dynaDYNADyna rev.fac.nac.minas0012-7353Universidad Nacional de Colombia10.15446/dyna.v86n211.80864ArtículosMetropolis: an emerging serious game for the smart cityMetropolis: un juego serio emergente en una ciudad inteligenteAguilarJoseaeDíazFranciscoaAltamirandaJunioraCorderoJorgecChavezDanilobGutierrezJosed CEMISID, Facultad de Ingeniería, Universidad de Los Andes, Mérida, Venezuela. aguilar@ula.ve, altamira@ula.ve, francisco.diaz@ula.ve, Universidad de Los AndesFacultad de IngenieríaUniversidad de Los AndesMéridaVenezuelaaguilar@ula.vealtamira@ula.vefrancisco.diaz@ula.ve DACI, Escuela Politécnica Nacional, Quito, Ecuador. danilo.chavez@epn.edu.ec Escuela Politécnica NacionalEscuela Politécnica NacionalQuitoEcuadordanilo.chavez@epn.edu.ec DCCE, Universidad Técnica Particular de Loja, Ecuador. jmcordero@utpl.edu.ecUniversidad Técnica Particular de LojaUniversidad Técnica Particular de LojaEcuadorjmcordero@utpl.edu.ec Dpto. Ciencias de la Computación, Universidad de Alcalá, España. antonio.gutierrez@uah.esUniversidad de Alcalá de HenaresUniversidad de AlcaláSpainantonio.gutierrez@uah.es GIDITIC Informática y Sistemas, Universidad EAFIT, Medellín, ColombiaUniversidad EAFITUniversidad EAFITMedellínColombiaOct-Dec201986211215224040720191609201911112019The author; licensee Universidad Nacional de Colombia.Abstract
Emerging serious games mark the beginning of a new era in video games. Emerging serious games introduce advances in distributed artificial intelligence into their design in order to guide the player’s manipulation of a specific subject in an adaptive way. In this article, we present a city simulator game, called Metropolis, which generates emergent properties. Metropolis can be used by a smart city for city planning, to make collective decisions, and for other purposes. This paper describes why Metropolis can be classified as a serious game. It also analyzes how its emergent properties can be used for managing a smart city, and especially how it promotes e-participation as an e-decision-making tool within the context of urban planning. In addition, this paper explores how Metropolis can be used to analyze a smart city’s emergent citizen and urban patterns (urban spatial distribution) based on strong e-participation.
Resumen
Los juegos serios emergentes son el comienzo de una nueva era en los videojuegos: la introducción de avances en inteligencia artificial distribuida en sus diseños, para guiar la comprensión de un tema específico de forma adaptativa. En este trabajo, proponemos un juego de simulador de ciudad con propiedades emergentes, llamado Metropolis, que puede ser utilizado por una ciudad inteligente para planificar la ciudad, tomar decisiones colectivas, etc. Este artículo presenta las características de Metrópolis como un juego serio, así como un análisis de sus propiedades emergentes en la gestión de una ciudad inteligente, en particular, para promover la participación electrónica, como una herramienta de toma de decisiones electrónicas, en el contexto de la planificación urbana. Además, este artículo explora el uso de Metropolis para analizar la emergencia en una ciudad inteligente del patrón de sus ciudadanos y su patrón urbano (distribución espacial urbana) debido a la participación electrónica.
The concept of the emergent game marks the beginning of a new era in video games [1, 3]. Emergent games represent the evolution of games that adapt to the player while maintaining the central thread of the game. E-participation in a smart city supports the participation of citizens in governance through the use of Information and Communications Technologies (ICTs) [13]. In a smart city, citizen participation is especially important; smart cities should be planned according to the needs of its citizens [19, 21, 23]. In general, e-participation encompasses different purposes: administration, policy making, service delivery, and decision-making.
In this paper, we analyze e-participation in the context of decision-making in a smart city. We define the tools that allow citizens to interact in decision-making processes that affect city life. We also define environments in which citizens learn how to participate, and specifically, to learn about the complexities of e-participation in decision-making processes in a smart city.
