Navigation – Plan du site

AccueilTransversalitésScience et Toile2012Video games and urban simulation:...


Video games and urban simulation: new tools or new tricks?

Jeux vidéo et simulations urbaines : trucs ou astuces ?
Samuel Rufat et Hovig Ter Minassian
Cet article est une traduction de :
Et si les jeux vidéo servaient à comprendre la géographie ?


Les jeux vidéo permettent de modéliser les systèmes complexes en révélant les boucles de rétroaction, en reproduisant l’auto organisation et l’émergence de structures hiérarchiques, en répliquant la différenciation fonctionnelle et la ségrégation sociale au travers d’interactions multi niveaux. Les travaux récents insistent, d’une part, sur l’amélioration de la qualité graphique des outils de modélisation et de l’attractivité des interfaces, d’autre part, sur l’utilisation de jeux vidéo pour stimuler l’enseignement et la recherche. Cette apparente convergence entre outils de simulation et jeux vidéo est analysée à l’aide d’une sélection de jeux de stratégie et de city builder. Ces comparaisons révèlent des ressemblances frappantes: les outils de simulation et les jeux vidéo aboutissent à des résultats similaires : ils permettent tous deux de simuler des dynamiques urbaines complexes (réseaux urbains hiérarchisés, ségrégation urbaine). Les jeux semblent même offrir des simulations plus abouties. Cependant, la principale limite des jeux vidéo découle de leur pouvoir didactique: les jeux vidéo et les logiciels de simulation accordent une place presque diamétralement opposée aux utilisateurs dans la production des règles et des modèles. Les jeux vidéo poussent les joueurs à s’approprier et à comprendre les modèles, mais pas à les dépasser, ni à produire de nouvelles connaissances.

Haut de page

Texte intégral

Paper first presented at S4 International Conference Emergence in Geographical Space: Concepts, Methods and Models, Paris, France, November 2009.

“Of course simulation models can be conceived as entertainment tools, and designed for building games, or imagining fictive worlds, as utopias always did. But we want to learn something about the real world from such exercises, by confronting the results of simulation with observation” (Bretagnolle et al. 2006).

1The SimCity video game series, created by Will Wright in 1989, is unanimously appreciated among geographers, urban-planners, and teachers. Aside from its playful aspects, a didactic role is often attributed to this game series. In the United States, several urban studies and town planning trainings are using video games (Adams 1998, Squire 2004, Gaber 2007, Gordon & Koo 2008, Nesson & Nesson 2008). Like models (Haggett 1965), video games can be both heuristic and didactic tools. Furthermore, the development of modelling tools enhances their similarities to video games. They use video games’ graphic models in order to improve their own user interfaces. Moreover, some modelling software have shifted into games for didactical purposes, such as MacSim (Augier et al. 2001), a macroeconomic simulation software. The Simpop (Bura et al. 1997, Bretagnolle et al. 2006) regional declinations and predictive scenarios have drawn it closer to the multi-level interactions and urban network emergence proposed by the Civilization video game series (Microprose & Firaxis 1991-2010). Finally, some agent-based modelling software, like StarLogo TNG or Mason, are also presented as game conception tools.

2Michael Batty and Paul Torrens underline that “models with emergent properties based on evolutionary principles are increasingly being adopted in game simulations. There is evidence that what is state-of-the-art game design today is often incorporated into the e-science of tomorrow” (2001). From the perspective of these two authors, is it possible to consider some video games as simulation tools, allowing complex systems’ modelling? Do modelling tools have something to learn from video games besides graphic quality and user friendly interfaces? Could we consider then video games as “efficient” modelling tools? And what do they teach us about the “real world”?

3This paper discusses the convergence between video games and urban systems modelling by focusing on emergence as a recurrent notion in both complex systems modelling and video game design. Jesper Juul distinguishes two major kinds of video games: “games of progression that directly set up each consecutive challenge in a game, and games of emergence, that set up challenges indirectly because the rules of the games interact” (Juul 2005: 67). Games of emergence, as chess, are based upon a small number of simple rules which can lead to a number of possible states that defy description (Juul 2005). In both video game design and complex systems science, emergence is based upon an asymmetrical relationship between the simplicity of the rules and the extent of the space of the different outcomes proposed by the video game or the model. However, video games and models do not interact in the same way with the player/user and do not share the same goals. In games, different outcomes and emerging properties arise from the interaction between the core code and the different choices of the player; in modelling, exploring the model commonly leads the user to reshape the rules and structures in order to fine-tune or to calibrate the model.

4We first explore the relationships between video games, models and representations. Then, we consider that video games as geographical meta-models able to simulate emergence. This is followed by case studies of emerging properties in various video games at different levels. Finally, we explore the limitations of using video games as modelling tools.

Video games as modelling tools

5Traditional and more recent research on video games have shown how challenging it is to define a game (Huizinga 1938, Caillois 1958, Koster 2005). However, three main dimensions characterize video games: multimedia, fun and interactivity (Juul 2005). Video games are spatial simulations designed for entertainment that require user participation through graphics, audio and mechanical interfaces.

6There is virtually no video game without representation of space, from the road that scrolls in front of a car to the recreation of Earth through imaginary universes. Spatial representations are central to various levels of video games. Video games open a distinct space (of play); space is also simulated by software, mediated by interfaces, represented by players, and space can itself become the stake of play (Stockburger 2006, Nitsche 2009). Representations of space within a video game must be considered to be at least “squared”: player’s spatial practices and representations are based on a simulated space (Rufat & Ter Minassian 2011). Thus, the analysis of the in-game space of video games can help us to deepen the comparison between modelling and representation.

From the game to the model and back

7From a process rather than an interface point of view, video games similar to SimCity are close to the modelling tools used in research. They use similar models, and the simulation relies on feedback loops, conditional loops and cellular automata. Many video games are based on simple spatial models (gravitation, diffusion, centre/periphery, land market, etc.), the combination of which lead to the emergence of archetypal forms of spatial organization at the meso or macro level. They also offer diagnostic tools to evaluate ongoing processes and their spatial results.

  • 1 In 1970, the mathematician John Conway suggested this game as the exemplar cellular automata. It is (...)

8For twenty years, the SimCity video game series has allowed users to play an all-powerful mayor who builds from scratch, modifies and manages a city. In Sim City, simple spatial rules are reproduced by cellular automata similar to Conway’s Game of Life1. SimCity uses a combination of gravitational models and cellular automata to model urban growth and the competition for space through the following:

  • the attractiveness of different parcels, which can affect their development;

  • the parcels’ value, which determines the type of population (poor, medium and wealthy) or activities they are likely to accommodate;

  • segregation, which homogenizes residential districts and activities; and

  • the invasion/succession of populations and activities over time.

