Gaming Simulation: a Tool for Empower Social Scale-Free Networks. Some Reflections about the Impact in Urban Planning.
The following is a recently slightly reviewed version of the original paper Rizzi P., Cossu R.(2007), "Gaming Simulation: a Tool for Empower Social Scale-Free Networks. Some Reflections on the Impact in Urban Planning.", discussed by Roberto Cossu at the 10th International Conference on Computers in Urban Planning and Urban management · CUPUM 2007 held in 11-13 July at Iguacu, Brasil.
Urban Gaming-Simulation stands as an alternative method to forecasting techniques and large-scale territorial models that in the ‘70s got in crisis. But in the early ‘80s UGSs got in crisis themselves because of the decline of algorithmic models of social systems. By the end of ‘90s sciences of complex networks puts more attention and give more persuasive mathematical explanation on such phenomenon called “small-world” and on the properties of scale-free networks.
The paper is focused on the application of small-world model and scale-free networks concepts in urban planning describing also two examples. Conclusions, by a vision of the urban environment as a genetic system based on complex social relationships, starts a reflection on how is possible to replace old social models in UGS by the small-world model tuning the overall with scale-free networks properties.
1. Urban Gaming-Simulation in the XXIst Century
1.1 What is Gaming/Simulation
Urban Game Simulations (UGS) are part of the large family of Gaming Simulation (GS). In order to grasp the big picture and its complexity it’s necessary and usefull to follow a path that goes by XXth to XXIst century during which singol steps are represented by definitions of GS given by major scholars and experts in gaming-simulation.- An educational technology: “...it’s clear that they constitute a new educational technology. New technology is usually preceded by basic scientific knowledge. [...] In the present case, however, the sequence has been reversed: we have a technology which works – but we do not really know why. This is rather unusual situation in behavioral science” (Boocock, Schild, 1968; p.22)
- An instructional tool: “Gaming-simulation, with its mixed strategy approach to the coverage of the quantitative, through mathematical terms, and the qualitative, through human representation, appears to be an appropriate vehicle for conveing and understanding [complex situations]” (Taylor, 1971; p.5)
- The future’s language: a powerful form of communication particularly suitable to get the GESTALT (Duke, 1974; p.10)
- A communication device: “When we argue that gaming-simulation my be a way to deal with the challenges [...] it is partly because we see games as communications devices, rather than holding to the narrow conception of them as pedagogic devices” (Greenblat, 1989; p.18)
- “Simulations of the effects of decision made by people, assuming roles that are subjected to rules” (Cecchini, 1988; p.656);
- An eclectic and multidisciplinary problem solving methodology (Duke & Geurts, 2004; p.1008)
- Social Systems: “While playing games, adults and children give shape to human organizations. [...] For that reason organization theory offers a fruitful frame-of-reference fro reflecting on gaming” (Klabbers, 2006; p.38)
- “An excellent tool in assisting people envisioning the future and striving to elaborate policies or taking important decisions in their organization [...and also...] a powerful analysis method for face complex problem areas” (Duke, 2006; p.34)
That review of definitions can’t and don’t want to be exhaustive, but is helpful for understand some points.
First of all GS has growth empirically and then the effort was on understanding why it works an how to develop a persuasive theory.
Second, the awareness about the power of GS had a progressive impact on professionals and scholars thanks to positive results in field experience and theoretical research.
To be more clear few recommendation has to be done: GS can’t be confused with Games Theory (Von Neumann, Morgernstern, 1944) BECAUSE GS is a methodology instead of Game Theory that is the theoretical corpus dedicated to the solution of decision problems by which as Operational Games they has been derived.
Nevertheless it’s quite important to have cognition about Game Theory to better evaluating and designing GS. Moreover, UGSs can’t be confused with business games (even if some training issues for general management are quite useful to learn or reflect on urban management skills) or with policy games even if in this second case boundaries are very shaded.
Another point which has to be considered is the impact due by computational power on GSU. Sofisticated simulation softwares as SIMCITY, CIVILIZATION, SIMEARTH an so on, are designed with entertainment purposes and not to make the gamer more conscious about urban o population systems dynamics, and even if they became very popular can’t be confused with UGS.
In those simulation software all take place within the mathematical model that processes gamer decisions as inputs for the algorithm. Actually videogames can be used to have a pleasant warming-up before being familiar with planning issues.
Nowadays it can’t also be ignored that computer simulations has become a powerful tool to implement in UGS but only in few sectorial fields as transport and mobilty they can be directly used in decision aiding processes.
But the reverse of the medal is that computer simulations regarding urban problems areas, on the contrary of UGS, are quick to elaborate scenarios but they are thirsty of good data that has to be grasped and elaborated in long time by humans.
So long time for grasping data for decision to be done too often quickly (Rizzi, 2004). Today we are still in the lack of robust methodology that underpins gaming and simulations methods. But we can say that “gaming and simulation are more than methods and tools. They are firstly a way of thinking, and secondly, a method and a technique” (Klabbers, 2006; pag. xi)
It’s difficult to grasp the entire picture and more difficult to draw a synthetic framework but it’s possible to recognize some general constitutive elements that put together all GS exercises.
1.1 General constitutive elements of GS
Gaming simulation has become common in the design and planning disciplines used as analysis and research instrument. By the ’90s, design and exertation of gaming simulation is also oriented to stakeholders involvement and participation purposes. GS have had in the past and will have unlimited possible classifications. Nevertheless, is useful to underline what are the principal elements that concur to explain the nature of GS: #roles, #rules, #model and #simulation. (Rizzi, 2005)
Generally the #role assumes a significate of a function with a determinate character in a particular context. The role is often related to expected functions and characteristics of an agent in particular activities or processes. Even if given and structured by the designer, the roles are never strictly defined. A given role imposes restrictions to participants and limitations to options and possible approaches during the game, but it doesn’t establish de facto strategies and behaviors running the GS. Sometimes roles are conceived in order to be explicitly modified or designed by participants. Gamers with the same role can pursue different strategies and their own goals can be interchanged and modified even constantly during the game.
The #rules are a set of elements that establish limits that can be controlled. In other words the rules establish what can be done or not and are calibrated to control conflict and competition level in order to run safely a GS. A rule can be qualitative/quantitative, temporal/spatial, verbal/gestural, rewarding/penalizing, negotiable/fixed, implicite/explicite and so on. Rules should reflect the aims of the GS even if they are not declared. It should be said that while the scenario can be considered an essential and intrinsic factor for a simulation so it doesn’t for the model.
The #model reproduce the basical characteristic of a phenomenon and it is a the misuration reference for the #simulation.
If the model isn’t available the game is called #MetaphoricGame. Those games are often not structured and their characteristics pretends to be the same of the real world but only hypothetically. In brief, model and simulation are related to a mechanism that joins by the linkage between causes and effects, roles and rules to generate possible results related with a given situation (Rizzi, 2005).
According to Klabbers (2006) the term simulation can be intended as “the process of simulating something that is, reproducing a set of conditions, or the result of simulating it” or “an attempt to resolve a problem or to work out the consequence of doing something by representing the problem or possible course of events mathematically, often using a computer” (Klabbers, 2006; pp.29-30).
