Simulation Modelling and Operational Thinking: An opportunity for systemic design

Laura Guzman-Abello and Camilo Olaya

Despite its widespread application in engineering, urban planning, public policy, and other design-driven fields, computer-based modelling and simulation remain underutilised in systemic design. This persists despite calls within the field to explore computational approaches (Bornes et al., 2024; Jamsin, 2018; Killingsworth, 2022). A key source of reluctance appears to stem from concerns that modelling attempts to “capture” human complexity or that it relies excessively on available data. As a result, systemic design has largely been confined to the use of visual representation tools, such as actor maps, causal loop diagrams, and leverage point identification (Meadows, 1999), without fully integrating the consequences of decisions and actions as they might unfold over time.

However, systemic design addresses complex problems that emerge from the continuous interactions and decisions of actors within social systems. These problems evolve dynamically, shaped by the very decisions actors make. When limited to static visual representations, systemic design faces several risks, including:

• Challenges in scaling and managing complexity as systems grow more intricate.
• Limited capacity to explore alternative scenarios and assess their long-term implications.
• Inadequate handling of uncertainty.
• Vulnerability to subjectivity and cognitive biases, which can distort system representations.
• Dependence on static depictions of inherently dynamic systems, failing to capture feedback mechanisms.
• Weak integration with decision-making processes reduces the applicability of proposed solutions.

Dynamic systems cannot be fully understood through static representations alone. Instead, they require a design approach that incorporates operational perspectives—one that integrates decision-making processes and their evolution over time (Olaya, 2015). This is essential for achieving sustainable, long-term solutions (Forrester, 1971; Meadows, 2008; Sterman, 2000). This article explores the potential of System Dynamics computer simulation modelling to enhance systemic design. System Dynamics provides a computational framework for analysing dynamic complexity, policy resistance, and the development of more effective interventions (Sterman, 2000). In particular, stocks and flows models enable the examination of how actor interactions shape system behaviour over time through continuous exchanges of information and material (Forrester, 1971; Lane, 2008; Olaya, 2015). These models explicitly define system structures in terms of accumulations, changes (flows), and decision rules (equations) that generate feedback loops—essential for fostering a truly systemic, endogenous perspective and for improving the mental models of decision-makers (Richardson, 2011; Richmond, 1994; Sterman, 2000). By distinguishing between different variable types and their interactions, stocks and flows models offer reliable representations of systems’ complexity and behaviour over time (Lane, 2008).

This talk highlights their potential to:

1. Mathematically represent decision-making processes that reflect diverse mental models and perspectives (Olaya, 2015).
2. Evaluate the impact of different assumptions and design scenarios (Olaya, 2015).
3. Shape and refine mental models to align with systemic complexity (Bijl-Brouwer & Malcolm, 2020; Sterman, 2000)
4. Engage stakeholders in the design process by incorporating their mental models (Hovmand, 2014).