Introducing emerging serious games into a smart city allows for the transparent integration of technology into city dynamics. Emerging serious games can be used in different contexts in a smart city. Considering that one of the main characteristics of a smart city is the application of e-participation to facilitate collective decision-making about citizens’ services and needs, we analyze how emerging serious games can be used in this context. In this paper, we explore the technology needed to construct a vision of urban development in a smart city and in a collective and secure fashion in order to manage the city’s assets (schools, hospitals, etc.). Specifically, we are interested in serious games that promote e-participation in the context of urban planning.
Ahmed et al. [27] have developed a Technology Acceptance Model (TAM) and Trustworthiness Model (TM) that facilitate the use of serious games in e-government services, and empower citizen engagement and participation. These models are based on serious games that assist governments in increasing citizens' engagement through their online services. Pflanzl et al. [28] discuss game design as a way to motivate, engage, and change citizens’ behavior with respect to public service improvement. Thiel [29] reviews recent academic projects concerning gamified participation tools. The gamified participation tools are a novel approach to encourage citizens to make use of their democratic rights by using digital participation platforms. Thiel et al. [30] provide a review of gamification strategies on e-participation platforms, and give an overview of the state-of-the-art of gamified participation initiatives. Thiel [31] also provides an overview of current trends for applying gamification to public participation. Together, these authors offer relevant insights for the design of future e-participation platforms, and establish the terminology for e-participation game research.
In the domain of urban planning games, Poplin [32] focuses on online games and serious games for urban planning. The author overviews the urban planning games currently available online and give some examples of their game stories. Poplin also explores the potential of serious games in participatory public planning. Lundström et al. [33] address urban and regional planning as a wicked game. They explore the obstacles and opportunities for participatory methods in a wicked game called The Citizens' Jury. Martin el al. [34] present the research of architects and computer scientists on mobile, context-sensitive serious games for sports and health (also known as exergames). Recently, Madani et al. have proposed a serious environmental management game to improve understanding of environmental sustainability [26]. These game-based learning initiatives increase soft skills such as critical thinking, creative problem solving, and teamwork.
In this paper, we are interested in a gaming subtype called construction and management, often used in smart cities. Construction and management games are simulations in which players build, expand, or manage fictional communities or projects with a set of resource limits [5,7]. In these games, the player's objective is not to defeat an enemy, but rather to build something in the context of an ongoing process [9]. Examples of these include city construction games such as SimCity and OpenCity [4,5].
Specifically, we have proposed a game, called Metropolis, in which social dynamics emerge from the decisions that players take [9,15]. The game aims to collectively plan the successful growth of communities. The premise of Metropolis is that cities can be self-managed based on decisions taken collectively in an environment in which all players have roles of equal importance. In Metropolis, there is no local authority such as a mayor, governor, etc. This game can be used to plan a city, to manage a city’s limited resources, to identify the collective interests of its citizen, as well as to meet other goals. It can be used to learn how to reach collective decisions. This learning aspect makes this game a serious game, since it offers this teaching capacity (learning how to reach collective decisions). At the same time, is fits the emergent system model, since the game generates results (city management).
Previous research focused on studying emergent behaviors of a city based on rules that govern the interactions between agents (players) that play social roles in this society. This paper uses a mechanism that reaches consensus opinions in Metropolis and establishes transparent forms of inclusion for e-participation. Additionally, Metropolis is designed to encourage the emergence of the collective urban vision of a city based on the interests of its citizens.
Hence, Metropolis can be viewed as an emerging serious game that teaches players about the complexity of e-participation in a smart city. Specifically, it teaches players how to do things together. As a result, Metropolis facilitates the emergence of smart city behaviors (urban patterns, city features, etc.) that are based on collective decisions.
This research contributes to three domains. It establishes the importance of e-participation in a smart city for democratic decision-making processes. It shows how emerging serious game can be used to teach e-participation. Finally, it confirms the emergence of smart city behaviors that are based on that e-participation.
Emergence [2, 8, 16, 17] is “what happens when a relatively simple system of elements organizes itself spontaneously, without explicit laws, and thus gives rise to intelligent behavior." In the literature, there are several definitions of emergent games. For example, [1,3] indicate that games with emerging properties appear when a relatively simple set of rules leads to complex game strategies involving different levels.
