Hopefully this article should give you a cursory glance into what systems are and highlight what to consider should you look into utilising games as a tool for learning about systems of any kind.

What is a system?

It can be deceptively tricky to provide a straightforward answer to a definition of what a system is, but fear not; as described in the Field Guide to Consulting and Organizational Development, systems are an organised collection of parts (or subsystems) that are highly integrated to accomplish an overall goal. (Authenticity Consulting LLC, 2005)

The main components that are necessary in order to classify virtually every system you can find are:

• Inputs, which are resources or facilities used by the processes to complete tasks and achieve goals of the system
• Processes or Activities that utilise the inputs in order to create the tangible results or…
• Outputs, which are the end goal of the system and can include the creation of specific products or
  complete specific tasks.

Basically, it is a bunch of things, people or subsystems, each with their own tasks, functions or processes which produce specific results that ultimately come together in order to accomplish or create things that would either take way too long to complete if carried out individually or would be impossible to complete without the extra assistance they would have access to whilst being part of a much larger system.

When laid out like this, you might notice that virtually anything that you do can be categorised as being part of a system and figuring out how these systems work and getting the individual parts to work together or combining them in a particular way in order to achieve the goal that you want is the basic idea of what systems learning is.

I feel that the best phrase to sum this up would be “The whole is greater than the sum of its parts.” This is especially true if you think of tasks such as baking a cake or building an engine as individually, each of their ingredients or components don’t do much, but when you carry out tasks to combine them in a specific way, you end up with things far greater than when you first started (In this case, an edible cake or a working engine for a vehicle). (Lumen Learning, 2017)

Different types of systems

Now that the basics are covered, one of the more interesting things that I wanted to discuss about systems as a concept is that there are a variety of different types of systems which are comprised of different actions or tasks to achieve certain results. Whilst the list of the many different kinds of systems the we interact with throughout life is far too long to show in this article, I feel that it is important to touch upon a much smaller and easier to remember way of categorising systems.

This set includes; Simple systems where there is only one single path to a single result (e.g., pressing a button or riding a bicycle), Complicated systems where there are multiple paths to the same answer (e.g., Chopping wood or cooking a recipe) and Complex systems which feature multiple paths to multiple answers (e.g., Assembling a football team or examining human social hierarchies).(Feld, 2019)

It is important to figure out which of the aforementioned categories the system you’re working with belongs to as it will make the development process much easier for you in the long run, especially if you’re looking to develop a product that you intend to use to teach people. This is where games can offer a unique opportunity in teaching people how to think about systems learning.

Games as systems and how they can be used for systems learning.

So, what does thinking with a systems-oriented mindset have to do with creating games? Well, everything, if we’re being honest here. Games (especially video games) fall into a unique category of adaptive complex systems with the added benefit of the different components within the game’s system being entirely customisable, in regards to the content found within the game being developed, and how it behaves based upon its interaction with its userbase.

This includes everything from the surface level; sounds, art and animations, to the calculations, processes and behaviours that occur under the surface; which form the core experiences for the user. As such, you have the potential to create games that can simulate virtually any system you wish, provided you have the firm understanding of the system you want to simulate and have the capability and the resources to translate it into a game setting.

Because of this innate flexibility in how you can structure games as a system, it would be much easier to answer the question of ‘how do you use games to teach people about systems learning?’ by simply saying ‘Just make games…’.

Whilst true on its own, based on games being customisable systems, it doesn’t really give a satisfying answer. It misses out on highlighting the true potential of being able to replicate any kind of system and displaying it in a way that gives the creator full control over the desired player experience.  It also offers  the chance to learn about specific systems through a more interactive, customisable, potentially safer (depending on the system you’re simulating) and playful way.

I feel I have some potential for punditry on the topic of games as a learning tool as I’ve had the opportunity to work as a designer within a serious games company, helping to develop a testing tool for doctors specialising in Neonatal resuscitation, with scenarios featuring different parameters such as patient conditions, time limit and equipment available to the user. In addition to testing the capabilities of the users taking the tests, the product also gathered analytics on their performance so that their supervisors could figure out what they are doing well and which areas they needed to improve. (Here’s the product in question: https://www.youtube.com/watch?v=0aDzjJTWUsc)

Things to keep in mind

Some of the things that you should try asking yourself or your team when looking to create a game that simulates a particular system include but are not limited to:

• What system do you wish to simulate?
• What type of system do you want to replicate or portray in your game? (Simple, Complex or
  Complicated)
• Do you want to simulate the whole system or only part of it? (Being a manager or a team
  member of a sports team)
• What type of product are you looking to create? (A testing tool, a teaching tool or a game
  with educational and/or entertaining aspects.)
• Who is your target audience? (Children, teenagers, adults, novices or experts of a particular
  field etc.)
• What type of game do you want to create to best represent your system? (Not just genre,
  I’m also referring to creating tabletop or card games as well, depending on your budget,
  audience and goals)

These questions should be taken simply as things to consider, should you not be too sure on how to
proceed or if you’re on the right track. Finally, don’t worry too much about getting your product
right in the first draft, that’s what testing is for and by far the most important thing to remember
whilst making a game is to have fun with it.

Hopefully this wall of text proves useful for you and your system oriented endeavours. Thanks for
reading, hope you’re all staying safe and well and here’s to a new year of interesting things to do!

The post Learning about systems using games. first appeared on Ludogogy.

The field of serious game development has reached a threshold where cinematic and literary elements can enter and help increase learning. Engagement is no longer a sufficient or necessary goal for serious game based learning (GBL). In this paper I discuss ways to work with competition and exploration as game dynamics for GBL. Games that are integrated into digital gameworlds or mixed reality simulations require a more sophisticated structure then simple points, badges and leaderboards. Harnessing these two powerful behavioral drivers in learners can lead to improvements in skill rehearsal, subject matter recall and cooperative learning.

Competition

Although the term ‘game’ denotes competition, the concept of winning or losing is not essential for this process of goal pursuit despite failures and obstacles. Although Bartle[i] and others report that many players are driven by competition, almost an equal number are averse to it or see it as non-productive. Competition needs to be carefully monitored in both design and execution in building gameworlds. It is best to have this as an optional activity rather than being core. Leaderboards work well when teams compete but can be discouraging for individuals who are faring poorly. They can provide disincentive for learning and they can fall prey to “kingmaker syndrome”. Kingmaker design flaws are those where once a player gets a lead in a game, there is no way to ever catch up. Good board games have rule sets (called game mechanics) that ensure that there is some randomness associated with outcomes; it is not a linear path. This might seem like a contradiction at first, that we want players to be able to win, but frustrate that win with chance and rules, but a nuanced view of this is essential. We have seen serious games based on competition fail in devastating ways, where the reward was so tantalizing that employees began to cheat and argue about who was winning. The cost of developing serious games is non-trivial, you might invest thousands of dollars in one, only to find that players are working against each other, rather than with each other to progress. Modulation of competition is key.

A typical leaderboard is shown below. Note that it is typically sorted into rank, listing the current score, in this case in reputation points, any badges or achievements and their time in the game system.

What we tend to build are player versus player (PvP) regions of the gameworld which players can enter or decline. Imagine a map you are exploring with a warning sign you locate which indicates that all players who proceed in this region can compete with others. Or have a leaderboard that you need to sign up to view. It should not drive the entire game. We will talk about how to build competition into immersive games later; for now our experience using it in team-based narratives is helpful to explain.

Using Group Vs Group Competition in a Role Playing Game in Medical Education

In one of my first role playing games for learning for nursing degree students, each group of 6 students formed a team who would take on medical cases each week. There were three levels of case difficulty and each paid progressively more in-game points. The teams competed over the term to see who could earn the most “healing points”. But the competition did not rest on a single variable such as number of cases solved. As they solved each case, they got 3 types of currency.

The first currency type was wisdom points, which were used to order further tests or do treatments. Those had to be managed so that one had the resources in place to treat virtual cases. The second currency type was healing points, these were used to track in-game progress. The third currency type was called mana points, and these were used to level members of the group up to gain more skills. Each group had a total of 6 players, and there were 4 character classes they could have, and any form of mix in the group they wished. One class was called the Seer. Seers could do all radiology investigations but only simple ones at a low level. At a higher level, they could order CT scans and other high tech investigations. The entire group received the points, no one student received them. The group earned these levelling up points by writing quizzes. Each pass for a quiz for each student in the group awarded them 1 point. Each score over 80% in the group awarded them 2 points. We had students staying after class begging to write more quizzes so they could level group members up. This begging for more quizzes behavior happened in Prof. Rob Bajko’s course in social media at Ryerson University when he used similar game mechanics. A student begging for more quizzes is something we rarely see in academia but it can be common in serious games.

So the competition had many elements to it. Teams had to accumulate game scoring points, points to take actions in the game and points to level up their team members to take on new challenges. The competition was not based only on how much people learned. It was based on a mix of learning, balancing priorities and each student’s contribution. Simplistic competition does not allow for strategy other than pushing at one goal to the exclusion of others. Strategic competition, where students have to balance several types of currency and optimize the timing and intensity of gameplay, synched to learning, is where we want to go. Games with simple competitive design do not have replayability, because the game activity itself is static. Just advancing a token along a gameboard, answering quiz questions is a game in the most rudimentary sense of the word, its really just a series of quizzes disguised as a game. These games are easy to build, they require no imagination. However, they may appeal to very casual users who are simply seeking a way to make reviewing content more interesting. They should not be discounted, but they favor a kind of childish competition where one cannot really get better at the game. They can only improve through recalling knowledge.

However, what we should aim to build in competitive games is the parallel tracks of learning connected to game strategy. We will discuss this more ahead, but for this discussion let us conclude with the idea that when we think of games, we think of competition. When we think of gameworld hypereality, we think of immersion. It is not necessary to include them both in design. Given that many people who play games enjoy some form of friendly competition, the key is to design that element so it has a few elements, compulsion loops, which when completed, advance player’s position. I will share two ways we used competition in creative ways in games designed for teaching.

Using App-Based Simulation Games for Individual Competition

In the SOS game discussed earlier, the app for simulating care of frail elderly, each case completed either paid $5,000 or $20,000, depending on its difficulty. There was also a power up system, where you had a limited number of action points for solving the case, but could set these aside in a pile, which multiplied case earnings. This meant you had less action points to solve the case with, ie, order tests and give treatments, but your earnings went up if you did solve. You could remove these action points from the power up stack if you needed them, but that would reduce the earnings on the case at the time of the solve. So, competition for earning demanded smart choices, both in selecting hard cases vs easy cases, where more time was required for the former, and in wagering a limited resource. Each game also had a timer, so that in the given time required, solving a case was better than making mistakes, since each reset brought the player back to the case opening screen. Now there were four elements needed for a successful win; knowledge, case difficulty, allocation of actions and wagering. This makes for deeper gameplay. Deeper games have high replayability, you can improve not only in knowledge, but in game tactics. However, the game tactics are tightly linked to knowledge demonstration.