9The player may interact with the physical environment and the destination (either residential, industrial, or commercial) of these cells, but he or she cannot operate directly their development, the type of population or activities established in these cells. The actions of players therefore have indirect effects through the choice of the location of amenities, transport infrastructures, and through budgetary constraints. Amenities have an influence or effect zone on neighbouring cells at different scales, from strict neighbourhood to remote effects, for example:

  • the effect zone of a conference centre on commercial clusters’ attractiveness has a radius of thirty-two cells against only twenty-two cells on the residential clusters;

  • the effect of a small park on residential clusters’ value has a radius of fifteen cells; that of a large park is forty-five cells.

10The total attractiveness of a cell results from the combination of the effects of all its amenities (fig. 1). Their evolution over time as well as network and environmental quality determine the cell’s land value, which in turn affects its attractiveness, population or activity.

11Therefore, SimCity is not based on a cellular automata stricto sensu but on a cell-space model, to follow the distinction made by Michael Batty (2005), since it incorporates remote effects, not only action on neighbouring cells. Furthermore, SimCity is based on bottom-up retroactions: neighbouring effects and local interactions are the ones that generate the emergence of urban structures at the meso scale. The software also includes top-down retroactions. The emergence of highest rank activities and services in the more attractive cells depends on the size and the general level of education and health of the population of the entire city. Since SimCity 4 (2003), the game has offered the ability to manage an ensemble of cities at the regional level. The vertical growth and the higher service level that can appear in this game depend on the neighbouring cells, on the size or attributes of the whole population, and also on the evolution of these settings across the urban area.

Fig. 1: Effect zone on neighbouring cells of different amenities in SimCity 4 (2003)

effect value on commercial zones

effect distance for commercial zones

effect value on residential zones

effect distance for residential zones

small park





large park





conference centre



- 50


research centre



- 50


stock market





12Finally, video games such as the SimCity series enable simultaneous viewing or overlay of diagnostic tools (maps, graphics, etc.) offering significant amounts of information. In SimCity 4, it is possible to overlay arrows showing in “real time” traffic, route, and transport modes. The player is therefore led to use GIS-like tools to read urban processes and assess its choices regarding the model outcomes (fig. 2).

Fig. 2: Diagnostic tools in SimCity 4 (2003)

Fig. 2: Diagnostic tools in SimCity 4 (2003)

13SimCity illustrates the modelling capacities of video games. By offering urban simulations that simulate self-organization and emergent properties such as hierarchical organization, functional differentiation and social segregation through multi-level interactions, it can be regarded as a modelling tool.

Model and video game as both simulation and representation

14A model is a “formalised representation of a phenomenon” (Brunet 2001). By definition, models are a simplification of some reality to some lesser representation (Batty & Torrens 2001).

“Of course, this representation passes through several filters, which all have their traps: the perception of the phenomenon; its representation; the construction of a model; the interpretation of the meaning of this model, its social logic; the ability of the model to take account of the phenomenon; the communication of the results. But who ever said research was meant to be easy?” (Brunet 2001).

15Modelling is an activity for testing, exploring, creating and communicating knowledge about certain urban phenomena. Over time, it involves testing hypothesis through a series of iterations, shifting from approximation to approximation (Bretagnolle et al. 2000 & 2006). However, these models may have opposite philosophical backgrounds (Sanders 1999). They expose the epistemological issues underlying the analysis of urban problems: complexity is an intrinsic feature of phenomena but also depends on the knowledge of the observer (Occelli 2002). Furthermore, the use of modelling in social sciences often relies on arbitrary choices and intuitive rules rather than testable assumptions (Batty & Torrens 2001, Epstein 1999).

16For more than fifteen years, the relation between the model and the “real world” (Batty 1994) and the mere sense and effectiveness of urban modelling (Wegener 1994) have been discussed:

“One is forced to conclude that most models that we are working with are arbitrary, based on a loose consensus of what seems plausible but not on any definitive evaluation of the appropriateness of model structures. Until we are able to move beyond this, then all complex systems model will remain contestable and inconclusive” (Batty & Torrens 2001).

17Complex urban models will always contain more assumptions about reality than are testable and involve contextual assumptions that remain implicit. These increasing difficulties in testing and validating models, and the fact that some arbitrary mechanisms have to be imposed so that realistic outcomes emerge, have raised serious concerns about the ability of models to predict or even reflect the “real world”. It has been argued that models are still useful, because every kind of model can be used for every purpose. It may depend on users, not on the models; urban models still largely remain didactical vehicles that are valuable to engender discussion and debate (Batty & Torrens 2001). These concerns are not specific to modelling. Trevor Barnes and James Duncan underline that “writing about worlds reveals as much about ourselves as it does about the worlds represented” (1992: 3). Writing (or modelling) reflects more our representations than the world itself. In short, urban modelling can be seen as “story telling” (Guhathakurta 2001), like video games (Frasca 1999). However, in both contexts, the extent to which the story told is believable is always at stake.

18Video games are spatial representations and they teach us the impacts of representations and ideologies on spatial practices (Ter Minassian & Rufat 2008). So, are video games better stories, better representations or even better models? This is not a rhetorical question: Steven Johnson made the game SimCity (Maxis 1989) one of the pre-eminent examples of complex systems:

SimCity would also inaugurate a new phase in the developing story of self-organising; emergent behaviour was no longer purely an object of study, something to interpret and to model in the lab. It was something you could build, something you could interact with (…) It is both the promise and the peril of swarm logic and the higher-level behaviour is almost impossible to predict in advance. You never really know what lies on the other end of a phase transition until you press play and find out… And then you see what happens” (Johnson 2001).

19Hence, the differences between video games, models and simulation tools may become unclear, even if SimCity is arguably a borderline case. It is open-ended, surprising the player with emerging properties, and has no clear goal. This has led to the assertion that it is not a “pure” video game (Juul 2005). In addition, it has been used worldwide by city planners and mayors for prediction or to explore the effects of various projects, budget choices and adjustments or public policies (Starr 1994) and more recently for urban hydrology modelling (D’artista et al. 2007).

Unfolding the representations behind the models

20By playing such video games, the player is made to adopt the same experimental posture as the researcher exploring a model, asking “What if I act in such a way?” All of these games propose a playground wide enough to explore the different conditions for self-organisation and the emergence of socio-spatial hierarchical and functional differentiation. This study analyzes the model underlying each game. Understanding the model requires playing a video game a multiple times in an attempt to reach the implicit or explicit objectives of each game. In a video game, the spatial structures’ emergence may depend on player’s choices. It is also the case of the user’s choices when exploring a model:

“The lines between the modeller, the modelled, and the user are increasingly blurred. This is no more or less than the idea that the user is part of the system to be modelled and is often no different in behaviour from the rest of the system that is being modelled” (Batty & Torrens 2001).

