Rationalism's Response to Planning Uncertainty——Rationalism in Recognizing and Responding to Uncertainty in Urban Planning (2)

3. Rationalism’s Response to Planning Uncertainty #

As the emergence and development of planning ideas often present a spiraling upward state, and many ideas and methods have appeared in the early stages of planning, undergoing several generations of updates and corrections, this paper mainly summarizes the background of rationalism that led to these ideas and the specific methods used to respond to uncertainty. The chronological order of this paper roughly follows the stages of Western planning thoughts in the 20th century as divided by Wu Zhiqiang and Qiu Baoxing [9,10], and appropriately combines and condenses them according to the development of uncertainty.

From the early 20th century to before World War II, urban planning generally lacked recognition of uncertainty. Although urban planning had moved beyond the excessive pursuit of urban forms and patterns, it still believed that the future of cities was predictable, controllable, and changeable to a large extent. Most planners thought that the task of urban planning was to draw a beautiful blueprint, detailing a wonderful vision of the city in several years, and believed that the realization of the blueprint could be accomplished through technical means [11]. The recognition and exploration of uncertainty mainly occurred after the 1960s, hence this paper focuses on the period from the 1940s to the early 21st century.

3.1 Initial Recognition of Uncertainty through Instrumental Rationality: Comprehensive Planning, Systems Planning, and Quantitative Analysis #

3.1.1 Instrumental Rationality’s Recognition of Uncertainty #

The concept of instrumental rationality was first proposed by the Frankfurt School in Germany in the 1940s, emphasizing the driving role of science, technology, and tools on social culture and art [12]. Weber further suggested that instrumental rationality solves the problem of maximizing benefits, but in this process, actions pursuing moral, cultural, and value goals are ignored, i.e., value rationality is neglected [13].

From after World War II to the 1960s, systems theory, information theory, and control theory saw significant development, marking an important climax of instrumental rationality. Its research and application scope moved from natural sciences to humanities and social sciences, significantly influencing urban planning thought and methods. Systems theory indicated that urban problems require a systematic approach to study the current status, development changes, and relationships of various elements [10]. Although cities are random and uncertain, their laws are not completely unknowable or uncontrollable. Information theory suggested that general laws of urban development could be explored through methods like measurement and simulation [14]. Control theory provided methods for guiding planning on how to control this complex system and reduce uncertainty after decision-making [11]. Against this background, planning attempted to adopt a path of “defining goals and problems, proposing solutions, and evaluating and comparing solutions” [15], and planning models such as comprehensive planning, systems planning, and procedural planning began to emerge.

3.1.2 Initial Understanding of Uncertainty Based on Instrumental Rationality #

Comprehensive planning is a typical product of instrumental rational thought, attempting to solve the problem of uncertainty in planning intervention by comprehensively evaluating the impact of each project, calculating a series of weights and impact factors, and selecting the most beneficial solution. Its scope of study has far exceeded the previous period’s material form and focused on a larger whole. Geddes proposed the three-stage theory of “survey-analysis-plan” in methodology. In 1968, the UK replaced the original development plan and master plan with institutional planning and local planning through legislation [14].

Systems planning further views urban planning as a dynamic adaptive process. Planning is no longer a final blueprint-style but a process-oriented one, requiring regular adjustments according to the system’s operation at different times, thus forming multiple cross-sections of different developmental states. Planning can monitor, analyze, and intervene in this process [3]. This cross-sectional analysis method analyzes the important characteristics and general laws in the process of uncertainty.

Clearly, although comprehensive and systems planning both acknowledge the uncertainty of the future, they both believe that the uncertainty in planning, or at least the uncertainty in the parts concerned with urban planning, can be eliminated through control and intervention, and these key parts will have a predictable impact on other parts. Instrumental rationality, while firmly believing in rational and effective future decision control, concretely provided two types of planning models to reduce uncertainty and propose the best future solutions.

Concurrent with these planning practices were numerous attempts to apply scientific analytical methods to quantitatively study urban laws, addressing the uncertainty in city cognition, but with limited success. Methods like comprehensive forecasting, mathematical models, and computer simulations were used to analyze the operating laws within the city system, attempting to fully quantify and simulate predict the future development path [14]. Some models about cities were also proposed, like Christopher Alexander’s notion of the city as a “semi-lattice structure” of multiple intertwined and overlapping aspects [14]. These approaches clearly contain a strong flavor of instrumental rationality, attempting to completely comprehend the general laws of the city through calculation, later proving that cities always have parts that are difficult to quantify and predict. However, it is undeniable that although cities cannot be fully quantitatively understood – now a part of the consensus in academia – these attempts and ideas were and still are of considerable positive significance for the cognition of cities.