Another game where competition was used in a unique way was called “El Stinko”. Here the mechanic was simple and drew on a few elements of psychology to win. This was a course training web designers how to use behavioral neuroscience in their build. The players were divided into teams of six and they were shown a series of web pages on a monitor for about one minute each. A total of four different web pages were selected. Each page had various design elements in it. Some were really bad examples and some were very good. The groups were shown the pages one at a time, in a random order. Groups had a total of 10 El Stinko vote cards, and they could award the worst page as many cards as they wished out of that total. So they were seeing web pages, one at a time, but being asked to wager on which was the worst, without being able to see what page would come next. This forced teams into relying on what they had learned about page design, because the worst page clearly violated these rules. It had dozens of images on it and a confusing interface with a variety of sales messages.

Now students had to anticipate and gauge which pages could appear, and bid their limited cards accordingly. The winners would be those who bid the most number of cards on the page which received the highest number of bids. In other words, they were rewarded for synching their learning to that of the group, but with uncertainty about how different groups would vote. This forced players to make decisions in a fun way and then during debriefing, express why they had voted that way. It is impossible to really use much strategy in that kind of competition, it is a light-hearted way to compare different qualities of something such as web page to ensure that learners rely upon knowledge presented. This game was played for the Canadian Military as an audience, a group who are not generally light-hearted about training, so it had novelty and the ability to get teams to share ideas.

Competition usually leads to some form of debriefing, to ask groups or players why they made the choices they did. This is not requisite and should not concern designers. A good game does not require debriefing. Poor games often do, because the competition is short lived and sharing ideas is the driving element. However, it is better to build game mechanics that permit players to compete without any form of outside intervention by the trainer. The learning and gameplay, if properly synched, should enable a team to take a lead. I use the term “team” a lot here and it is deliberate. One of the most powerful ways to compete is to pit team against team, which creates a learning event in the groups as they now have additional incentives to perform. Just learning in an open world lacks suspense and what we need to do is ensure that adequate levels of stress occur in the game. We can conclude this section by looking at the role of cortisol in competition builds and how it fits into the overall ACES model.

Cortisol is a hormone released during stress and in high amounts, can inhibit learning. But in short bursts, it not only wakes up a sleepy audience but also energizes players and increases their emotional investment in the outcome. By adding timers to serious games we introduce a moderate level of short term stress, and this is what competition can do as well. Competition in a limited time frame, in particular, leads to some of the best gut-level decisions you can draw out of players. If you, for example, introduce an emergency to the learners, such as someone having a medical crisis, or in business, a threatened insolvency, and attach a timer to it, then players have to figure out a solution where stress drives the action. This is often done digitally in the form of daily challenges which appear on your cell phone as a notification. When we tighten up time and force teams to solve a problem we bring out the best in them and this is where competition can really shine. Using short bursts of competition we do not make it the focus of the game, it is simply another activity which will appeal to players who favor it.

Timers and Competition in Science Fiction Based Games

In one game we built, we used timers in a rather sophisticated way. This was a build for personal coaching in major, high Gartner-ranked company (one of the top 1000 in the world). They had issues with getting their management team to use personal coaching and apply it in their work, where they needed to learn how to coach teams. Further, they had undesirable outcomes using competition-based  learning in the past, to the point that they had to cancel the initiative, because there was animosity bred using that model. Our game was based on the idea of quantum tunneling. The mechanic was based on simulations, having managers come up with coaching ideas for a number of simulated cases.

The player had a limited resource, called M-25. This was used by players to open up quantum portals which provided an link to a series of simulations. As you completed one simulation with a one page summary of your action plan, it was vetted by the trainer briefly. That opened up another simulation which lead to another, each simulation point was called a node. Some portals were two nodes deep, some were up to 10 nodes deep.

When a player completed a single node, shown above as an arrow, they were awarded a standard reward of M25. The player had a choice of many portals, but they did not know how many nodes were in each one. So they had to invest M25 to open a portal, but, could not only recover it from solving cases in that node chain, but also got a healthy completion bonus of M25 when they finished the chain. The problem is that they had no idea, when they opened a portal, how many nodes it had. There were three to five portals open at any one time, each with a mystery of how many simulations it contained, ie, how many nodes to complete to get a completion bonus.

There was a timer that would go off about every two weeks, but the precise time was not made known to players. It was just about every two weeks. At that time, the game would freeze and no player could take any actions for 24 hours. Then the game would resume 24 hours later. During this time, players could convert M25 into game points, which showed who lead. The risk was that if you spent too much M25 on advancing on the leaderboard, you might not have enough left to open new portals. So when the game re-opened 24 hours later and the leaderboard was shown, it revealed your position in the game. We built the leaderboard using an electrophoresis gel model, where you were advancing up a “race track” of sorts, the higher you were on the board, the more you were in the lead as shown below;

The player on the extreme right is leading and the bars represent their position in previous rounds. The player in the column just to the left adjacent to them, is lower and the player in the third position from the right edge was even lower. Each player was attempting to reach the top of the gel graph first, like an Olympic swimmer hitting a button when they finish their laps.

The beauty of this design is that no one knew, at any point, who was going to emerge as the new leader after the 24 hour game freeze. So the kingmaker syndrome, with a runaway lead, was not possible. If you overspent M25 to get ahead now, you would threaten your chances of opening new portals during the two week game period and fall behind. If you hoarded M25 you had no position on a leaderboard gel. But you could hoard M25 all through the game and then, in the last week, dump all those saved resources on your lead position. This made it impossible to judge who would lead at any time and produced a secondary game faking out opponents and developing fairly deep strategies for advancing.

Competition can take the form of timed drills to see who can beat the best time. Simulation-based games are very well suited for this mechanic. In a game we designed for teaching cyber security, our goal was to reduce the documents they had to memorize, with contact numbers, procedures to follow if their workstation was hijacked and other things, into a playful experience. Security protocols are dull and are so similar to each other that it is hard for learners to remember them all. The key is repetition, multiple repetitions in fact, so that the content gets memorized.

We built a game based on the fact you were a cyber security expert who was responsible for a space colony of 100,000 inhabitants. When the game started, you were on a timer to save the most number of citizens that you could. The plot line had an evil hacker shutting down your life support systems randomly, including oxygen delivery, medicine units and food storage, thus killing off your ship citizens. Each minute the game advanced, you lost 1,000 citizens. Your goal in the game was to navigate through the ship from room to room to seal off security breaches. The rooms were connected by tunnels, and to enter each room you had to answer a randomly generated question from the security documents. Those who knew the answers were able to move fast through the ship to beat the timer, those who did not know lost thousands of lives.

Static images like the one below were used in the game which was coded for Android as a mobile learning solution. This was consistent with the narrative and the competition was against one’s own self, to beat your best time each time you played. Best times were shown on a leaderboard. Best times meant you knew your cyber security protocols in the real world. So competition need not be against other players, but against one’s own personal best.

Cooperative competition games can be designed as well, which reward players for helping each other learn. To do this in one game for a Ministry of Health approved program on long term care for seniors, we developed the idea of “Kudo” points. These were points you could award to other players for making helpful contributions on discussion forums. With enough Kudo points, one would achieve the status of “most valuable player – MVP” on the forums, which signified that according to the class, you were a good person to come to with questions or help even in future classes. So this status of MVP persisted over time, so that in future incoming classes, you could, if you wished, stay in the game to help the newer players. This type of competition is not based on winning, but on personal achievements against oneself, to be so helpful that other players would reward you by an up vote.

Other competition mechanics consist of having teams complete learning challenges such as solving a simulation problem and then having the group vote on the best solution. This adds an element of entertainment to the experience but is prone to creating an adversarial atmosphere, so it should be used sparingly. The other model is that of PvP matches, where two players compete in a series of challenges against a timer. We will discuss this and the use of other “combat systems” in chapters ahead. The key take away message is that competition is a vital component of the ACES learning model and almost 40% of your students, according to Bartle’s data, will expect it and enjoy it. Don’t let your own aversions and philosophies on competition direct the design, the evidence is clear from the video and board game industries, almost half your learners like to compete. I don’t like competition much myself, it reminds me of being picked last for baseball teams as an awkward child. But I still build them into almost every game. The ACES framework dictates that we use all four components in game design, to satisfy variations in audience demand. You need to give moviegoers a film they want, not what you insist they watch. The first is entertainment, the latter is propaganda.

For those who dislike this style of play, however, it can be a turn off, and so we add to achievement and competition as mechanics our third component of ACES, exploration. Exploration is something that many people thrive on in learning, needing to know what lies ahead or what can be discovered. Exploration is the heartbeat of doing science, archeology or any form of research. This is the area we will explore….next.

Exploration Based Learning

Exploration is a game mechanic that has been widely used to increase engagement and provide a gateway to new experiences. Experiential learning, the strongest new trend in higher education, is about participating in work rather then learning about it as the core training activity. Linking experiential learning to exploration and discovery is the core structure of an ACES-derived curriculum. The umbrella narrative in any serious game should be based on exploration, to omit this element is to preclude any form of sustained motivation in a game system. What makes discovery so compelling is that our brains are hard-wired to do so. To fail at this would be to starve in neolithic times. Our entire nervous system is built to detect threat and abundance and to seek these out actively. Our psychological apparatus, from flavor, to scent, to vision and hearing is all designed to do one thing, detect small changes in patterns. Those detection mechanisms can be measured in human subjects and indicate that we parse the environment for any deviation from salient backgrounds. We are walking through a forest, it is quiet. We hear a wolf, we focus on the wolf. Everything else disappears, as we evaluate threat or opportunity. Exploration is simply searching for new opportunities or threats.

In open world games it is common to award achievements based on exploration. This is used in World of Warcraft as a key quest line to unlock powerful game upgrades, such as being able to fly instead of ride a horse everywhere. As the player explores the open world, which is scaled to represent hundreds of real world miles, they might get a notification on their screen saying “Explore Broken Isles Completed!”. They player gets immediate feedback when they complete exploration, which usually involves travelling to every region of that map.

It is not difficult to adopt exploration mechanics to learning gameworlds. In order to do this, however, one needs to write a storyline where the narrative demands that the player explore. It is not enough to simply let people wander about in a curriculum, we need to build attractor regions and domains. So when we design gameworlds we need to create a fictional place which then can be travelled through in some way to discover domains and attractors.

