Moving Pieces and Allocating Budget Together: A Framework for Using Analog Serious Games in Sustainable Collaborative Planning

Abstract

The process of seeking games as tools for participatory and collaborative approaches applied to planning and public engagement is thriving. Despite the growing literature, and in contrast to the increasing number of experiences, there is a lack of methods for developing game-based approaches. We propose using the MIQUAPA method to support low-tech and low-cost serious games for collaborative planning and participatory budgeting. We designed two serious games using modern board game mechanisms and tested them to address two sustainability problems related to a university campus. The games engaged the participants and delivered collaborative planning experiences. However, the experiences revealed several simulation limitations of this method. The participants’ preparedness, context, and motivation also affected the game outcome. This paper proposes a method to develop future game-based approaches, informing the advantages and limitations of such approaches and proposing data collection and facilitation solutions. It warns future practitioners about the required preparedness to use game-based approaches.

1. Introduction

Games are trending and re-emerging as form of planning support tools [1,2]. From their use as teaching tools to activities that can support stakeholders and experts in decision-making exercises, games seem promising [3,4]. One possible application is supporting participatory budgeting (PB), aiming to foster more collaboration and sustainable, accountable solutions [5] as ways to climb the participation ladder [6]. However, there are several challenges to using them. There are game design, facilitation, and data collection methodological design challenges. Developing games is complex. It is a user-centered creative process, prone to considerable uncertainty that must be framed. Data collection and validation of the game’s impact and outcomes are also challenging aspects. The literature on applied games, gamification, and serious games for spatial and urban planning highlights the advantages of the games but recognizes these difficulties [2,7]. We argue that games have not yet been explored to their full potential, mainly because we have not fully mastered how to develop and use them in practice [8,9]. Despite the considerable literature on game-based approaches for more general civic and public participation processes [10], their application in activities for deepening democratic societies like participatory budgeting is scarce. Our goal is to present a game-based tool that is easy to use and adaptable to different budget allocation case studies, including PB. Despite the potential of game-based approaches, we tested whether a set of modern board game design elements (mechanics and pieces) and co-design principles that reinforce participants’ decisions can contribute to the deepening of PB processes.

This paper proposes a versatile method based on analog game design that can also be transferred to digital and hybrid platforms (MIQUAPA). Our method aims to deliver participatory and collaborative decision-making planning processes that can mimic or support participatory budget allocation processes, enhancing engagement in PB through game-based approaches. From a game design perspective, our method departs from modern board game design elements [11] like voting, tracking, and tile placement mechanisms [12]. It can be combined with maps representing the spatial planning issue [13,14]. We argue that this method is suited to participatory budget allocation processes and defines priorities and a hierarchy of choices. In addition to this, it includes co-design dimensions, allowing users to define some game elements. The use of physical board game components makes it tangible for newcomers. The low-tech and low-cost approach is easy to prototype from a functional and economical perspective [15], simplifying what can be achieved even more by using low-cost digital technologies for the same purposes [16].

After defining the game-based planning method (MIQUAPA), we tested it in case studies with two different planning challenges (and public) related to sustainable planning issues affecting a university campus (Faculty of Engineering from the University of Porto—FEUP). The first case study (TCT) addressed the trips made by students to the FEUP campus. In the TCT, the purpose was to reduce car dependency and introduce new alternatives. Transport system and spatial planning researchers attended the TCT session. Civil engineering undergraduate students participated in the TCS session as part of a voluntary contest about spatial planning. In the TCS, the challenge was changing the FEUP campus to achieve higher sustainability, considering all the functional and physical dimensions of the campus. In both case studies, the players worked in teams, defining the cost for each proposal and managing a collective budget with a certain degree of uncertainty (co-design). According to the game economy restrictions, the participants played with the game pieces and map elements to define and locate the proposals.

The case studies revealed that the tested method applies to different game-based planning processes and seems suited for game-based participatory budgeting processes. Modern analog game design elements, which were low-cost and easy to implement, delivered meaningful experiences, allowing us to propose the MIQUAPA method for future game-based processes. Participants recognized the potential of game-based collaborative planning approaches. However, participants recognized that the games must be supported and based on expert knowledge to deliver more realistic and practical results. Competition, motivation, and collaborative contexts can influence the results. Data collection and analysis were problematic despite our efforts to experiment with two different approaches (filming post analysis and direct surveys).

2. Games for Participation through Planning and Budgeting

2.1. From Planning Decisions to Games

According to Arnstein [6], having the freedom to decide on public issues relates to higher stages of participation, which means that a deeper level of involvement happens when these participants (agents) have the freedom (agency) to plan (decide) in practice and aim for collaboration. Planning practices can be compared to playing a game [17]. The agents decide what to do in the present, expecting that their decisions achieve a result, a goal, or a purpose. Planning is making choices for the future [18], usually influenced by political powers, even when supported by planning experts and planning methodologies [19]. In the case of spatial and urban planning, these social and political dimensions are even clearer. Some planning practices have supported dominant powers [20], which can lead to different types of social conflicts [21].

With the rise of democratic societies, the ideals of sharing power and increasing participation have been enforced [22,23]. Many different options have been tested. Game-based approaches are among them, with some successes and failures [24,25]. If game structures can engage citizens and promote more participatory attitudes, thoughtless gamification can lead to forgetting the reason for participating in the first place. Playing the game just for the entertainment experience or other extrinsic motivations like rewards and prizes can dominate the experience [26].

Before exploring the relationship between games and planning (participatory decision), we need to define what games are. Salen and Zimmerman [27] collected several different definitions, and tended to adopt a systemic approach where games are considered as interactive systems able to deliver engaging experiences. Then, games as planning tools can support participatory and collaborative planning. Both approaches (games and this specific planning approach) focus on the users’ experience and engagement. We avoid terms like fun because they are very subjective. Koster [28] establishes that the fun part of games is the accomplishment, leading to learning and mastery. There are other types of experiences that move players, as Zagalo [29] systemized in three streams (socializing, problem-solving, and exploring). Fullerton [30], Hunicke et al. [31], and Martinho and Sousa [32] explored more of these experiences and player profiles. For practical purposes, we will use the definition of a game provided by Boller and Kapp [33] (p. 4): “A game is an activity that has a goal, a challenge (or challenges), and rules that guide achievement of the goal; interactivity with either other players or the game environment (or both); and feedback mechanisms that give clear cues as to how well or poorly you are performing. It results in a quantifiable outcome (you win or lose, you hit the target, and so on) that usually generates an emotional reaction in players”.