The above planning epistemologies and solutions in response to uncertainty began to be questioned and criticized in the late 1960s. On the one hand, comprehensive planning and systems planning did not truly solve the uncertainty in urban cognition. The completely rational planning model requires a comprehensive examination of related factors and comparison of various schemes, which is almost impossible to complete in actual operation, still leaving a large amount of uncertainty in planning formulation. The preparation of planning took a long time, with the average preparation time for a plan in the UK once exceeding four years [15], making the lengthy and complex planning process lose flexibility and less favorable in practice for solving uncertainty.

On the other hand, the above planning models struggled to answer objective questions about planning goals and the effects of planning intervention. Their criterion of “public interest” for maximum benefit evaluation was vague and even substituted in actual operation. Neo-Marxism pointed out that when planners, as social elites, make decisions, they often become spokespersons for capital and politics under the guise of “science” [15]. In implementation, the comprehensiveness of planning included too many tasks and requirements, becoming a comprehensive plan for social development, far beyond the control range of planning departments, turning “comprehensive” and “systematic” into mere paper visions.

3.2 Bounded Rationality’s Response to Uncertainty: Incremental Planning, Mixed-Scanning Approach, and Scenario Planning #

3.2.1 Theoretical Background of Bounded Rationality #

With the development of rationalism, there has been a growing recognition of the uncertainties in the real world, leading to the emergence of schools of thought that offer corrections, including bounded rationality. This school reflects on the certainty of rationality, asserting that individuals, constrained by real-world conditions, cannot possess complete and sufficient rationality. The concept of bounded rationality, first introduced in decision-making research, was brought forth by Herbert Simon, who highlighted the presence of value judgments in decision-making as a characteristic of bounded rationality, indicating that uncertain decisions are widespread [7]. Alongside the critique of comprehensive and systems planning, bounded rationality gradually shifted the focus of planning to decision-making behaviors, operational planning, and the achievement of planning goals under real-world constraints [3]. On this basis, planning approaches such as incremental planning, mixed-scanning models, and scenario planning were developed.

3.2.2 Planning Methods Under Bounded Rationality to Address Uncertainty #

In 1959, Charles Lindblom, addressing the limitations of comprehensive planning in terms of time, finances, and information, which hindered reliable predictions and achievements, proposed “disjointed incrementalism” in planning, also known as “separate-incrementalism.” This approach defines realistic and limited objectives within the uncertainties of the future and uses limited schemes to solve current practical problems [16]. In 1973, Branch proposed the theory of continuous urban planning, revising disjointed incrementalism and suggesting that urban planning is a process. Planning issues can be classified into certain and uncertain, thereby determining the planning timeframe [3]. Compared to Lindblom’s theory, continuous urban planning emphasizes continuous actions and outputs, with the belief that uncertainty can be partially resolved through a continuously developing process. That is, each small, short-term decision is relatively certain, and a roughly continuous direction can potentially reduce future uncertainties.

Both types of planning modes focus more on what can be done at present, showing a relatively passive attitude towards future uncertainties. In 1967, Etzioni combined disjointed incrementalism with comprehensive planning, proposing the mixed-scanning approach [17], which coordinated comprehensive planning framework with separate incremental actions, encompassing both macro-strategic and micro-execution levels.

Scenario planning, which emerged during the same period, is a planning mode specifically aimed at addressing uncertainty, focusing on solving uncertainties in planning cognition and intervention. Scenario planning adopts a mindset oriented towards future scenarios, deducing development pathways from key elements, and representatively describing future city events and trends [18]. Unlike comprehensive planning, scenario planning discusses future plans but focuses on effective mechanisms for responding to potential future situations, not on the precise determination of plans. Moreover, scenario planning moves beyond extrapolation from historical data, instead exploring key factors in urban development from various information sources.

