Modelling & Simulation

Distinct from ‘big data’ and ‘analytics’, modelling and simulation offers a variety of advantages to firms willing to incorporate it into business planning and strategy.

  • Predictive insight: predict the future based on the past and present state of a system.
  • Hypothesis confirmation: complement expert understanding of processes, markets, and environments by exploring intuition with generative models, to understand the key drivers behind changes.
  • Counterfactual analysis: run ‘what-if’ experiments to determine precisely how a change in strategy or variables will impact outputs, even in complex adaptive systems with moving parts.

From locating additional evidence to help explain trends you already suspected were occurring to encouraging a more quantitative mindset among your colleagues in industries where intuition and expertise have always been the order of the day, there are plenty of reasons other than forecasting to incorporate models into your organisation’s approach.

We use a variety of modelling paradigms, each best-suited to a different set of problems, to enable speedy, productive systems simulation:

  1. System dynamics models take a global and systematic view of how dependencies and flows within households, organisations, and societies.
  2. Discrete event models can be used to monitor processes and sequences, often in industrial, manufacturing and supply-chain contexts.
  3. Agent-based models are employed to capture the ’emergent’ impacts of entities interacting within systems, the results of feedback loops, and the consequences of adaptation in complex systems where entities are able to respond to their environments.

Using HASH, we combine unlimited numbers of models and modelling paradigms together into single unified meta-models.

Further reading on combining modelling paradigms


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