But it have to be added that a simulation is also an activity that projects different situations in time and space, usually bringing them by the real/existing world into hypothetical situations. Simulation creates a potential dynamic that permits to a scenario or a model to become “alive”. Simulation is ever based on a scenario that in some cases can assume the shape of a model but it can be even the description of a situation or a narration (Rizzi, 2005).
Given those common elements, UGS can be dinstinguished non only because of the complexity of the issues it face but also for its history of rising and crisis that don’t seems to have reached a persuasive solution.
1.2 Urban Gaming Simulation: rising and crisis
Nathan Grundstein and Richard Meier are with the first in designing and running UGS.
In 1959 Grundstein in cooperation with William Kehl, they designed the COMMUNITY GAME, a city simulation covering a time of 30 years; in the same period Meier conceveid the idea to design and to run GS to explore concepts and political, social and cultural processes.
The result was WILDLIFE, a simulation that even if not focused on urban environment introduced in GS the concept of community. But the game common intended the very ancestor of UGS is POGE (Planning Operational Game Experiment) by Francis Hendricks. (Rizzi, 2004).
In 1958 Richard Duke developed the game METROPOLIS which ufter a modification and enhancement was called METRO (Michigan Experimental Teaching and Researching Operation).
In 1958 Richard Duke developed the game METROPOLIS which ufter a modification and enhancement was called METRO (Michigan Experimental Teaching and Researching Operation).
That game was used in the city of Lansing, Michigan. Thanks to the success of those experience, Duke decided to develop his first complex UGS, METRO/APEX in which was introduced computer simulation. Takes part of that family also the game CLUG (Community Land Use Game) by Allan Feldt in cooperation with the urban geographer Brian Berry: it’s a game based on a model of land use inspired by two games WILDLIFE and SQUARE MILE. The second game attempts to provide a simplified description about city development and urban planning. Allan Feldt, inspired by a game by Paul Goodman, called INTER-NATION which was played in teleconference, developed a new family of large-scale games for 50 to 100 players known as MAGG (Metropolitan Area Growth Games) (Feldt, 1995).
GSU in those years were characterized to be firstly training tools for planners and managers, instructional tools for students and, finally, research tools for scholars.
GSU in those years were characterized to be firstly training tools for planners and managers, instructional tools for students and, finally, research tools for scholars.
Furthermore, there were a general confidence about the possibility to use UGS as analysis, prediction and setting tools for policies in the field of urban planning and design.
Yesterday as today, scholars of urban planning was interested to get the behavior of urban systems that are regolated by a more large number of variables respect the past. Moreover those variables are hidden, unknown or impredictable so that is impossible to face them with simple or linear mathematical algorithms (Cecchini, 1998).
Despite of that limitations the diffuse trend was to integrate in the design of UGS hard large-scale models whose crisis – that was due to the need of large amount of data, the wrong-headedness of ideas, design complexity, high costs, tendence to be hypercomprehensive, gross and mechanical (Lee, 1973) – had a reflection on the future development and use of UGS.
Even if apparently the need of large amount of data, the wrong-headedness of ideas and design complexity seems to be overreached (Harris, 1994), large-scale models are having still today the pretence to predict future and behaviors of complex systems like urban ones.
It not was a secret that in nature complex systems are extreamely sensible to little variations of surroundings conditions and that urban ecosystems are more complex than it was believed in past: the behaviors of agents aren’t always totally rational or based on simple reasoning, but they are the consequence of a continous interaction that sees singular preferences connected to each other in a co-evolutive way (Allen, 1997).
The crisis of models and of urban planning in general that lost its social connotations for approaching hard sciences (Hall, 1988) (Alexander, 1992) gave an acceleration to the progressive obsolescence process and crisis due to epistemological, methodological and practical reasons. (Cecchini, Rizzi, 2001).
The epistemological reasons for the crisis are related to the tendency of many scholars of “weak” disciplines like urban planning to believe that the methods and tools of physics that had proved so successful might migrate into social sciences giving birth to hard urban models.
The epistemological reasons for the crisis are related to the tendency of many scholars of “weak” disciplines like urban planning to believe that the methods and tools of physics that had proved so successful might migrate into social sciences giving birth to hard urban models.
“Moreover the aim of urban planning is not only to describe, analyze, and explain urban development, but its aim is above all to predict and advise strategies to reach feasible futures” (Cecchini, Rizzi, 2001; p. 511).
So putting complex algorithms like large-scale models promising predictional performances into UGS did make belive that the gol was reached. The methodological reasons of the crisis are related to the necessity to extend the domain of UGS to predictive aims and to training and analisys aims at the same time encountering much problems about the good nature of the models used in UGS. In particular it wasn’t clear if it had better to use purely algorithmic urban models ore hybrid models based on mechanical submodels connected to a system of actors.
The practical reasons of the crisis emerged around the ‘80s. UGSs were considered effective planning tools exactly as plans devides by competent scientist were viewed as valid governing tools. That belief was so rooted to consider that conflicts could be rationalized and once understood they could be settled. Actually facing ratial conflicts and economic stagnation contributed to a genel regional disequilibria and UGS exposed its unfectiveness so that public administrations stopped using UGS (Cecchini, Rizzi, 2001).
For sure by the first conference of the International Simulation And Gaming Association (ISAGA) held in 1970 many things has changed.
For sure by the first conference of the International Simulation And Gaming Association (ISAGA) held in 1970 many things has changed.
In fact, at the time a considerable part of ISAGA members was interested in urban planning, environment and sociological issues. That tendency during the ’80s has strongly changed and teaching, learning and training issuesin different matters drived the interests. Rizzi, 2004). It was like backing to the roots or probably a sort of wiping the visible difficulties and perplessities that UGS was showing.
1.3 Signs of vitality and new perspectives
Signs of vitality by GSU comes thanks to the mutation of the general reference framework of urban planning: in face to epocal changes al the global level that drives without chances the growing of urban areas we have a change in the perspective of observation of phenomena considering to combine quantitative and qualitative models; it’s also in progress a reflection on the planner’s role and on his skills; we see the even more powerfull dimension of stakeholders participation without some ingenuousness of the past.
Those signs, in order to be amplyfied, have the need of crossfertilization among diverse disciplines (Cecchini, Rizzi, 2001).
New perspectives, that we want to explore in the present paper, are given by the evolutions in the study of complex graphs. Researchers of different fields (from physics and computer science to biology and the social sciences) have found that networks are suitable for successfully represent a great variety of systems, and that there is much to be learned by studying those networks.
The study of networks has had a long history in mathematics and in the sciences and begins in 1736 when Leonard Euler gave a solution to the mathematical riddle called Könisberg Bridge Problem. Then the theory was perfectioned by Cauchy, Hamilton, Cayley, Kirchhoff and Polya (Biggs et al., 1976).
The study of networks has had a long history in mathematics and in the sciences and begins in 1736 when Leonard Euler gave a solution to the mathematical riddle called Könisberg Bridge Problem. Then the theory was perfectioned by Cauchy, Hamilton, Cayley, Kirchhoff and Polya (Biggs et al., 1976).
The way with it’s possible to draw complex graphs and the study of their structure and properties begin foundamental in the study of complex phenomena as the social network are.