Emergence in games is enabled by the definition of simple global rules for the behavior and properties of game objects, as well as by the interactions between the game world and players. Emergent forms can be expressed in different ways [1-3,8,15]. 1) Emergence occurs when, due to the decisions of the players, a pattern of collective behavior, shared social knowledge, or a common result appear in a game. 2) Emergence also occurs when the properties of game objects interact to create a completely new game. In this paper, we focus on the first type.
Taking into account the “serious” aspect, serious games are simulations of real-world events and processes for the purpose of engaging with serious topics or problem-solving [10-12]. Serious games train or educate users, introduce new topics, and deliver messages [11, 18, 25]. There are many different categories of serious games, including [10, 20] educational games, advergames, simulation games, political games, games oriented to religion, and health-oriented games.
<italic>2.2. Simulation of cities</italic>
City simulation is a type of game where the players build and manage a city using limited resources. An example of a city simulator is SimCity, a city-building game where the goal is to create, develop, and manage “SimCity” [4, 5]. The player starts the game with a blank map of a city, which she or he expands with budget purchases. The city must provide basic services to its citizens, such as water, electricity, urban waste management, etc. In addition, the citizens must create sites for health care, education, security, and entertainment, all represented by different buildings. The main sources of SimCity income are taxes, services, and specialized buildings located within the city (casinos, military bases, etc.). Similar city simulators include LinCity and Opencity [6].
Metropolis does not follow the same philosophy of games as SimCity, LinCity or OpenCity, which use a self-management model inspired by the Prisoner's Dilemma game. These games are clear examples of non-zero sum problems. In the Prisoner’s Dilemma type game, the game assume that each player independently tries to maximize their own advantage regardless of other players’ decisions [9]. In standard game theory analysis, to determine the Nash equilibrium, each player can betray the other (but interestingly, opposing players obtain better results if they cooperate) [2].
This is the key point of these games. In our game, as in the Prisoner's Dilemma, cooperation is obtained as an equilibrium result. The game is played repeatedly, and as the game is repeated, each player has the option to punish the other players by not cooperating in previous games, or to cooperate. Thus, the incentive to cheat can be overcome by the threat of punishment, leading to the better result, which is cooperation.
<italic>2.3. E-participation and urban planning in the smart city</italic>
A smart city should be understood far beyond its application of ICT [13, 14, 19, 24]. For example, giving citizens the possibility to participate online in the city’s management activities is an important component of what makes a city smart, not only from a technological point of view, but mainly because this type of city addresses and tries to satisfy the needs and requirements of its citizens [21, 22].
E-participation is considered an essential component of smart city functioning. It helps individuals get involved in the policy-making process through the use of electronic means. Classically, the three types of e-participation are [13, 14, 23]: i) e-decision-making; ii) e-information; and iii) e-consultation;
In general, e-participation helps develop the smart city, since it provides [13, 14] more focus on citizen needs, improved government responsiveness, greater government transparency, and increased citizen involvement. There are several mechanisms for e-participation: electronic voting (also known as e-voting), reputation systems, online communities, and online social networking. Our research analyzes how the use of e-decision-making in emerging serious games introduces, promotes, and lets users learn about e-participation.
We should also mention that urban planning is an interdisciplinary field that includes architecture, civil engineering, and public administration. It is a technical and political discipline related to the development and design of land use in an urban environment. Urban planning takes into account the physical layout of human settlements, the protection and use of the environment, as well as the repercussions of social and economic activities. It also encompasses the planning and development of water resources for common use, rural and agricultural land, parks, etc. In a smart city, urban planning is a collaborative process, in which citizens participate in the construction process using e-participation tools.
3. Game description<italic>3.1. Game philosophy</italic>
The key strategy in Metropolis is that each player tries to enhance their individual advantage [9, 15]. However, inspired by the Prisoner's Dilemma, cooperation is crucial to our game. Cooperation balances the wishes of all, leading to collective happiness. Cities are punished when the people do not cooperate, since the quality of city life worsens. In Metropolis, a player’s happiness is linked to construction activites based on the player’s personal characteristics. For example, if a player has health problems, then the player should construct health care facilities. The percentage of health buildings in the city then determines the happiness of this player. Finally, the overall happiness of the city equals the average happiness of all the players. The incentive to cooperate can thus emerge, because the goal is to create a city in which all players feel better, i.e., reach consensus on community life.