Building a Virtual Hospital Using Exploration as a Core Mechanic

In “The Grid’ we built a virtual hospital, a VLE. There are four wards in this hospital and the student is free to enter any of them at any time and see virtual patients. When they see these patients, as we discussed earlier, they only have access to some of the vital information needed to solve the case. By purchasing in-game items such as “Spectral Goggles” or a “medical consult” they can obtain information buried in that case to enhance the solve rate and learning associated with it. So it is not enough simply to have a fictional map, there must be something to do once you discover something. It is also nice to be able to explore more deeply if you have earned an achievement in the game. It is even  more compelling if there is some competition linked to this, as in being the “first” to find a rare item. Being “the first” scientist to discover insulin or the “first” astronaut to  land on the moon is very satisfying. The race to the summit of Mount Everest that took place between 1924 and 1956 is an example of how competition to explore can fuel nations to take pride in discovery. Competition is not so much a case of beating other players by exploring a map faster, but in what they achieve as a result of exploration. So exploration is just a means to an end, that of increasing a sense of pride and ownership in the journey of one’s avatar.

Exploration maps need not take place in virtual space, they might just be learning course content in any way they wish, what is called a “reading course” in Canadian universities, a series of essays. But this is not what we are aiming for in hyperreality design. Maps are essential in gameworlds because the very name suggests a world. The key decisions to make are what the theme is going to be, what the storyline is and where this all will take place. Basic writing 101. However, we can also make exploration the key activity which yields the highest reward as one pursues an in-game goal.

The above map is typical of the video game open world. You can travel from location to location to unlock quests by locating quest givers.

The quest giver depicted above is from World of Warcraft, the bold exclamation sign indicates that this non-player character has a quest to offer. When you click on this character, the dialogue box opens to the left. This tells you how the quest fits into the storyline and what you need to do to complete it. It also shows the rewards.

Note that there are choices of rewards in many quests, not just a bit of gold. In the case above, the player must fight many tough enemies as part of a 5-person team to have a choice of 6 different weapons, each with its own abilities for a specific type of player. Warriors will like an axe, but mages will like a staff. This linkage of exploration to quest givers is the key component of open world game design. There is no motivation to explore if there is nothing awarded for doing so, time is always precious for players. And rewards can substantially improve the players ability to do two things; a. gain increased status in the game and pride in achievement and b. open up further exploration that is forbidden without these elements. There are high level dungeons in Warcraft that can only be enjoyed once one has the right “gear level”. One improves their gear level by questing enough to unlock access to a new place, a set of dungeons, which have the most powerful weapons and armor in the game.

Each dungeon has a map, as shown above. In the dungeon, there are patrols, groups of mobs that do not reward you, but challenge you. Eventually you “clear” that patrols (called pats, or ‘trash mobs’ by players) and get to the boss fight. Each boss you fight is different, they can kill you in all kinds of ways . The final boss is the toughest challenge. This is similar to medical school, you go through lectures, then clerkship, then internship and so forth, so progression is the core element here.

Linking progression in the game to exploration is therefore vital in order for the player to have any motivation at all to travel and locate new regions. This compelling blend leads to a basic curriculum design model for open world builds shown in the table below.

This replaces a standard course outline as we have come to know them.

Note that course outlines, aside from being a kind of legal document to inform students about what they need to do to pass, are a map. They can be a good map, or a dry boring one. But they do serve as templates for designing exploration in the ACES model. Each week there are things we teach that let us talk about new things in the following session. Instead of this being a linear progression however, it can be randomized and linked to map locations. Imagine that in learning coding above,  you had to visit each topic that was located on an island. Your goal might be to conquer that island, or even better, to get an exploration achievement for doing so. One island might be on number theory taken from the course outline, another island might be on encryption theory, the outline above is on programming secure internet sites. But that is not so interesting to do, exploring the island of encryption theory. This is where we now link our theme, narrative and map to learning. This is where the art of open world design begins

The art of open design is based on having a vivid imagination to see an entire world populated with learning attractors[ii]. An attractor region for learning encryption might be inserted in a location. Let us imagine, for a moment, what this game could look like. First, let us set the entire game in 1943, and have the student play the role of an intelligence expert working to fight the Nazis. In order to beat them at intelligence and block their communication, you would have to locate scholars or scientists in the open world of 1940s Europe, travelling from town to town. This gets engagement going. Now the whole theme has to make sense as a story and it will all be constrained by reality. Immersion is based on suspension of disbelief, the willingness to enter a gameworld and pretend to participate in it. This is why we go to the movies. Immersion is not something we build for millenial learners only, all people love the movies and the feeling of being transported to another place that they provide. So lets take a second look at theme and maps for the course outline above. Let us set this in a high technology but interesting, non conventional space this time instead of locating it in reality. Both are good. Let’s see what other juicy things we can do.

I love to use steampunk themes in design, they fuse tradition with technology. Below is a typical steampunk character. Note the mix of modern and old, as though her entire wardrobe was cobbled together from bits and parts.

Note the appearance of the avatar. In the gameworld “Second Life” people are free to pick the appearance of their avatar and customize it using “mesh skins” which are photorealistic body parts. The vast majority of players love to customize their appearance. In video games like Warcraft, you can customize your appearance as well, but in a way compatible with the theme and storyline.

So now we have a steampunk world, let’s put it in outer space. Let’s say that you are being hacked by evil beings, like our cyber security game proposed. Let us say that you have to explore a new planet which has technology to decipher or cipher for intelligence. Let us now create some tension. All stories need conflict to be effective. So let us now be searching different planets for pieces of a puzzle, how to encrypt or decipher data to protect it from the evil beings. On each planet, students will find attractor regions with a ton of simulacra, case studies, which will guide them in their learning. They will access Google Scholar and other online resources to learn the basics, as they apply them using experiential learning in simulated space. Do you see how we are building an experience for the student, based on the desired content? We are not designing for content presentation any more, we are designing for experiential learning in virtual space, creating a symphony of elements. A broad, deeply constructed emotional palette.

Forming teams of explorers is a key element needed to drive socially-based learning. In order to explore some hazardous regions of space, for example, we might insist that players form small teams, each with its own ability among members. This means we would build the game using character classes, such as the Seers or Alchemists in my earliest role-playing game, “Healers’ Quest”. You simply could not solve cases as an individual, because your character might only be able to get diagnostic data or provide drug treatments, depending on your class.

Another key element in exploration is not so much in travelling through virtual space or locating learning experiences, but in trying out different combinations of in-game assets to customize. In our travel game design, built for hospitality students, each time you created a one-minute video to practice selling a destination, it unlocked new destinations. Different combinations of destinations could be scored as a set. Different sets were used to unlock further reward experiences. So by posting one destination pitch in the North American hub and one from the Asian hub, this unlocked a set of new ways to earn in-game currency. Players could personalize their simulation experience by selectively unlocking various types of travel packages they could offer, thus increasing their earnings.

This can be taken to a next step where you can choose “talents” from a talent tree, which customizes how your character can interact with the world. These talents are rule-breakers, that is, they permit you to do something special in the game.

Each point on a talent tree is called a node and as the player advanced in experience in the game, they are able to make some choices about what abilities their avatar has. Typically this is linked to experience points (XP), which increase as the player progresses in the game. For a typical game we usually build about 10 levels to start, then add to that later in the form of creating “expansion packs”. So at level 2, you get to pick a talent point, at level 3 another and so forth, each new level you achieve rewards you with a customization decision. These decisions impact game play and permit learners to select the way they progress in the game.

In the real world, we do this all the time and its so engaging that there are names for its excess. Gear acquisition syndrome (GAS) is something widely discussed on web-sites like the Acoustic Guitar Forum. GAS refers to buying new gear all the time, it becomes almost compulsive. A similar thing happens with “gear freaks”, usually men, who love buying things related to the outdoors, or photography. Its a dopaminergic cycle of addiction in some cases. But in most instances, it is the desire to explore that drives the behavior. In purchasing my gear for cycling throughout our cold Ontario winters, I went on a $1,000 spree. The first ride I took I was freezing, but my head was warm, my ears were cold, my hands were okay. Then I bought a down vest and layered my clothing more, adding some cold resistant gloves. Then I was too hot when going uphill and I was drenched from sweat. So I had to consider a breathable jacket, but they were $400 and I searched until I found one for $70. Then it rained. That was cold rain, and my feet and hands were wet, my head was cold. So now I bought GoreTex boots, neoprene cycling gloves and a rain pants. But I could not put the pants on fast if I got caught in a downpour. So I had to find pants that unzipped at top and bottom of the leg so I could wear them easily over work pants. Then it got really cold and I bought a waterproof parka.

At each phase of this spree, I was testing out combinations of gear. Eventually, over a month, I was able to cycle comfortably in bitter weather. The reward was supreme, I was gliding through snow flurries on a bike with studded tires, racing past traffic that was gridlocked. I saw Fall leaves in their last majesty, intense crimsons, sunset yellows, I smelled the leaves and felt the wind. My body was filled with energy, the feeling of cycling has the same thrill as flying for me. The point of my story is that I was exploring to find a solution and enjoying evaluating each solution, learning as I went, and now was able to do things I could not before with deep rewards. These clothes will last me the rest of my life, good outdoor gear is like that. Think of each course you design now, as this kind of thing for your students. Let them try different combinations of learning experiences, see what fits, what works for them and keep the options expanding all the time. Don’t just design an experience and dump it on them like so much offal in a fish market. Sadly, that is what we often default to. “Here, learn this”. Unlearn what you know about teaching. Let emotional artistry guide your class.

In a game we built for an award-winning personal coaching company, they wanted the theme to be set in space, where each planet of three had simulations of different types. For example, the red planet had cases about conflict resolution, the blue planet had cases about creativity and team building. In order to visit these planets you needed to spend a limited resource called Iridium. The first thing players did was arrive at the ship console where they could see a map of the three planets. They were all located at different distances from the main ship, and it cost Iridium to use as fuel. In order to access the furthest planets you needed to earn Iridium in the game. At each level you were permitted one talent tree option. One choice was to be able to conduct more than one mission (solve more than one simulation) at a time. Without this talent you could only work on one case study before you opened further ones. When you finished case studies, that awarded more Iridium, which could be used as fuel for travel, or could be traded for in-game assets such as better armor on the ship, or things to enhance your ability to create diplomacy and recruit new crew members from each planet.

The talent trees then directly influenced game play and by doing that, learning. Another talent choice was to travel faster with less Iridium cost for all further missions. So a learner could either take the talent for more cases to be opened, thus increasing the potential earnings over time when they all were solved instead of proceeding one case at a time, or be able to travel more cheaply and unlock different types of cases. Personalization of learning is very easy to build into games and is a core exploration mechanic. We are not so much exploring virtual space as we are exploring how different combinations of abilities can be used. A student in this kind of game can test out talent “builds”, but has to make hard choices each step of the way. Hard choices, where we want to do three things, but must only pick one, make games compelling. They force the learner to think about how they want to learn. This makes training fulfilling, one really has a sense of agency. These are forward-decision games, the same type we used in building SOS to solve medical issues for the frail elderly. You cannot go back and undo your talents or your decisions, you must live with the consequence – which makes decisions actually matter.

