4. Systems Gaming

4. Systems Gaming


‘Decision-making will always be a risky business for professional and responsible gamblers, but complex systems science can bring in reality-connected and fast-evolving support systems.’

– Dr Pascal Perez


Image from Boho’s Best Festival Ever: How To Manage A Disaster

Embedded within Systems


As human beings, we are surrounded by and embedded within systems, both natural, such as climate and the ecosystem, and human-made, such as the economy, the internet or even society as a whole.

Rarely do the problems that face us arise in isolation. Our world is made up of many different domains (the biophysical, the economic, the political, the social) that are linked, often in surprising ways. Things take place over different timeframes and at different scales – sometimes problems emerge from the bubbling up of many smaller issues, sometimes they trickle down from the larger picture.


We cannot solve these problems without considering the system within which they take place, or without taking into account the broader implications of our solutions. This is the Systems view, and it is an important perspective to bear in mind when addressing the complex problems we often face.

All too often, though, our decision-making and management systems are based on mechanistic and simplistic representations of the world.

One of the most valuable benefits of adopting a systems lens to look at problems is that it affords you the chance to take into account the different stakeholders within a system. Often, different members of our society have different needs, desires, perspectives and behaviours. What seems natural to one group is blasphemy to another. A greater adoption of the systems lens in reflecting on a problem allows for more thoughtful and constructive debate and negotiation.

In short, a systems approach offers the chance to find acceptable compromises.

What is Systems Science?

The study of Systems is a relatively recent scientific endeavour, one which has been steadily gaining momentum since its origins in the 1970s.

Systems science is an interdisciplinary field that studies the complex systems that exist in nature and society. It is a way of analyzing the dynamics of our world by looking at it as a whole rather than separating it into parts. Systems science concentrates as much on the links and interactions between things as it does on the things themselves.

Some of the key insights from Systems science include:

  • Complex Adaptive Systems – How complex systems, both natural and human-made, have their own behaviours and properties including the ability to spontaneously self-organize and adapt;
  • Interconnectivity – How the different parts of a system are interconnected, and how those links can often operate in surprising and unexpected ways;
  • Feedback Loops – The idea that links can form feedback loops –some parts of the system feed into other parts which feed back again, and so on, and how those loops can sometimes get out of hand;
  • Tipping Points – How a system can reach a threshold and then suddenly and unexpectedly undergo a rapid transformation into something that looks and behaves very differently;
  • Resilience – How some systems can absorb shocks and retain their functioning, while others can suddenly collapse or transform – what is it that makes a system fragile or robust, and what does it mean to be resilient?
  • Stakeholders – That a complex system involves different stakeholders who want and value different things and those different priorities need to be kept in balance to keep the system flourishing.
  • Trade-offs – Managing a system is all about trade-offs and compromises –squeezing the most out of one part of the system will inevitably involve making sacrifices somewhere else;
  • Scales – How complex adaptive systems work on a range of space and time scales – and how dealing with a problem or understanding an issue is often a matter of viewing it at the right scale.

Over the last three decades, scientists in the field of Systems Science have gained a new understanding of these issues, creating a toolkit of vital concepts for understanding and grappling with real-world complexity.

Increasingly, Systems scientists are using these tools to help people – from farming communities to tourist resorts to government agencies – understand the systems they are part of, so that they can make better decisions.

One of the key tools Systems scientists use to help people understand the behaviour of complex systems are models.


A model is a mental or formal representation of a system which is used to anticipate its future behaviour.

Modelling is a universal activity. All of us, scientists or not, construct models to help us understand the world around us and to guide our planning and decision-making. When we store information from the past and use it to predict the behaviour of the future, we are modelling.

Every living creature on the planet stores information from the past and from it extracts regularities. These regularities are a model of the environment which that creature uses to anticipate the future.

CSIRO ecosystems scientist Fabio Boschetti explains: ‘Whether it is a tree responding to shortening day length by dropping its leaves and preparing its metabolism for winter – in advance of winter – or a naked Pleistocene ape storing food in advance of winter for the same reasons, both are using models.’

In his 2008 address on social simulations at the Santa Fe Institute, Joshua Epstein elaborates: ‘Anyone who ventures a projection, or imagines how a social dynamic – an epidemic, war, or migration – would unfold is running some model…’When you close your eyes and imagine an epidemic spreading, or any other social dynamic, you are running some model or other. It is just an implicit model that you haven’t written down.’

There are a number of reasons to construct models, including:

  • Prediction – To create predictive scenarios that allow us to prepare for the future;
  • Understanding – To illuminate the workings of the system being modelled;
  • Training – To teach skills and attitudes useful for dealing with complex systems.