The basic activity of the game is decision-making in the form of a council. On the council, each agent belonging to a group has the opportunity to vote for or against certain types of building constructions by making collective city budget allocations. There are two types of agents, one representing the players, and others that are randomly generated and controlled by the game. Personal characteristics are assigned to the agents, and these determine the types of construction agents will support. In other words, votes on the council represent construction choices. The city evolves as the agents decide to build or destroy facilities. Each player's actions directly affect the city.
In essence, each player represents a percentage of a city’s population (a community). When someone wants to build in the city, a project is submitted to popular vote on the council. The game has a scoring system calculated for each year. The score is based on development rates in the city and on the happiness of its population, where the happiness of all agents determines the city’s Happiness Index. Development rates in the city are calculated according to the balance between different development sectors (environmental, educational, etc.). They are individually calculated, then added together to determine the development rate of the city. The happiness of the people derives directly from the happiness of its agents.
3.1.1. Game rules
The game can be played with one or more players on one or more teams, where each team is represented by an agent. A time limit is set equal to the number of years needed to finish the game. Each agent may vote for or against the construction of a particular building in a particular area.
In order to simulate the percentage of citizens who cannot be controlled by the players, every city has 10 agents, of which at least five are artificial agents. Artificial agents are generated randomly and make decisions in the same way as other agents. Non-artificial agents have personalities, either generated randomly or defined by the user. (Refer to the explanation below.) Players request to build buildings that they want in their preferred areas by specifying site coordinates. The characteristics of a city used to calculate its development rate are health, education, environment, trade, industry and technology.
3.1.2. Characterizing constructions (Buildings)
Each type of construction in a map box (coordinate grid) is represented with a numeric value. Different types of construction have different effects on the game and on surrounding areas. [2, 9] present the details for each type of city construction, i.e., its effect: the radius of each building in the city (its coverage), the cost of the construction, the construction’s group affiliation, and the bonus or penalty that it adds to the development rate. There are two types of buildings: primary buildings, which add/remove points to the development rate; and secondary buildings, which add/remove a percentage of points to the development rate. When the range of two identical buildings (primary/secondary) does not intersect, the buildings are combined for a total score. When ranges intersect, a positive bonus decreases by 50%, while a negative penalty remains unchanged.
3.1.3. Agent personalities
At the beginning of the game, players must choose among four types of personalities. Artificial agent personality types can either be chosen randomly or selected by the player. Personality determines the behaviour of the agent during the game.
The performance of human players and artificial agents differ. Human players are used to calculate player happiness. Artificial agents determine their own happiness and behaviours during the game. For example, an artificial agent assigned the role of environmentalist always votes against proposals that harm the environment.
An agent’s personality determines its basic needs and its concominant happiness. Other personality traits that are not activated for a given agent remain by default at normal levels (1000 points); for activated personalities, the range is 1250-750 points. [2, 9] describe the personality types (Saver, Healthy, etc.) their relationship with construction types, and how they are used to calculate player happiness. For example, a Healthy player’s Index of Happiness for health care facilities is 750, meaning that this type does not require a lot of city health facilities.
3.1.4. The council
The council makes decisions based on what each player wants to build, and according to the personalities of the agents voting for or against a building project. The votes of 10 players are taken into account to make a decision. The decision is determined by majority vote.
3.1.5. Assessing building proximity
Details in [2, 9] specify the penalties or bonuses related to the proximity of certain types of buildings. Proximity is related to the distance between construction types. For example, the game awards a bonus for types of buildings that players prefer grouped together, otherwise it issues a penalty. That means, our game promotes or penalizes the clustering of certain types of construction projects in the city according to type. It allows cities to define zones (e.g., industrial zones, educational campuses) because buildings attract other buildings with similar functions. For example, the proximity of a hospital (primary health care) to an industrial building would result in a -6 point penalty applied to both buildings based on their proximity. In contrast, if a research center (technological institution) is built near a school or university (educational institution), both construction projects would receive a bonus of 3 points. The area that each type of building type covers, i.e., its coverage, determines its proximity. (See Section 3.1.2.)