It’s interesting to note that the study of the structure and dynamics of networks has followed a parrallel path: on one hand sociologist have driven empirical and intuitive researches and on the other hand mathematicians and physician studying natural phenomena like sincronism were discovering new models that seems to be applied broadly in nature as well as in social sciences.
In physics and mathematics, on the contrary, since 1998 with the pubblication on the journal Nature of Watts and Strogatz article on the Collective Dynamics of “Small-World” Networks, the behavior of networks in general had a modelization made by a random graph where a node is connected to the others without a clear pattern (Salomonoff, Rapoport, 1951) (Erdos, Rényi, 1959).
The experiment was led in two steps. First it was explored the capability to create a cooperative behaviour giving to a simple cellular automata a small-world behavioral pattern obtaining positive results. The second step of the experiment was led using Game Theory in order to observe the effects due to the variations of the coupling topology in two sub-cases: in the first, to evaluate the emergence of a cooperative behavior inside an homogeneous population where each node is able either to cooperate or to compete; in the second, to evaluate cooperative behavior evolution in an heterogeneous population in which satisfing rules are carried on for new generations. For this kind of simulation Axelrod methodology was used: repeting much times the Prisoner’s Dilemma appling generalized “Tit-for-Tat” and “Win-Stay, Lose-Shift” strategy. In the first sub-case players assumed an intrinsicanly cooperative behavior in the sense that each player repeated the behavior of the other player in the previous turn. In the second sub-case players didn’t assume a cooperative behavior and each player changed his own behavior only if he lost in the previous turn (Watts, 1999).
The results, corroborate by following studies (Cassar, 2007), showed that in first sub-case the emergence of cooperative behavior in small-world network need the initiale presence of a population rather incline to cooperation by the start, if we want that cooperative behavior spreads quickly; if the population is only marginally incline to cooperation, cooperative behavior can spread but slowly. In the second sub-case, in which a lone cooperator was introduced inside a group of competitors, competitors attempted to change the behavior of the lone cooperator; if the attempting was successful cooperator became competitive. Nevertheless, it can be experienced that some competitors decided to cooperate even if they don’t obtained visible benefits. That type of strategy drives the system to situation that reaches a stable state only asymptotically and in a long time (Watts, 1999).
Recent researches on craze are based on the classical model elaborated in the ‘70s by the sociologist Mark Granovetter even if by 1953 Rapoport published a study about Spread of Information through a Population with Sociostructual Bias (Rapoport, 1953). Granovetter’s model was based on an hypothetic crowd of about 100 people where a revolt can happpen and on the assumption that each decision to participate or not to the revolt is a variable dependent by the choice of each one and it follows a distribution with a determinated curve of probability. Inside the crowd is possible to see fomenters and followers. The variations to the model show that the unpredictability of a crowd is related to internal dynamics that depends on the particular composition of the crowd (Granovetter, 1978).
Recent studies by Watts take in account the more realistic case in which inside a large group individual choices are influenced by a sub-set of friends and collegues and that influence is exerted through simple communication forms as word-of-mouth. Which is very interesting about this model is that a general behavior can be observed: two phase transitions corresponding to as much tipping points are there. When the model reaches the first tipping point diverse groups of people gathers forming a grid where fast transmission of information is possible; that dynamics facilitates epidemics of innovation. Increasing the connectivity among nodes the phenomenon amplyfies reaching the second tipping point where a diluition effect with a loss of intensity of the phenomenon can be experienced. From this point the system become extremely unpredictable but stable respect to changes. New epidemics of innovation are possible but rare and capable to reach high levels (Watts, 2002)
The concept of “tipping point” as that of “clustering” was studied in sociology and then mathematical evidence was found studying complex networks. In fact, the concept of tipping point was introduced the first time by two authors interested in studying the dramatic withdrawal of white people from neighborhoods when a threshold, corresponding to a number of coloured people the moved to the neighborhood was trepassed. That number was called tipping point. The first scholar was Morton Grodzins in 1957, the second was Thomas Shelling in 1971(Grodzins, 1957) (Shelling, 1971) (Gladwell, 2000)
According to how has been reported in the Paragraph 2 we have new knowledge in models and networks structures and behavior. So, how the networks properties are used in urban planning? A useful starting point for discussing all that is analyzing two examples by sociology and urban planning using a theoretical and a pragmatic point of view. The first case is about the Master Plan for the strijp Philips at Eindhoven, The Nederlands, based on the concept of “genetic metropolis”; (Branzi, 2006) The second case discusses of an emerging concept in sociological interaction model named “space-participation model” (Ciaffi, Mela, 2006)
Branzi defines the genetic metropolis as “...the metropolis where the biological rules that reach their maximum level of melting, fully invading an infrastructure and spreading out from every possible designed retaining shape, [and still] the vision of a territory inside that architecture doesn’t fulfil any traced fragmentation of the space, but it becomes the theater of a wide elastic modification activity (that is reversible) from the bottom of the infrastructures, of services, ad it is metropolitan sub-systems” (Branzi, 2006; pag. 24)
That idea of metropolis in the informational era puts together technology and the “human being territory” representing the space of relationship into “connecting self DNA with the DNA of business spreading its gene in a close network of parental and enterpreneurial relationships” ad it refers to the ethics of the informational networks that “teaches us the existence of the great provided by social interconnetion”. (Branzi, 2006; pag. 24)
Following those ideas, the design for the regeneration of the strijp Philips is based on a weak-urbanization model. Eindhoven was, since the beginning of the past century, the storic headquarters of Philips industries that are on the cutting edge of the world technological innovation. In fact there were developed industrial projects and producted the first electric bulb, the first cd-rom, the first walkman in the world. In the ‘70s over 90% of its population had been working on Philips plants untill the dramatic decline that hitted european districts with a consequential delocalization of productive functions in other Countries. That huge area, about 982.000 square meters, has been proposed to the european market as the ideal city that can become the birthplace or the incubator of micro-imprenditorial iniziative related to technological and artistical mass design creativity that, to grow, needs an environment where it’s possible to activate intensive relational spaces. In order to drive that forces, the masterplan is based on a mixed land use model within productive farm lands are made permeable to physical connection and to the localization of research laboratories, residential, commercial and leisure functions, that are able to work in sinergy changing their dimension and functioning model on demand.
The interaction model of the design process is based in the transformation of an “uncomplete” starting network (Figure 5) to a new one rich of bio-diversity.
The author represents the structure of the network, its topology and its future behavior in an extremely complex way (see Figure 6) where the nodes have to become specialized hubs capable to attract young generations but it’s not clear if the network wil be set up to be a small-world a scale-free network or something in-between.
The second example offers a conceptual model that can be collocate in a emergence pespective of research definined “spatialist sociology” that [...] even if in [its] evident heterogeneousness, [faces out] a theoric node that in the history of urban sociology, has been often leaved on the backstage or, anyway, not solved: that node of the relationship between action and social systems on one hand, and the space (or, better, the spatial-temporal dimension) on the other hand (Mela, 2006, p.34).
“Space-Participation” model, was developed starting with a cross observation of some recurrent elements tha can be discovered in urban regeneration design experiences in european countries underlining how the principal action that can be started within a society are the result of declining participation into 4 categories of action: communication, animation, consultation, empowerment (Ciaffi, Mela, 2006).
