Description

This curriculum spans the iterative research practices of a multi-phase systems thinking engagement, comparable to those conducted by internal strategy teams or external consultants mapping complex organizational dynamics, integrating data and stakeholder perspectives, and sustaining models through governance and operational feedback.

Module 1: Defining System Boundaries and Scope

Selecting which organizational units, processes, or external stakeholders to include in a system model based on influence and data accessibility.
Documenting assumptions about boundary exclusions and justifying them to stakeholders during governance reviews.
Negotiating scope adjustments when new feedback loops emerge during stakeholder interviews.
Deciding whether to model a supply chain as a closed or open system based on regulatory reporting requirements.
Handling conflicting definitions of system scope between operational teams and executive sponsors.
Using boundary critique techniques to expose power dynamics influencing what is considered "in" or "out" of the system.

Module 2: Mapping System Structure and Relationships

Choosing between causal loop diagrams and stock-and-flow models based on client modeling maturity and data availability.
Validating feedback loop assertions with historical performance data or expert triangulation.
Resolving disagreements among team members about the direction or strength of causal links in a policy implementation model.
Integrating qualitative insights from ethnographic observation into formal system structure diagrams.
Deciding when to abstract or decompose subsystems to maintain model clarity without losing critical dynamics.
Managing version control when multiple analysts update interdependent relationship maps in parallel.

Module 3: Data Integration and Model Calibration

Assessing the reliability of legacy ERP data for populating stock variables in a production-distribution model.
Imputing missing time-series data using proxy indicators while documenting uncertainty margins.
Selecting calibration time windows that capture both stable operations and disruption events.
Reconciling discrepancies between self-reported behavioral data and observed system performance metrics.
Applying smoothing techniques to noisy operational data without obscuring critical thresholds.
Documenting data transformation steps to ensure auditability during regulatory or internal review.

Module 4: Identifying Leverage Points and Intervention Pathways

Evaluating whether to target policy rules or information flows when addressing persistent delays in service delivery.
Assessing political feasibility of proposed interventions in highly unionized operational environments.
Ranking leverage points using a weighted matrix that includes impact, cost, and implementation lead time.
Anticipating second-order effects when proposing changes to incentive structures in a sales organization.
Mapping intervention dependencies to avoid sequencing conflicts during rollout planning.
Engaging frontline staff to surface informal workarounds that may undermine formal interventions.

Module 5: Stakeholder Engagement and Mental Model Elicitation

Designing interview protocols that surface implicit assumptions about cause-and-effect in crisis response systems.
Facilitating cross-departmental workshops where participants assign different meanings to the same system element.
Managing power imbalances in group sessions to ensure input from lower-ranking but operationally critical staff.
Translating conflicting mental models into comparative system archetypes for structured discussion.
Deciding when to use anonymous input mechanisms to capture dissenting views on system performance.
Archiving stakeholder inputs with timestamps and attribution to support traceability in audit scenarios.

Module 6: Scenario Testing and Policy Simulation

Defining scenario parameters for a demand surge event based on historical peaks and climate projections.
Setting simulation run durations long enough to observe delayed feedback but within computational constraints.
Interpreting oscillatory behavior in model outputs as either artifact or plausible system dynamic.
Communicating probabilistic outcomes to risk-averse executives who expect deterministic predictions.
Adjusting model granularity when simulation results are too sensitive to minor parameter changes.
Validating simulation edge cases against documented past failures or near-misses.

Module 7: Governance, Ethics, and Model Transparency

Establishing review cycles for model updates when underlying system conditions evolve rapidly.
Disclosing model limitations in executive summaries to prevent overreliance on simulation outputs.
Implementing access controls for sensitive models that contain proprietary or personally identifiable data.
Assessing potential for algorithmic bias when system models inform staffing or resource allocation decisions.
Creating model documentation that enables independent replication by internal audit or regulatory bodies.
Handling requests to repurpose a validated model for a new domain with different boundary conditions.

Module 8: Iterative Learning and Organizational Embedding

Designing feedback mechanisms to capture real-world outcomes for comparison with model predictions.
Integrating system model insights into existing management reporting dashboards without overloading users.
Training operational leads to recognize early indicators of model-invalidating structural shifts.
Scheduling periodic model revalidation sessions aligned with strategic planning cycles.
Managing resistance when model findings challenge long-standing performance metrics or KPIs.
Establishing cross-functional review panels to assess model relevance and accuracy over time.