The previous definition fits the concept of serious games, where the games must keep their characteristics and engagement potential while addressing other purposes [34,35,36]. If games can deliver experiences similar to planning processes (e.g., decision-making in a scenario/system to generate outcomes), it is not surprising that games seem to be promising tools. Before using games, it is recommended to recognize their strengths and limitations for planning purposes [2]. Serious game approaches, where games are created to achieve planning goals/purposes, seem to avoid some of the gamification problems stated before. Playing a serious game tends to deliver intrinsic motivation, where there is no need to reward the players beyond the game’s outcomes [34].

Although drawing a line between gamification and serious games might be difficult [37], developing a serious game where the only purpose is delivering a better planning solution is viable. An example of this is the UrbSecurity (Urbact project), where the game-based co-design process defined the priorities to increase urban security in the city of Leiria (Portugal) [38,39]. Defining the best solution, according to the participants’ perspective, can be the core drive of the game. This possibility represents why games have been explored as ways to deal with complex planning, wicked problems, and cases where rational and standard optimization approaches are not a good fit [17,40,41]. When the games allow the participants to define what is more important, the priorities, and the proposals, we enter the domain of participatory and collaborative planning [42]. Participatory and collaborative planning approaches are unfit to address every planning problem [43,44]. They are just two more approaches planners and other practitioners can use, and games can be the vehicle to implement them.

2.2. Budget Allocation as a Planning Process

In this section, we address some concepts from game theory to support budget allocation games. In these games, participants decide where to spend a limited amount of money, defining real-life options [45]. Through participatory budgeting (PB), participants can influence public decision-making [46].

Since the 1990s, many PBs have been tested worldwide and have generated many scientific reports [47,48]. Despite the growing interest and the potential to deepen democratic institutions and processes, there are problems such as manipulation from political institutions and the need for more know-how [49]. One recommendation is higher citizen control and providing information to support decisions and the resources to implement them [50]. The lack of political motivation and resources has affected these policies, even in places were PB was very popular [51]. One possible tool to reinforce PB is the use of playable models, where engagement driving for goal-oriented tools can benefit from delivering reality-based models that participants can play to define, learn, and test solutions for more efficient resource allocations. Serious games and collaborative planning approaches are options to explore [3,42,52].

However, a quick search on Scopus and Web of Science using keywords like “public budgeting & games” reveals a gap. In the Scopus database, of the 41 results, only 4 papers refer to games for users and human participants (all the others report to game theory and non-human agents). None are about modern analog board games. Gastil and Broghammer [10] talk about the use of digital games as support tools, Hassenforder et al. [53] address the use of role-playing games (RPG) in a practical case study, Gordon et al. [54] idealize the use of generic RPGs, and Bogost [55] reflects on the political representation of videogames.

Despite Web of Science revealing 326 results for the same keywords, no new papers about player-centered games emerged (excluding the classic game theory approaches). Searching for other keywords like “public budgeting serious game” revealed nothing new. These results show that proposing a framework to develop serious games for participatory/collaborative planning and alternative forms of public budgeting to strengthen new democratic approaches is yet to be explored. We believe that game studies can provide some answers and solutions for enhancing PB for deeper levels of participation and democratic development.

The game theory studies literature considers limited budget allocations to generate complex games for analysis [56], which may even seem boring from the participants’ perspective [45]. Choosing where to allocate budgets and predict outcomes is like playing a strategy game [57], which might not be engaging as a purely mathematical exercise according to player profiles in entertainment games [30,31].

Considering that participatory budget allocation processes are created to foster citizens’ participation [58,59], delivering a tedious process of calculating all possibilities might represent a problem to be solved. Gastil and Broghammer [10] are among the few authors who connect participatory budgeting with games to foster more public participation and to connect participatory and collaborative planning approaches. Authors like Lerner [25] provided several case studies that can be inspiring. However, game-based solutions seem underexplored. Gastil and Broghammer [10] propose linking the participants’ motivations, which are highly emotional and rooted in territorial attachment (emotion and situation), with game-based tools to empower participants (identifying possible game mechanics).

2.3. Modern Board Games and Planning

Many modern board games address city-building themes and the urban development of real cities [38]. The use of these games to learn how to develop serious games for planning and citizen participation is still scarce [2]. The same is true for game-based learning contexts [60]. Considering that modern board games are thriving and engaging a growing number of players worldwide, they are growing beyond a niche activity [56,57,58,61,62,63]. This is a phenomenon connected to the post-digital movement [64], where users seek to engage in face-to-face social activities. The social dimension of modern board games results from the combination of physical components and mechanical systems, making for meaningful interactions [32,65,66,67].

Developing games is complex due to the uncertainty of the players’ behavior and the need for the game to be engaging [68]. Developing a serious game is even more complicated because it needs to balance playability with other purposes beyond entertainment [35,36,69]. Game design practice recommends creating early prototypes of the games and testing them continuously until the game’s purposes are met, which is valid for any game type [15,30]. Even digital games result from low fidelity and technology prototypes. Making board games is a standard method for developing digital games and a learning exercise recommended to game designers and developers [70,71].

Another advantage of analog games is their innate ability to foster collaboration when playing face-to-face with other players. Analog game systems reinforce collaboration to deal with the rules and the social contract of playing a game, requiring collaboration between players to function without external enforcement [72,73]. Modern board games have been characterized by games where conflict between players can be reduced or removed (i.e., Eurogames) [65]. By replicating these design elements, we can adapt and design collaborative entertainment games [74,75] and serious games [14,76]. The case of Cities: Skylines, in its digital and analog versions, is an example of the possibilities and limitations of modern board games. The analog version can simulate some details of urban simulation and establish an easy-to-grasp collaborative planning exercise [77].