Those categories are quite shaded and any of that empowers each other in a relational space settled in concentric circles tipical of community psicology literature: the inner circle is the space of intimacy (private space); the intermediate circle is the space of familiar relationships and friendships (local public space); the broad circle is the rest-of-the-world (super local public space)
Those concentric circles are configured as real “ecological niches” within the category of local relationship is disembodied by propinquity ties. So, the inner circle, or private space, is an ecological niche that contains the home but it contains residential and semi-residential structures for adults as assistance communities; the intermediate circle, or public local space, is another ecological niche that contains public spaces perceives as familiar and therefore regularly frequented even if distant from house; the external circle, the super local space, is a broad ecological niche that contains well known, marginally known or unknown public spaces ad that are perceived as no familiar (Ciaffi, Mela, 2006).
The importance of that model is not only because it attempt to reconstruct the whole with a systemic view putting in relation actions, social systems and personal life space but we can see evident connections with the concept of genetic city that itself put together social interaction principles, physical space modification and ecology recognizing the city as relational ad exchange system, as a structure that connects. But also in this second case we can’t see operative reflections on how to activate processes working on the structural components of the social network within the concentric circles framework.
According to Castells and Sassen the crisis of urban systems is related to economical, social and environmental issues triggered at the global level and amplyfied by the increasing velocity in communication and social transformation. It seems to be impossible to face urban planning only as a transfomation of the physical space, using traditional communication techniques or only attempting to regenerate them.
The new knowledge we have on structure and dynamics of complex networks as the social networks (see paragraph 2) represent a great motivation to deeper how it can be useful in design and run UGSs that can be helpful in activation of time long cooperative behaviors in urban planning processes.
Is almost fascinating the idea to consider the concept of collaborative behavior as a germ to vehiculate and fast spreading: a sort of positive epidemic that helps us to recognize tha even if sometimes seems that cooperation gives a minor personal payoff the final result is a major payoff for the whole system also considering the modification of the physical space.
We propose to elaborate new criteria and requirements for designing and realization of a new generation of UGSs based on small-world and scale-free models and we are working on that attempting to study and develop a prototype.
What can be said right away is that the approach we are using is more weak than the engineering one and we define it the “bricoleur” approach. This approach brings inside all that cognitive micro-processes that urban planning of engineering type don’t permit to grasp and also facilitate to enlight that structure that Bateson called “the structure that connects” that can indeed be compared to those networks of nodes and relationships that in conclusion for us represents the city.
REFERENCES
a) Books and Books chapters
b) Journal papers
2. Small-World Networks and Scale-Free Networks
In the sociological field the study of networks was strongly influenced by the fascinating theory of the “six degrees of separation” (Milgram, 1967) but first by a research in anthropolical analysis in a norwegian village (Barnes, 1954) and subsequently by a study on different typologies of social networks: ego-centric networks, partial network, ego-centric partialized network (Hannerz, 1980) and by the concept of open network (Kadushin, 2002, 2005); then by the discover of the “power of weak ties” (Granovetter, 1973, 1983) and also by the discover of the “fraction of transitive triplets” (Wasserman, Faust, 1994) (we’ll see forward that this is the same concept defined in formal mathematical language as “clustering” by Watts and Strogatz in 1998); then by the evidence that subjects that take part to a social network are not all the same but they can be of two types: “hubs” and “connectors”. First type of nodes are very special because they have connections and acquaintances with a large number of others nodes in the network and they establish the average degree of separation between all the individuals of the network.In physics and mathematics, on the contrary, since 1998 with the pubblication on the journal Nature of Watts and Strogatz article on the Collective Dynamics of “Small-World” Networks, the behavior of networks in general had a modelization made by a random graph where a node is connected to the others without a clear pattern (Salomonoff, Rapoport, 1951) (Erdos, Rényi, 1959).
Around 25 years ago it was possible to derive mathematically the equations of Erdos and Rényi establishing that in that random networks the distribution on the average degree of connections is described by a bell curve (Bollobás, 1981) that is that in a social network each node has the same number of acquaintances.
That could be right far in the past or today in little and closed communities (See also the “caveman model” in Watts, 1999). Today we can see that starting by the industrial revolution a strong phenomenon of mixing of races and growing of the number of people who lives in the cities is going on and this dynamic is changing old relationships based on clan belonging (Tönnies, 1887) in new less strong types of relationships based on the exchange of goods and services (Weber, 1922).
Mathematical modellization of the “small world problem” was discovered switching a regular net to a random one. To represent an intermediate network architecture two statistical concept are necessary: the characteristic path lenght that is the typical distance between every vertex and every other vertex (that is the degree of separation); and the clustering coefficient that characterises the extent to which vertices adjacents to any vertex are adjacent to each other (that is a measure of local density). The first can be measured calculating the steps of the shortest chain that separates a couple of vertex, then calculating the distance between each couple of vertex we can finally calculate the average path lenght. The second provide the average value of the overlapping between nodes and it’s explained by the probability that two vertex that are linked to a common vertex are linked each other: by a social point of view it represent the probability that two persons that have a common friend they are friends too. The average path lenght depends on the way the whole network is linked giving a measure of how much it is extended but it doesn’t exist any local data that can give information on it. The clustering, on the contrary, provides informations on the local structure. (Watts, Strogatz, 1998) (Strogatz, 2001). On the beginning of the switching between a regular and random network one the few random connections acts as a shortcut between distant vertex or nodes. The activation of these shortcuts triggers a powerful non-linear effect by which those two vertex can connect not only two distant nodes but iven entires world far away. Putting only few shortcuts cause a shrinking world but the clustering value seems to remain the same. Hence the transition in a small world network is impossible to perceive at the local scale on the contrary of what happens in random or regular nets where dimension and local density going on together. Random nets are small with a little clustering; regular nets are large and with a high clustering (Watts, Strogatz, 1998) (Strogatz, 2001).
Intermediate nets that are at the same time small and with a heavy local density were called small-world networks taking in account the field of human relationships where even we take part of small groups we are potentially connected by short paths to persons apaprently distant.
By our point of view it’s important to know if the small-world architecture gives advantages if implemented as a simple model of interaction within UGSs. And if it possible to do that, can we simply replace models in existing UGSs or it become necessary to develop new design requirements and hence a new generation of UGSs? I consider the second option but for now we can only create the basis for a deeper study that can allow to give a brief answer and we need further elements.