Agency refers to the feeling that a player has control of their character. If you have a game where the player can only do one thing, there is little agency. Studying player experience ratings are a new way that serious game designers are approaching selection of game mechanics. High experience or player ratings are instructive. They reveal what kinds of things we like to do in gameworlds. One of the highest ratings in studies conducted recently was for the game Bioshock. It satisfied several criteria of replayability; a good story line and real character development synched to the main narrative. It had high player agency. What you did in the game mattered and there were tons of things to do, places to explore and ways to use your abilities. A game named Darfur with very low scores was one designed to teach students about civil war and its effects on society. In this game you played the part of a refugee whose only goal each day was to get water from a well one mile away. The theme should have been an amazing opportunity to really feel what it is like to live in a war zone to create empathy.

However the game mechanics defeated the learning due to weak implementation of agency. The only thing to do in the game was get water – not so compelling. To make matters worse, and, this is key, military vehicles would appear as you tried to do so, and they would run into you and kill you, pushing you back to the village to start again. The vehicles sounded ugly and were irritating to encounter and avoiding them was difficult.

After some kills by vehicles, the game unlocked a new location and mission. Players began to run into the moving vehicles just to die and get onto further missions. There was no real agency. They had one thing to do and got killed doing it most of the time. Depressing. Other games that failed on exploration mechanics were based on fighting in Viet Nam, where when your player got ambushed, you had to start a tedious mission all over again. Equally frustrating was the permadeath of the original role playing video game, Everquest. If you died in that game, you were gone. Everything your player had collected through blood, sweat and tears for years, could be erased in one bad second. The game Remission was designed to teach kids about cancer, where you searched the body for tumors to kill them off, a great idea! But it failed because once you found cancer, you just shot a beam at it and hoped for the best. Each tumor looked the same so nothing was unique about a quest. These are plodding, well-intentioned game designs which held great promise but failed due to poor mechanics. Everquest, in fairness, was very popular, but when the new generation of MMORPGs was released, where you could resurrect after being killed with a small penalty, it faded. Now players could try crazy things to see how to use their abilities and if they died, they learned from it and improved.

Bad mechanics which punish players for mistakes are referred to as digital leashes. They are ways to force players into learning and deny any form of agency. Games with high agency such as Black and White, by designer Pierre Moulineux and Fable both had fascinating agency – without the leash. In Black and White, you played the part of a God, who oversaw an entire Island. Your job was to create more followers by helping your people do things, or to just play on the Island and create things of your own. These “God” games give high levels of agency and exploration. In the game Black and White, your game was open ended, it was the first game of its genre. You were there just to explore what you could do with your God abilities and see how you could shape your Island’s progression. In Fable, Moulineaux had you playing the part of a character travelling through a mythological kingdom. As you did things that were morally right, your character looked nicer and nicer. If you did morally bad things like killing random characters in the game, your “toon” (gamer’s name for avatar) would get uglier over time. So in strong agency exploration games, you can explore consequences of your decisions.

When I interviewed the design lead for Deus Ex, he discussed this extensively when asked about why he thought this game had been a AAA commercial success. In this game, depending on moral choices you make, new game worlds would open up. So you could try playing the game a few different ways, and see what kinds of experience that provides. In World of Warcraft  you can level a character as either Horde or Alliance, two different factions in the game. They both provide completely different experiences. They take place in two different continents, with entirely different storylines connected to the main theme and overall narrative. So in really strong agency exploration games you can play again and again, each time changing talents, game story, visual experiences and challenges. Very few serious games anticipate these player needs and most are composed of simplistic elements where you do one or two things. Remember, we are aiming in open world design to build conglomerations of player satisfaction, not just one or two goals, such as selling real estate or doing well at a quiz.

Exploration mechanics build highly motivating and engaging elements into learning of subjects as un-sexy as accounting or coding by creating agency and curiosity. However, none of these things works as well as it might for learning unless it is socially connected. This is where the video game world informs, but does not dictate best practices. There are thousands of video games you can play by yourself and they are highly successful. Complete agency. But there are many which also demand you play with other people which have achieved success such as Warcraft. In learning we can use one of the most powerful tools available, teamwork, to advance a game narrative. To fail to take advantage of this potent learning strategy, problem-based learning (PBL), would be a major oversight in game development. The impressive literature on team-based case simulation learning produced over the past 40 years is difficult to ignore for people with even a passing interest in education.

The post Beyond Engagement: Competition and Exploration in Serious Games Using Digital Narrative first appeared on Ludogogy.

It is in looking at the behaviour of the game that it really comes into its own.  In many ways, a game does not actually exist until it is being played. Although games designers often talk about designing experiences, in reality they cannot do this. They can use their domain skills and expertise to create and design system components, and characteristics, that they believe will elicit specific behaviour and experience (and of course, they should also have thoroughly tested these beliefs), but the experience of the game can only happen with the active participation of a player.

A system displays emergence when it can be seen that the system as a whole exhibits characteristics or behaviours which cannot be seen in any of its constituent parts. Life, for example, is considered to be emergent from the biological components and phenomena of plants and animals (as well as the physical and psychological).  A single celled organism is ‘alive’ but the molecules which constitute it are not.

Inextricably linked with the concept of emergence is complexity. We can use a reductionist approach to understand the structure and function of a component, but this gives us no idea of how it will behave when combined with other components at greater levels of complexity. To give another example from biology, we can look at the components and function of a single cell within a body, and understand them very thoroughly, but the emergent properties of that cell when combined with other cells in a tissue, cannot be deduced by studying that cell in isolation.  Similarly, we cannot look at the tissue and deduce how its component cells are arranged or how they function.  How much less, therefore, could we understand the whole organism, of which these are tiny parts, or the societies into which those organisms gather?

To return to games – in both designing and playing them – this presents us with some interesting problems and opportunities – which can be usefully compared as opposite sides of the same coin, as below…

It makes games design a complex activity.  When designing games, we need to iteratively and thoroughly test the experiences and behaviours that will emerge from the systems we are constructing. There is an additional burden of time and effort needed for player testing, as compared to the creation of other cultural artifacts such as written or cinematic stories, because much of the emergence derives from the active participation of a player, who is not simply consuming, but is co-creating the experience.

Conversely, this can make the creation of complex experiences much easier than it might be if one needed to fully create an experience to be consumed by another. Simple rules and characteristics can combine to create complex behaviours and narratives which do not therefore have to be themselves designed or created. An often-cited example of emergence is the behaviour of cellular automata in ‘The Game of Life’.  A grid contains ‘cells’, squares which are either ‘live’ or ‘dead’ and which interact with their eight neighbours using the following rules:

1. Any live cell with fewer than two live neighbours dies, as if by underpopulation.
2. Any live cell with two or three live neighbours lives on to the next generation.
3. Any live cell with more than three live neighbours dies, as if by overpopulation.
4. Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction.

An initial seed pattern is entered by a ‘player’ which then plays out, either dying completely after a few ‘generations’ or settling into a repeating pattern of entities which live and continue to reproduce until the game is stopped.

Although this is an excellent example of complex emergence from simple rules, it is effectively a zero-player game, and does not represent the kinds of experiences we wish to build for real human players.  So better examples of emergent gameplay might be the relatively simple frameworks of character formation and actions, determined by dice throw which make up RPGs such as Dungeons and Dragons, or the open-ended play of ‘sandbox’ games such as Minecraft, where the only ‘win-states’ are those invented by the players themselves and which may not even have been imagined by the games’ designers.

Even under the most thorough testing protocols it is unlikely that all eventualities will emerge, and the more complex your game system, the more likely it will be that there will be unintentional emergence in play. Even if we disregard actual faults in the game (such as ‘glitches’, which could be seen as a type of unintentional emergence), there is often plenty of scope for players to change game objectives, or to use in-game objects in ways they were not intended to be used.

If you are unlucky, this can lead to the game getting a reputation for being ‘broken’.  But more positively, it can lead to entire new genres of creativity or styles of play. Notable examples of this include ‘speed running’ which has maintained the cult status of some games for decades, and Machinima, where in-game action is recorded to make ‘movies’ with narrative that was not part of the original game.

From the perspective of games-based learning, emergence is one of the characteristics of games which makes them so suitable for learning about complex systems, systemic issues, and situations in which exact prediction and determinism are not possible.  We can simulate, for example, the emergence of racial or economic neighbourhood segregation, from individual behaviour which would not necessarily be considered particularly ‘racist’ or ‘snobby’ – a simple ‘rule’ where cells in an automata game exhibit a ‘preference’ for being next to cells like themselves. In the above Excel simulation we can see the result of running nine generations of the game, where the cells began randomly spread, and each cell would be ‘satisfied’ so long as 50% of its neighbours were like itself – which also means it did not mind if the other 50% were unlike – not particularly prejudiced behaviour.

We can demonstrate the interdependency of a functioning ecosystem (and how easily that function can deteriorate into complete collapse, given human interference), using simple transfer of tokens from one element (player) to another.

Systems, simple and complex, are everywhere, and provide endless inspiration to create these simple ‘toy games’ and thought experiments as well as complex player-centred experiences

The post What Lies Beneath – Emergence in Games Systems first appeared on Ludogogy.

The field is a rich one, encompassing formal structures of systems, how they dynamically change over time, how system elements interact and many other aspects.  For the purposes of this article, I will focus on a relatively narrow aspect of games as cybernetic systems – Feedback Loops.

In their simplest form, cybernetic systems consist of three components. A sensor, takes in information (feedback) from the environment, and provides it to the controller. The controller uses this information to decide whether the system is deviating from an established norm, and provides instruction to the actuator which produces some form of output which affects the environment. In many Systems Thinking textbooks, the first example of this that a learner will be presented with is a heating thermostat, where the sensor detects the temperature in a room, the controller compares that to the ideal temperature set by the user, and then instructs an actuator to switch the heating on or off (or issues no instruction if tno action is required).

The thermostat example shows a negative feedback loop – not because its effects are unwanted, but because it causes a situation of equilibrium, maintaining the status quo. Positive feedback loops on the other hand create a cumulative change.  These can obviously go in one of two directions; producing more of something (rising temperature, economic growth, increasing infection rates) or less, ultimately moving towards zero or a standstill (population decline, economic recession, absolute zero). The heating thermostat could be set to operate as a positive feedback loop if was designed to activate if the sensor detected that the temperature was greater than a certain minimum – resulting in an ever-rising temperature.

So, two questions present themselves with respect to (learning) games design. First, how might we use these concepts to inform games design? Second, how might we create games which can be used for learning around the concepts of complex systems?

One game which definitely displays a positive feedback loop, and is, as a result, one of my least favourites, and my go-to example of a crappy game experience, is ‘Monopoly’.  Once a player has money, or property, there is a tendency for them to gain more.  It is extremely difficult, if not impossible, without a great deal of luck (which a well-designed game experience should never rely on too heavily) for a fiscally-challenged player to eventually triumph. While this is probably a very accurate representation of the way that deregulated capitalism actually works, as a ‘fun’ experience, it sucks – especially given that players may be playing as escapism (possibly precisely because of the adverse effects of deregulated capitalism).

