Speaker
Description
Introduction and research problem
Urban planning operates through incomplete knowledge, competing objectives, uncertain forecasts, and interactions among institutions, communities, markets, infrastructures, and environmental systems. Therefore, uncertainty is produced through disagreements over goals and means, changing conditions, distributed authority, and the limited capacity to anticipate how urban systems will respond to intervention. Contemporary planning increasingly relies on digital models, simulations, and data-intensive decision environments to make such complexity legible and actionable. These tools establish particular ways of understanding urban processes and possible futures through choices about variables, relationships, indicators, and interfaces.
City-building games offer a productive but comparatively underexplored setting in which to examine this process. Games such as SimCity and Cities: Skylines translate urban systems into computational environments in which players zone land, construct infrastructure, allocate services, adjust taxation, respond to population change, and observe environmental or economic feedback. Building on this, the study examines whether games accurately represent cities to how they transform uncertainty itself. The paper asks: How are key urban planning processes translated into variables, rules, indicators, and feedback mechanisms in city-building games? Which forms of planning uncertainty are represented, simplified, displaced, or excluded? What assumptions about legitimate planning action are produced by the relationship between simulation mechanics and player agency? The central proposition is that city-building games redistribute uncertainty by retaining, computationally producing, converting into deterministic relationships, or removing from the decision environment altogether.
Conceptual or theoretical framework
The study approaches city-building games as model-mediated environments. Any model necessarily selects certain features of the world, formalizes relationships among them, and excludes others. In a game, this selection is additionally conditioned by playability: processes must become perceivable, actionable, and capable of generating feedback. Urban phenomena such as housing demand, congestion, pollution, land value, employment, or service accessibility are therefore operationalized through numerical indicators, spatial categories, agent behavior, thresholds, and causal rules. Once formalized, these relationships define what the player can treat as a problem and what types of intervention become possible.
To investigate this translation, the analysis distinguishes four interrelated forms of uncertainty. Epistemic uncertainty concerns incomplete knowledge of how an urban system works or how variables are related. Predictive uncertainty concerns the inability to know the future consequences of an intervention. Social uncertainty arises from changing or heterogeneous behavior among residents and other urban actors. Institutional-political uncertainty concerns conflicting objectives, distributed authority, negotiation, regulation, opposition, and changing relations between actors and institutions.
The distinction is particularly relevant to city-building games because simulation can increase complexity in one dimension while suppressing it in another. A transport system may contain thousands of simulated agents and generate emergent congestion, for example, while the decision to construct a new road may remain institutionally frictionless. Similarly, a game may represent fluctuations in demand, taxation, land value, or pollution but provide the player with centralized authority to alter zoning, demolish buildings, relocate infrastructure, or modify fiscal policy without the negotiation that characterizes planning practice. Therefore, the study examines uncertainty together with player agency by questioning what the model contains, who is authorized to act upon it, with what information, and under what constraints.
Methods
The paper uses a comparative qualitative systems analysis of three influential city-building games: SimCity (2013), Cities: Skylines (2015), and Cities: Skylines II (2023). These cases belong to a shared genre and planning-oriented gameplay tradition while differing in simulation granularity, agent behavior, economic systems, and the amount and form of information presented to players. The comparison intends to examine how successive computational models construct different relationships between urban complexity, uncertainty, and decision-making.
The analysis combines structured gameplay observation with examination of interfaces, game rules, in-game indicators, feedback systems, and publicly documented mechanics. A common coding framework is applied across the three cases. Six planning dimensions are examined: land use and development; housing and population; mobility and accessibility; economy and employment; environment and urban services; and governance and decision authority. For each dimension, the analysis records what urban phenomenon is represented, how it is operationalized, what information is made visible to the player, which interventions are available, how the system responds, and which forms of uncertainty remain present or are suppressed.
A second analytical layer traces the transformation from planning concept to game mechanism. For example, housing may be translated into demand indicators, residential zoning, density classes, land values, household agents, or construction responses. The analysis then asks which real-world relationships are retained within this chain and which are compressed or omitted, such as tenure, affordability, speculation, displacement, informal housing, or political contestation. Similar mappings are developed for mobility, land development, environmental effects, and public services. Cross-case comparison is used to identify recurring patterns and differences associated with increasing simulation detail.
This methodology combines perspectives from planning and game-system analysis. It examines how rule systems, feedback loops, resource constraints, and player affordances participate in constructing a particular model of planning. This interdisciplinary approach allows the computational logic of gameplay to be interpreted alongside the institutional and spatial complexity of planning practice.
Results
The analysis is expected to show that city-building games represent uncertainty selectively. Predictive and system-performance uncertainty can remain significant: traffic patterns may evolve unexpectedly, population and economic conditions may fluctuate, and interventions may generate delayed or indirect effects. Such uncertainty supports gameplay by requiring observation, experimentation, adaptation, and repeated decision-making. Increasing simulation detail may further multiply these interdependencies without necessarily making outcomes fully predictable.
At the same time, institutional-political uncertainty is expected to be substantially compressed. Planning authority is generally concentrated in the player, while land acquisition, intergovernmental coordination, public opposition, electoral change, legal contestation, competing ownership, and negotiation among actors are either absent or translated into limited system variables. The model can therefore create a city that is dynamically uncertain while making the institutional capacity to intervene unusually certain.
A second expected finding is that indicators transform ambiguous urban conditions into actionable categories. Demand bars, traffic overlays, land-value maps, budgets, service-coverage areas, pollution levels, and population statistics allow the player to diagnose problems and connect interventions to outcomes. This legibility is central to gameplay, but it can also produce an impression that planning problems have stable definitions and measurable solutions. What cannot be operationalized through the game’s model risks becoming invisible to the decision process.
The comparison is also expected to reveal that greater simulation granularity does not necessarily resolve these limitations. Later or more detailed models may represent households, firms, travel behavior, economic processes, and infrastructure interactions with greater specificity, yet retain a centralized model of intervention. Therefore, uncertainty may be redistributed from political and institutional relations toward the performance of the simulated system itself.
Discussion and conclusion
By reading city-building games as model-mediated planning environments, this study contributes to debates on digital planning with the proposition that models reorganize uncertainty. What appears to be a more detailed or data-rich representation may simultaneously increase uncertainty within simulated processes and eliminate uncertainty surrounding authority, negotiation, legitimacy, or implementation.
This perspective is relevant beyond entertainment games. Planning increasingly interacts with simulation platforms, urban digital twins, scenario models, and data-intensive decision-support systems, while game technologies themselves are being adapted for participatory and geodesign applications. Recent research demonstrates an expanding interface between city-building games and real-world planning practices, making critical examination of their underlying logics increasingly relevant. City-building games provide a particularly visible laboratory for examining how every model determines which uncertainties become calculable, which actors become legible, and which interventions become imaginable. It examines how that simplification is structured and what conception of planning it produces. By identifying where uncertainty is preserved, transformed, and removed, the paper aims to examine which uncertainties are made visible, who is given the capacity to act upon them, and which dimensions of urban change remain outside the model.
Keywords: city-building games; planning uncertainty; urban simulation; model-mediated planning; game systems
References
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