Identifying modern board game mechanisms [12,78] that fit spatial decision-making and budgeting allocation exercises is a way to use the potential of modern board games for participatory/collaborative participatory budgeting processes. Profiting from the fascination of moving and playing with pieces [32,79] and the ease of organizing the pieces in a tangible way to support decisions [66,80] was explored in our case studies.

Regardless of the previous advantages of analog games, they have limitations. High-detail simulation and massive participation are difficult to achieve with low-complexity analog games [2,81]. Modern board games can achieve higher levels of simulation than standard board games due to their design elements [11,77]. However, they might be complex for newcomers to play [61,62]. Our case studies also tested these perceptions.

3. Materials and Methods

The proposed method is divided into several steps and is adaptable to different game-based planning and participatory contexts. It departs from a user-centered design to adapt more to the users (players). It follows the recommendation of co-design with the participants from Champlin et al. [82], transforming it into a faster solution, already departing from a set of game mechanics from modern board games (resource allocation and voting) and generic game pieces (easily acquired and able to be used for players’ interpretation) [13,14,83]. The analog dimension of the playable elements of the method facilitates these adaptations. However, digital implementations through specific controllers and interfaces are possible. Despite not being tested in our case studies, we believe that finding specific controllers and interfaces would make the method viable through different digital game platforms, as is common in digital game development [30,70,71].

The conceptual method comprises the following five steps (MIQUAPA):

• Modeling spatial representations of the planning problem/goal (S1).
• Identifying problems/priorities to solve/respond to (S2).
• Quantifying costs and effects (S3).
• Allowing for playable decision-making and budget allocation (S4).
• Conducting serious game support and evaluation (S5).

S1 is defined by the game designers. S2, S3, and S4 introduce the concept of co-design, including the participants’ decisions to set the game’s economy and narrative dimensions. Participants establish the game components’ quantities, meanings, and representations (narrative/fiction).

One parallel step (S5) is the data collection dimension, used to evaluate the serious game dynamic and results, and to analyze the game outcomes and the players’ behaviors and decisions. It is typical in game research to compare the game impacts before and after the play sessions using pre- and post-play tests. However, these methods might break the flow of the games and be considered intrusive by participants. One alternative is to observe the players’ behaviors and analyze them later. Through surveys and semi-structured interviews, the researchers can explore the game experiences. If this is performed long after the sessions, we can learn whether the experience was impactful or not, according to the participants’ perceptions [84]. The surveys collected information about the personal experiences of the participants with participatory approaches and serious games, detailing questions and the classification of the experience according to the creativity, collaboration, and decision-making involved in the process, identified as core elements to improve PB [48]. In addition to this, we also evaluated the perceptions of playability and engagement following serious game standards [47,85], and if participants see value in the analog solution (related to a low-tech and sustainable solution for serious games for PB).

After S4, and as part of the evaluation (S5), debriefing is a way to discuss and reflect, with the support of the game facilitator, on the playable experience and the serious game outcomes. When the game facilitators are also the game designers, it is easier to support the co-design processes where the participants change the game economy and the narrative meaning of the game elements (e.g., the physical game pieces) [14,83].

To test the MIQUAPA serious game method, we defined two case studies to address the same spatial entity (a university campus) through the lens of different sustainable planning problems. Data collection/analysis was also distinct, aiming to test which alternative can provide better insights into the continuous development of the MIQUAPA methodology. Each game context, game design element, and the data collection are presented in the following sub-sections.

3.1. General Game Design Elements for MIQUAPA Serious Games

We propose using specific game mechanisms previously tested in other game-based planning and participatory approaches [38]. The chosen game mechanisms can transform printed maps of an intervention zone into serious games. Engelstein and Shalev explore the identified game mechanisms in detail [12]. Each stated game mechanism is identified with the GM acronym in brackets. Game pieces are all the three-dimensional game components (e.g., coins, building miniatures, car miniatures, disks, sticks, etc.). They can be in different colors.

The established narrative was inspired by the uncertainty of public funding availability, combining it with dice rolling (GM). Setting limited time (GM) for the decision-making increased the tension and stress of managing the funds.

The games follow the cooperative structure (GM), where all players collaborate in teams to achieve the best results. However, we introduced a competitive structure (GM) where each team’s overall proposal received a score, resulting from a voting (GM) system. Each player had a defined number of votes to distribute among the proposals (except their own).

We explore the concept of scarcity using resource limits (GM), constraining the participants’ choices to define priorities. Players cannot use more than a certain number of physical game pieces. Players obtain income cycles (GM) from turn to turn (GM). They can spend their income (in the form of coins) on the available options. Players can negotiate (GM) and give their coins to select an option. Some solutions cost more coins than any player will ever have, working like a contract (GM) that is achievable by mobilizing collective resources.

We used tracking bars (GM) in a table format that the game pieces could fit into to track information. Removing and adding pieces that represent the solutions and costs was inspired by the tableau building (GM) principle (adding/removing pieces) and tile placement (GM) over the map. Adding the pieces onto the map changes the game state and represents the planning decisions. Placing the coins (money) in the track bars represents the budget allocation. Figure 1 presents a schematic representation of the dynamics created by the combination of game mechanisms. The standard pieces had no predefined specific meaning but were related to generic urban elements of different colors; players defined what each of them represented as a co-design dimension of the games (modeling the planning problem and the solutions). Players also defined how much each piece cost (reinforcing agency and the need to research and put solutions in perspective, from one to another).

3.2. Adapting to the Case Studies: The Campus Travels (TCT)

The Campus Travels (TCT) serious game aims to find proposals to transform private automobile travel into more sustainable transport solutions. The session (three hours), including the briefing and debriefing, took place at the Faculty of Engineering of the University of Porto (FEUP). Ten researchers/lecturers from CITTA (Research Center for Territory, Transport and Environment), divided into two groups of five, attended the session (n = 10). The TCT serious game followed the MIQUAPA method, adapting it to the planning context at stake. One week after the session, the participants took the post-play test. The session was filmed and analyzed.

