
Polkinghorne (1983) and others describe the pluralistic nature of epistemology, that is, the existence of multiple knowledges. A single truth corresponding to reality does not exist; instead, communities accept some truths, and this multiplicity of views composes reality (Ogilvy 1977; as cited in Polkinghorne 1983). Polkinghorne recognizes Ogilvy’s “communities” as varying “systems of inquiry,” such that each system of inquiry represents a particular epistemology. Epistemological pluralism allows for more than one type of knowledge. Indeed, only by combining and contrasting differing knowledges can one’s own framework be contextualized and understood. A fuller understanding develops by admitting alternate frameworks of knowledge. Multiple knowledges enrich our understanding of the world. PAR seeks to empower participants not only to produce this knowledge, but also to recognize themselves as sources of legitimate knowledge (Rahman 1991).
The need for participation in the planning process is well established. PAR relates to public participation on two levels. First, PAR can be used in its traditional sense, in order to collaborate with the public in pursuit of research (discovering new knowledge), education (group learning), and action (empowering citizens). In this framework, PAR emphasizes the production of local knowledge, deemphasizing but not devaluing formal or scientific knowledge. Citizens become the sources of knowledge (Fals-Borda 1991), rather than the passive recipients of it.
Second, PAR points the way toward a specific framework of public engagement — one that seeks to discover and incorporate local knowledge into the process. Among other goals, PAR generates group knowledge that may indicate a course of action (de Roux 1991). In the planning process, PAR can serve to define a problem and seek solutions to that problem, rather than using the public engagement process simply to rubber-stamp decisions already made by experts. PAR also dissolves the hierarchy between researcher and researched (Gaventa 1991), such that all participants are on equal footing; Fals-Borda (1991) describes this breakdown in asymmetrical relationships as the essence of participation. Action research provides a means of increasing community capacity, particularly in leadership (Forester 1999). Viewed through the communicative action lens of planner as facilitator, PAR points us toward a more effective engagement process, one where citizens’ knowledge is valued, all participants learn together, community members build their individual and collective capacities, and new courses of action emerge.
Games as Participatory Action
Games in the participatory action research (PAR) framework engage two types of potential game participants: planners and the public. My study will address the first type, with planners as stakeholders in the ultimate results of my work. I intend my game framework as a planning tool; planners, as the end users of this tool, have a stake in the results of my work. By including a group of practicing planners in my research as participants, my work becomes collaborative. The game will allow for a collection of voices, rather than my own voice having the final authority. My study will contribute what Shdaimah and Stahl (2012) call a “thoughtful reflection” to society by introducing a new planning tool directly to those who will use it. However, my approach is not prescriptive; it is collaborative, seeking to collect the experience and practical judgment of professional planners and learn with them through the implementation and interpretation of my study.
The second type of game participant is members of the public. As a public engagement tool, playing games highlights and makes explicit a multitude of voices. While participants are challenged to act the part of other characters in the gameplay itself, the debriefing period is perhaps more important for the process of learning (van Ments 1999), especially communal, collaborative learning. During the debriefing, participants have the space to express their opinions and talk about their perceptions of the process. Forester (1999) notes that transformative learning in planning occurs through reframing ideas and critiquing expert knowledge (among other activities). In my game structure, participants will reframe their ideas about the scenario first by acting the part of a character in the game, and second by reflecting on this shift of viewpoint. Players will also have the freedom to critique expert knowledge during the game. I will present the “expert knowledge” as part of the scenario in the form of solutions to the problem. I will give participants some hints about their practical, local, lifeworld knowledge about how these solutions may operate in the real world once they are implemented. The structure of the game deemphasizes the formal, expert knowledge and provides permission to critique it using practical knowledge. Players are thus given the safe space to critique formalized knowledge — enhancing the transformative learning potential of the process — while practical knowledge is put on equal footing with formal and technical knowledge. The stakeholders integrate fully into the process as equal participants.
Games provide opportunities for learning based on experience and mistakes. My research achieves this experiential learning for both the study participants and the researcher. For the study participants, gameplay affords the freedom to make mistakes in a safe environment without lasting consequence. If the characters in the game fail to make a recommendation about the preferred course of action, no real-world situation, institution, or relationships are at risk. Instead, the planner-participants are afforded a learning opportunity, discovering why no solution was reached, discussing the difficulties of compromise in the face of multiple viewpoints, and speculating how future processes (a replay of the game, perhaps) may be structured to favor different outcomes. In addition, after playing the game, participants will have the experience of gameplay in a planning setting — experience they can continue to learn from and use in future professional situations.
For me as researcher, the experiment is essential to the applied aspect of my research. The final result of my thesis will be a framework for practicing planners to use in the design and implementation of games. While I could simply prescribe a process for designing and implementing games, my process will remain untested without the opportunity to apply my research. The test serves as an opportunity to learn from experience and mistakes, and iteratively incorporate that learning into my work.
Summary
Three branches of theory provide frameworks for describing, understanding, and using games. Rational planning corresponds to positivism. This framework emphasizes the importance of testability, experiential learning, and defensibility in planning. Games used for testing fit into this framework. Games can be used to test the effects of policy changes on outcomes, to understand the functioning of a system, or as empirical evidence for policy-making.
Incremental planning corresponds to complexity theory. This framework emphasizes the importance of understanding historical path-dependency and limited prediction ability. However, games used for prediction fit into this framework, particularly when used as scenario planning exercises, in order to understand the bounds within which outcomes are likely to fall. Games can be used to predict the range of outcomes likely from implementing new policies or new rule changes.
Collaborative planning corresponds to communicative action. This framework emphasizes the importance of communally constructing knowledge through dialogue. Games used for problem-framing and public engagement fit into this framework. Games can be used as part of deliberative planning practice, to enhance individual and group learning, to build bridges across social divides and build social capital, and to express conflict with less tension.
Games provide many opportunities for learning and participating in a safe environment. This learning is transferable to real-world situations. Careful attention to the design and implementation of games can overcome some of their potential roadblocks. Participatory action research (PAR) provides a lens for understanding and designing public participation processes. Games are a type of PAR and provide many learning opportunities for participants and the facilitator.
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