2.1 Small-World Networks
Studying the behavior of coupled phased oscillator arranged in a regular grid, Duncan Watts guessed that to better understand the behavior of a large number of elements that belongs to a network is necessary to understand the structure of that connections; paraphrasing Bateson we could say that is necessary to take a look to the “structure that connects”. And it could be intuitive to think that processes of spreading information o epidemic processes in a social network are strongly influenced by existing relationships between the structure and and the function covered by each node. And it is more true if we consider social networks as complex and dynamic systems within each element can face modification passing the time. But this way of thinking was in conflict with the tendency of scholars that skirt the issue of the connectivity studying only regular or random nets not because of their aptitude to explain real systems but because they were more easy to manage in complex situations (Strogatz, 2001).Mathematical modellization of the “small world problem” was discovered switching a regular net to a random one. To represent an intermediate network architecture two statistical concept are necessary: the characteristic path lenght that is the typical distance between every vertex and every other vertex (that is the degree of separation); and the clustering coefficient that characterises the extent to which vertices adjacents to any vertex are adjacent to each other (that is a measure of local density). The first can be measured calculating the steps of the shortest chain that separates a couple of vertex, then calculating the distance between each couple of vertex we can finally calculate the average path lenght. The second provide the average value of the overlapping between nodes and it’s explained by the probability that two vertex that are linked to a common vertex are linked each other: by a social point of view it represent the probability that two persons that have a common friend they are friends too. The average path lenght depends on the way the whole network is linked giving a measure of how much it is extended but it doesn’t exist any local data that can give information on it. The clustering, on the contrary, provides informations on the local structure. (Watts, Strogatz, 1998) (Strogatz, 2001). On the beginning of the switching between a regular and random network one the few random connections acts as a shortcut between distant vertex or nodes. The activation of these shortcuts triggers a powerful non-linear effect by which those two vertex can connect not only two distant nodes but iven entires world far away. Putting only few shortcuts cause a shrinking world but the clustering value seems to remain the same. Hence the transition in a small world network is impossible to perceive at the local scale on the contrary of what happens in random or regular nets where dimension and local density going on together. Random nets are small with a little clustering; regular nets are large and with a high clustering (Watts, Strogatz, 1998) (Strogatz, 2001).
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| Figure 1 Regular graphs: simple (a) and totally connected (b) (by Strogatz, 2001) |
Intermediate nets that are at the same time small and with a heavy local density were called small-world networks taking in account the field of human relationships where even we take part of small groups we are potentially connected by short paths to persons apaprently distant.
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Figure 2 transition by a regular graph to A random graph (p= probability of random links) (by Watts, Strogatz, 1998) |
Another aspect is that coupled oscillators in a small world network can sincronize faster than in a regular network because the shortcuts provide a high capacity communication channels which allows fast spreading of mutual influences inside the whole population. And it happens at a lower cost than if any oscillator has been linked to each other. This fact stimulated the idea to verify if the small-world architecture provide advantages in other scenarios where it’s necessary that information spreads rapidly through a complex and large system (Watts, 1999).
By our point of view it’s important to know if the small-world architecture gives advantages if implemented as a simple model of interaction within UGSs. And if it possible to do that, can we simply replace models in existing UGSs or it become necessary to develop new design requirements and hence a new generation of UGSs? I consider the second option but for now we can only create the basis for a deeper study that can allow to give a brief answer and we need further elements.
2.2 Scale-Free Networks
A distribution is defined scale-free, that is without scale, if it can’t be represented by only one scale (on the contrary of the classic case in which the average value is a characteristic dimension tipical for any member of the population, an example is the average height). That particular condition is described by a curve with few nodes that have a very high numbers of links following and the most nodes that have just few connections that is that follows a power-law. If by a mathematical point of view a power-law doesn’t mean anything of particular, by a physics point of view they are signals of a possible presence of a self-organizing system. Those equations are tipical of situations in which a system is at a tippig point (we’ll see that ufter), between order and chaos and are characteristics of fractals (Shroeder ,1991) (Strogatz, 2001) (Barabasi, Bonabeau, 2003). |
Figure 3 Architecture and Distribution about Random Networks and Scale-Free Networks. Hubs in black (by Barabási, Bonabeu, 2003) |
What surprises is that very diverse networks shows the same three tendencies: short chains, high values of local density and power-law distribution of linkages: “In the last five years, new concepts of small world networks and scale-free networks triggered the explosion of empirical studies with the aim to analyse in detail complex networks structure. In a series of different cases, when the surface layer is removed, the same skeleton structure ever appears. The skeleton of Internet and primates brain? Two small-worlds. And also food cycle, the network of cellular methabolic reaction, interchained boards of the 1000 most important american companies according with Fortune and even the structure of English language are small-worlds. And in the most of cases, even if not for all, those network are also scale-free [...]” (Strogatz, 2003)
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Figure 4 Simulation of a scale-free network growing by 2 to 11 nodes (by Barabási, Bonabeu, 2003) |
In complex systems each node can be distinguished by the number of links but also for its intrinsic characteristics. Observing Figure 4, some nodes even if come late can attract the most of links becoming hubs instead of earlier nodes that wasn’t able to do that. It has to do with competition inside networks, as social ones, and it is related to the ability of people to build friendships and acquaintances, or the ability of a company to attract more clients than the competitors. That characteristic is defined “fitness” and provides a measure of the competitive ability of each node. A new node when is gathering the network evaluate the product between fitness value and connectivity value of each node available, choosing the one that at the moment offers the high product value. Between two nodes with the same connectivity value become predominat the one who has the highest fitness value, whereas connectivity and fitness have same value for each node the older become predominant. This model is the “fitness model” and explains in a more sophisticated way how scale-free networks evolve. According to this behavioral model, networks topology can be classified in two categories: those in which competition is not such important and those in which competition selects the strongest node that wins all the links becoming the unique hub in the network. In the first case we have the typical free-scale distribuited topology. In the second case we have a star-network (or as seen before in sociological terms, an ego-centric network). But untill the fitness establish the strenght of the nodes competitions generates a scale-free configuration (Bianconi, Barabási, 2001).
2.3 Strenghtness, Redundance, Feedback, Resilience.
The more interesting characteristic the distinguish scale-free network by the random networks is its strenghtness due to its distribuited topology: even if we want to eliminate a big portion of nodes or links the scale-free network don’t crashes because the eliminated paths - thanks to the feedback and the initial redundancy of links and shortcuts - can be replaced by existing ones or new ones. Hence, links redundancy due to distribuited topology coupled to feedback assures a high degree of resilience to the scale-free networks exactly as we see for all natural networks. It has to be said that this resilience of the network to random fails o attacks is balanced by a vulnerability to organized attacks to the hubs (Barabasi, Bonabeu, 2003).2.4 Infection and Spreading
Watts has conducted empirical studies in order to have evidence that small-world structure can bring visible benefits if applied to dynamic systems specially to favour information diffusion through communicative shortcuts working in the same way of spreading a disease starting with a single germ.The experiment was led in two steps. First it was explored the capability to create a cooperative behaviour giving to a simple cellular automata a small-world behavioral pattern obtaining positive results. The second step of the experiment was led using Game Theory in order to observe the effects due to the variations of the coupling topology in two sub-cases: in the first, to evaluate the emergence of a cooperative behavior inside an homogeneous population where each node is able either to cooperate or to compete; in the second, to evaluate cooperative behavior evolution in an heterogeneous population in which satisfing rules are carried on for new generations. For this kind of simulation Axelrod methodology was used: repeting much times the Prisoner’s Dilemma appling generalized “Tit-for-Tat” and “Win-Stay, Lose-Shift” strategy. In the first sub-case players assumed an intrinsicanly cooperative behavior in the sense that each player repeated the behavior of the other player in the previous turn. In the second sub-case players didn’t assume a cooperative behavior and each player changed his own behavior only if he lost in the previous turn (Watts, 1999).