It is useful for a games designer to use the lens of Cybernetic Systems when deciding on the rules of their games because the rules constitute the sensors, controllers and actuators in your game, and will help to create the play experience in terms of its ‘flow’. Will your game tend towards a steady state, or will the experience wildly oscillate? Do you risk positive feedback loops that lead to unbalanced experiences (as in Monopoly), or the tendency for the game to reach unplayable ‘stalemate’ situations? How do feedback loops maintain competition (or cooperation)?

At any point in a game, there is a current game state – represented by, for example, the positions of pieces on a board, the current score or other stats of players or the location in which play is currently set – or any other similar ‘snapshot’ information.  To some extent, this is ‘outside’ of the system, in the same way that the temperature of the room is external to the thermostat because it is neither sensor, nor controller, nor actuator (although the current information contained in those, or the impact of them, may be represented)

The scoring system is the ‘sensor’ of the game in that it is a measure of how the player is performing.

In most games, the player would be able to use the scoring mechanism to see the impact of previous decisions they have made in the game, and to hypothesise what might be the effect on the score of the next decisions they are planning.  Even in games which don’t ‘keep score’ in a recognisably numeric way, there are ways of a player discerning how well they are doing and linking that performance with their past and future decisions. In Chess, for example, you could see the effect of your moves in the relative material advantage of yourself and your opponent, whether pieces are safe or threatened, and so on.

The controller in this situation is the player (usually a human), who receives the information from the scoring system, and uses that, along with their knowledge of the game system, skill in play etc. to make a decision about what to do next. They can then set in motion a game event or set of events – the actuator (casting this spell, moving that piece, or even declining to play at all, dependent on the actions that the game structure, rules and components, makes available to them).

The above is adapted from a formalised model which can assist you in games design, created by Marc LeBlanc. His way of modelling games as feedback systems provides us with useful tools when we are creating our own rulesets. See his collected ‘rants’ for a much more detailed description of the above, and guidance on using positive and negative feedback loops, illustrated through two games – ‘Negative Feedback Basketball’, and you’ve guessed it ‘Positive Feedback Basketball’.  He is also the originator of the MDA design model, which is the subject of a previous article in this magazine.

As a counter-example to Monopoly, and to show that positive feedback loops are not necessarily always bad games design. I am happy to report that while recently playing ‘Guitar Hero’ (‘Heartbreaker’, by Pat Benatar, if you are interested), I managed to successfully complete a sequence of star-shaped notes, thus activating star power and therefore narrowly saving myself from being booed off stage.*

A currently popular game mechanic (which I, personally, am obsessed with) is ‘engine-building’ (or tableau-building), which is used in some really great games – ‘Wingspan’, being the most famous, but honourable mentions have to go to ‘Everdell’ and ‘Castle Dice’.  This mechanic is an elegant implementation of positive feedback loops to build almost endless permutations of advantageous growth scenarios.

So how might we create games for learning around these concepts?  Search of the available literature reveals that many researchers have posited that lots of off-the-shelf games contain excellent raw material for learning about complex systems. For example, Dana Nicula and Sorina Constantinescu of Bucharest University wrote a paper about using Catan as a learning tool.

My particular favourite cybernetics game dates from a time closer to the birth of cybernetics as a discipline (as you can probably tell by the width of the collar on the box).  Trippples was published in 1973, by an actual psycho-cybernetics researcher, William T Powers.  According to the rulebook,

‘Trippples requires that its players consider each move from the “feedback” game theory point of view:

Each move is not only an advance of the player’s piece, but puts definite limitations on the “move options” of the opposing player – and vice versa’

The playing pieces are 64 wooden tiles of three kinds

• Depicting either a circle or square (starting and end pieces of two players/teams (four tiles)
• Blank tiles showing ‘no-go’ areas (four tiles)
• Showing all the possible combinations of sets of three directional arrows (56 tiles)

The tiles are laid before play to create a playing surface upon which the players will make their moves with transparent markers.  The aim of the player is to win by being the first to reach the ‘end’ tile (e.g. an unfilled circle tile), which is positioned in the diagonally opposite corner from their ‘start’ tile (e.g. a filled circle tile).  They do this by moving their marker by one tile at a time either vertically, horizontally, or diagonally, while avoiding the four central blank tiles.

So far, so simple, but where the game models the feedback systems of complex adaptive systems is in its central rule.  The moves that a player can make are constrained by the directional arrows underneath their opponent’s marker.  In the words of the rulebook

‘Trippples is played in the arena of psycho-cybernetics, where the mind is continuously challenged to process rapidly changing information and formulate new strategies as each move is made and new options are opened.’

in other words, when your opponent makes a move, you will be presented with three options for the way in which you can move (or fewer if constrained by moves which would take you over the surface edge or into the ‘no-go’ blank tiles).  You must then try to find the move which best meets the requirements of:

• Moving you closer to your own win-state
• Restricting the next move of your opponent so that he does not gain an advantage
• Forcing your opponent’s next move to provide you with advantageous options for your next move
• …and so on, to as many levels of recursion you can manage to hold in your head

Think back to the thermostat, and the concepts of positive and negative feedback loops.  What we have here are two feedback loops (imagine a heating system trying to raise the temperature to x, operating in the same room with a cooling system trying to achieve a much cooler y).  A tug of war ensues, which is where the competition in the game comes from.

The rules above represent just one of the suggested ways of playing Trippples given in the rulebook, and other versions are suggested.  There is even a plea in the rulebook for serious players to write to the inventors (postal address given – this was the 70s) with their ideas for the as yet ‘undiscovered’ versions of the game.  In some senses, Trippples was born into the wrong time (and this makes me a little bit sad).  If it had been created in the age of the Internet, there might well be thriving communities of players/creators making new games for this Games System (see the ‘Focus on…’ article on other games system, also in this issue), as there are for systems like Looney Pyramids.  Maybe Ludogogy readers could start a Trippples revival!!

Looking at games design, and its potential application, through the lens of cybernetic systems, emphasises the suitability of games for creating learning experiences about and within complex systems. In this age of complex systemic issues; ecosystem degradation, systemic racism, climate crises, the spread of mis- (and dis-)information, pandemics and so on, the opportunities are greater than ever for creating playful experiences that are not only ‘fun’ but which help us to make better futures.

*Yes I know it’s been out for years, and isn’t especially hard, but I really struggle with hand-eye co-ordination.

The post Beer and Trippples – Games as Cybernetic Systems first appeared on Ludogogy.

While playing videogames, children seem to reach immersion levels not found anywhere else. They go to sleep thinking about how soon they can be awake again so they can keep playing. While they are playing, you can scream; you can do summersaults; they will not break concentration. And if you question them about the game, they can recite you so much lore that it could easily fill a history manual. Of course, we want children to learn with as much excitement and engagement as they play!

But if games are mostly used for fun and to escape reality, using games for learning could break that purpose and become paradoxical. And that is where educational game development gets stuck.

That has not stopped researchers, game developers and educators from producing millions of educational games, some more successful than others, using different genres, different processes, mechanics, game elements, to the point where the variety of such tools is becoming too much – and for so little result.

There have been so many failed attempts. And with each experiment that did not give the results developers desired, they looked at creating more complex games and using more advanced technologies such as 3D, AR and even VR; searching for what was missing. 

The answer cannot be found in technology, or by giving the user a wider range of graphic and mechanical possibilities to explore in the games, just as that is not the answer for any sort of videogame. 

What is missing in educational games is, literally, the size of a world

Developers and educators often confuse the reasons why gamers play. If they have not played themselves, it is easy to place all games in the same “bag”. In reality, the reasons for playing are many and not limited to fun elements. Curiosity, development, creating alternatives, looking for possibilities, exploration, relaxation, are all possible motives.

And that is where worldbuilding becomes an interesting system.

By creating games that children play, not only because they are fun, but also because they become intertwined with the story and immersed into the adventures and the game world, we find that even when the game is forcing players into doing meaningless and repetitive tasks, they will happily grind if it allows them to progress and evolve.

Any D&D player knows that. As WoW gamers can tell you, while they take eight-hour-long strolls, playing is not always fun. Consider those hardcore old school Runescape players, who would  spend weeks in a poorly designed Java-based game simply fishing. Yes, you read that right – not catching monsters or fighting dark powers – fishing.

And that is due to the existence of a complex and complete world. Just as in fantasy literature, success comes when a game is full of elements such as geography, weather variations, a societal structure, a socioeconomic fabric, important characters, a massive amount of history that adds depth, and a narrative that places the gamer inside the story and the events which unfold. Developing games is also about creating worlds for the players. 

Without a whole world to explore, playing might become repetitive, boring even, and if you remove the fun element, you remove any motivation for the gamer to play. It still might be better than simply studying, but not by a lot. 

Research says as much, with gamified learning environments producing lower long-term retention learning rates than schools (Putz & Treibmaier, 2019). On the other hand, role playing games show higher immersion rates and, even better, a positive connection between gameplay and learning (Sancho, et al., 2009).

Worldbuilding as a System

Worldbuilding is not just a tool for creating good and immersive games. It can also be a system, and a good one at that. Designing worlds is a complex procedure, which is full of dangers, just like the cliffs that surround the Argonath. Very much like going down the rabbit hole for the first time, it is not for the faint of heart. But if you have the talent of a seeker and the wits of a Citadel scholar, you just might find that this tool can become your dæmon.

Even when designing the simplest of games, you probably need characters. And those characters are alive. They communicate with the player. How do they talk; with an accent? If yes, why? Where do they come from; some far away land? Sure. Why there? What are they doing so far from home?

The scenarios where the game occur are there for a reason, your player must move through mountains. Which ones? How are they named? Where are they located? Now the action moved to the moon. Does that make any sense? Probably not. 

Here is one last example. Your player has to complete a set of tasks; let us say, a series of mathematical equations. It is fun, surely. It is also educational. If completed, the player receives a badge, an achievement and wins a hat for their avatar! Cool, right? Wrong! They are working just so they can customize their avatar?! 

What about the story behind the equations? Why those? Why not create a scenario where those equations help rescue a walrus from being captured by evil pirates? Wouldn’t that be more fun? Now let us expand! The Walrus is named Tom and he is very friendly. The player is now in the Arctic. Why? You were sent on an expedition by the Royal Academy of Walrus Protectors of the Kingdom of Wallabe. This is just one of the missions you see! Walruses are in grave danger, especially those from the Droughtnought Tribe because their chief is very stubborn. Those pirates you managed to avoid earlier, remember? You did that by completing a set of equations to calculate the strength necessary to break the metal bars in their cages. Then you followed them to Ground Kindir, their hideout. What next? Maybe another calculation to avoid detection? And so on.