In S1, the participants modeled the spatial representation of the FEUP campus (Figure 2b). The FEUP campus is represented in the center of Figure 2a at a different scale. Adjacent hexagons simulated the area around the campus (5 hexagons for the campus and a belt of 11 for the surroundings). Over the hexagons, participants could place a set of white wood pieces (20 cubes, 4 slabs, and 2 sticks) to represent the urban and building morphology (including parking, entrances/exits, and pedestrian paths) (Figure 2a). S1 was intended to be a creative exercise dependent on the participants’ agency (co-design dimension).

In S2, participants addressed the problems at stake. Since it was a regional planning problem, the model included the regional distances from the surrounding municipalities to the campus and the number of students traveling from each center (municipality). Figure 2a shows the physical implementation of the model preparation. We collected information from previous data collection from CITTA, identifying the distance and number of students traveling to the campus. Each hexagon represented a travel origin having a determined number of meeples (wooden anthropomorphic pieces). Bigger meeples represented 100 students and smaller ones represented 10. Each hexagon exhibits this information, complemented by the distance, which is represented by the white wood sticks (10 Km per piece) and the cardinal location referring to the campus (e.g., south, northwest, etc.) to help the participants set up the game (Figure 2a). Setting up the model in S2 according to the instructions helped the participants to identify the problems at stake. We challenged the players to represent the campus with the available white wooded pieces (Figure 2b). The game included this co-creative process to fuel discussion and the sharing of information about the campus.

In S2, participants defined proposals to make the modal transportation shift (co-design dimension). Participants received a table with tracks to place board game pieces from an available set (colored sticks, slabs, houses). They grouped the game pieces and placed them onto the table tracks (with pairs of rows), writing the meaning of the proposal next to each track/pair of rows (Figure 3a). In this case, the players had decided how the tableau building dynamic represented in Figure 1 would affect their former decisions and simulation details.

S3 happened immediately after S2. The participants defined the monetary costs (money) and the expected impact of the transport modal shift, as well as how many students (meeples) would stop using their private automobiles (Figure 3b). Each group defined their budget and the value of each monetary unit (coin piece), represented by the yellow discs. This requirement led the groups to discuss the costs, the effects, and how many they plan to be necessary when defining the global solution for the planning problem.

S4 was the core part of the game. Each group had to decide where to locate the solutions identified in S2 and S3. By placing a game piece in the model (campus and travel schemes), participants had to spend money (coins). Participants staked the coins (budget allocation) over the track squares where the game pieces (proposal unit) were available. Players defined how many meeple pieces were removed (modal shift) by each proposal piece, moving them from the model to the tracks above the allocated money (Figure 3b).

During S3, the facilitator informed the participants that the total budget was available. However, when S4 started, participants received an information update that the money came from a governmental fund, released in small amounts of one/eighth (eight turns) of the total (game turns) (Figure 4a). This change introduced a narrative dimension to the simulation (engagement by fiction and surprise). After the first turn, the facilitator started to track time (5 min per turn), informing the participants if they did not decide quickly on the money allocation, they would lose all coins not spent (allocated). The narrative climax occurred when the facilitator informed participants there was a problem regarding fund management for the coming income turns (fictional narrative). To simulate this, we used a D20 dice. If the dice roll resulted in a value lower than 10, the players would receive half of the income fund. This cut happens in three income turns. In the fourth turn, the facilitator informed participants that it would be the final turn. No more money would be available.

A facilitator supported the game by presenting the narrative and rules of play and controlling the game progression. After the play session, the facilitator debriefed the participants, asking them their opinions directly to foster discussion (Figure 4b). Each table presented its general proposals and debated them with the other participants. The model and the overall game experience were also addressed. The facilitator challenged the participants’ critical analysis of the experiment and the usage of games as a planning support tool (Figure 4b).

3.3. Adapting to the Case Studies: The Campus Sustainability (TCS)

We adopted a pre-test and a post-test for the TCS, with less time to reflect and provide comments than in the TCT. There were 12 participants, forming 4 teams of 3 participants each. The session was part of an academic challenge about urban planning for students. In the end, there was a winning team.

In S1, the participants modeled the spatial representation of the FEUP campus, detailing the urban surroundings. The campus was represented by an orthogonal grid map extracted from a satellite image. The participants had a limited amount of white wood pieces (20 cubes and 5 slabs) to represent the buildings, green spaces, parking, and other infrastructure existing on the campus. S1 resulted from the participants’ agency, allowing them to place the wood pieces over the grid map in a creative and co-design process (Figure 5a,b).

In S2, the participants addressed the problems at stake when interacting with the map. Next to the map, the participants had a table with five rows. In this table, the participants identified the five most important problems/priorities to increase campus sustainability. Then, the participants used the plastic pieces, wrote the respective number (1 to 5), and placed them in the model (co-design dimension). Figure 6a shows the tables and the plastic pieces used to identify the proposal’s locations on the campus map.

In S3, the participants placed wood pieces (houses, cubes, and slabs) and furry wires (blue, red, green, and yellow) on a printed table with pairs of rows. Each pair of rows defined a track for a proposal type (determined by the players), including the available pieces (proposal units) in the top row and the costs (also defined by the players) in the lower row. Figure 6b shows one of these tables. The money allocations in the figure are a part of S4 (co-design dimension).

In S4, the participants spent the budget received each turn (income), replacing it with the piece representing the proposal unit (Figure 1 and Figure 7a). Then, the piece was placed on the map to identify the intervention (proposal) location on the model (Figure 7a).