3.1.6. Total score for the development index
In a city, each Development Index is linked to a type of personality and type of construction. For example, for a development in higher education, the city must already have a lot of primary and secondary educational facilities. The Development Index is determined by the impact and quantity of each construction on the city’s development: It is derived from the bonus or penalty for each construction type and for each type of development. In general, the total score for each Development Index j is calculated as:
where l represents all primary buildings and m all secondary buildings in the city, respectively; CERik represents bonuses and penalties depending on building proximities i and s (or k and s, respectively). CERik determines the closeness between two buildings if not more than 3 boxes separate them, and PCij is the total score of building i for Development Index j to which it pertains. Thus, the total score of the index is calculated.
3.1.7. Calculation of city happiness
The game calculates the happiness of a city. For that, it determines the relationship between each of the city’s Development Indexes (calculated previously) and the personality of each player. The overall relationship determines the Happiness Index of each player. The calculation for the score of the Happiness Index for each player p is:
where TRJpj is the total required by player p for Index j, according to her or his personality. The general index of happiness in a city is the average of the Happiness Index of each agent.
4. Testing
This section defines several experiments undertaken with Metropolis within the context of e-participation in a smart city. Each scenario tests a different characteristic provided for Metropolis to facilitate e-participation and to support the development of the smart city. For the testing, we assumed that each player represents a specific community within the city.
The hypothesis states that emergence in a smart city is very important because it allows the smart city to adapt to the needs of its citizens. We tested two properties of Metropolis: whether the citizens (players) were able to learn about the importance of e-participation for democratic decision-making processes, and whether e-participation generated the emergent behaviors and characteristics of a smart city, specifically, whether urban patterns emerge due to collaborative urban planning. With these experiments, we wanted to prove whether e-participation contributes to the emergence of urban planning in a smart city, and whether Metropolis can be used to teach e-participation skills that support decision-making.
<italic>4.1. Case 1: emergence of urban patterns</italic>
In this first test, we set the playing time to 3 years and the number of real players to 2 (the rest of the players were artificial agents managed by the game-see Section II.B for more details. Most player personalities were randomly generated. In the upper right of Fig. 1 below, in the turn (shift) button (Proximo Turno, Next Turn), the game displays information about the current turn (Turn 1 of 36) and the current player (Jugador/Player 1 de/of 2).
The initial screen in Metropolis (<italic>Dinero</italic>= money<italic>, jugador</italic>=player, <italic>turno</italic>= turn)
Source: The Authors.
Each turn in the game represents one month for each player. In this case, because we have selected 3 years of play, the game will take 36 turns to finish. Every 12 months a window displays the development rates of the city, as shown in Fig. 2. For the first year in this example (see Fig. 2), the highest development rates were for education (13%), technology (22%), and industry (12%).
Development Index of a city for year one (<italic>Salud</italic>=Health 9%, <italic>Ambiente</italic>= Environment 9%, etc.)
Source: The Authors.
At the end of each year, an amount of money is added to the city budget (equivalent to the taxes, donations, etc., received by the city), and the development index changes accordingly for each year. Urban patterns begin to emerge, for example, zones with only educational institutions. Fig. 3 shows the emergence of the industrial zone on the left side after turn 29.
Case 1, turn 29
Source: The Authors.
When this game was completed, we studied the density of each type of construction in the city. Fig. 4 indicates the density of educational institutions in the city once the game ended.
Density of the Educational Institutions in the city
Source: The Authors.
The densities show an emerging urban behavior based on decisions taken by the agents. Urban zones have emerged, based on the players’ personality profiles, i.e., the city’s communities. Basically, this behavior consists of patterns that are generated within the city based on groupings of similar buildings within the same zones. If we use Metropolis as a tool for e-decision-making in a smart city, the patterns that emerge are products of the city’s adaptation to its citizens. The patterns are determined by the collective decisions of the citizens. If we assume that their decisions are based on their personalities (their needs and requirements), then an adaptation process has clearly emerged in the city.
