What’s the problem? Link to heading

When an organisation needs to respond to a new situation, be it threat or opportunity, there are numerous decisions to take, often in short order. Building a common understanding of the situation and generating insights to assist with such decisions is time consuming, expensive, and stressful, particularly if the consequences are significant. What data do you need? How do you compare qualitative and quantitative evidence? How do you resolve differing perspectives? How do you keep key stakeholders informed and involved? How do you keep track of what you’ve decided and why? How do you update your strategy as new information emerges?

Getting these things wrong costs money, slows things down, and impacts on both delivery and reputation. In some circumstances, it can have catastrophic consequences. Decision makers need all the help they can get.

What’s the solution? Link to heading

There are three stages to rapid decision making in conditions of deep uncertainty: understand, plan, and refine.

Understand Link to heading

• Use whatever data and expertise you already have to rapidly build a shared mental model of the system you are dealing with.
• Dig into “gut instincts” and intuition.
• Describe the causal relationships and identify dependencies.
• Compare and combine different perspectives.
• Visualise the results.

Plan Link to heading

• Test your hypotheses and options using appropriate decision science methodologies
• Red Team potential actions and understand potential enablers and detractors to achieving your objectives
• Identify and compare relative merits of potential actions or options for achieving objectives
• Represent complex adversarial systems, going into detail where you need to, filtering out irrelevancies.
• Simulate outcomes based on the logic and quantitative data you have generated using stock and flow, decision trees, system dynamics and other mathematical modelling approaches.

Refine Link to heading

• Share your understanding and insights with stakeholders using visual analytics, simplified but without losing the nuance.
• Demonstrate the rationale for your conclusions for both forward decision-making and retrospective audit.
• Refine the system based on feedback and as new information and insights emerge.

How we can help Link to heading

Cosimmetry has combined the mathematics of models and systems with a deep understanding of how people work under pressure to build intuitive, easy-to-use software that supports decision makers throughout their journey from Day-1 Hour-1 to Business As Usual. Our tooling and methodology facilitates decision making in complex situations with speed and confidence, compressing processes without compromising their integrity. Existing users include military commanders, policy makers, engineers and finance professionals.

Our world class research and development team conduct highly specialised work that underpins the next generation of decision support tools. These tools help teams of people to collaborate in developing and testing new products or systems, strategies and policies, in volatile situations, providing valuable insights that would be impossible using conventional approaches.

We are focused on solving the following perennial challenges:

• Helping senior leaders access and explore the wisdom of experts = bridging the gap between analysts and decision-makers, uncovering unknown knowns
• Surfacing and combining the diverse thinking and knowledge in the team = avoiding groupthink
• Assembling, testing and then adapting a plan = countering the natural bias towards the plan continuation
• Coping with massive scale, uncertainty and complexity in short time frames = avoiding information overload
• Building confidence where there is a lack of high quality data = avoiding undue focus on known knowns
• Resolving competing priorities between departments, teams, or organisations = making space for quieter voices
• Surfacing unintended consequences before they materialise, and understanding how to counteract them once they do = accelerating the test/pilot phase.
• Combining qualitative and quantitative evidence = resolving the tension between complete data, emerging data and gut instinct
• Integrating human expertise with data analytics = keeping common sense in play