The evaluation step (S5) was composed of different parts. First, the students filled out a form to identify how their proposals would improve the campus according to the following dimensions: environmental, economic, social, and cultural. Then, the teams presented their overall proposals to the other teams (2 min for each pitch). Each participant received six votes (red stickers) to distribute as they pleased over the proposals (Figure 7b). After this voting stage and identifying the winning team (most votes), the facilitator challenged the participants to discuss the experience and results. The session ended with the participants filling out the post-tests.

3.4. Similarities and Differences between Methods in Each Case Study

Despite both following the MIQUAPA method, the TCT and TCS serious games delivered different experiences and outcomes. The duration was similar (approximately 180 min), and there was a comparable number of participants (TCT n = 10; TCS n = 12). The support and evaluation step (S5) was different between the games. The TCT had a post-test a week after the sessions (no pre-test), filmed gameplay, and a long final debate (debriefing). The TCS had a pre-test and post-test, no filming, and a shorter discussion (debriefing).

The TCT was played by planning experts. The goal was to discuss and find solutions for sustainable mobility and ways to deliver participatory and collaborative planning approaches for planning practices while testing games as planning support tools. Defining a winning condition was not relevant. The game outcomes and goals were as follows: identifying the overall proposal, the management, and the priorities to deal with sustainable transport. Discussion and critical analysis were important outcomes because they allowed us to contextualize the outcomes of the games (map, piece location and interpretation, voting results). The evaluation methodology (S5) included filming each group and analyzing the content, the decisions and discussions, the engagement, and the participants’ behavior. The reduced number of groups allowed the facilitator to better support the play experience and challenge the participants to go deeper into the discussion during debriefing (S5).

The TCS was played by undergraduate students, some in their first year of study. The purpose was to introduce decision-making and collaboration skills while defining a winning team for the contest at stake (using a voting system, GM). The higher number of participants and groups (four groups) diminished the facilitator’s intervention and allowed less time to discuss and write the post-test comments. Filming each table would require more video equipment, and it would be infeasible for only one facilitator to control this. The adopted option was to deliver a pre and post-test to the participants.

3.4.1. Pre- and Post-Test

The pre-test and post-test were very similar. Repeated questions of the pre and post-tests allowed us to quantify the impact/change perceived by players during the play experience [81]. These repeated questions in the pre-test and post-test are identified with an R in the following list. The participants had a Likert scale from 1 to 7, where the lower numbers were the following:

• Game habits/preferences:
  • Traditional/classic board games.
  • Modern board games.
  • Digital games.
  • Applied games (serious games, gamification, educational games, etc.)
• Games as urban planning tools:
  • Games can be tools for urban planning.
  • Easiness to access and use games for planning.
  • Prejudice about the use of games for planning.
  • Analog games have advantages over digital games for planning.
• Game session experience and dimensions (of serious games):
  • Simulation and modeling accuracy.
  • Promoting discussion and debate.
  • Learning about urban planning.
  • Testing and experimentation.
  • Foster creativity.
  • Foster collaboration.
  • Foster decision-making.
  • Solution quality
  • Playability.
  • Motivation, engagement, and fun.

In addition to the previous topics used to classify the session experiences through the Likert scale (1 to 7), the post-tests had a section where participants could write free commentaries about their perspectives. The quantitative results were compared and analyzed using the average and median values. We analyzed the qualitative information following the grounded theory principles, identifying emergent clusters related to the participants’ commentaries.

3.4.2. Video Content Analysis

To analyze the filmed data, we set a list of events that would be expected to happen in a serious game about urban planning, considering the previous literature review [1]. We divided the events into four clusters related to engagement (game) dimensions and purpose (serious) dimensions:

• Rules and generic playability issues (core game dimensions);
• Engagement, collaboration, and accomplishment (e.g., laughs, surprise reactions, etc.) (behavior dimensions);
• Modeling, testing, and simulating reality (serious dimension).

By observing and analyzing the video, we were able to identify these moments. More importantly, we transformed these moments into comparable data, which form the basis of our discussion on the game experience.

4. Results

Due to the low sample size (n < 20), we did not expect undoubtedly conclusive statistical results. Nevertheless, we used the ANOVA and Kruskal–Wallis statistical test to analyze the changes (before and after play) between the pre- and post-test in the TCS. Using the median values (fit for Likert scales) complemented the analysis of the average values and standard deviation.

Table 1. Questions/affirmations and asked dimensions in the TCT and TCS after the sessions (average, median, and standard deviation values).

Questions/Asked Dimensions	TCT			TCS
	$\overline{\mathit{X}}$	$\tilde{\mathit{X}}$	σ	$\overline{\mathit{X}}$	$\tilde{\mathit{X}}$	σ
Game habits/preference: Traditional/classic board games	3.80	4.00	0.75	4.92	5.00	0.95
Game habits/preference: Modern board games	3.60	3.00	2.06	4.92	5.00	1.80
Game habits/preference: Digital games	3.10	3.00	1.37	5.67	6.00	1.49
Game habits/preference: Applied games	1.90	2.00	0.70	4.83	5.00	1.72
Games can be tools for urban planning *	5.90	6.00	0.83	5.92	6.00	1.26
Easiness to access and use games for planning *	3.00	2.50	1.18	4.42	4.50	1.71
Prejudice about the use of games for planning *	4.10	4.50	1.45	5.67	6.00	0.94
Analog games have advantages over digital games for planning *	4.60	4.50	1.43	4.75	4.00	1.59
Game session: Simulation and modeling accuracy	5.00	5.00	1.10	5.00	5.00	1.08
Game session: Promoting discussion and debate	6.20	6.00	0.60	5.50	6.00	1.38
Game session: Learning about urban planning	5.40	5.50	1.36	5.58	6.00	1.11
Game session: Testing and experimentation	5.50	5.50	1.02	5.33	5.00	1.25
Game session: Foster creativity	5.80	6.00	1.17	6.17	6.00	0.80
Game session: Foster collaboration	6.50	7.00	0.67	6.25	6.00	0.72
Game session: Foster decision-making	5.60	5.50	0.92	5.92	6.00	0.76
Game session: Solution quality	4.60	4.50	1.02	5.83	6.00	0.69
Game session: Playability	5.70	5.50	0.78	5.42	5.00	0.95
Game session: Motivation, engagement, and fun	6.60	7.00	0.66	5.83	6.00	0.83

* Considering the results from the post-test of the TCS to compare to the TCT results.

presents the results of the post-tests of both game sessions (TCT and TCS). Despite the games’ similarities, the age groups varied considerably. In the TCT, the average was 37.40 years, while in the TCS it was 21.75. This difference might explain the game habits/preferences of the TCS participants for digital games (nearly six), but this was noticeable in all other types of games. The TCS participants had higher game habits/preferences than the TCT participants. The TCS participants identified more prejudices about using games for planning purposes and considered accessing games for planning purposes easier.