This development is very important, because recognizing urban patterns in a smart city can help it to define public policy, including the local urban plan for the city, and other specific policies, meaning that the city can now make intelligent decisions about future investments, social rules, etc., during city planning sessions. Specifically, used as an e-participation tool, Metropolis allows smart cities to tailor development closer to citizen needs and to improve a city's responsiveness. (See Sections II.D and [13], where we outline the pillars of e-participation in a smart city.)
In this first scenario, we see that e-participation allows urban patterns to emerge, in a context in which Metropolis allows its citizen-players to recognize its importance. Our tests show how Metropolis behaves like an emerging serious game that educates players about e-participation.
<italic>4.2. Case 2: study of city patterns based on the specific needs of agents</italic>
In the second case, Agents 6 and 8 were configured, and the rest were generated randomly. Playing time was set to 2 years and the number of players to 4, the other two being artificial agents--see section III.B. The configuration for Agents 6 and 8 was:
- Agent 6: hypochondriac, industrial, difficulties with education.
- Agent 8: hypochondriac, environmentalist, thrifty, and self-taught.
Fig. 5 shows the state of construction in the city after the game has ended. The Development Indexes of the city for the first year are: Health (27%), Commerce (0%), Industry (33%), Environment (8%), Education (9%), and Technology (6%). In the case of the second year, the Development Indexes are: Health (34%), Commerce (4%), Industry (41%), Environment (8%), Education (23%), and Technology (11%).
Map of the city after the game has finished (Case 2)
Source: The Authors.
The final results for the city are consistent with the personalities of the agents, since their basic needs were concentrated on health, education and industry. (See Fig. 6.) We see a pattern emerge in the city that satisfies those needs. In the case of the industrial index, even with an environmentalist player (Agent 8), we obtain an industrial rate (41%) higher than the environmental rate (8%), due to the fact that the rest of the players are industrialists (Agent 6 and the artificial agents).
Case 2: agent happiness (<italic>Estado de satisfacción final por colectivos,</italic> final state of satisfaction per collective).
Source: The Authors.
We also observe another type of emergence, the personality of the city, which is basically the union of the different personalities of each agent. This personality is used to achieve a common goal in the smart city, that of improving the city’s rate of development.
In Fig. 6, the personalities of the agents are translated into an overall pattern in the city, i.e., its personality, or in other words, the appropriate pattern of behavior for society’s members.
The personality of a city guides the interests to be addressed by city planning efforts. This personality specifies the needs and requirements of each citizen, and a smart city should devise a plan for fulfilling them.
Again, using Metropolis as an e-participation tool allows players to develop a smart city better focused on citizen needs. Additionally, it increases citizen involvement and their capacity to get involved, since it incorporates a learning process for collective decisions within an emerging serious games framework. Thus, the smart city develops in a transparent way. See [13] for more details about the pillars of e-participation in a smart city.
In this second scenario, we show two relevant properties that Metropolis analyzes: the importance of citizen e-participation in the democratic decision-making processes of a smart city, and how the smart city can adapt to the real needs that its citizens have. The results of the game reveal to the players that their participation in the life of the city is required, if they want the city to adapt to their needs. That is, e-participation in smart cities allows cities to adapt to their citizens. This adaptation occurs when Metropolis is implemented as a serious game, thus allowing players to learn about the importance of e-participation.
4.2.1. Metropolis in the context of e-participation
We conducted several tests to probe the decision-making process for different characteristics of the players. In one scenario, we defined 2 player types: 1) players representing social communities, and 2) players representing individual interests. Possible profile relationships between players included (a) antagonistic interests, (b) similar interests, (c) different but non-conflicting interests, and (d) different interests that require collaboration. Based on these, we calculated the Happiness Indexes that emerged in the city. (See Table 1.)
Happiness Index of the city for different types of players
Scenario
Happiness Index for the city
1-A
25.3
2-A
3.2
1-B
69.4
2-B
10.3
1-C
24.7
2-C
8.6
1-D
30.2
2-D
16.7
Source: The Authors.