The bigger the world, the more depth you can give the story and the context in which it plays out. This also means that, even if you decide not to use 90% of the world created, it now has room to expand; without breaking away from the narrative built; without “betraying” your players who are more curious than ever about the fate of that world and its characters. Worldbuilding can be, as you see, an enormously powerful system.

Worldbuilding for Game-based Learning Environments and Collaboration

Creating worlds for educational games is an interesting challenge for two reasons. First, it implies a collaboration between several professionals, game developers, designers, publishers and, most importantly, teachers; all of whom have their own set of constraints and desired outcomes. 

Game developers, for instance, focus on game mechanics, which might or might not be useful to teachers, who are more concerned about the means of getting the educational contents inside the game. Publishers might be worried about what type of content is appropriate for a school scenario. Will it  include any sort of violence or will it, even accidentally, create of negative impact on the learners? Designers, therefore, are also under a lot of pressure to create acceptable content.

The second challenge can also be an opportunity. Worldbuilding for education means creating a mixture of game content such as narrative, quests and adventures and educational contents. The more learning moments and information you can insert into a narrative without breaking immersion, the better. This requires imagination. And a lot of people have more creativity than just one. Each team element can bring their own ideas, scenarios, adventures, characters, and all their individuality.

But even if you do need to “steal” a few moments from the game to teach the player something they absolutely need to learn for the educational objectives you set for the game; if your story is engaging enough, then, just as they are willing to spend hours grinding away in any other RPG, they will listen to all the teachers have to say.

A Small Note on Discrimination

Plenty has been said about looking down on people who are gamers, but extraordinarily little seems to be changing. Viewed by a large part of society as antisocial by nature, they are geeks, freaks, weirdos. And even among those who accept gamers, at least superficially, what I find even more intriguing is the negative mindset towards anything game-related. 

The same applies to anything fantasy-related. Worldbuilding requires anyone who attempts it to get out of a world-sized box and create a new one. It is, not only a massive challenge, but a creativity test. It most definitely should not be looked down upon, as anyone who can create a whole new world just through their mind should be, at the very least, encouraged and commended.

“May the worlds you build be better than the one you inherited”

– Trent Hergenrader

The post Worldbuilding in Game-based Learning Environments – A System and a Tool first appeared on Ludogogy.

When I read this in the book What Video Games Have to Teach Us About Learning and Literacy by James Gee, I must have had an expression on my face similar to all the characters in the books written by Sir Arthur Conan Doyle when Sherlock explains how he solved a crime ‘it just makes sense’ I thought.

Yet, in both cases, to solve those complex fictional crimes that took place in  England between 1880 and 1914, and to understand that “all well-designed games are learning games” requires first that we are aware of the existence of the many parts that make up the systems. Second, we require a good understanding of how these parts work. And finally, we need a solid understanding of how these parts interact with and influence one another.

If we assume that “well-designed games as learning games” is itself a system, its constituent parts can roughly be listed as: a) games; b) how people learn; and c) how the education system works. And this system goes by many names:

1. Game based learning
2. Serious games
3. Project based learning
4. Active learning; and
5. Gamified learning -among many others.

It’s no wonder that we feel overpowered by complex systems. There seems to be too much to be learned and our contribution may not even amount to much in the end. But it doesn’t have to be this way. And games can help us realise this.

Imagine creating a society from scratch; watching your decisions about law, the economy and politics shape society. How about creating basic organisms and watch them interact with the environment? All of this, and more, is possible through games that enable the player to manipulate many of the parts that make up the system. And by doing so, gain a much deeper understanding of the parts that make up a complex system.

Going one step further, and one step sideways, I decided to test this theory with university students. Instead of simply playing games, I designed a learning solution (a physical game, an evaluation and development framework, and a series of YouTube-style classes) that allowed students to create their own games. This is what I mean by ‘one step further’.

And when I said ‘one step sideways’ what I mean is that the games the students create are tabletop games (as opposed to digital games. There’s already extensive research on this area, including that done by Mitchel Resnick and his team).

In this experiment the students of The University of Economics Krakow were instructed and coached on the creation of games that would help to teach learning outcomes related to organisational behaviour. In the first round of prototypes, over 95% of the teams decided to teach and measure knowledge through the use of questions on cards*.

While a small number of teams changed, and improved, the way of coaching players on the learning outcome they worked with in their games, a large majority remained using questions on cards all the way to the last iteration.

One of the premises of this experiment is that by exposing students, in a controlled way, to the complex system of game development ** they work harder to master the learning outcome they chose to work with. You can see more about this research on this the TEDx talk.

To understand a topic well, to understand the parts that make it up and how these relate to one another, students need to go beyond abstract learning. We need to see how these parts work in the real world, James Gee calls this “situated learning”. But of course, in most cases this is not an option. And here is where games and simulations support education.

Games and simulations can provide learners an opportunity to immerse themselves in the topic they are studying. Games in education afford learners the opportunity to learn about the many parts that make up complex system.

* Preliminary data analysis.

** Product development and Agile techniques. Design Thinking. Communications and collaboration.

The post What Video Games Have to Teach Us about Learning first appeared on Ludogogy.

Sustainable development is a complex problem encompassing an interrelationship between different domains such as society, environment, and economic agents in different levels (local to global) (Weijs, Bekebrede and Nikolic, 2016). To address the complexity of systems thinking and sustainable development, we designed a board game, Green Economy and developed a game-based learning approach utilizing this game (Gatti Junior, Kim, et al., 2020; Gatti Junior, Lai, et al., 2020). The game design began with a simple prototype (Figure 1) and finished after 10 weeks.

Why a game?

Games are models of systems (Kim and Bastani, 2017) and systems themselves (Fullerton, 2008) which makes game play and game design promising learning tools for complex issues. Playing a game that invites the players to participate in the system itself helps to cultivate systems thinking in diverse age groups and contexts. For example, Goodwin and Franklin (1994) designed a Beer Distribution Board Game for adult learners in management development programs to experience the product distribution system. Castronova and Knowles (2015) also explored how a board game about climate systems can be played by university students to learn about and (hypothetically) participate in climate policy making. More recently, Nordby, Øygardslia, Sverdrup, and Sverdrup (2016) observed the potential of their digital game about ecosystems for elementary students’ experiencing and learning about the system. From the constructivist and situated learning perspective (Lave and Wenger, 1991), games situate knowledge within the modeled system and, therefore, simulate a meaningful context for systems thinking.

Why a board game?

Green Economy encompasses both the gameplay and game design experiences in one experiential game-based learning activity. By incorporating a unique feature that enables players to change the rules during the game play, we invited the players to act as game designers during the game play. Both play and design engages students in systems thinking as they need “to think about how various parts of a system (e.g., different subsystems within a system) or different systems interact with each other” (Gee, 2009, p. 6). The board game as a tool embodies design possibilities based on low-cost resources and can easily be used in formal and informal learning settings without computers, internet access, or other technical devices. Additionally, a board game requires much prior experience for learners to play or design (e.g., coding) and provides an immersive learning experience.

In Green Economy, players are invited to engage in the reasoning of sustainable development. They lead a nation through two distinctive stages. They gather and manage resources (including land and Gold) in the first stage to build facilities that will allow them to evolve as a civilization into the second stage.

The game board is formed with hexagons (Figure 2), and each hexagon represents a land; the cards include resources, chances cards, and rules cards. The game encompasses other elements that represent population, Gold, factories, and army. The first nation that reaches a certain degree of wealth without negative environmental points wins the game.

In the first stage, players must have land, Gold, technology, and mineral to build factories and armies. The Factory is the element of the game that provides Gold for players. In each turn, a player receives for each factory owned 1 Gold and 1 negative environmental point. Yet, the army can move throughout the board one land per turn and only to adjacent land. The players may (but are not required to) build or use armies to protect their own lands and factories, to conquer an available land and/or to attack other players’ land. When players use their army to conquer an empty land, they receive 1 negative environmental point. When they use the army to attack another player, they receive 2 negative environmental points.

To win the game, a player must present 10 Golds, 1 stage two factory, and 0 (zero) environmental points. It means that some decisions in the first stage (the use of the army and build factories) will lead players to deal with a critical burden at the second stage. Thereby, we argue that this game play mechanism helps players (as students) reflect on their decisions and the consequences of their actions for the environment.

The feature introduced in our design that fosters the system thinking in-depth is the rule change cards (Figure 3). These cards introduced learning opportunities in our game anchored in new design possibilities that emerged during the game play. The players often have the chance to transform the result of the game completely as we could observe in one of the tables in playtesting with master students when the group started playing collaboratively to attack a player who would win the game. In this particular game, a joint attack was possible when one of the players, who had a rule change card, allowed players to move their armies more than one land per turn. One of the students shared his observation, and acknowledged,

“Once the rule change cards came into play the objective began to focus on how to extend or manage the play between the entire group. The change of rules began to happen to instigate events in creating game play that would promote a deep group interaction.”

One of the important moments from the learning process using games is the discussions conducted by instructors after the game play. During the debriefing sessions, players learned from the decisions made and their consequences in the game. Similarly in the Beer Distribution Game (Goodwin and Franklin, 1994), players acted in the roles of the factory, distributor, wholesaler, and retailer, aiming to consider cost-effectiveness. After the gameplay, players in different positions drew a graph of the pattern of customer demand. While explaining what happened in the game, most students thought other players’ behaviors had ripple effects on their game performance, but seldom noticed the impact of the larger game structure and how their own behaviors contributed to systems result. After collaboratively reviewing and analyzing how the system worked with videotapes of their gameplay, students were able to interpret with systems perspective (Goodwin and Franklin, 1994). The study by Nordby et al., (2016) similarly encouraged elementary students’ reflective practices of writing diaries on ecosystems based on their gameplay and holding debriefing sessions. In Green Economy, through the reflection on the decision making during the game play, we could see that students were involved in systems thinking recognizing elements and their relationships in the game, as well as organizing those components and the process of the game system. Through the interaction of different game design elements that supported social interaction and the formulation of emerging strategies, it was possible to see how players were engaged in systems thinking emerged in a social gaming experience.

Our research seeks to contribute to a growing wave of game design for educational purposes (serious games) that encompass not only the creation of digital games but also card and board games (Kwok, 2017). Our work contributes to a critical discussion concerning the integration of elements of game design and learning theories for developing a board game that enables educators to enhance player’s systems thinking.

Acknowledgment: The authors acknowledge the work of Liping Liu and Xingru Lai (former master’s students at the University of Calgary) who were part of the game design team.

The post Board games to engage in systems thinking first appeared on Ludogogy.

What does my work on this committee mean for the university? What does buying Marvin Gardens mean for the long game of Monopoly? Will selling off my AMEX stock really affect the London Stock Exchange?

This is SYSTEMS thinking and there are four main ways to think about them: big (macro), small (micro), simultaneously (synchronous), or linearly (asynchronous).

You got to have a system to navigate a SYSTEM, right? Keep reading to find out how.

What is Systems Thinking?

