We’ve all been in situations where the way forward feels a bit like driving down a foggy road. You can see just far enough to keep moving, but not far enough to know what's ahead. When dealing with complex systems (organizations, communities, industries...) it often feels the same. Decisions are made with partial visibility, guided by assumptions that may or may not hold true once the fog lifts.

Imagine a small town that’s considering building a new community centre. On the surface, it seems straightforward: find a site, raise some funds, and open the doors. But anyone who has tried to create lasting community change knows it’s rarely that simple. Questions quickly pile up: Who will the centre serve? How will it be funded? What programs will it offer? How will it connect to what already exists? The road ahead looks foggy. You can see a few steps forward, but not the full path.

This is where system inquiry matters. Before rushing to act, it gives the community tools to see more clearly. It helps untangle the structures, relationships, and hidden feedback loops that produce today’s challenges. And once the road is clearer, the town can act more wisely; not just by building a centre, but by shaping it in ways that strengthen the whole community over time.

At Hostile Sheep, we work in the same way. Whether it’s research that sees the system, prototypes that evolve from trust, or strategies for the long haul, system inquiry guides us. It’s how we help organizations and communities move from uncertainty to clarity and from clarity to meaningful, lasting change.

There are two main ways we do this:

• Current system inquiry, where we understand the forces producing today’s outcomes.
• Future system inquiry, where we explore how the system might evolve to produce more desirable outcomes.
• In between is an overlap zone, where today meets tomorrow. This is where we figure out how to change the current system in order to achieve that more desirable future.

"Today’s problems come from yesterday’s solutions." - Peter Senge

When we talk about current system inquiry, we mean taking a step back to see the whole picture of how the system operates today. Too often, organizations and communities look at isolated parts of their system (a single program, a budget item, a staffing challenge) and wonder why the same problems keep resurfacing. If we only ever treat the parts in isolation, the deeper dynamics that drive those problems never come into view.

Current system inquiry is about shifting the lens. It’s about unpacking the inputs, outputs, and the messy middle that connect them. It's about seeing how they interact to produce the outcomes we experience today.

There are a few tools we like to use to help us do this:

• Stakeholder worldview mapping is a way of surfacing who is part of the system and how they see it. It makes visible the perspectives, priorities, and power dynamics that shape the way the system operates today.
• Causal layered analysis is a method for digging deeper into what lies beneath the surface; the narratives, values, and even cultural myths that keep current patterns in place.
• Current stock and flow modelling is a structured way of showing how resources such as time, money, attention, or influence accumulate or deplete as they move through the system, creating pressures that drive its behavior.
• Causal mapping is a technique for sketching out how different forces connect and influence one another across the system. While many of these forces exist in the current system, understanding these system dynamics will enable us to better predict how changes to the system will impact the larger system.

By looking at the system holistically, what once looked “stable” or “inevitable” starts to reveal itself as the product of many interconnected choices and forces working together. Stability comes from the system reliably producing the same outputs over time. But reliable isn’t the same as desirable. A system can be very good at reproducing patterns that no longer serve the community.

This is why we begin here. Before imagining what could be different, we need to understand what holds the present in place. Once we’ve surfaced the inputs, outputs, and messy middle that define today’s outcomes, we’re ready to explore what tomorrow might bring.

If current system inquiry is about seeing today clearly, future system inquiry is about stretching our vision into what might be possible. Once we understand what holds today’s patterns in place, the question becomes: how could this system evolve to produce more desirable outcomes?

Unlike the current system inquiry, a future system inquiry is not a linear process. We don’t simply move from one tool to the next and declare the work done. Instead, we cycle back and forth refining models, testing scenarios, and deepening our understanding with each pass. Every iteration sharpens the picture of what’s possible, while also helping us spot risks and trade-offs before resources are committed.

When we start shifting our thoughts to the future, we use these tools:

• Current stock and flow modelling builds on the baseline we established earlier. It clarifies the benefits of the current system that we want to preserve, while also highlighting bottlenecks and delays that are ripe for improvement. Each new idea can be tested against this foundation.
• Causal mapping helps us explore the forces driving system behavior. As we move into future thinking, the goal isn’t to collapse the current system but to refine it. By cycling between causal maps and other tools, we can spot leverage points where a small shift could create outsized improvements.
• Causal loop diagramming shows how feedback loops might amplify or dampen change. We often return to these diagrams again and again, adjusting them as new insights emerge, and using them to turn early ideas into coherent strategies.
• Future stock and flow simulations let us explore “what if” scenarios before investing real-world resources. By simulating different interventions, we can surface both intended benefits and unintended consequences. This information allows us to refine our models further and in an ongoing way.