According to Table 1, players that represent community interests always obtain the best results (Case 1). Additionally, when individuals follow their own interest and do not collaborate, the city’s overall Happiness Index is very low (Cases 2-A and 2-C).
4.2.2. The relationship between metropolis and smart cities
The previous scenarios allow us to verify our hypothesis about the important role that emergence plays in a smart city, mainly, that emergence allows the smart city to adapt to its citizens’ needs.
One of the main features of smart cities is their use of ICT in all aspects of life [19, 24]. But one point of tension in smart city implementation concerns citizens engagement and participation, and the relative lack of both in actual practice and research results related to this issue [21, 22]. E-participation addresses this issue, since is a core component in the process of developing communities with socially inclusive governance [13, 14, 23]. E-participation becomes inclusive when it focuses on citizens' needs and requirements. Although technology is a main component of e-participation, we must also consider the capability and willingness of citizens to participate in decision-making processes and to collaborate with public institutions. E-participation is one tool that engages citizens and promotes collaboration between citizens in governance [22]. E-participation can be applied [13,14,21]:
- In the context of administration.
- In the context of defining, executing, and monitoring public policies.
- In the context of service delivery, so that citizens can participate in both the provision and use of services.
- Finally, in the context of decision-making processes, where democratic schemes are required.
The last case is where Metropolis can be used to teach citizens about the different aspects of the decision-making process: its components, control mechanisms, the citizens’ roles, etc.-all necessary for a real democratic, decision-making process guided by a society’s collective goals.
Metropolis is a game that facilitates training in how to participate in democratic decision-making processes. Thus, it is a serious game in which players can learn to make collective decisions, where common goals are more important than individual goals. With Metropolis, users can learn to define these common goals and to take rational collective decisions based on these goals. Metropolis presents different situations that require collective decisions that must balance the interests of specific citizens with the collective goals of the society. Specifically, Metropolis facilitates:
The previous test scenarios show how Metropolis’ capabilities adapt a smart city to its citizens.
<bold>4.2.3. A comparison between <italic>metropolis</italic> and smart city applications</bold>
In this section, we compare Metropolis with several recent applications based on the following criteria. (See Table 2): a) whether they fall within the domain of smart cities; b) whether they propose e-participation approaches; c) whether they constitute serious games; d) whether they analyze emergent behaviors; and e) how they fit in the context of urban planning.
Comparison with other applications
[6]
[12]
[14]
[24]
[26]
[33]
[35]
[36]
Met
a
x
x
x
x
x
x
x
x
b
x
x
x
c
x
x
x
x
d
x
x
e
x
x
x
x
x
x
Source: The Authors
Overall, there are several urban planning applications based on serious games within the context of smart cities, but they don't explicitly incorporate e-participation, let alone an analysis of the emergent behavior of the city based on collective decisions. Metropolis is the only game that encompasses all these criteria.
5. Conclusions
The city is a living entity that is constantly changing and evolving [3]. The way that its residents interact generates effects that can be analyzed at a deeper level. We face a phenomenon in which simple behaviors generate complex patterns and organization on a larger scale [3]. The underlying factor at play is the exchange of information between urban components.
The city is a dynamic system whose evolution does not depend on one, two, or several agents, but depends on what emerges within it as the product of the collective decisions of its agents, which produces upward forces that directly affect its overall structure. These forces are unpredictable [2, 3].
The Metropolis game studies the dynamics of the city. City behaviors emerge from the decisions taken by its agents regarding the type of constructions that interest them. The behaviors that emerge correspond to urban patterns configured within the city structure, e.g., certain buildings attract similar constructions within their proximity, resulting in the emergence of urban zones composed of like buildings. Another behavior is the pattern of the city, reflected in its development rate. Towards the end of the game, the city’s pattern stabilizes in order to incorporate the needs arising from the fusion of the residents’ personalities within the society, combining in some way these personalities.
We show how Metropolis can be used in a smart city context. Specifically, its emergent behavior produces two very interesting results [2, 16, 17]: the city’s urban and citizen patterns. They are constructed as the result of playing the game. A smart city can extract this information and apply it to urban planning problems. One of Metropolis’ unique features is that it can be used to build these patterns for a smart city’s self-management operations.