21The possibilities granted to the player are limited by game rules (Salen & Zimmerman 2004), interaction degree, details and the elements with which the player can interact (Juul 2007). Jesper Juul shows that this “level of abstraction” does not depend on the technical capacities but on the choices made by the designers. Indeed, they have to reduce information and the possible interactions in order to turn a set of possibilities into a game. From this point of view, game design can be compared to modelling.

  • 2 One can also take the example of the Grand Theft Auto series (Rockstar Games 1997-2008), although t (...)

22Our hypothesis is that the underlying model of video games does not depend solely on rules and game design but also on incitements framing the player choices and behaviours. Player’s behaviour is oriented by the goals, when clear goals exist, and by some incentive and coercive mechanisms of the game that we called “regulations” (Ter Minassian & Rufat 2008). Responding to these regulations, the player has to develop a reflexive analysis of his/her successful choices, effective behaviours, and painful failures. For example, in city builder games, the player has indeed the freedom to experiment with different strategies in order to develop a city and to choose different goals. Nevertheless, choices in accordance with the model coded by the designers into the software will ensure faster growth and more diversity, thus raising funds. The SimCity series favour an urbanization type very close to conventional representations of the North American city2. The city in the SimCity series is an urban model, both as an ensemble of simple spatial rules and as an ideological representation of the flourishing city based on functional zoning and urban sprawl.

23These choices are gratifying, because they grant more possibilities, so the player may tend to fold up the model even if he/she does not consciously understand it. On the other hand, those rules and regulations have to be sufficiently subtle so that the scope of winning strategies will not be too narrow. The player should not identify too easily or too quickly which strategies are to be led to win almost every time in order to ensure game length (Koster 2005). In short, it is necessary to play a video game multiple times and to test its evolution in an attempt to reach the implicit or explicit objectives of each game in order to spread out the model, no matter who the player actually is.

24To test our different hypotheses, we analyze the interactions between urban system models and game mechanics in some commercial video games.

Video games and the emergence of hierarchical and socio-spatial differentiation

  • 3 This article will not discuss multi-player games, because they present specific issues due to their (...)

25We focus on “city builder” and “strategy games”, to examine the self-organisation and the emergence of hierarchical, functional and socio-spatial differentiation through bottom-up and top-down interactions. These two categories of video games involve multi-level urban development and offer great flexibility for experiment3. Then, we set a typology of the outcoming urban structures in the selected video games to show that, despite some similarities, they rely on different modelling choices and different representations of urban areas.

  • 4 City builder’s games are sometimes called “god games”: the player with his overhanging vision of th (...)

26Strategy games and city builders are the two categories of video games that most closely reflect geographical issues (Ter Minassian & Rufat 2008). In the former, played at the scale of an urban system, a continent, or even an entire world, the player must control the land and resources in order to win. Game mechanics are directly inspired by traditional wargames. In the latter, the game is played at the intra-urban scale, the player acts as both the mayor and the urban planner4. For the geographer, the distinction seems relevant. In the first ones, geography is about “making war” (Lacoste 1979) and space is mainly presented as a battlefield. In the second ones, geography is about urban planning, and space is a kind of territory that the player must urbanize from scratch. The video games on which we focus can be sorted in two groups:

  • Open-ended games with no clear goal, in which the player can build a city by controlling planning, budget, transport, etc. The atmosphere and graphic representations may change from game to game, from Antiquity in CivCity Rome (2k Games 2006) and Caesar IV (Sierra 2006) to Western contemporary urbanism in SimCity 4 (Maxis 2003) and City Life (Monte Cristo 2006). The similarities and differences between these games allow the identification of basic trends in the simulation choices as well as in the attempts of designers to make games more original than their competitors.

  • Strategy games with an explicit goal: to develop an urban system in order to master resources and to slay opponents. These games repeat the same simulation principles at a different scale, for an entire country, as in Civilization IV (2k Games 2006) and Rise of Nations (Microsoft 2003).

27A cross-study of all these games allows varying the analysis from the intra-urban micro level to the networks of cities macro level.

Micro-management, emerging territories at different scales

28The examples of City Life and Civilization show that video games can simulate the emergence of structures at various scales: from urban structures to the organization of full city networks.

Social segregation in City Life

29In City Life, the player must ensure the social cohesion of the city, but the underlying definition proposed by the game mechanics in City Life is clearly problematic, far closer to social segregation than to social mixity. Social cohesion, along with competitiveness and governance, has become the new credo of mayors, public authorities and urban planners since the beginning of the twenty-first century (Gordon & Buck 2005). Social cohesion is a key component for a successful urban growth, although it can take multiple definitions (Parkinson & Boddy 2004). Indeed, the main interest of the video game City Life, graphically close to the SimCity series, is to promote social cohesion by considering the inhabitants’ social profile as a genuine parameter. There are six profiles: elite, yuppies, white collars, artists, blue collars, and unskilled workers. The player has to guarantee the pacification of their social relations through physical separation, although they have to team up at their workplace. Indeed, it is not enough to build cities uniformly occupied by some yuppies or to separate homogenous neighbourhoods by several kilometres of buffer zones as in other city builders. In City Life, development is based on companies that require the collaboration of several types of social classes.

Fig. 3: Accessibility in City Life (2006)

Fig. 3: Accessibility in City Life (2006)

Increase of the employment zone of a single company (in green) when shifting its connection to the city from a 2-line road (left) to a 4-line highway (right)

30Thus, the evolution of employment across the whole city has effects on the local demand for housing. It is a top-down retroaction. Furthermore, the effect zone of any building is determined by its accessibility, which varies according to the transport infrastructure type. A plant or a company located too far does not succeed in attracting employees, even if its attractiveness can be improved through higher level infrastructures (fig.3).

Fig. 4: Urban segregation in City Life (2006)

Fig. 4: Urban segregation in City Life (2006)

31The organization of the 6 classes in more or less homogenous zones (left) and a zoom on a building at the border of two zones (right), with “intellectuals” (little men in red) and “unskilled workers” (in black) annoyed to have to coexist.

  • 5 It was not possible to reveal the exact algorithm that manages those social tensions. Our testing e (...)

32In City Life, the spatial proximity of two “communities” automatically generates conflicts, called “cultural tensions” in the game. Tensions appear as soon as different populations coexist in contiguous housing, but not at their workplace5. The game is based on bottom-up retroactions. Each social class has specific needs in terms of jobs, leisure and equipments. At the scale of each neighbourhood, the localisation of companies, services and leisure is therefore a powerful tool to shape the socio-economic fabric. This leads to the social segregation of the entire city, which is also a spatial segregation by neighbourhoods (fig.4). Thus, the game generates a segregated city through micro level interactions, according to MAS derivate from Schelling’s model (1978), as proposed by Michael Batty (2005), and taking networks into account, as suggested by Arnaud Banos (2010).