The TCT and TCS participants considered that the games delivered high collaboration dynamics. Although all values reinforce the game sessions’ potential for collaborative planning dynamics, the TCT participants considered the sessions’ ability to foster debate, test experimentation, and collaborate higher than the TCS participants did. All the results related to collaborative planning practices and purposes were above 4.5. The lowest result was the solution quality (4.5), classifying the game outcome proposal, resulting from players’ decisions in each group, as the weakest dimension.

Although they presented high scores, the TCT participants perceived the games as more fun than the TCS participants. This distinction might be related to the competitive structure of the session. The TCS was a competitive game and was part of a student competition to win prizes. The TCT players had only one motivation in addition to participating in the session itself. The TCT participants were researchers interested in finding new planning tools and methods.

In

Table 2. Pre- (B) and post-test (A) questions about the use of games for urban planning in the TCS. Underline means the values are statistical significant p < 0.05.

	$\overline{\mathit{X}}\mathit{B}$	$\overline{\mathit{X}}\mathit{A}$	$\overline{\mathit{X}}\mathit{A}-\overline{\mathit{X}}\mathit{B}$	$\tilde{\mathit{X}}\mathit{B}$	$\tilde{\mathit{X}}\mathit{A}$	$\tilde{\mathit{X}}\mathit{A}-\tilde{\mathit{X}}\mathit{B}$	ANOVA (p)	Kruskal–Wallis (p)
Games can be tools for urban planning	5.67	5.92	+0.25	6.00	6.00	0.00	0.0035	0.0868
Easiness to access and use games for planning	4.83	4.42	−0.41	5.00	4.50	−0.50	0.0236	0.1525
Prejudice about the use of games for planning	4.67	5.67	+1.00	5.00	6.00	+1.00	0.4580	0.4499
Analog games have advantages over digital games for planning	3.08	4.75	+1.67	3.50	4.00	+0.50	0.2394	0.2512

, we compare the pre-test (B) and the post-tests (A) of the TCS session to verify if the analysis method is applicable for serious game evaluation and data collection. The higher robustness of the Kruskal–Wallis did not generate statistically significant changes. However, the p values were low (near 0.05), and the ANOVA test revealed a significant change in the perception that games can be tools for planning and that it is easier to access these games than expected.

When analyzing the average and median value changes for the other dimensions, Table 2 shows apparent positive changes in perception (no statistical significance).

The video recording of the TCT session tested a qualitative approach to collecting data from serious game sessions.

Table 3. The approximate percentage of events according to the total events in each Step 1–4 (%) and examples of typified events recorded during the sessions’ film recordings (e.g.).

Clusters of Events		S1—Modeling the Campus	S2—Identifying the Problems (Travels) and the Solutions Type	S3—Defining the Costs and Impacts of Each Solution (GP)	S4—Allocating Costs and Adding/Placing Solutions
Rules and generic playability issues	%	40%	30%	20%	10%
	e.g.,	Asking questions to the facilitator and confirming interpretation with colleagues.
		Need to use all the GM?	All H connected to the campus or between each other also?	Can we search online for the costs.	Will we MC from turn to turn?
		How to place the different H?	Should the GP be staked?	Is there a limit to the costs?	How to identify the best proposal?
Engagement, collaboration, decision-making, and accomplishment	%	20%	30%	30%	60%
	e.g.,	Discussing the model.	Discussing problems and solutions.	Discussing values and the importance of the solutions.	Time stress.
		Laughing because of the constraints of space inside the H.	Laughing because of the available GP.	Using the GP to represent places and complex solutions.	Loss aversion due to the reduction in the budget (MC).
		Laughing when making narrative interpretations of the spaces.	Surprise with the game available GP (mainly the meeples).	GP colors and shape interpretation according to the solutions.	Making jokes about the bureaucracy and uncertainty of funding.
		Surprise with the model physical result (H and GP).	Surprise with the distribution of the travel origins (majority near).	Excitement, explosions, and jokes when receiving the MC.	Asking for help and assuming leadership facing stress.
Modeling, testing, and simulating reality	%	40%	40%	40%	30%
	e.g.,	Adjusting the GP placement to represent buildings, green spaces, parking, and accessibility.	Quantity of GP representing distances and agglomeration of travels in H.	Establishing a reference value (one MC worth X money in reality).	Establish priorities for the first MC. Complementing solutions in the next rounds MC.
		Identifying different uses for the GP according to location.	The spatial distribution of travel origins H. Some connected with each other.	Consider the isolated effects of each solution and try to define some combined effects between solutions.	Acknowledgment of the effects of time pressure to spend funds.

H—hexagons; GP—game pieces; MC—money coins.

presents the observation according to the previous classification of a cluster of events that could emerge during a serious game session. The percentage (rounded to 10) of events identified in each cluster during the gameplay is complemented with some observed examples. If the result was 30%, it means that 30% of the events in that session were of that type.

Participants reacted to the narrative dimension of the game’s unexpected events. Participants expressed visible laughs, smiles, and comments when challenged to be creative by representing the campus and identifying proposals. Positive feelings of nervousness (dealing with the novelty and challenge) and an eagerness to explore emerged when participants had to quantify and define the proposal costs. The material representation of the model generated expressions of understanding the problems at stake, like the dominant distribution of automobile travel across short distances. This setup surprised some participants when they saw the quantity of staked meeples (student groups) over the hexagons at a short distance from the campus. Participants criticized the model and changed it accordingly. For example, to connect travel origins to each other to form a connected network beyond a radial system.