Types of Models


There are numerous different types of systems models, ranging from the extremely simple to very complex. At the simple end are conceptual or flowchart models. These are simple depictions of a system, often simply drawn using pen and paper, which indicate which parts of the system are connected where.

earthsystem copy

At the other end of the spectrum are huge software models that simulate incredibly detailed processes at high speed. An example of these are the Global Climate Models (GCMs) which simulate the trajectory of the earth’s climate over the coming decades, used by the Intergovernmental Panel on Climate Change (IPCC) to prepare its periodic assessment reports on the current threat of climate change.

Participatory Co-modelling

malinga-scenario-workshop-south-africa A Stockholm Resilience workshop in Malinga, South Africa.

In the last fifteen years, Participatory Co-modelling has emerged from Systems Science as a way of using scientific modelling to help non-scientists deal with complex systems.

It’s a method of engaging stakeholders in a constructive, factually grounded dialogue, examining and reflecting on complex problems. Rather than focusing on precision in their simulation of real systems, participatory co-models focusing on supporting collective learning and decision-making.

Co-modelling highlights the process rather than the outcome of the model. The aim is to involve the stakeholders who will be affected by the model’s outcomes in the creation of the model.

Garcia Barrios - MESMIS 02

Co-modelling projects often takes place over two key phases:

1.    Creating the model

Scientists will travel to the area being modelled. This may be a river system, a region of farmland mixed with dry forest, a town by a lake, a national park bounded by cattle country, or so on.

The scientists will begin to construct a model of the system. They will gather data themselves, but importantly, they will also undertake repeated consultations with local stakeholders from across the system, bringing in a broad range of specific expertise.

The model is created gradually. Over a number of meetings with different stakeholder groups and organisations, each party gets to contribute their full understanding of how the system works.

The scientists gather their own data, but also take into account all of the contributing perspectives. When they encounter disagreements in the way that different groups perceive the system working – for example, if the farmers and the local parks authorities disagree on how much fertiliser runoff is lethal to native fish – the scientists negotiate with both parties to try to resolve it. Failing that, they ensure that the model includes both contradictory viewpoints.

Accommodating many different perspectives and opinions on the nature of the system in question guarantees that the model will have limited capacity for prediction. However, the advantage of this approach is that the model has been socially validated by a wide spectrum of stakeholders, and consequently can be used as a legitimate platform for decision-making.

2.    Bringing stakeholders together

Once the model is built and functioning, and provided it satisfies the minimum standards of each of its contributors, stakeholders from across the spectrum are invited to take part in workshops, discussions or scenario exercises.

At these events, participants engage in role-playing activities and problem-solving exercises facilitated by the systems scientists. The model is used to either generate scenarios or to calculate the consequences of peoples’ decisions.

These events try to fulfill a number of goals:

  • For participants to experience the system from other stakeholders’ perspectives and to better understand the motivations behind their behaviour.
  • For the community to address potential system-wide crises before they occur in real life, to experiment with different strategies for addressing and managing them.
  • To facilitate debate between opposing groups. By creating a ‘fictional’ version of the system, the model lowers the stakes for conflicting parties to engage with one another.

In short, Participatory Co-modelling offers a means to facilitate effective negotiation, decision-making and compromise at the community level.


Garcia Barrios - MESMIS 01

An example of a participatory co-model is Garcia Barrios et al’s MESMIS project, which focuses on ecosystem management in rural Latin America. In one iteration of this model, stakeholders from a Mexican river-system were invited to take part in a co-modelling process.

These stakeholders included a maize-farming community from along the river, tourist operators from the nearby lake, and local government officials. Representatives from these three groups joined scientists for a three-stage modelling exercise.

In the first stage of the model, the participants explored the situation of the maize farmers, who were seeking to increase the yield of their land by introducing a new fertiliser. By experimenting with the model, participants discovered the quantities of fertiliser required to sustain the members of this community.

In the second stage of the model, the participants took on the role of the tourist operators on the downstream lake. The model demonstrated the impact of fertilizer run-off on the lake ecosystem, and consequently the value of the land for tourism. By experimenting with the model, the participants explored the safe quantities of fertilizer which could be added to the river system in order to sustain the tourist industry.

In the final stage, the participants took on the role of the local authorities, seeking to balance the demands of the two communities with the complex behaviour of the ecosystem.


The result was a consensus agreement on the best approach to managing the river system for all concerned.  As is almost always the case, the consensus was a compromise which did not deliver the optimum results for any single group but the best results that all three could accept.