Christallerian cities’ network in Civilization

33In the Civilization series, the player is the leader of a country (or a nation) called “civilization” and has to ensure its development from 4000 BC to present. Player actions are conditioned by the cities’ performance, measured through manufacturing output or wealth. City development is therefore crucial in the success or failure of the game. The emergence of a national, hierarchical, and specialized urban network relies on the actions of the player which intervene at interacting scales, from the micro level of the cities to the national level of spatial planning and the international scale of the geopolitical relationships between “civilizations”.

34In Civilization, each city controls a production area (fig.5). The more resources located within its production area, the better they are used, and the more powerful the city becomes. A first specialization level begins at the micro level: a city that has many mineral resources will benefit from specializing in industrial development.

35At a higher level, the player must consider the best options for national territory development. A compulsory minimum distance avoids overlapping production areas (fig.5). Conversely, the regulations of the game discourage significant distance between cities: a huge territory is harder to defend; transport infrastructures are more difficult to build; and each city’s productivity is inversely proportional to its distance to the capital city. Players are free to build roughly the same equipment in all cities, but regulations lead to specializing cities, thus saving resources. For example, environmental determinism is a strong regulation toward specialization: industrial investments will be more effective in cities near mineral resources, and rivers and ocean will favour commercial activities. Moreover, it is possible to place the national stock exchange in the smallest city, but this is utterly counterproductive. It is better to build it in the wealthiest city in order to maximize funds and use them to develop other cities.

Fig. 5: Cities’ network in Civilization IV (2006)

Fig. 5: Cities’ network in Civilization IV (2006)

Each city controls a 21 cells production area (left), the model and regulations lead the player to specialise cities and to create a hierarchical cities’ network (right)

36Finally, international geopolitics is a major factor with a direct impact on the micro or urban scale. Degraded diplomatic relations can incite the player to strengthen his defences in border cities. This may lead to increasing the density of the local transport network and to constructing military equipment and infrastructure at the expense of economic growth. Eventually, cities are progressively specialised into a hierarchical network, depending on their size and their functions (fig.5). The results appear to be convergent with the Christaller’s model, despite the environmental determinism of the game’s model (Fortin et al. 2006, Ter Minassian & Rufat 2008).

Distinct spatial differentiations according to modelling choices

37Previous examples have shown that video games tend to model the emergence of identical geographical phenomena. However, a more detailed analysis highlights dissimilarities between video games. For example, three types of urban hierarchical organization and functional differentiation can be observed according to modelling choices: centralized, tiered, polycentric.

Centralized urban space

38In CivCity Rome, the game’s model and regulations determine the emergence of a centralized urban area. In this Roman Empire urban simulation, the growth of cities is based on equipment, amenities and services proximity (e.g. barber, school, temple, etc.) and certain products availability (wine, olive oil, etc.). The more diverse products to which inhabitants have access, the higher they climb the social ladder, which is graphically shown by the improvement of their dwelling. This improvement is also rewarded by an increase in local taxes levied; they will directly feed public funds and hence increase the player’s options. Like in City Life, product availability and services proximity depend on the accessibility of catchment areas (mainly markets) or equipment, measured by Euclidian distance and by the presence of roads. In order to get the most cost-effective outcomes and faster development, the player needs to favour neighbourhoods where the first signs of quality emerge. In CivCity Rome, the distance is tied to dwelling: the better a house is, the further its residents can travel to obtain products and services (fig.6), thus accessing more diversity and increasing their chances to upgrade again. This creates a positive feedback loop: the presence of a wealthy population encourages demand in higher level amenities and products, to which the player responds by developing them, in turn consolidating the emergence of wealthy neighbourhoods, all increasing financial revenues and player possibilities. A centralized urban area emerges, because regulations make the player concentrate its efforts in areas that are already well equipped and supplied (fig.6), especially since the game allows the player to move existing houses to the best equipped neighbourhoods.

Fig. 6: Centralized urban space in CivCity Rome (2006)

Fig. 6: Centralized urban space in CivCity Rome (2006)

Dwellings near the equipments concentrated in the centre upgrade (left), and the better a house is, the further its residents can travel to catch products and services (right).

39This process is related to the choice of designers to base the calculation of accessibility on the position and the quality of the housing in an urban area. This generates an organisation in concentric areas, with higher-ranking equipments and services located around the most luxurious housing, while those of lower rank are found on the outskirts, surrounded by the poorer dwellings.

Hierarchical urban space

40In City Life, a few settings are enough to model from a blank game map a city with socially differentiated neighbourhoods, sometimes separated by buffer zones, and with a central area concentrating social diversity. The game is based on a subtle balance between spatial proximity and urban segregation to ensure both proximity of a diverse labour force for each type of activity and social homogeneity inside each neighbourhood. The emerging organisation stems from the definition of centrality as both a dense and socially diverse area. This diversity can only exist around specific equipments: city hall and “cultural” centres. The “cultural” or neighbourhood centres have a smaller effect radius than the city hall, which is always the first building to be found. The result is a city polarized by a few hierarchized centralities, ensuring density and diversity, at the fringe of the homogeneous zones (fig.7).

Fig. 7: Hierarchical urban space in City Life (2006)

Fig. 7: Hierarchical urban space in City Life (2006)

Dense and diverse central area around the city hall (left) and smaller centrality spot around the nearby “cultural” centre (right).

41The same is true for Caesar IV, although the modelling is based on different factors. In City Life, hierarchical differentiation and socio-spatial segregation emerge from the interaction of residential choices and amenities distribution, which works because all dwellings are potentially available to all inhabitants. In Caesar IV, however, there are three types of dwellings which correspond to the three population categories (plebeians, equestrians, and patricians). Each type must exist in sufficient number in order to accommodate “in real time” the labour force needed by the developing city. The spatial distribution of these different housings leads to emerging properties such as urban segregation. Collective facilities such as hospitals or baths are suitable for all population categories, but recreation is indispensable for patricians. Urban space tiering stems from the distinction between common devices and markets, which form the city’s core, and specialization of higher rank housing and amenities for each population, which are structured in poles of variable importance.

Polycentric urban space

42Conversely, in Sim City the measure of distance is tied to amenities and not housings. During the game, interactions between land zoning (housing, industrial, and commercial zones) and distance to amenities and services first generate concentric urban areas, as in the Burgess model. Depending on its level of development, the city can then be structured in multiple kernels, as in the model of Harris and Ulmann, and some edges-cities may emerge (fig. 8).