We complement the qualitative data from the filmed gameplay. Through the interactive analyses, based on the grounded theory [77,83,86], the participants’ (Px) comments generated two-level clusters.

Table 4. Examples of TCT participants’ comments and their classification based on the emerging clusters (based on grounded theory).

Example of Commentaries from Participants (Px)	1st	Game General Experience				Game Application/ Serious Uses				Game Specific Outcomes
	2nd	Positive/Interesting	Surprising/Powerful	Adaptability	Playability Issues	Learning about SG	Introduction to Planning	Complex Decision Making	Interaction/Participation/Collaboration	Low Quality and Detail/Unrealistic	Need More Time to Play	Need Expert Knowledge/Support Tools and Actions	Simplicity Is Good	Can Simulate Stress
It was a great experience! We realized the enormous potential of games as tools for promoting interaction and supporting decision-making in the field of planning. However, for the quality of the solutions generated to be satisfactory, more time and preparation are needed. (P1)		▪				▪		▪	▪	▪	▪	▪
This session demonstrated that games can be a powerful tool in Planning, allowing the problem to be conceptualized in simpler terms and with well-defined rules. Despite these limitations, which can be seen as a negative point because they don’t take all the constraints into account in order to create a more efficient solution, their simplicity and collaborative spirit force the debate of solutions, which attenuates differences that may exist between participants and makes it possible to find the various facets of a problem that weren’t even considered at the beginning when creating the game and then channel them into a common solution. This tool is even more pertinent for those who don’t have much knowledge of planning, as it is much easier to understand and manipulate than more complex digital tools based on GIS. The games are more dynamic and have greater potential, measured by our creative capacity, which allows for constant improvement with everyone’s participation and customized to each situation. (P4)		▪	▪	▪			▪	▪	▪	▪			▪
Modeling the study area/object was quick and intuitive. So was the generation of options. On the other hand, given the limited time for modeling and simulation, the economic aspects of the game (benefits VS investment) will not correspond well with reality. The biggest difficulty in terms of the game was simulating/applying the multiplicative effect in terms of benefits, generated by the complementarity of measures (which is fundamental in the reality of planning). Another difficulty was playing with the small set of “meeple” pieces provided (more diversity and intermediate sizes would have been needed). An interesting learning during the game was to see how the approaching time limit leads to hasty, ill-considered, and more individualized decision-making, with the sole aim of spending the money available (which seems a fairly close representation of reality). (P5)		▪			▪		▪	▪		▪	▪	▪	▪	▪
This type of game has great potential for application in planning, particularly in participatory planning, as it provides a means of explaining complex processes and can therefore more easily involve stakeholders with less knowledge in the area of planning (namely the population). However, it seems to me that their application in more technical and strategic planning processes will perhaps be more limited due to the difficulty of representing an approximate model of reality (especially when compared to other available tools such as GIS systems). (P6).		▪		▪			▪	▪	▪	▪			▪	▪

1st order and 2nd order clusters.

shows examples of the participants’ full commentaries and how we classified them into clusters.

Reading the detailed commentaries allowed us to collect deeper data than the previous Likert scales. Despite being collected one week later, adjectives like “great” (P1, P6) and “powerful” refer to the impact of the game. The critical analyses cleared the potential and limitations of the tested approaches. P4 highlights the model limitation but states that the game’s simplicity makes it engaging for participatory planning processes. P6 reinforces P4’s comment. P6 argues that the game-based approaches can deliver easy ways to address complex planning problems. P6 also warns us about the limited application of the experimented game-based approaches compared to other powerful simulation tools (e.g., GIS-based).

Considering the research goals, the game mechanisms (GM) allowed the participants to track information and materialize decisions (agency). The decisions were tracked in tracking bars and spatial models (see Section 3.1), using limited game components and following the game economy principles of scarcity to enforce priorities. The cooperative gameplay delivered the collaboration decision-making dynamics from which PB may profit. The freedom to determine the meaning and cost of standard modern board game pieces (solutions/proposals in the model) reinforced informed decision-making and a co-designed result. These simplifications fostered critical analysis from the participants, who were able to identify the limitations of the simulation while highlighting the engagement potential of such methods.

Table 5. Quantitative results from the emerging clusters based on the TCT participants’ comments.

Based on the Participants Commentaries	1st	Game General Experience				Game Application/Serious Uses				Game Specific Outcomes
	2nd	Positive/Interesting	Surprising/Powerful	Adaptability	Playability Issues	Learning about SG	Introduction to Planning	Complex Decision Making	Interaction/Participation/Collaboration	Low Quality and Detail/Unrealistic	Need more Time to Play	Need Expert Knowledge/Support Tools and Actions	Simplicity Is Good	Can simulate Stress
Total		7	3	3	2	2	4	6	5	7	3	6	3	1

presents a quantitative summing up of the clusters identified by the commentaries of all the TCT participants. Participants described the experience as highly positive, stating the potential for introducing planning processes and dealing with their complexity. However, they considered that the games might deliver low-quality details and unrealistic results that require expert knowledge and other support/complementary tools to achieve their full potential.

5. Discussion

The serious game literature concludes that there are few tools for practitioners to use in order to reach games’ potential for participatory and collaborative planning and decision-making processes [2,9,82,87]. The MIQUAPA method, based on the modern board game design elements and co-design approaches, delivered two games that addressed different sustainable problems (transport planning and spatial planning). Participants recognized the potential of the played games to foster collaboration in a decision-making process where budget allocation was the core mechanism. The game mechanisms defined the tracking systems and helped the participants quantitatively determine the priorities and meaning of the proposal (deterministic approaches, cause, and effect, as modern board game design delivers for strategic entertainment games). The quantities, costs, scale, and spatial units (maps and simplified distances) generated a sandbox for collaborative decision-making, where participants defined the solutions (co-design).