In conclusion, Metropolis is an emerging serious game that can be used in a smart city to achieve various goals. It can be used as an e-participation mechanism to reach consensus opinions, in order to build a collective urban vision of a city that takes into consideration the interests of its citizens. Additionally, it can be used as an e-decision-making tool to facilitte the emergence of urban patterns, products of the adaptation of a city to its citizens, thus allowing the development of a smart city that focuses more on citizen needs.
In summary, this research makes many contributions. It defines the importance of e-participation in the decision-making processes of the smart city. It show how Metropolis can be used as a serious game to teach citizens about e-participation in a smart city. Finally, it describes the emerging characteristics of a smart city (e.g., the city pattern, urban spatial distribution) derived from e-participation.
This last aspect is very important, because emergence is a core characteristic of the smart city that enables behaviors adapted to the necessities of smart city citizens [2]. Metropolis teaches players about democratic and collective decision-making processes. It helps players understand the different aspects of these processes, e.g., budget management, interaction mechanisms, etc.
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is BSc. in Systems Engineering in 1987 from the ULA, earned an MSc. in Computer Science in 1991, from the Paul Sabatier University-France, a PhD in Computer Science in 1995, from the Universite Rene Descartes-France, and a postdoctoral degree in Computer Science at the University of Houston, in 2000. He is professor of computer science at ULA, a member of the Academy of Sciences of Mérida and of the IEEE CIS Technical Committee on Neural Networks. He has completed postdoctoral research at several Universities and has published more than 500 works on parallel / distributed systems and computational intelligence. ORCID: 0003-4194-6882
is BSc. in Systems Engineering in 2002, earned an MSc. in Computer Science in 2006 and holds a Dr. in Applied Sciences, all of them from the ULA. He participates in research internships at the University of Rennes I in France. He also conducts research in the area of data mining, intelligent computing, multiagent systems, bioinformatics in the CEMISID at ULA. ORCID: 0000-0003-0339-3244
is BSc. in Computer Engineer in 2004 from the UVM, Venezuela, earned an MSc. in Applied Computing IUPTJE in 2010. He has been a tutor in the Masterdrez PROEA learning community of UPTMKR since 2014 and a PhD student in Applied Sciences in the ULA, since 2017. Currently, he is a Computer Engineer for the Autonomous Service of Notaries Registration (SAREN) ORCID: 0000-0002-3859-476
received the BSc. in Engineering in Computer Systems and Computation from the Universidad Técnica Particula de Loja, Ecuador, in 2006. H received an MSc. in Computer Science and Technologies at the Universidad Politécnica de Madrid, Spain, 2014. He has worked as a university instructor and researcher since 2007 in the Computer Science Departament at the Universidad Técnica Particular de Loja. His research interests include artificial intelligence, ambient intelligent, social robotics, affective computing, and machine learning. ORCID: 0000-0001-8789-9343
is a Dr. of Engineering in Control Systems and professor of control systems and human-machine systems at the Escuela Politecnica Nacional in Quito, Ecuador. He specializes in human-machine systems and robust control systems. other interests areas include robust control, artificial intelligence, mobile robotics and logistic. ORCID: 0000-0002-7529-9006
obtained the BSc. in Computer Science from the Polytechnic University of Madrid, Spain in 1987, a BSc. in Mathematics from the University Complutense of Madrid, in 1997 and a PhD from the Math Department of the University of Alcala, Spain, in 1999. From September 1992 to April 1994, he served as a lecturer in the Mathematics Department of Alcalá University. He then joined the computer science Department of University of Alcalá. From 1998 to 2001, he was the chief of computer services at the University of Alcala. His research interests focus mainly on software engineering and knowledge representation. ORCID: 0000-0002-3073-4369
Aguilar-Castro, J. L, Altamiranda-Pérez, J. A, Díaz-Villarreal, F. J, Cordero, J, Chávez, D. and Gutiérrez de Mesa, J.A, Metropolis: an Emerging Serious Game for the Smart City. DYNA, 86(211), pp. 215-224, October - December, 2019.