Fig. 8: Polycentric space in SimCity 4 (2003)

Fig. 8: Polycentric space in SimCity 4 (2003)

43During the game, the consolidation of this polycentrism is ensured by progressive provision of equipments or specific amenities, which ensures a sometimes-considerable attractiveness bonus for the spots located in their area of influence. Since SimCity 4, high-ranked amenities must be unlocked, for example, when the city reaches a certain population size or level of education. This new system allows the player to accelerate the emergence of a hierarchal and specialised urban area around the different levels of centrality generated by the presence of these particularly attractive amenities.

44Playing and observing the emergence of different urban hierarchical organization and functional differentiation, resulting in European, North American or hybrid situations, seems very close to the exploration of urban simulation models. For example, the agent-based Urban Economics model (Lemoy et al. 2010) includes choosing the location of different amenities and adjusting the inhabitants’ utility functions in order to monitor the resulting city structures. Playing games and exploring or fine-tuning models require the same operations: locating amenities, monitoring self-organisation and inspecting the emergence of urban structures and segregation. Moreover, the limitations admitted by this model’s designers are actually addressed by video games: vertical growth, spatial competition between firms and household, impact of transportation network, etc. Finally, video games can appear as more complete representations (besides their graphic appeal) or even more efficient models of urban life. So, what does that mean that video games can be used to explore possible future issues in complex urban systems?

The limits of using games as models or simulation tools

45Obviously, it is possible to have fun exploring models and video games present convergent results with simulation tools; they even seem to go one step further. However, using video games as simulation tools has some limits. First, the code of video games is protected by intellectual property, and only few settings of the software are editable. Second, other limits arise from the aims, practices and user actual possibilities in video games.

Playing a game is not conceiving a model

  • 6 Nevertheless, the core-code of the first edition of Sim City (1989) has been edited online; the GPL (...)
  • 7 See for example on the first American on-line fan site dedicated to the Civilization series: (...)

46The video game market is highly competitive; the legal protection of the code restrains the precision that may be reached in the analysis of modelling processes. Only explanatory factors and some mechanisms can be isolated, not the exact algorithms underlying them6. However, online forums publish reports of experiments by some players trying to understand the underlying models of their favourite game in order to improve their strategy7. Some video game settings are editable, but players cannot intervene in the core-code of the software, which is held by designers. For example, the very definition of centrality is hard-coded in the games’ software and seems to vary depending on designers’ culture: in Sim City, the central criterion for the American designers was density, whereas in City Life, the European designers considered both density and diversity. As a consequence, the players who do not have access to the core-code of the game are not in the same position as scientists designing models.

47Even among apparently similar city builder’s video games, City Life has a social focus, offering to micromanage different populations’ localisation through activities and amenities, when SimCity has an economic focus, allowing to manage activities through transport infrastructures, amenities and differentiated tax levels. Players can change some rules and parameters through mods or fine-tune the model through patches, but they cannot make one game behave as the other. Only game designers have full access to the underlying model. Both video games and modelling can lead to a reflexive approach of urban processes and to adopt the “what if?” experimental posture. Game design can be compared to modelling; game patches and mods can be associated with calibrating and fine-tuning the model. However, playing a game is more similar to exploring the model. Moreover, designing and exploring models rest on almost opposite man/machine interactions.

48Self-organization theories stipulate that observable structures at a specific scale emerge from interactions between elements at a lower scale. Simple mechanism repetition produces relatively complex organized structures, without there was ever an intention to create them (Allen 1997). In a self-organised model, the user sets the starting parameters and fine-tunes the model, but simulation is what supports the dynamics of evolution over time (Pumain et al. 2009). Conversely, players can rarely set up the starting parameters (or only part of them) and reshape all rules and structures of the game’s software. On the other hand, exploring the model means systematic variations in parameter values, model structures and rules that encode different processes into the model (Batty & Torrens 2001). Likewise, players’ choices lead them to explore the model, introducing the time dynamics and taking part in feedbacks and bifurcations of the system, without intervening on initial conditions.

49In a video game, the model moves toward the stabilisation of urban structures. However, if spatial differentiations seem to emerge “naturally” from the interaction of agents and phenomena occurring at different scales, then player choices are oriented toward the emergence of specific socio-spatial structures. So, intentionality appears to be a serious issue in both video games and modelling: once targeted, a behaviour or a property is no longer surprising. The intention exists, from both users and players, to reach hierarchical differentiation and the emergence of socio-spatial new properties. In some sense, once modelled and simulated, a system is thus no longer complex. However, it is trickier in video games, because the main intention of the player may not be to understand or to explore the model.

Beating the model or winning the game?

  • 8 Borrowing to sociology and urban studies, the “actor” designates an individual who possesses indepe (...)
  • 9 In some cases, it may be assumed that some settings are deliberately left aside. In City Life, if t (...)

50When playing a game, players are induced to have fun and to try to win the game. Even in open-ended games with no clear goals, players are challenged by emerging problems and swayed to define their own goals in order to make the game more fun or more awkward. Solving the game or simulation is supposed to provoke pleasure (Koster 2005). Paradoxically, video game players cannot do everything; their possibilities are conditioned by goals, rules and regulations. Goals and regulations specific to each game bond players’ choices; they push them to learn the model. Therefore, players may be seen as agents rather than as actors8 of the simulation (but not of the game). For example, in Sim City, the sudden merging of the high-tech industries is dependent on a set of factors (level of fees, average education and health of the inhabitants, crime rates, etc.) on which the player can indirectly act through the game. Players can try to master those parameters in order to facilitate the high-tech industries’ emergence, but their choices are mainly determined by their will to reach some goals or to win the game9. In other words, the players are themselves “played” by the game; they are induced to discover, learn and fulfil the underlying model.

51The modelling proposed by a video game requires relentless vigilance from its user to constantly adapt choices to the outcomes of the software. This reflexive decision-making process is the key to video games. It conducts the game simulation toward the emergence of hierarchical organization, functional differentiation and social segregation. It also stimulates the didactic power of games: players have to explore their possibilities in order to learn the underlying model and then adapt their choices. In turn, it generates almost immediate gratification, provided that the user/player controls the main mechanisms of the game, which in turn creates the pleasure of playing a video game. Thus, as when exploring a model, there are two different ways of playing a game: by experimenting all possible actions and reflexively observing their outcomes, as when fine-tuning the parameters of a simulation, or by studying what other players have discovered and achieved, as when extracting from surveys the attributes of a model’s agents. Nevertheless, the game’s goals and regulations lead the player to spread out the model, perhaps to learn it, but clearly not to improve or to challenge it.

How are arbitrary choices made?