The framework of scenario planning involves complex classifications and possibilities (Figure 1), which are not elaborated in detail in this paper but only discussed in terms of their approach to solving uncertainties. In general, scenario planning addresses uncertainties on three levels: first, it acknowledges cognitive uncertainty and categorizes the known and unknown. After defining key elements, it discusses possible scenarios in a way that combines subjective and objective approaches, rather than relying solely on extrapolation from the current situation. Second, it reduces uncertainty in planning intervention through scenario simulation. Scenario-based adaptive planning models test and vet existing strategies through multiple scenarios, while generative strategies in scenario models optimize intervention decision-making by influencing decision-makers’ experiential perspectives [8]. That is, although the future is uncertain as a whole, influencing the choice of intervention decisions by delineating a few relatively certain causal chains. Third, it provides a potential platform for resolving uncertainty in planning goals. The simplicity and choice of scenario models allow planners to move from the position of decision-makers or drafters to “decision consultants,” enabling multiple parties, including the public and government, to make joint decisions based on existing scenarios. This effectively avoids unilateral decisions and the pursuit of short-term interests or grand appearances [8]. Overall, scenario planning offers a relatively rational cognition and targeted solutions for the uncertain characteristics of planning and continues to be widely used in formulating major strategies.

3.3 Communicative Rationality’s Response to Uncertainty: Communicative Planning #

3.3.1 Theoretical Background of Communicative Rationality #

Another school of thought that revises instrumental rationality is communicative rationality, which posits that rationality is a common human nature and needs to be understood through reasonable communication and analysis of contemporary social relationships to grasp the scale of rational judgment [19]. Communicative rationality directly addresses the issue of uncertainty in planning goals, suggesting that goal determination can be achieved through communication among subjects, i.e., rationality manifested in interpersonal interactions. This idea gradually entered the field of planning in the 1980s and significantly altered the role and task definition of planners. Planners shifted from being decision aids influencing urban development to mediators among different interest groups, with the core task of planning turning towards balancing multiple interests and seeking common goals.

3.3.2 Planning Methods Under Communicative Rationality to Address Uncertainty #

Communicative planning is an important paradigm of communicative rationality applied in planning. It establishes the rationality of planning on the collective effort of subjects, where planning is the pursuit and establishment of consensus, achieved through discussions and debates [3]. This effectively resolves the issue of uncertainty in planning goals. While it includes many specific ideological branches, most emphasize equality in the planning decision-making process, without expert privilege or marginalization of weaker groups. The quality of planning should be measured by the planning process, where a good plan should fully reflect democratic values, and the significance of the process outweighs the importance of the planning results [20].

Communicative planning also indirectly reduces the uncertainty of planning intervention. Through comprehensive discussions among various parties, planning decisions often become more direct and specific. In the framework of communicative planning, communities and constructors may even consider details like facades and window sills [21], promoting identification and support for the project by various groups, making the intervention process smoother and subject to broader supervision during implementation.

3.4 Neo-Rationalism’s Reconsideration of Uncertainty: Complex Adaptive Systems Theory #

The important scientific theoretical foundation for the emergence of neo-rationalism is the “complex adaptive systems” theory, evolved from “dissipative structures,” “mutation theory,” and “synergetics.” The former three further advanced the understanding of uncertainty in cities, recognizing the imbalance, mutation, and high complexity of cities. Complex adaptive systems theory further points out the significant “bottom-up” balance characteristics of cities: First, cities can “self-renew” and “self-evolve” by processing information and extracting laws about the objective world from experience. Second, the collective decision-making of cities is an adaptive result after considering external environments and development goals, meaning cities have “self-adaptive” capabilities. Third, cities have a symbiotic and co-evolutionary relationship with surrounding societies and natural environments [22]. The theory of complex adaptive systems, compared to earlier views, emphasizes more on micro-individuals and the collective laws formed by them, proposing a “bottom-up” intervention approach and the capacity for public self-organization in dealing with urban uncertainties.

Strictly speaking, research in complex science has mostly guided urban planning at the epistemological level and has not yet established a complete planning model. Existing practices, such as smart cities and twin cities [23], adaptive simulation in traffic planning [24], and attempts in social governance [25], often involve recognizing and revising related issues within the framework of existing planning models. In terms of planning decision-making, the paths of simulation and public participation brought by complex adaptive systems theory are more about providing references for decision-making rather than intervening in the decision-making itself. It can be said that although complex adaptive systems theory provides a feasible path for building city models with lower uncertainty [26] and efficient public participation, due to current limitations in value recognition and technical methods, planning has not yet produced an effective response.