Description

This curriculum spans the breadth of a multi-workshop organizational modeling initiative, covering the technical, social, and governance aspects of building and deploying systems models in real-world decision-making contexts.

Module 1: Foundations of Systems Thinking and Modelling

Selecting between hard and soft systems methodologies based on stakeholder consensus and problem ambiguity in organizational redesign initiatives.
Defining system boundaries when modeling interdepartmental workflows, balancing comprehensiveness with model manageability.
Choosing causal loop diagrams versus stock-and-flow models based on whether feedback dynamics or accumulation behaviors are central to the issue.
Documenting mental models of key stakeholders to expose assumptions before constructing formal system representations.
Deciding when to model qualitative influences (e.g., trust, morale) as semi-quantitative variables in feedback loops.
Establishing model purpose early—whether for insight, prediction, or stakeholder alignment—to guide modeling depth and validation criteria.

Module 2: Problem Structuring and Stakeholder Engagement

Facilitating cross-functional workshops using rich pictures to capture divergent views of a supply chain disruption.
Mapping power and interest of stakeholders to determine whose mental models require formal integration into the system diagram.
Resolving conflicting problem definitions among departments by co-creating a shared problem statement before modeling begins.
Using boundary critique to challenge whose interests are included or excluded in a public policy simulation model.
Deciding when to use nominal group technique versus open dialogue to structure input in politically sensitive modeling projects.
Integrating qualitative interview data into causal loop diagrams without oversimplifying complex social dynamics.

Module 3: Causal Loop Diagramming and Feedback Analysis

Identifying dominant feedback loops in a customer retention model to prioritize intervention points.
Distinguishing between reinforcing and balancing loops in workforce attrition models where multiple loops interact.
Labeling link polarities in causal loops when intermediate variables are implicit or contested by stakeholders.
Managing model complexity by clustering variables into aggregates (e.g., "market confidence") without losing explanatory power.
Validating loop structures with historical data or expert judgment when quantitative calibration is not feasible.
Communicating delay effects in feedback loops to decision-makers who expect immediate results from policy changes.

Module 4: Stock-and-Flow Modeling and Dynamic Simulation

Defining stock units (e.g., full-time equivalents, backlog items) to ensure dimensional consistency in workforce planning models.
Specifying inflow and outflow structures for a product inventory model under intermittent supply disruptions.
Calibrating model parameters using historical throughput data when expert estimates are unreliable.
Implementing nonlinear functions (e.g., saturation effects) in adoption curves for new technology rollout simulations.
Testing model behavior under extreme conditions (e.g., zero hiring, sudden demand spike) to expose structural flaws.
Using sensitivity analysis to identify which parameters (e.g., training duration, attrition rate) most affect long-term outcomes.

Module 5: Model Validation and Confidence Building

Conducting extreme condition tests to verify that model behavior remains plausible under implausible inputs.
Comparing model outputs with historical trends to assess face validity in organizational change scenarios.
Using behavior pattern tests to check if the model reproduces known cycles (e.g., boom-bust in project staffing).
Documenting structural assumptions (e.g., fixed delay times) that limit model applicability to specific contexts.
Engaging skeptics in model walkthroughs to address concerns about omitted variables or oversimplified relationships.
Deciding when to halt refinement based on diminishing returns in predictive accuracy versus stakeholder understanding.

Module 6: Scenario Planning and Policy Testing

Designing policy levers (e.g., hiring caps, training investment) as adjustable parameters in a talent development model.
Running comparative simulations to evaluate the long-term impact of centralized versus decentralized decision-making.
Assessing unintended consequences of a new incentive scheme in a sales performance model over a 36-month horizon.
Communicating trade-offs between short-term cost savings and long-term system resilience in infrastructure investment models.
Using simulation results to identify policy resistance mechanisms, such as delayed feedback or goal erosion.
Structuring scenario narratives around exogenous shocks (e.g., regulatory change) to test adaptive capacity of the system.

Module 7: Integration with Organizational Decision Processes

Embedding system model outputs into existing management reporting dashboards without distorting interpretation.
Training operational managers to interpret simulation results without requiring modeling software proficiency.
Establishing review cycles to update models when organizational structure or key metrics change.
Negotiating data access for model calibration when departments treat performance metrics as confidential.
Defining ownership roles for model maintenance when multiple units rely on a shared supply chain simulation.
Archiving model versions and assumptions to support auditability and institutional memory.

Module 8: Ethical and Governance Dimensions of Systems Modelling

Disclosing model limitations when presenting simulation outcomes to executive decision-makers under time pressure.
Assessing whether a model reinforces existing power structures by privileging quantifiable over qualitative outcomes.
Managing consent and anonymity when incorporating individual performance data into organizational models.
Documenting assumptions about human behavior (e.g., rationality, compliance) that may embed bias into policy recommendations.
Resisting pressure to adjust model parameters to align with pre-determined strategic outcomes.
Establishing review protocols for models used in high-stakes decisions, such as workforce reduction or service closures.