What is decision science?
Decision science brings together analytical techniques, systems thinking and human expertise to improve the quality, transparency and confidence of decision-making.
Some problems require structured workshops.
Others require simulation.
Some benefit from operational research or mathematical optimisation.
Others are best explored through collaborative wargaming.
Our role is to select the right combination of approaches to help decision makers understand their options, evaluate trade-offs and make decisions with confidence.
Decision Science Methods Link to heading
No single technique is appropriate for every problem. We draw from a broad toolkit of complementary methods.
Systems Thinking Link to heading
Understanding how people, processes, technology and organisations interact as a connected system rather than as isolated parts.
Typical applications include:
- Complex programmes
- Organisational transformation
- Capability planning
- Policy development
- Enterprise architecture
Operational Analysis Link to heading
Applying structured analytical methods to improve operational effectiveness and support evidence-based decisions.
Typical applications include:
- Defence
- National security
- Infrastructure
- Emergency planning
- Government policy
Multi-Criteria Decision Analysis (MCDA) Link to heading
Many important decisions involve competing priorities rather than a single “correct” answer.
MCDA provides a structured way to compare options against multiple criteria, making assumptions explicit and helping stakeholders understand trade-offs.
Typical applications include:
- Investment decisions
- Procurement
- Capability assessment
- Option appraisal
- Portfolio prioritisation
Wargaming Link to heading
Wargaming enables organisations to explore decisions, behaviours and consequences before committing resources in the real world. Our team is highly experienced in running large-scale, complex wargames, and have designed, facilitated and run wargames at a national level.
We’ve also built and developed novel technologies to enable wargames in areas and for problems considered impossible.
Different forms of wargaming provide different insights.
Examples include:
- Kriegsspiel and matrix games
- Seminar wargames
- Tabletop exercises
- Adversarial planning
- Campaign analysis
- Red teaming
- Crisis management exercises
- Decision games
Wargaming is particularly valuable when human behaviour, uncertainty and competition play a significant role.
Scenario Planning Link to heading
Exploring multiple plausible futures rather than attempting to predict a single outcome.
Scenario planning helps organisations prepare for uncertainty, identify resilient strategies and test long-term decisions against different future environments.
Modelling and Simulation Link to heading
Building representations of real-world systems to explore behaviour, test interventions and understand potential outcomes before implementation.
Depending on the challenge, this may include:
- System dynamics: Understanding how feedback loops, delays and interactions influence behaviour over time. Particularly useful for strategic planning, organisational change and policy analysis.
- Agent-based modelling: Representing individual people, organisations or assets as autonomous agents whose interactions produce larger system behaviours. Useful where individual behaviour influences overall outcomes.
- Discrete event simulation
- Monte Carlo simulation
- Network modelling
- Digital twins
- Hybrid simulation approaches
Optimisation Link to heading
Identifying the best allocation of limited resources while balancing operational constraints.
Typical applications include:
- Scheduling
- Logistics
- Workforce planning
- Resource allocation
Network Analysis Link to heading
Understanding relationships between people, organisations, assets or information.
Applications include:
- Organisational analysis
- Supply chains
- Critical infrastructure
- Intelligence analysis
- Social networks
Risk and Uncertainty Analysis Link to heading
Helping organisations understand not only what could happen, but how likely different outcomes are and how uncertainty should influence decision-making.
Methods include:
- Sensitivity analysis
- Probabilistic modelling
- Bayesian approaches
- Decision trees
- Influence diagrams
- Risk modelling
Causal Analysis Link to heading
Understanding why things happen rather than simply identifying correlations.
We use causal mapping and structured reasoning techniques to identify dependencies, root causes and intervention points within complex systems.
Benefits Realisation Link to heading
Ensuring programmes remain focused on delivering measurable outcomes rather than simply completing activities.
Benefits mapping helps organisations understand how investments translate into operational value.
Human-Centred Decision Support Link to heading
The best analytical models are useless if decision makers cannot understand or trust them.
Our methods place people at the centre of the process by making assumptions explicit, encouraging collaboration and presenting evidence in ways that support transparent decision-making.
Combining Methods Link to heading
The most valuable insights rarely come from a single analytical technique.
A typical engagement might combine:
- stakeholder workshops
- systems mapping
- causal analysis
- scenario planning
- simulation
- MCDA
- visualisation
- collaborative decision support
This allows organisations to move progressively from understanding the problem to identifying options, evaluating alternatives and selecting the most appropriate course of action.
Decision science is not about applying sophisticated mathematics for its own sake.
It is about helping people make better decisions.
Our combination of expert people, rigorous methods and intelligent tools enables organisations to understand complexity, challenge assumptions and see relationships that might otherwise remain hidden.
Because the best decisions often come from changing perspective rather than simply analysing harder.