We argue that following the MIQUAPA method would allow the development of game-based participatory and collaborative processes in the future for practitioners (including participatory budgeting) with low game design skills. We identified several game mechanisms and how they can support simple models, including map representations. Figure 8 summarizes the MIQUAPA methodology and proposes an implementation sequence of tasks and roles for future applications. The set of game mechanics and pieces was tested in previous experiments [13,14,83].

Figure 8 presents a serious game-based process centered on the participants, identifying their decisions and interactions related to co-design and the playable sequences (S2, S3 and S4). The game designers and facilitators define the game processes (S1) and continue to adapt them, supporting the participants’ decisions and evaluation by collecting data for later analysis (S5). However, S3 and S4 depend on knowledge and player agency, highlighting the participants’ need to learn more about the decisions they are facing (agency, self-awareness, and co-design). We argue that MIQUAPA can easily be replicable, requiring minimal game design, facilitation, and data collection/treatment skills and knowledge.

We observed that exposure to games can affect the participants’ perspectives on the novelty and potential of using games as tools and the expertise level required to model and simulate (e.g., proposals and costs) the planning issues at stake. The context and motivation to play can affect the experience. The TCT participants were voluntarily looking to find new tools. The TCS participants wanted to win the challenge. Excessive competition can jeopardize the collaborative experience, especially when the participants set the game goal (e.g., the concept of the best solution in a complex context). During debriefing, participants in the TCS admitted that the voting and associated scores for the proposals were not satisfying or fair. This extrinsic layer added to the game can generate negative effects in gamification approaches that are merely based on points and leaderboards. The TCT participants enjoyed the freedom of exploring a new tool and the possibilities it opened for practical purposes.

Participants’ commentaries, occurring during the debriefing, must be recorded. If not, the participants might not share all the relevant information in the post-test commentary section. This absence of information happened in the TCS.

The introduction of game pieces to simulate solutions and proposals contributed to the playable mindset. The uncertainty of the dice rolls, as well as time limits, fueled the dynamic and addressed the decision-making simulation under the stress of depending on external funding (e.g., public funding). As P5 admitted, it generated fewer reflected decisions. Despite the low quality of decision-making proposals, we avoided the possibly boring experience of abstracted and excessive mathematical decision-making. This also helped to control the duration of the session.

The data collection process can affect the game experience, breaking the game flow and engagement. The TCS participants had to perform a pre-test and post-test, while the TCT participants just discussed the session experience, and one week later conducted the post-test. Quantitative data are less subjective and easier to analyze. However, qualitative data can show information we might never obtain by other means. Through the qualitative analysis, we identified the potential of using game-based approaches for participatory engagement and the difficulties of conducting detailed and realistic simulations with the proposed method. These perceptions match the literature review for participatory and game-based planning, including the perception of prejudice against using games as serious tools [2]. Despite our efforts to present a method to collect and analyze data for two different serious games that use the same game elements, a more controlled environment and the ability to record and perform post-analysis of each playgroup is recommended. Our focus on the game design elements and applied playability might have affected this dimension.

6. Conclusions

Games are not new, but their broader and generalized use as tools for participatory and collaborative planning and decision-making (or planning support tools) is still considered an innovation. There is a growing collection of literature on game-based planning. However, the practical use of games in planning processes and their use as processes to deepen democratic practices like participatory budgeting are still scarce. There is a growing collection of literature, but on the contrary, practices and guides to replicate game-based methods for higher engagement processes are scarce. Developing games is not easy, even when applying established game design frameworks or adapting/modding existing games. Developing games becomes even more challenging when designing them to deliver other purposes beyond entertainment; for example, to address specific topics and real-case scenarios while still maintaining the engagement dimensions. We had to make these adaptations from other game approaches to provide a fast, simple, and low-cost solution to address participatory budgeting for the case study, since the literature review did not provide other alternatives. The games reinforced the participants’ agency and collaborative dynamics, which are key dimensions to improve PB processes. This was possible because modern board game design elements allow for tracking information decisions without compromising player agency and collaboration dynamics. We believe these design approaches can help to generate new tools for PB, to achieve new levels of engagement, meaning, and collaboration. They reinforce decision-based knowledge represented through simplified and sand-box models that experts and non-experts use to collaborate. They are also used as tools to identify the limitations, causes, and effects projected for the future, which is helpful to avoid manipulation and justify resource allocation (problems identified in Section 2.2).

Balancing playability with detailed simulation is a challenge for game-based planning and participatory approaches. Combining our low-tech approach with other low-cost digital planning tools is an approach worth exploring in the future [16], and it connects to the need to focus more on the sustainability dimensions for PB. Based on modern board game design, the proposed game elements proved to be playable even when the participants defined the meaning and cost of the game pieces, establishing a co-design approach. However, the data collection and evaluation processes still need to be improved.

The MIQUAPA methodology proposes one alternative to address this gap and aims for higher levels of participation and engagement. It uses low-tech and analog solutions applicable to spatial planning and other participatory approaches. The method delivers games based on co-design principles that foster collaboration, simplifying complex realities. The MIQUAPA method proposes one of the few game-based approaches for PB and applies modern board game elements through co-design that reinforce autonomous learning and participants’ agency. As seen before (see introduction, Section 2.2), there needs to be a clear record of games applied for PB, as there is a scarcity of modern board game applications for educational activities [56]. Following MIQUAPA (see Section 3 for game design details), it is possible to use game-based elements to support a PB process (with low-cost solutions as tested in the TCS and TCT) with player agency (seeking information and applying it) and tracking/measuring decisions. When applying MIQUAPA, the context, players’ motivation, debriefing process, and data collection process can affect the game impact and learning outcomes despite using similar game systems like the TCS and TCT. We also tested different planning themes (i.e., transport systems and building facilities) with the same budget allocation game system, proving its ease in supporting various planning processes.

The simulation dimensions of the generated games have limitations, mostly depending on the participants’ motivation, preparedness, skills, and knowledge. The context and purpose of play can also affect the results.