The results, corroborate by following studies (Cassar, 2007), showed that in first sub-case the emergence of cooperative behavior in small-world network need the initiale presence of a population rather incline to cooperation by the start, if we want that cooperative behavior spreads quickly; if the population is only marginally incline to cooperation, cooperative behavior can spread but slowly. In the second sub-case, in which a lone cooperator was introduced inside a group of competitors, competitors attempted to change the behavior of the lone cooperator; if the attempting was successful cooperator became competitive. Nevertheless, it can be experienced that some competitors decided to cooperate even if they don’t obtained visible benefits. That type of strategy drives the system to situation that reaches a stable state only asymptotically and in a long time (Watts, 1999).
2.5 The Tipping Point
Epidemiological studies, spreading deseas theories plus who studies effects of spreading information and craze in sociological field seems to be in according to the concept of threshold: if it is exceeded the epidemic starts. Scale-free networks, according to what discovered in 2001 by Pastor-Satorras e Vespignani, have a null threshold that is: any epidemic phenomena even those not much contagious spreads and become persistent even after so much time. That phenomenon can be better understood if we consider that in the moment a hub contract the desease by one of the nodes to which it’s linked the epidemic spread with extreme velocity (Barabási, Bonabeu, 2003).Recent researches on craze are based on the classical model elaborated in the ‘70s by the sociologist Mark Granovetter even if by 1953 Rapoport published a study about Spread of Information through a Population with Sociostructual Bias (Rapoport, 1953). Granovetter’s model was based on an hypothetic crowd of about 100 people where a revolt can happpen and on the assumption that each decision to participate or not to the revolt is a variable dependent by the choice of each one and it follows a distribution with a determinated curve of probability. Inside the crowd is possible to see fomenters and followers. The variations to the model show that the unpredictability of a crowd is related to internal dynamics that depends on the particular composition of the crowd (Granovetter, 1978).
Recent studies by Watts take in account the more realistic case in which inside a large group individual choices are influenced by a sub-set of friends and collegues and that influence is exerted through simple communication forms as word-of-mouth. Which is very interesting about this model is that a general behavior can be observed: two phase transitions corresponding to as much tipping points are there. When the model reaches the first tipping point diverse groups of people gathers forming a grid where fast transmission of information is possible; that dynamics facilitates epidemics of innovation. Increasing the connectivity among nodes the phenomenon amplyfies reaching the second tipping point where a diluition effect with a loss of intensity of the phenomenon can be experienced. From this point the system become extremely unpredictable but stable respect to changes. New epidemics of innovation are possible but rare and capable to reach high levels (Watts, 2002)
The concept of “tipping point” as that of “clustering” was studied in sociology and then mathematical evidence was found studying complex networks. In fact, the concept of tipping point was introduced the first time by two authors interested in studying the dramatic withdrawal of white people from neighborhoods when a threshold, corresponding to a number of coloured people the moved to the neighborhood was trepassed. That number was called tipping point. The first scholar was Morton Grodzins in 1957, the second was Thomas Shelling in 1971(Grodzins, 1957) (Shelling, 1971) (Gladwell, 2000)
3. Urban Planning in the XXIst Century and UGS
Actually, the action field for planners is represented by a reality in which global dynamics shows urban areas facing problems of social disequilibrium driven by the impetuous phenomena of expansion (Charlesworth, 2005; Friedmann, 2005; Gregotti, 2005; Perlman, 2005) that can be represented by a reticular model of organization of the space apparently not hierarchical (Nichols, 2005). That spatial structure is the field into super-local economic and social dynamics face strongly on the local dynamics contributing to describe the cities as complex social communication systems (Meier, 1969). In the areas affected by territorial expansion and human concentration that complex communication system is founded even more on telecommunication infrastructures that accelerate the production, exchange and elaboration of data and informations instead of communitarian ties: face to face communications are powered by can on-line communications in the traditional way (web sites, e-mail, chat, forum) or enriched by the opportunity to have tridimensional virtual spaces as Active Worlds or Second Life: this fact if at one hand shows a weakness in the traditional face to face relationships on the other hand enlarges the perspectives and modifies times and spaces for dwell and their perception. The conditions with which urban planning face don’t permit to describe the city through cause-effect models and at the same time trace specific boundaries in using predictive and quantitative techniques. The cultural and socio-economic composition of local communities that are even more eterogeneous all over the world; the growing environmental pollution also caused by settlements and life models give contributions in the increasing of complexity, the uncertainty and instability in the future and the relationship detached from physical contexts (disembedding) (Giddens, 1984).3.1 Networks in Urban Planning and in Sociology: two examples
At different levels networks become part of the common language of different disciplines as well as with gaming (roles, rules, strategies). In particular an imagine become even more strong in urban planning perspective: the network of the global cities with their capability to escape from the national boundaries boosted by global economy. (Sassen, 2000, 2006). If we consider cities as the living space of a organized local society, designing the city is an activity that brings opportunities to envision new configurations of the physical space through a communicative and interactive process and in which quality and desireability of the design increases with the improvement of the organizational behavior of the whole system. Within complex organizations the metaphor of network is assumed as comunicative and interactive paradigm. But a network to be considered as well and to properly function needs that nodes (not necessarily constituted by a singular people but also bu groups) was been able to interact among them and to bring to the network little innovations that in the long term can assure the ability to answer of the system to external sollecitations. So, if the quality of the network is given by the quality and by the consciousness that every single node shows by its own organizative behavior, how can we improve the capability of the nodes to interact?According to how has been reported in the Paragraph 2 we have new knowledge in models and networks structures and behavior. So, how the networks properties are used in urban planning? A useful starting point for discussing all that is analyzing two examples by sociology and urban planning using a theoretical and a pragmatic point of view. The first case is about the Master Plan for the strijp Philips at Eindhoven, The Nederlands, based on the concept of “genetic metropolis”; (Branzi, 2006) The second case discusses of an emerging concept in sociological interaction model named “space-participation model” (Ciaffi, Mela, 2006)
Branzi defines the genetic metropolis as “...the metropolis where the biological rules that reach their maximum level of melting, fully invading an infrastructure and spreading out from every possible designed retaining shape, [and still] the vision of a territory inside that architecture doesn’t fulfil any traced fragmentation of the space, but it becomes the theater of a wide elastic modification activity (that is reversible) from the bottom of the infrastructures, of services, ad it is metropolitan sub-systems” (Branzi, 2006; pag. 24)
That idea of metropolis in the informational era puts together technology and the “human being territory” representing the space of relationship into “connecting self DNA with the DNA of business spreading its gene in a close network of parental and enterpreneurial relationships” ad it refers to the ethics of the informational networks that “teaches us the existence of the great provided by social interconnetion”. (Branzi, 2006; pag. 24)
Following those ideas, the design for the regeneration of the strijp Philips is based on a weak-urbanization model. Eindhoven was, since the beginning of the past century, the storic headquarters of Philips industries that are on the cutting edge of the world technological innovation. In fact there were developed industrial projects and producted the first electric bulb, the first cd-rom, the first walkman in the world. In the ‘70s over 90% of its population had been working on Philips plants untill the dramatic decline that hitted european districts with a consequential delocalization of productive functions in other Countries. That huge area, about 982.000 square meters, has been proposed to the european market as the ideal city that can become the birthplace or the incubator of micro-imprenditorial iniziative related to technological and artistical mass design creativity that, to grow, needs an environment where it’s possible to activate intensive relational spaces. In order to drive that forces, the masterplan is based on a mixed land use model within productive farm lands are made permeable to physical connection and to the localization of research laboratories, residential, commercial and leisure functions, that are able to work in sinergy changing their dimension and functioning model on demand.