Whenever you think about a large organization like a university or a large program like orientation you are also thinking about a system. Since you’re already thinking about a system you also need to think about how it works.  How do individual components (like people, places, and things) interact with each other?

Systems thinking is knowing and understanding how those different components work and relate to one another. Some of these systems are HUGE and complex like the United Nations or the ecosystem of the Pacific Ocean.

Some of these systems are really small like an individual game of Pandemic or a single flip of a coin.

All systems have at least two components: micro (small) elements and macro (big) elements. Each one requires a different understanding in order to understand the whole “system”

That understanding contributes to whether or not the system works, works well, or completely breaks down…

The Micro

Micro systems are really the “nuts and bolts” of the entire operation. In an organization these micro actions are the motions of the individual people inside of it. These can include everything as small as a mouse click and as big as a text message.  These are the small components that make up the larger system.

In games, the micro play occurs during individual players’ actions.  At a poker table these can be the decision to check or fold during any hand. In Scrabble it can be a decision to save that 10 point “Q” in your rack for a bigger bonus later. In a game of Hanabi it could be a decision to play a card or give a teammate a clue.

At their very basic level, micro decisions are small and personal. Sometimes people think rationally about these decisions, but for the most part we are on auto-pilot when we decide to smile at a beautiful stranger or stop at the yellow light instead of trying to make it.

The micro is about the small individual picture – it doesn’t always take into account the BIG picture – the Macro.

Macro Systems

Macro systems are the very opposite of micro systems. Macro systems are thinking about the world’s economy, our planet in the solar system, and the Pacific Ocean as a whole. Macro systems look at the forest instead of the trees.  They deal with BIG changes that affect all of the little nuts and bolts in micro systems.

In games, the macro play occurs when players take their actions, opponents’ actions, and the game’s action all together.  A sports commissioner looks at the records of teams playing games throughout the league. A player at the World Series of Poker sees which players are building chip stacks on a daily basis.  A macro player in a game of Risk decides whether to hold a big continent like Africa for the army bonus or give it up by holding two smaller continents.

Macro play is about making LARGE sweeping decisions that affect yourself, your opponents, and the game as a whole.  They take into account the decisions of many others and try to find trends in what’s happening in the big picture.

Macro play at the college level involves the creation of a new major. At the state level it involves the creation of a new campus. At the national level it involves the trend of student loan debt for college graduates.

Macro systems think big. They think HUGE!  They focus on what’s come before and what’s to come next.

Synchronous

What does synchronous mean? Synchronous means that actions take place at the same time. Meetings are a great example of this: one person might be speaking but multiple people are listening (or not) and forming their own opinions. The same thing can be said about lectures, concerts, or any sort of live entertainment.

Games that have synchronous actions are Taboo with its frequent yelling, Escape: The Curse of the Hidden Temple with its crazy dice rolling, or the manic clicking of the real-time strategy game StarCraft 2.

Synchronous actions take place at the macro level like at the trading floor for the New York Stock Exchange. Millions of transactions are taking place every second to determine stock prices rising or falling.

Synchronous actions also take place on the micro level like when a running back receives the ball or when a counselor determines a follow up question based on what a patient has just said.

Synchronous actions are made in real time and are often decided based on immediately available information.  But sometimes, you don’t have to think off the top of your head. Sometimes you have time to formulate a move ahead of time…

Asynchronous

Asynchronous moves are actions taken in order. In a game of Monopoly a player waits until another person’s turn is over before acting. This means that there is currently only one thing going on at a time, and it happens in a specific order.  Board gamers call this “resolving” before other actions are taken.

Asynchronous actions can also take place in a large organization like a college or university.  Examples of this are when a committee meets, the faculty senate holds a vote, or when students complete homework for a class the next day.  In all of these situations things happen before the next person acts (i.e. committee members read an agenda, a faculty senate reviews initiatives up for vote, and students listen to a lecture in class before doing the homework).

Big asynchronous actions often take place in macro environments where large scale decisions have a big impact. You can see this whenever congress meets to vote on legislation or when the United Nations holds its general assembly. Many meetings, conversations, and other events led up to a large action now taking place.

Alternatively, asynchronous actions can also take place in micro environments like in email or chat conversations.  How many times has meaning been lost in an email because the conversation spanned hours or days?

Takeaways

Things operates in systems. Colleges and universities are just as a diverse or complex as the human body or a nation.  To navigate a system well you need to know how the different parts of it work together.

Some of these systems work on a really small (micro) level while others work on (macro) larger levels.  Understanding both helps you understand how the entire system works.  Micro actions take place in games with individual players’ actions.

On the other side Macro systems involve large organizations or groups of individuals.  A single bet in a poker game is a micro action. Passing sweeping new financial regulation is a macro action.

No matter if it’s a macro or micro action they all take place either synchronously (at the same time as other actions) or asynchronously (in a specific order).  Synchronous macro actions can be as big as a day’s trading on the New York Stock Exchange or as small as a game of Taboo.

Asynchronous actions take place in sequence.  At a micro level they involve students doing homework before the next class or on a macro level when a governing body like the senate meets to debate previously presented legislation.

Your ability to determine 1) the micro or macro scale of an action and 2) whether or not it is synchronous or asynchronous will help you play your game and navigate your system.

Sometimes it’s beneficial to wait until you have more information before taking a turn or placing a bet.

Other times you need to act quickly to resolve an emergency like a leaking toilet or a historical financial collapse.  Knowing is half the battle!

This article covered game systems in games-based learning. To learn more about how systems thinking affects gamification check out the free course on Gamification Explained.

This article was originally published by Dave in his blog What’s Your System?

If you have enjoyed this article – consider getting yourself lifetime access to his Games-Based Learning Digital Library containing all of the content from the past two Games-Based Learning Virtual Conferences; past webinars and courses he’s created; as well as his complete back catalog of articles; podcast episodes; and videos. And more content is being added all the time.

Readers of Ludogogy can get a $50 discount on this valuable resource by using this link.

The post What’s your System? first appeared on Ludogogy.

Thirty-odd years later, one particular day found me googling feudal succession laws, and that night, dreaming about taxation and vassals. It was, of course, a game that made the difference; a PC game, Crusader Kings III. In it, you play the head of a dynasty, ruling a kingdom (or sometimes a smaller or larger holding). When you die, you play your heir, and theirs in turn until (if you’re successful) the end of the game’s timespan (from either 867 AD or 1066 to 1453).

It grabbed my attention mainly by modelling complex systems incredibly well. At first glance it just appears to be a very complex game with lots of things going on; something similar to the more famous Civilisation series but with more detail and more focus on the people, including yourself. But look closer and the game is made of a number of interlocking systems that model medieval life.

By looking at how this commercial game does this so well, I’ve learned a lot that I think will help me in designing learning games.

The titles, succession and claims system

Modelled very closely on real life, the game presents the (‘old’) world as a jigsaw of empires and kingdoms, each broken down into duchies, baronetcies and holdings. Control of each of these is by title, and individuals can inherit or lay claim to titles. If you want to grow your kingdom, you need claims (even to wage war, you generally need a claim to justify it), and parts of this system model claimants joining your court, changing of succession law, disinheritance and many other associated factors.

All of which has often found me furiously searching for somebody in the game with a claim to the Duchy of Lancaster or the Kingdom of Aquitane – or else, manufacturing a claim. And the succession elements and the fact that you will play as your heir give you a real interest in your lineage and what your heir will inherit.

The lifestyle system

Different rulers in this time had different reputations; a good steward, a schemer, a pious scholar. The lifestyle system simulates this by giving you experience which you can spend towards ‘perks’ in one of five lifestyles: learning, stewardship, intrigue, diplomacy or martial. Choose a diplomacy lifestyle and you could persuade rulers to accept being your vassal without the need for war. Choose intrigue and you could murder your rivals without being exposed.

Once you’re invested in the perks for one of these lifestyles, it gets easier to gain more powerful perks in the same one, so the system channels you into specialising, but without it feeling like you’re being corralled. When my current character is a stewardly ruler, I delight in being able to build up to bonuses that allow me to build more castles and tax more efficiently.

The stress system

The modelling goes beyond laws, land realities and worldly concerns to human behaviour and emotions. Your character has traits (which also tend to dovetail with their chosen lifestyle). You may be greedy or generous, honest or deceitful, and a host of others. Throughout the game, events and developments give you choices: which way to take a conversation at a feasts, how to educate your children, how to navigate encounters on pilgrimages and hunts.

These choices can have material gains and losses, but your choices are also constrained by your traits: if your character is greedy, giving away money causes you stress. If they’re honest, even a white lie may raise your stress levels. This is systems modelling of cognitive dissonance. And there are health and effectiveness impacts to stress. The result is that you find yourself playing the character rather than the numbers.

Interlocking systems

There are many more systems in the game. A faith system models the dynamics of the crusades, indulgences, the shame of adultery and heresy, witch hunts and religious leaders. A culture system models the way cultures intermingle and develop, including technological advances. There’s a system around money, taxation and vassals, and another around types of holding such as cities and bishoprics, and the castles and other buildings you can have. A system around wars, and another around intrigue and schemes.

In many ways the drawing of lines around each of these systems is somewhat arbitrary, but that’s a little like real life systems modelling. The systems interlock, and what emerges is something lifelike; something imperfect, absolutely, but this is implicit in the definition of ‘model’; perhaps most importantly, something thoroughly engaging, with the capacity to make players think deeply about the system they’re engaging with, and learn about it.

Emergent properties

Because of the focus on modelling rather than offering paths to a specific goal, the game is very open-ended. There are some achievements, but the only ultimate win state is your dynasty surviving to the end, and even failing in that feels not so big a thing, if you had some great moments along the way. The game has more of a sandbox feel than many games that might be categorised with it, because — like life — you have to set your own objectives, find your own meaning, and experiment. This puts the player at the very heart of things, which is one of the most important reasons for using games in learning. In a way, the player is recruited as game designer to some degree, choosing the shape of the game. Choices become even more meaningful than usual, and wider. The level of engagement to be gained is of the order that sets one off googling on your own time, or dreaming about it.

With this level of engagement, learning about the real-life system being modelled is guaranteed. In the senses in which the model is perfect or near-perfect, I’ve learned directly from the game (certainly more about medieval history than from my history classes). But also where it’s not, I’ve been inspired to ponder, investigate and discuss more. Did the way land was apportioned after a successful crusade really work like that? What was really happening with the competing Kings of León?

Simpler learning games

I’d have learned a lot more in my history lesson if my homework assignments had revolved around Crusader Kings III. Any history teachers reading this, take note. And it’s not limited to history. I could have written this article about social systems and The Sims, or manufacturing and any one of the current craze for factory simulation games out there. But what if you don’t teach or train medieval history or something that’s been similarly modelled in a great commercial game?

Fortunately, I don’t think models need to be this complex to gain some of the benefits. Modelling can be of any system, and we can take lessons and inspiration from such well done examples even if ours are simpler and cruder. I’ve built learning games around how talent development and management works, how manufacturing works, or how project management works.