52Video games and models are representations. They rely on arbitrary choices and intuitive rules to offer simplification of some reality. Video games are designed to be challenging and enjoyable over time. According to Sid Meier, designer of Civilization, “a game is a series of interesting choices” (Rolling & Morris 2000: 38). For both video games and modelling, the simulation relies on arbitrary parameters or structures that may not reflect, or even try to reflect, reality. The difference is that in the case of modelling tools, those structures are (or at least should be) designed to bring new knowledge or new understanding (Pumain et al. 2009). There is also the common assumption that arbitrary implementations should be explicit (Batty & Torrens 2001). Conversely, in a video game, the reason to establish, for example, the power law (income)⅜ is that it works fine to balance the game and keep it entertaining and challenging. What is more, this arbitrary choice is not apparent to the player; it is buried under the code.

53In order to balance a game, game designers suppose that the player is a homo œconomicus (Smith 2006), that is, a rational agent making best use of the information at hand and trying to maximize his/her gain. This has several limitations. First, it is not obvious that all players behave in a strictly rational manner. Some players do not play only to beat the best score, reach the maximum level or rush to finish the game. Second, the examination of all possibilities (and their consequences) present at a certain time is difficult in practice; the rationality of agents is therefore “bounded” (Simon 1956). Finally, rational logic and the pursuit of performance do not always get the upper hand in a game. If not choosing the best possible choice often leads to failure, then favouring it systematically may prevent access to certain contents of the game. Rational choice and reflexive analysis may guide players, but if they replace the game, they kill the play (Caillois 1958). Also, if regulations are too strong and limit possibilities too much, then players may be frustrated or discouraged by the game. On the contrary, if the regulations have no effect or very little effect, then they break the interactivity and turn the player into a spectator. When extreme, effectiveness and regulations may kill the game, but this threshold is probably relative and may vary from one player to another (Juul 2005).

54When designing video games, arbitrary choices and intuitive rules are not meant to simplify some reality or to bring some new understanding; they are designed to ensure player’s entertainment and the game length. For example, game balance is used to prevent a choice or a strategy from becoming prevalent, according to the rock-paper-scissors principle (Rolling & Morris 2004), or to favour cooperative behaviours (Smith 2006). Generally speaking, player’s sanctions, from point loss to the death of characters and game over, is destined to challenge player’s choices and strategies in order to make players confront new challenges, which prolongs the gaming experience (Juul 2009). Furthermore, many of the arbitrary choices made by game designers are strongly ideological (Bogost 2008).

55Finally, the main difference between modelling and video game is their goals. A game aims to be fun and entertaining, while a model aims to produce knowledge and engender debate. In short, game mechanisms are often guiding player actions without accordance to any spatial model.

Conclusions: are video games really one step further in modelling?

56Video games are generally a good approach to complex systems, by stressing feedbacks and retroactions that the user must understand and control. They use classical models and may have didactical applications. They even appear to go one step further than models and simulations used by scholars.

  • 10 If the presence of trees arises the value of the adjacent land, it is because they contribute to li (...)

57Besides their graphic qualities, video games may be seen as more complete models than existing simulations. Video games appear to be less caricatured simplifications, because they take into account vertical growth, transportation networks, spatial competition, firms, households, environment, etc. However, it is far from obvious that featuring all of these interacting elements at once makes them more efficient models. This can be illustrated by their implementation of the environmental framework. Video games take into account a wide range of environmental effects. The topography constrains constructions and networks as well as the availability of natural resources (Civilization), the presence of rivers facilitates trade (Caesar) or pollutions and recreational opportunities (SimCity, City Life). The SimCity and Civilization series promote a specific representation of the environment, in which natural determinisms remain decisive, while human technology will always triumph in the end. The environmental framework is malleable virtually at will: its presence in the simulation is mainly cosmetic. A belief in the advance of technology in order to overcome “natural” limitations, following a technicist vision of human evolution (Baark & Svedin 1988), underlies all of these games. In SimCity or City Life, for example, to flatten land is very inexpensive, and the environmental framework has no landscape or hedonic value10 (a private housing estate will not have higher value on a hill with a beautiful view over a river). The physical environment is mostly considered as available land to be urbanized.

58This stresses the most essential difference between video games and simulation software: they implement rules and models using nearly opposite methods. Complex systems models are difficult to validate, which is why models and simulation are constructed in an explicit way (or at least they should be): explicating assumptions, exposing arbitrary choices and divulging chains of relations. Conversely, in video games, most of the simplifications remain hidden to the player, and the underlying models are not explicit; rather, they are buried under the core-code of the software. Players have to discover the rules by exploring their possibilities and the different outcomes of the game in order to establish their own strategy and to “solve” the game. This is supposed to induce pleasure from the game. However, it is possible to do it without taking full notice of the model.

59As Paul Krugman puts it, models are metaphors, not truth (1997: 80). The same can be said for video games, even the apparently most realistic city-builder games. Indeed, video games are squared spatial representations and metaphors whose main purpose is to induce pleasure and fun. They are appealing because of their graphic qualities and appear as less schematic simplifications because they feature more interacting elements at once. Moreover, playing at city builder games can lead to a reflexive approach of urban dynamics. This is why urban studies scholarships have recently employed video games.

60However, the main limitation of using video games in research emerges precisely form their didactic power: models and rules remain hidden, to be discovered by the player’s explorations. In short, video games and simulation software implement rules and models in almost opposite means. Therefore, the game may induce the players to learn the model, but not really to understand it or to produce new knowledge. Nevertheless, video games could be an invitation to reinsert human factor into models, particularly for online games which rely on interactions between many players. The difference between City Life and Schelling’s model is the introduction of player diversity as a new uncertainty level; the range of their reflexive behaviour may add a political constraint to the model. Could “serious games” be the solution? The response is not so easy. Serious games are video games explicitly developed for educational or communication purposes. They are already the result of this apparent convergence between video games, models and simulation tools. Indeed, the design and exploration of serious games rely on practices other than those related to commercial video games (Alvarez et al. 2011). Furthermore, playing is always a serious activity. The software called “serious games” often fail on all accounts: they appear as less compelling, and few studies have shown that they may have better didactic power than commercial video games.

61Ultimately, the apparent convergence between video games and urban complex modelling seems to result from the desire to replicate the graphic appeal of video games. It is more a question of making these tools candy eyed than fun in order to widen their public and to sustain the development of simulation software. However, even the most powerful or innovating 3D engines bring no new knowledge to their users. Nonetheless, certain resemblances between the design of certain simulations and former video games are striking. For example, many models and simulations of urban growth or segregation have categories close to SimCity: three types of population (poor, average, wealthy), a strict zoning (residences, sometimes services and industries), the same kind of amenities, etc. This account leads us to question the possible cultural transfers, conscious or not, between video games and simulation software. If you are using urban models and complex simulations and also playing video games, please feel free to contact us!

