Introduction to Systems Thinking and Systemic Change via the Deep Demonstration methodology

This is a summary of my speech at ‘Museums Facing Extinction’ Conference organized by We Are Museums, MO Museum and EIT Climate-KIC on November 16th 2020.

Limiting our thinking and actions just to certain areas will not result in reshaping the world, current development models and our role in them. For this reason, we need to take into consideration entire systems, existing paradigms and new concepts which will leverage change. It is essential to relay on a framework in this transformative journey, and the Deep Demonstration methodology can guide us through complexity.

From my perspective, the systems thinking can be compared to the spatial planning. Discovering and learning a system looks like getting to know a new city, its citizens, and institutions. This experience is gained at a street, community, neighbourhood, and municipal level. It leads to the creation of a personal map which helps to understand connections between the elements of a city, as well as how to navigate through them. In the same way, we discover and acquire knowledge about a system, its main actors, and relations between stakeholders.

The Deep Demonstration methodology has been developed by EIT Climate-KIC as a vehicle to catalyse systems thinking and systemic change aiming to solving complex climate challenges with experimental and impactful approaches.

Currently, the Deep Demonstration methodology is deployed in multiple locations across Europe while working with cities, governments, regional authorities, and industries.

There are four phases of this methodology:

• Intent
• Frame
• Portfolio
• Intelligence

1 — Setting up a transformation

The first step is to identify the system boundaries and the main challenges — their roots, constitutive elements, and crucial actors to engage with.

This stage of the Deep Demonstration is designed to understand features and dynamics of a system, its cultural context, and cooperation environment within which involved parties operate. This phase aims also at envisioning a future system and interventions that will leverage it. Here, innovation and experimentation serve as a bridge to transform this vision of a future system into a reality. During the Intent phase, it is essential to respond to following questions:

• Why do we want this transformation to happen?
• What direction of development do we want to take through this transformation?
• What are the boundaries of the system?
• Who do we need to engage with– stakeholders, communities, actors of change?
• What are the factors that influence the system?
• What new perspectives do we need bring to the system?

2 — Map out everything you can

Normally, we start ambitious plans with strategies. Then, we align actions to the objectives. The Deep Demonstration approach offers a new perspective on it — instead of starting from a strategy, the Frame phase focuses on extracting data, information, and knowledge from conducting extensive mappings which enrich the understanding of a system on several levels — local, national and international. It allows to specify areas in a system where innovation can play a crucial role in materialising the vision defined in the Intent phase.

The Frame has been designed to create many opportunities to co-create, interpret collected data collectively, and engage with stakeholders and communities. Thanks to it, the process does not operate only in silos, but it penetrates the entire system and includes multitude of perspectives and voices.

The crucial element of this phase is about defining areas of a system within which we plan to begin with experimentations and interventions to accelerate the transformation.

3 — From project- to portfolio-thinking

The next piece of the Deep Demonstration framework is focused on activating a portfolio of experiments in the areas identified in the previous phases. The experiments function as interventions that might impact positively the entire system. At this stage, it is essential to observe, manage and learn from deployment of the whole portfolio of experiments, not just singular interventions.

This phase opens an opportunity to identified actors of change, communities and stakeholders to co-design and co-develop their own experimental projects, as well as to co-host this phase by sharing responsibilities and ownership.

Conducting several experiments simultaneously lowers the risk of failure. If we invest all resources in one project, we need to be aware of a collapse of the entire transformative process if this one experiment does not meet expectations. In turn, if we disperse experiments, we create an ecosystem of innovators and innovative initiatives which reinforce each other — even if some of them are not successful.

4 — Learn from the very first second

The Deep Demonstration methodology is characterized by its iterative and ‘learning by doing’ approach. Generally, we tend to include evaluation phase at the end of a process, but here we create many learning spaces to gather insights, data, and knowledge. In this framework, it is fundamental to learn and analyse data constantly as we tackle a complexity of a system and its dynamics. Thanks to this, we can react to unexpected events and discoveries by readapting the process to a faced context.

In conclusion, it is worth to emphasize that a systemic change happens only if we engage with every actor of a system. Everyone has a role to play in a transformative process and can contribute to it. This is the reason why we need to empower equally decision makers, actors of change, communities, and vulnerable groups to be part of a new, hyper-inclusive solidarity movement towards fairness, prosperity for all, and climate neutrality.