Systems Gaming


Participatory Co-modelling takes the tools of Systems Science into a community facilitation / workshop setting. At these events, participants are faced with invented scenarios to explore, puzzles to solve, role-playing exercises and other creative activities.

Lacking expertise in creating or facilitating these kinds of social experiences, systems scientists began engaging writers, theatre-makers, visual artists and game designers to help devise and coordinate these events.

The involvement of artists in creating these events opened up the possibility of employing a much more sophisticated palette of activities. Game designers are able to facilitate richer and more meaningful game events, offering participants more control and constructing a more fluid experience. Visual artists and graphic designers are able to create more evocative representations of the systems models, encouraging greater identification with the issues and ideas being explored. Writers and performers can construct more detailed, nuanced depictions of the different outcomes and consequences for the system and its inhabitants.


The new possibilities emerging at the interface between these disciplines gave rise to the emerging field of Systems Gaming, in which techniques from theatre, visual art and gaming are applied to ideas and principles from Participatory Co-modelling to generate interactive scenarios illustrating the behaviour of complex systems.

Unlike Participatory Co-modelling, Systems Gaming projects tend not to undertake extensive consultations with specific communities, creating bespoke models and scenarios in response to that community’s particular needs (though they can). Instead, these practitioners often create much simpler and general-purpose models and games that can be presented in a range of contexts to diverse audiences.

Rather than examining the complex interconnections and behaviours of specific ecosystems, Systems Games often use extremely basic systems models that demonstrate only one or two specific concepts. These ‘toy models’ use the bare minimum of detail required to generate the behaviours their creators are interested in.

Rather than gathering their own data from the system they are modelling, Systems Games generally use archetypal settings (‘desert’ or ‘beach town’) or model completely fictional systems.

Systems Gaming events engage participants with scenarios illustrating different aspects of Systems Science, using theatre, gaming and visual design to facilitate the experience. Sometimes these events are presented as entertainment or in arts or festivals contexts. More often, though, they are delivered as educational or training experiences to teach systems thinking concepts to participants.

What do Systems Games teach?

Systems Games illustrate general principles of how systems behave, highlighting phenomena such as feedback loops, tipping points, trade offs and emergence.

Through the experience of participating in different kinds of Systems Games scenarios, participants develop useful cognitive attitudes for dealing with complexity, including:

  • The ability to interpret outcomes against expectations
  • The ability to balance emotional responses (humility, curiosity, frustration, blame-shifting)
  • The ability to tolerate high levels of uncertainty
  • The ability to search for counter-evidence
  • Illuminate core uncertainties
  • The ability to recognise trade-offs and suggest efficiencies

In their paper ‘What is a model, why people don’t trust them and why they should’, ecologist Roger Bradbury et al argue that: ‘Computer models, resembling flight simulators, can be designed to train individuals to better understand the basic processes of real world significance for decision making, including management of limited resources and unexpected feedbacks. The belief underneath this approach is that managing and predicting complex behaviours can be learned and that models can represent systems in a manner appropriate for learning and training.’

Game Design principles

One of the key innovations of Systems Gaming is the involvement of game designers in the practice of creating and facilitating scenarios based on Systems Science models.

There are two key tasks that are absolutely non-negotiable in making and delivering Systems Games (and which are of significant benefit in a Participatory Co-modelling context as well) to which artists are integral:

  • Enriching the aesthetics of the system model to elicit greater investment from participants. A more lavishly created, fleshed-out and carefully realised game experience will cause players to care more, commit more and take the whole exercise more seriously.
  • Crafting sophisticated game experiences that generate the desired interactions and responses from participants.


Artists specialise in creating experiences. More than any other group, artists possess the sensibilities and the technique to strike the right balance of playful / serious, stimulated / given space to reflect, structured activities / freedom to play.

Game designers, whether digital, boardgame, interactive performance or live action role-playing (LARPing) – have a detailed understanding of the player experience. They possess the sensibilities and technique to effectively imagine:

  • How players first encounter the work
  • How they are contextualised within the game
  • How they come to understand the rules of the game and the affordances offered them
  • How they interact with other players and the facilitators
  • What the emotional arc(s) of the experience might be
  • How players may be guided if lost, supported if stressed, or nudged if disengaged.

All of these skills require extensive training, practice and the study of the sophisticated body of game design literature.

With these tools, game designers can help scientists construct provocative experiential games which generate the kinds of actions, behaviours and insights in players that the scenario demands.

Employed properly, Systems Gaming practice has the potential to convey an understanding of the behaviour of complex systems to a broad audience.