Haut de page


Adams P. C., 1998, “Teaching and learning with SimCity 2000”, Journal of Geography, vol. 97, No. 2, 47-55.

Allen P., 1997, Cities and Regions as Self-Organizing System: Models of Complexity, London, Taylor and Francis.

Alvarez J., Djaouti D., Rampnoux O., 2011, “Typologie des serious games”, in Rufat S., Ter Minassian. (dir.), Les jeux vidéo comme objet de recherche, Questions théoriques, 46-66.

Augier P., Brillet J.-L., Cette G., Gambin R., 2001, Macro économie européenne : MacSim, un logiciel de simulation, Paris, Lavoisier.

Baark E., Svedin U., 1988, Man, Nature and Technology: Essays on the Role of Ideological Perceptions. Houndmills, Macmillan Press.

Banos A., 2010, “À la recherche d’effets réseaux dans la dynamique du modèle de ségrégation de Schelling”, Cybergeo: European Journal of Geography, mis en ligne le 09 mars 2010. URL:

Barnes T., Duncan J. (ed.), 1992, Writing Worlds: discourse, text and metaphor in the representation of landscape, London, New York, Routledge.

Batty M., 2005, Cities and Complexity. Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals, Cambridge MA, The MIT Press.

Batty M., Torrens P. M., 2001, “Modeling Complexity: The Limits to Prediction”, Cybergeo: European Journal of Geography, 04 décembre 2001. URL:

Batty M., 1994, “A Chronicle of Scientific Planning. The Anglo-American Modeling Experience”, Journal of the American Planning Association, 60, 7-16

Bogost I., 2008, Unit Operations. An Approach to Videogame Criticism. Cambridge: MIT Press.

Bonnefoy J.-L., 2003, “From households to urban structures: space representations as engine of dynamics in multi-agent simulations”, Cybergeo, mis en ligne le 31 janvier 2003. URL:

Bretagnolle A., Daudé E., Pumain D., 2006, “From theory to modelling: urban systems as complex systems”, Cybergeo: European Journal of Geography, No. 355,

Bretagnolle A., Mathian H., Pumain D., Rozenblat C., 2000, “Long-term dynamics of European towns and cities: towards a spatial model of urban growth”, Cybergeo: European Journal of Geography, mis en ligne le 29 mars 2000. URL:

Bura S., Guerin-Pace F., Mathian H., Pumain D., Sanders L., 1997, “Simpop: Multi-Agents Systems for the Study of Urbanism”, Environment and Planning B, No. 24, 287-305.

Brunet R., 2001, “Models in geography? A sense to research”, Cybergeo: European Journal of Geography, 18 décembre 2001. URL:

Chess S., 2005, “Playing the bad Guy: Grand Theft Auto in the Panopticon”, in Garrelts N. (ed), Digital Gameplay: essays on the Nexus of Game and Gamer, pp. 80-90.

Caillois R. 1958, Les jeux et les hommes. Le masque et le vertige, Paris, Folio.

D’artista B. R., Hellweger F. L., 2007, “Urban hydrology in a computer game?”, Environmental Modelling & Software, No. 22, 1679-1684.

Epstein J. M., 1999, “Agent-Based Computational Model and Generative Social Science”, Complexity, 4, 41-60

Fortin T., Mora P., Trémel L., 2006, Les Jeux vidéo : pratiques, contenus et enjeux sociaux. Paris, L’Harmattan.

Frasca G., 1999, “Ludology meets narratology : similitude and differences between (video) games and narrative”, Parnasso, n° 3.

Frye B., Frager A.M., 1996, “Civilization, Colonization, SimCity: Simulations for the social studies classroom”, Learning and Leading with Technology, No. 24, 21-23.

Gaber J., 2007, “Simulating planning: SimCity as a pedagogical tool”, Journal of Planning Education and Research, No. 27, 113-121.

Gordon I., Buck N., 2005, “Cities in the New Convention Wisdom”, in Buck, N., Gordon I., Harding A., Turok I. (eds), Changing cities. Rethinking urban competitiveness, cohesion and Governance, Basingstoke: Palgrave, Macmillan.

Gordon E. & Koo G., 2008, “Placeworlds: using virtual worlds to foster civic engagement”, Space and Culture, No. 11, 204-221.

Guhathakurta S., 2001, “Urban Modeling as Storytelling: Using Simulation Models as a Narrative”, a paper presented to the World Planning Schools Congress, July 1-15, 2001, Shanghai, PRC

Haggett P., 1965, Locational Analysis in Human Geography, London: E. Arnold.

Holland J. D., 1998, Emergence: From Chaos to Order. Oxford: Oxford University Press.

Huizinga J., 1938, Homo Ludens, trad. Cécile Seresia, 1988, Paris, Gallimard.

Johnson, 2001, Emergence: The Connected Lives of Ants, Brains, Cities, and Software, New York, NY: Scribner.

Juul J., 2009, “Fear of Failing? The Many Meanings of Difficulty in Video Games” in Perron B. and Wolf M. P. (eds), The Video Game Theory Reader 2, New York, Routledge, 237-252.

Juul J. 2007, “A Certain Level of Abstraction”, in Akira Baba (ed), Situated Papers: DIGRA 2007 Conference Proceedings, Tokyo, DIGRA, 510-515. Available at

Juul J., 2005, Half-Real: video games between real rules and fictional worlds. Cambridge MA, The MIT Press.

Koster R., 2005, A Theory of Fun for Game Design. Scottsdale, Paraglyph Press.

Krugman P., 1997, Development, geography, and economic theory.

Lacoste Y., 1976, La géographie, ça sert, d’abord, à faire la guerre, Paris, Maspero.

Lemoy R., Raux C., Jensen P., 2010, “An agent-based model of residential patterns and social structure in urban areas”, Cybergeo, mis en ligne le 6 décembre 2010. URL:

Lévy J., Lussault M. (dir.), 2003, Dictionnaire de la géographie et de l’espace des sociétés, Paris, Belin.

Nesson R. & Nesson C., 2008, “The case for education in virtual worlds”, Space and Culture, No. 11, 273-284.

Nitsche M., 2009, Video Game Spaces. Image, Play, and Structure in 3D Worlds, Cambridge MA: MIT Press.

Passeron J. C., 2001, “Acteur, agent, actant: personnages en quête d’un scénario introuvable”, Revue européenne des sciences sociales, No 121, 15-39.

Parkinson M., Boddy M. (2004), “Cities and the policy agenda”. In Boddy M., Parkinson M. (eds), City Matters. Competitiveness, Cohesion and Urban Governance, (eds.), Bristol: Policy Press.

Pumain D., Lane D., Leeuw S. E., van der West G. (eds), 2009, Complexity perspectives on innovation and social change, New York, Springer.