The interaction model of the design process is based in the transformation of an “uncomplete” starting network (Figure 5) to a new one rich of bio-diversity.
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Figure 5 Eindhoven as an incomplete nework (by Branzi, 2006; pag.41) |
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Figure 6 Eindhoven as complex network that attracts young generations (by Branzi, 2006; pag.41) |
The second example offers a conceptual model that can be collocate in a emergence pespective of research definined “spatialist sociology” that [...] even if in [its] evident heterogeneousness, [faces out] a theoric node that in the history of urban sociology, has been often leaved on the backstage or, anyway, not solved: that node of the relationship between action and social systems on one hand, and the space (or, better, the spatial-temporal dimension) on the other hand (Mela, 2006, p.34).
“Space-Participation” model, was developed starting with a cross observation of some recurrent elements tha can be discovered in urban regeneration design experiences in european countries underlining how the principal action that can be started within a society are the result of declining participation into 4 categories of action: communication, animation, consultation, empowerment (Ciaffi, Mela, 2006).
Those categories are quite shaded and any of that empowers each other in a relational space settled in concentric circles tipical of community psicology literature: the inner circle is the space of intimacy (private space); the intermediate circle is the space of familiar relationships and friendships (local public space); the broad circle is the rest-of-the-world (super local public space)
Those concentric circles are configured as real “ecological niches” within the category of local relationship is disembodied by propinquity ties. So, the inner circle, or private space, is an ecological niche that contains the home but it contains residential and semi-residential structures for adults as assistance communities; the intermediate circle, or public local space, is another ecological niche that contains public spaces perceives as familiar and therefore regularly frequented even if distant from house; the external circle, the super local space, is a broad ecological niche that contains well known, marginally known or unknown public spaces ad that are perceived as no familiar (Ciaffi, Mela, 2006).
The importance of that model is not only because it attempt to reconstruct the whole with a systemic view putting in relation actions, social systems and personal life space but we can see evident connections with the concept of genetic city that itself put together social interaction principles, physical space modification and ecology recognizing the city as relational ad exchange system, as a structure that connects. But also in this second case we can’t see operative reflections on how to activate processes working on the structural components of the social network within the concentric circles framework.
3.2 Why UGS?
In UGS contributes from urban planning, sociology, economy are merged and into that mix it’s extremly important to develop common language and concepts without epistemological barriers. The gaming simulation field is constitutively an ideal place to start all that.
In fact, from strong technical practice, urban planning becomes even more a communicative, interactive and collaborative practice (Healey, 1997) that claims the ability to observe and to represent reality at different levels, and also the ability in communication and interaction with stakeholders. This need in renovating of helpful instruments in the design of the urban space gives the opportunity to put together pragmatism and visionaries skills acting deliberative practices (Forrester, 1999) creating the opportunity to really preserve public interest and to perform effective conflict management.
In the big picture still too much complicate, two positions seems to be helpful in reconstructing a more solid base to that discourse: John Friedman (a planner) and Manuel Castells (a sociologist).
Friedman reminds us that on the contrary of traditional models of rationality the wolrd is real and explorable through an empathic form of survey that is the way to ask to a social reality talented to answer our questions. The new epistemology transforms scientific and planning surveys in a dialogic process that procedes on the basis of a tacit and disembodied knowledge using a language capable to explain subjective realities putting together human, social and behavioral sciences (Friedmann, 1993; p. 519).
Ufter over ten years social transformations due to the pervasive penetration of new interaction and communication technologies (NICT) are further changing the big picture of Friedman and the first argument that Castells underlines is how “[...] transformation of the space has to be conceived in a more huge context of a complexive social transformation: the space isn’t a reflection of society but its expression. Cities born on the consequence of the creation of a new social structure, the network society, characteristic of the informational Era” (Castells, 2004; p.50).
In the full era of the informational society and ufter the pubblication of his famous trilogy (The Information Age: Economy, Society, And Culture), Castells reflect about the physical space and space of flux as precious materials for the urban planning in the XXIst century. In particular “Space of flux establishes an electronic connection among place physical separated, creating an interactive network of relationship not related with the specific context of reference between activities and individuals. The physical space organize the experiences with the limitation of geographical collocation” (Castells, 2004; p.57).
That transformation and empowerment of the experential dimensions has heavy reflections on the every day life specially in metropolitan cities in wich is rapidly increasing the widespreading of electronic communication technologies intended as integrate technological system based on the horizontal communication via Internet. All that means an acceleration of the fragmentation and self referentiality processes in communication. Then interaction assumes the role of dominant urban issue on three levels: the physical level (face-to-face communications, real communities); social communication level (citizens and local administrations); elctronic communication level intended as a new shape of sociality (real communities are replaced by virtual communities) (Castells, 2004).
Regarding those aspect Castells remarks that “It doesn’t exist yet a real and proper theory, because Internet is still only at the beginning as a broad social practice. But we can remark that on-line sociality is a specific sociality, not a sub-product of the real one and that geographic localization contributes with inexpected results to the configuration of the network of electronic communication” (Castells, 2004; p. 57)
The debate on the effectiveness of the common communication and interaction tecniques shows that they are useful to “sound” different knowledge with the will to enlarge the complex knowledge base needed to do planning practice. We can classify those techniques into these categories: technical knowledge techniques like future studies (environmental scannings, cross-impact matrix, Delphi, previsions e strategic management, cellular automata and GIS); technical and common knowledge layered techniques (scenario studies like European Awareness Scenario Workshop, Community Visioning Processes and various combinations of brainstorming sessions like metaplan and focus group); others methodologies (Open Space Technology e Appreciative Inquiry).
Friedman reminds us that on the contrary of traditional models of rationality the wolrd is real and explorable through an empathic form of survey that is the way to ask to a social reality talented to answer our questions. The new epistemology transforms scientific and planning surveys in a dialogic process that procedes on the basis of a tacit and disembodied knowledge using a language capable to explain subjective realities putting together human, social and behavioral sciences (Friedmann, 1993; p. 519).
Ufter over ten years social transformations due to the pervasive penetration of new interaction and communication technologies (NICT) are further changing the big picture of Friedman and the first argument that Castells underlines is how “[...] transformation of the space has to be conceived in a more huge context of a complexive social transformation: the space isn’t a reflection of society but its expression. Cities born on the consequence of the creation of a new social structure, the network society, characteristic of the informational Era” (Castells, 2004; p.50).
In the full era of the informational society and ufter the pubblication of his famous trilogy (The Information Age: Economy, Society, And Culture), Castells reflect about the physical space and space of flux as precious materials for the urban planning in the XXIst century. In particular “Space of flux establishes an electronic connection among place physical separated, creating an interactive network of relationship not related with the specific context of reference between activities and individuals. The physical space organize the experiences with the limitation of geographical collocation” (Castells, 2004; p.57).