Take the idea of a traditional game about talent management, based around a win state and a simple mechanism like roll and move, with quiz questions and simple encounters. I’m sure it could be fun. But now contrast that with the idea of modelling the systems inside your organisation that impact on talent management; individual ambitions, career paths, mentoring schemes, salary structures, development options. Imagine building a model of each of the systems involved, and then thinking how they interact.

If it seems like that would be difficult, you could recruit your players in modelling the system in the first place. Either way, imagine the richness of allowing them to play with the system and decide their own goals; to debate how closely the system modelled life, and what would make it more realistic.

Breathing life into models of systems, by making them into games, by opening them to our players makes us let go of some control and structure. But our potential return is something special. In Crusader Kings III discussions online, players trade stories about rising and falling dynasties as if it was a writer’s forum discussing novel plotlines, and discuss the differences and similarities with real history. I think even a small portion of that engagement and learning is something to strive for with learning games.

In a way, it’s like when we read a fantastic book or watch a truly classic film. We love it because it says something to us about life, and it can only do that if it speaks to us in the language of the rich systems that make up life.

The post Engagement and Learning as Emergent Properties of Systems Modelling: What we can Learn from Crusader Kings III first appeared on Ludogogy.

The goal of this article is to explore how these two lenses (System Thinking and Game Design) can work together.

Instead of a tedious lecture to explain how this connection works, I thought to use a technique from ancient Greece which was also commonly used in the Renaissance period; the dialogue. In a dialogue the author invents a conversation between people who have knowledge, but different perspectives, around a specific topic that he wants to explore. In this way he could present different views, objections and responses, performing a true exploration of the subject. Just to give an example the “Dialogo sopra i due massimi sistemi del mondo” is the book where Galileo Galilei presented his disruptive vision of the universe.

Clearly, we are not here to discuss such an important topic, but we can imagine a dialogue between a Game Designer named GorDon (GD) and a System Thinker named STephanie (ST), and listen to their conversation about the creation of a brand new game, while trying to understand the different roles in this process.

NOTE. To distinguish the different speakers we are going to mark each comment using their references (GD or ST). I have highlighted, in bold, the unique contributions to the conversations coming from the different speakers.

GD: Good afternoon Stephanie and thanks for accepting my invitation to spend this warm day in the Italian Gardens of Hyde Park taking about a new game I am thinking of.

ST: You are welcome Gordon. It is a pleasure to listen to you while you think of a new game, to give some contributions, and at the same time, enjoy this wonderful place and weather.

GD: That’s true Stephanie, we can walk while talking.

I was asked to create a new game for the leaders of a big Company, to explore the advantages of scaling processes across large enterprises. I have identified some requirements of this game but I am a little bit stuck on the mechanic.

ST: Let’s start with the boundary conditions of this system, what are your requirements?

GD: The subject of the game is scaling, and the game should be (obviously) scalable and eventually involve a large number of players. Let’s assume we have 100 players. It should be a sort of icebreaker to prepare people around scaling and synchronicity across the organisation. Players should be leaders of business units of a large organisation who have some anti-patterns while collaborating.

ST: What are the interactions among the different parts of the systems (players, components…)?

GD: We need to define these, but ideally, as an icebreaker, should be easy to play like the Rock-Paper-Scissors game (later RPS) and easily scalable like Bingo.

ST: Let’s explore the RPS game and try to clearly understand this system, and let’s also use a mathematical perspective . This game is perfectly balanced for very specific attributes (conservative, essentially polyadic, strongly fair, and nondegenerate). Let me summarise the effect of these attributes on the new game:

• First each element should have that same number of winning and losing cases. This requirement implies the number of elements should be odd (3, 5, 7…).
• Second if you want to scale the RPS game you need to manage an exponential number of combinations (3→9, 5→25, 7→49); this could create situations not easily manageable with large number of players (100→10000)

GD: That’s an interesting, and challenging, starting point but something we can start from.

To simplify the process of collecting the choices we can use cards (instead of players’ fingers/hands), but clearly we need to define a different interaction model also because, as described in the linked RPS article, apparently the position of the player is relevant, and this is not feasible in the situation we want to create.

ST: Sometime, in Nature, when groups of individuals are interacting with each other, you can consider the clusters of individuals that have something in common. The model assumes that this “something” must be relevant for the interaction: we can call this a “role” and when a role is clear, we can model the interaction among clusters instead of individuals.. For example, people in organisations and Business Unit organisation.

GD: So we can cluster all players’ choices (read collect cards in decks) by similarities and see the results. But again scaling is too complicated and also where is the fun to draw a card if we miss the goal?

ST: We need to add some strategy. This could lead to a goal and also to a reason to take a decision to pick a different card and lead to win the game. The original RPS doesn’t have any strategy (randomness is not a strategy), but each system should have a goal. Again, we can use Nature as a reference: when you have some consumable resources you need a strategy to keep or collect resources.

GD: We have cards that can be consumed in some way and everyone is starting with the same set of cards. Maybe we can use double sided cards where the choice on one side could influence resources on the other side, so we could create different combinations of front and reverse sides where the players could pick (and consume) one of the possibilities, defining their own strategy on future actions. Each player can follow/predict different strategies so we have differentiation.

ST: That’s a great idea! In this way we could also override the limitation of the odd number of combinations, because we could create combination of the front/reverse excluding repetitions: ideally we could create a mechanic with four elements where, for each of then, you can have the other three elements, on the reverse side.

GD: That’s trivial to achieve. We can have a “called element” that is the attribute to play a card (front side) and use the elements on the reverse side (”hidden element”) to cluster cards and collect combinations. So you have the called element as a consumable resource and the hidden element to apply a strategy for future choice. Given that back and front could be identical, you can have a lot of different combinations and so apply different strategies.

We can create a game mechanic with four elements, where four is a very common number in Business Strategy. For example we could use the Plan, Do, Check, Act (PDCA) cycle that is very related to many processes of innovative organisations. In this way we can use the game to educate organisations in the PDCA cycle.

[Gordon starts drawing the PDCA cycle on the ground with a rod]

[Stephanie takes Gordon’s rod and draws diagonal connections to the cycle]

ST: Each step of the PDCA cycle has a previous and consecutive step and in combination with the last one we can have a tie (impossibility). So the game is balanced following exactly the same logic as RPS. Also scaling is easy because every player has the same set of cards and clustering can be managed even with 100 players.

We need to work on the double sided cards from the statistical perspective (combinations).

[Stephanie draws a matrix just beside the cycle]

This is the matrix of possible combinations front and back of the cards.

       P         D         C         A

P      X         –         =         +

D      +         X         –         =

C      =         +         X         –

A      –         =         +         X          

(“+” beats, “=” equals, “-” loses)

You can observe the balance of the signs by row and column.

As you can see the diagonal is forbidden (give the constraint to remove repetitions) and this diagonal actually create two identical areas above and below, so we can use just one of these. In this configuration we can have six double sided cards.

GD: Six is not a good number for cards. If we’ll use poker size cards, these are printed in A4 sheets of nine cards. Is there some way to create 9 balanced cards?

ST: Actually we could duplicate the two tie combinations (“=”) so we can create one more option for each of the elements. In this way we can arrive at eight.

GD: That’s fine. We can create a ninth card with instruction of the game or something to identify players. In this way if we want to scale to 100 players we need just to print the same sheet 100 times and distribute one to each player that could personalise it. Scaling the game could be absolutely trivial.

ST: As per the above matrix you can see that given any called element, at the beginning of the game players have four possible choices for hidden elements, and player’s choice at every turn influences following decisions. At the beginning, in theory you have 16 choices in total, four for each element.

GD: That’s great. So we can define that in each game you need to consume all the cards, so it is based on eight turns and the first player changes at every turn. In this way your choices are reduced every turn and at the end of the game you will have just one card. We need a specific rule to manage the condition of not having the “called element”.

Can we explore the resulting clusters of the played hidden elements?

ST: With this configuration of cards, in each turn, we can have three different possibilities of clustering  the hidden elements: one cluster for all, two clusters, three clusters. Because of the structure of the PDCA cycle, we can use the cycle itself to identify winning elements

GD: I see that we can use the cycle to assign points:

• three clusters: we can have lower (0 point) higher (2 points) and middle element (1 point);
• two clusters we can have lower (0 point) higher (2 points); in case of a tie combination 1 point for all players
• one cluster same points to all (0 points)

and make ‘maximise score’ as the goal.

ST: Now back to one of the initial questions: we have the components, we have the mechanic, we have scoring rules, but why do our players want to play? Or, from the system perspective, what is the goal of the system?

GD: The mechanic of the called and hidden element can work from a competitive point of view, however we could use a collaborative perspective and make ‘maximise score’ a goal. This means to select the same hidden element and, from the scaling perspective, the message is, to be synchronised. So we could change the score, in the case of one single cluster, to 3 points to make it clear that this is the best situation for the system.

However, we need also to introduce a further rule: no communication and no planning in advance of the sequence of the cards. This is a strong message from the organisational perspective because we need to know what other units are doing without any communication and this requires being synchronised in some way.

ST: Finally, the goal of the system in the collaborative mode is exactly the message you want to deliver to the players: scaling is more effective if units are synchronised. Maybe we can use competitive mode as a warm up and collaborative as the final game to generate constructive conversations among players.

GD and ST together: This game is ready to be implemented. [both are smiling each other]

Conclusions

I hope you enjoyed this “divertissment” (a la française), but I also hope you had some doubts on who Gordon and Stephanie could be in reality. They are not different persons but actually are different aspects of our thought processes: we have the more creative, intuitive and divergent perspective (Gordon), and we have a more rationale, analytical and convergent perspective (Stephanie). So you can understand why creating a game is a complete activity from the intellectual point of view that can be compared to other “more serious” tasks.

Now, what about this game?

PDCAelements is a real game, and is available on the DriveThruCards portal in an implementation for 12 players using a standard poker double deck based on 12 different business components to distinguish players. Clearly the game could be scalable to a larger audience.

The real process of creation of this mechanic (called “elements”) was not very different from the one described above and you can find some track of it in a couple of threads on BoardGameGeek, where the game has been introduced and is one of the game selected for the 54 cards game context 2021 (the only serious game in the list).

The”element” mechanic here described is absolutely flexible and can be used for many different situations: for example I have created a Christmas theme (also the rules) and its first implementation is based on the 4 natural elements and the zodiac signs.

As an Agile coach I really loved this PDCA themed implementation because this is probably the only game available on this subject, it is easy and quick to play and every play test highlights different discussions.

If you are interested the mechanic is available under Creative Commons 4.0,  (also the rules) the only request is to mention the name (elements) and the author (@agileDex). Feel free to contact me for more details.

The post A dialogue about creating a new game using two different lenses first appeared on Ludogogy.