Occelli S., 2002, “Facing urban complexity: towards cognitive modelling”, Cybergeo: European Journal of Geography, mis en ligne le 19 mars 2002. URL:

Rollings A. & Morris D., 2004, Game Architecture and Design - A New Edition. Boston, New Riders.

Rufat S., Ter Minassian H., 2011, Les jeux vidéo comme objet de recherche. Paris, Questions théoriques.

Sanders L., 1999, “Modelling within a self-organising or a microsimulation framework: opposite or complementary approaches?”, Cybergeo, mis en ligne le 24 mars 1999. URL:

Salen K., Zimmerman E., 2004, Rules of Play: Game Design Fundamentals, Cambridge MA, The MIT Press.

Schelling T., 1978, Micromotives and Macrobehaviors, New York, W. W. Norton.

Simon H., 1956, “Rational Choice and the Structure of Environment”, Psychological Review, No. 63, pp. 129-138

Smith J. H., 2006, Plan and Purposes: How Video Games Shape Player Behaviour, PhD dissertation, IT University of Copenhagen. Available at

Starr P., 1994, “Seduction of Sim: Policy as a Simulation Game”, The American Prospect, vol. 5, No. 17, pp. 19-29.

Squire K., 2004, “Review: Sid Meier’s Civilization III”, Simulation & Gaming, No. 35, pp. 135-140

Squire K., 2002, “Cultural framing of computer/video games”, Game Studies, No. 1, vol. 2, pp. 19-29

Stockburger A., 2006, The rendered arena. Modalities of space in video and computer games, Ph. D thesis, University of the Arts, London.

Touraine A., 1984, Le retour de l’acteur, Paris, Fayard.

Ter Minassian H., Rufat S., 2008, “Et si les jeux vidéo servaient à comprendre la géographie?”, Cybergeo, No. 418,

Wegener M., 1994, “Operational Urban Models”. Journal of the American Planning Association, No. 60, pp. 17-29

Haut de page


1 In 1970, the mathematician John Conway suggested this game as the exemplar cellular automata. It is a cellular grid where any cell can be “alive” or “dead”, and there are two rules: a cell is giving birth if there are exactly three live cells in its neighborhood; a cell remains alive if there are two or three live cells adjacent to it. Fewer than two adjacent cells implies the cell dies from isolation, more than three and it dies from overcrowding. This game allows exploring the countless kinds of complexity that emerge from such simplicity and to find the self-perpetuating configurations. Its popularity lead to numerous variations: Day & Night, HighLife, Immigration, etc. (Holland, 1998: 136-142).

2 One can also take the example of the Grand Theft Auto series (Rockstar Games 1997-2008), although they are not city builder’s games. Although these games allow the player to act as a criminal, they re-enact the values of the Western legal system by teaching him the futility of crime (Chess 2005).

3 This article will not discuss multi-player games, because they present specific issues due to their game design (particularly in the case of “persistent worlds”) and the socialization contexts in which players interact in the game.

4 City builder’s games are sometimes called “god games”: the player with his overhanging vision of the playground and the capacity to freeze the in-play time acts more as a god than as a mayor. But this is not a specificity of city builders. Other games actually offer to play a divine character, as Populous (Bullfrog, 1989) or God of War (Sony Computer Entertainment, 2005).

5 It was not possible to reveal the exact algorithm that manages those social tensions. Our testing established that tensions spring up as soon as two different classes coexist in the same building or in contiguous housing. The more these populations are different and numerous, the faster conflicts become violent (from quarrels to riots).

6 Nevertheless, the core-code of the first edition of Sim City (1989) has been edited online; the GPL open source code version is called “Micropolis”:

7 See for example on the first American on-line fan site dedicated to the Civilization series:

8 Borrowing to sociology and urban studies, the “actor” designates an individual who possesses independent power of decision, whereas an “agent” designates those whose decisions are bounded for a more or less long time (Touraine 1984, Passeron 2001, Lévy & Lussault 2003).

9 In some cases, it may be assumed that some settings are deliberately left aside. In City Life, if the program took too often into account the presence of some communities in the vicinity at the very moment an inhabitant installs, there would be too high inertia of the social composition of the city, therefore an uninteresting challenge proposed to the player, and actually little interest in the game. Thus, there is a necessarily arbitrary search for balance between the various factors considered, in order to avoid both boredom and frustration, which is critical for game designers.

10 If the presence of trees arises the value of the adjacent land, it is because they contribute to limit pollution, and not for themselves. This is why in Sim City an urban park is a more “efficient” amenity than a forest. Furthermore, players may consider their city on aesthetic criteria, but this is not part of the game.

Haut de page

Table des illustrations

Titre Fig. 2: Diagnostic tools in SimCity 4 (2003)
Fichier image/jpeg, 580k
Titre Fig. 3: Accessibility in City Life (2006)
Légende Increase of the employment zone of a single company (in green) when shifting its connection to the city from a 2-line road (left) to a 4-line highway (right)
Fichier image/jpeg, 316k
Titre Fig. 4: Urban segregation in City Life (2006)
Fichier image/jpeg, 196k
Titre Fig. 5: Cities’ network in Civilization IV (2006)
Légende Each city controls a 21 cells production area (left), the model and regulations lead the player to specialise cities and to create a hierarchical cities’ network (right)
Fichier image/jpeg, 244k
Titre Fig. 6: Centralized urban space in CivCity Rome (2006)
Légende Dwellings near the equipments concentrated in the centre upgrade (left), and the better a house is, the further its residents can travel to catch products and services (right).
Fichier image/jpeg, 276k
Titre Fig. 7: Hierarchical urban space in City Life (2006)
Légende Dense and diverse central area around the city hall (left) and smaller centrality spot around the nearby “cultural” centre (right).
Fichier image/jpeg, 348k
Titre Fig. 8: Polycentric space in SimCity 4 (2003)
Fichier image/png, 824k
Haut de page

Pour citer cet article

Référence électronique

Samuel Rufat et Hovig Ter Minassian, « Video games and urban simulation: new tools or new tricks? », Cybergeo: European Journal of Geography [En ligne], Science et Toile, document 622, mis en ligne le 19 octobre 2012, consulté le 27 février 2024. URL : ; DOI :

Haut de page


Samuel Rufat

University of Cergy Pontoise, Paris, France

Articles du même auteur

Hovig Ter Minassian

University of Tours, France

Articles du même auteur

Haut de page

Droits d’auteur


Le texte seul est utilisable sous licence CC BY 4.0. Les autres éléments (illustrations, fichiers annexes importés) sont « Tous droits réservés », sauf mention contraire.

Haut de page
Rechercher dans OpenEdition Search

Vous allez être redirigé vers OpenEdition Search