That transformation and empowerment of the experential dimensions has heavy reflections on the every day life specially in metropolitan cities in wich is rapidly increasing the widespreading of electronic communication technologies intended as integrate technological system based on the horizontal communication via Internet. All that means an acceleration of the fragmentation and self referentiality processes in communication. Then interaction assumes the role of dominant urban issue on three levels: the physical level (face-to-face communications, real communities); social communication level (citizens and local administrations); elctronic communication level intended as a new shape of sociality (real communities are replaced by virtual communities) (Castells, 2004).
Regarding those aspect Castells remarks that “It doesn’t exist yet a real and proper theory, because Internet is still only at the beginning as a broad social practice. But we can remark that on-line sociality is a specific sociality, not a sub-product of the real one and that geographic localization contributes with inexpected results to the configuration of the network of electronic communication” (Castells, 2004; p. 57)
The debate on the effectiveness of the common communication and interaction tecniques shows that they are useful to “sound” different knowledge with the will to enlarge the complex knowledge base needed to do planning practice. We can classify those techniques into these categories: technical knowledge techniques like future studies (environmental scannings, cross-impact matrix, Delphi, previsions e strategic management, cellular automata and GIS); technical and common knowledge layered techniques (scenario studies like European Awareness Scenario Workshop, Community Visioning Processes and various combinations of brainstorming sessions like metaplan and focus group); others methodologies (Open Space Technology e Appreciative Inquiry).
But the limits of those traditional techniques are recognised both by Anglo-Saxon (Sanoff, 2000) and European and in particular Italian (Bobbio, 1996) authors that remarks how rare or totally absent are cases in which those instruments are able to make permanent the interaction and participation capabilities of local communities finding solutions to problems related with organized life.
So we have the need not of a regeneration of traditional techniques but of an innovation in planning methodologies and approaches by searching for those communication and interaction techniques that really might be put inside a planning practice that just now, and even more in the future, is experiencing the impact of the computer mediated relationships. One of these is surely the Gaming Simulation thanks to its position between communication, simulation and predictive techniques.
Cities has ever been the place of exchanges: it seems to be built much more on ideas than on bricks and it’s even more at the same time the space of places of flux. Flux means informational and communication flux.
So we have the need not of a regeneration of traditional techniques but of an innovation in planning methodologies and approaches by searching for those communication and interaction techniques that really might be put inside a planning practice that just now, and even more in the future, is experiencing the impact of the computer mediated relationships. One of these is surely the Gaming Simulation thanks to its position between communication, simulation and predictive techniques.
Cities has ever been the place of exchanges: it seems to be built much more on ideas than on bricks and it’s even more at the same time the space of places of flux. Flux means informational and communication flux.
In the last years some games appearead: CAN YOU SEE ME NOW?, UNCLE ROY, ALL AROUND YOU and I LIKE FRANK IN ADELAIDE by Blast Theory and CITITAG by Kmi of Open University; the virtual labirinth of PAC-MAN that becomes real in PACMANHATTAN by NYU’s Interactive Telecommunication graduate program; or MOGI by NewtGames where the physical game board interacts with the digital one.
The news is that the game board onto play the game and interact using the broad availability of electronic communication devices that enables extremely dinamic interaction (cellular phones, comuters radio, PDAs) is the city that becoms the space where to get lost and also meet. These experiences introduce the virtual level in the urban space transforming it by pysical to space of flux.
UNCLE ROY players interacts by cellular phones with other players that can be in the same city discrict or they can interact from home with a PC. But the inter-exchange levels follow non-linear sequences creating scenarios that game designers define “of theater”.
UNCLE ROY players interacts by cellular phones with other players that can be in the same city discrict or they can interact from home with a PC. But the inter-exchange levels follow non-linear sequences creating scenarios that game designers define “of theater”.
At the same time CITITAG players are “laboratory animals” for a techno-social experiment because authors wants to examine which potentialities can have social spontaneus behaviors in interactions mediated by PDAs or cellular phones in public spaces. Players of the read or the green team attempt to trap the most large number of opponents in a confined space: a piazza, a grass or a public garden.
The role-play GUNSLINGERS can be defined more a play exercise than an academic exercise: players can have the traditional experience of role-play in adventure scenario facing battles in the real scenario of Singapore metropolitan area.
Play reaches highest levels in PACMANHATTAN and MOGI: the first is a large-scale urban game using the urban grid of New York as gameboard: the declared aim is to experiment what can happen passing from the “small world” made by game consoles, televisions and PCs to the “great world” that is the real city. The team has 5 players physical competing using the physical map of New York as the PacMan gameboard.
In MOGI the players are a community composed by groups created spontaneously; they have, moving in urban environment, to collect some elements and then they can negotiate and exchange them with other players using cellular phones, PDAs, PCs. The players can also activate alliances and can help each other to collect the necessary elements. At the end urban space newly become the space where flux and places dialogues permitting, perhaps, the reconstruction of the relationship between function and meaning so essential for the existence of communities and cities.
4. Conclusions: Computers, NICT, Social Networks, Urban Planning and UGS
The integration among gaming, NICT and urban space described below, shows a general approach that define the city at the same time a game board and a playground promoting the exploration of the urban space as an activity that involves players in all experential, perceptive and sensorial levels. Nevertheless, the entertainment dimension prevail and much more has be done in the theory field both of UGS and urban planning.According to Castells and Sassen the crisis of urban systems is related to economical, social and environmental issues triggered at the global level and amplyfied by the increasing velocity in communication and social transformation. It seems to be impossible to face urban planning only as a transfomation of the physical space, using traditional communication techniques or only attempting to regenerate them.
The new knowledge we have on structure and dynamics of complex networks as the social networks (see paragraph 2) represent a great motivation to deeper how it can be useful in design and run UGSs that can be helpful in activation of time long cooperative behaviors in urban planning processes.
Is almost fascinating the idea to consider the concept of collaborative behavior as a germ to vehiculate and fast spreading: a sort of positive epidemic that helps us to recognize tha even if sometimes seems that cooperation gives a minor personal payoff the final result is a major payoff for the whole system also considering the modification of the physical space.
We propose to elaborate new criteria and requirements for designing and realization of a new generation of UGSs based on small-world and scale-free models and we are working on that attempting to study and develop a prototype.
What can be said right away is that the approach we are using is more weak than the engineering one and we define it the “bricoleur” approach. This approach brings inside all that cognitive micro-processes that urban planning of engineering type don’t permit to grasp and also facilitate to enlight that structure that Bateson called “the structure that connects” that can indeed be compared to those networks of nodes and relationships that in conclusion for us represents the city.
REFERENCES
a) Books and Books chapters
- Alexander, E.R., (1992) Approaches to Planning: Introducing current planning theories, concept and issues, Yverdon, Switzerland, Gordon and Breach
- Allen, P., (1997) Cities and regions as self-organizing systems: Models of complexity, Amsterdam, The Netherlands, Gordon and Breach
- Boocock, S., Shild, E., O., (1968), Simulation Games in Learning, Sage Pub., Beverly Hills (CA)
- Bobbio Luigi (1996), La democrazia non abita a Gordio. Studio sui processi decisionali politico-amministrativi, Franco Angeli, Milano
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