The Shaftesbury Partnership's systems-change approach is backed by a rigorous emphasis on Data Science tools, which consists of three key areas:
Simulation - Measurement - Prediction
We believe agent-based modelling is the one of the premier simulation methods to better understand the complexities and dynamics of today's most intractable challenges.
Together with our partners, we harness this approach to best understand how inequality and other aspects of broken systems can be simulated and modelled. Visit our Inequality Simulator page to explore an early prototype under development.
Our approach is to focus on particular sectors such as healthcare, migration, and the climate, and model the systemic interactions between agents using continuously improving agent-based simulations.
This information can then be used to inform decision makers (in combination with the use of war gaming and red-teaming decisions processes), through simulating interventions in these and other models.
Randomised control trials have been well established as best-in-class for measuring intervention effectiveness.
We are interested in taking this to the next level through working with our partners using mobile and other sensing technologies to bring down cost, generate more real time data, and make the practice of such approaches mainstream.
Bringing this data into contexts beyond healthcare, and other sectors such as education and offending, with tools to speed up the process of measurement will enable greater insights to be brought into practitioner contexts beyond pure academia.
As the challenges we face evolve and spring up today so rapidly, we believe this approach will enable more rapid decision-making, faster and more adaptive innovation, and counter the analysis paralysis and bureaucracy that can build up in over-regulated sectors.
With our partners, we harness the power of Artificial Intelligence to help teams make better predictions, and therefore better decisions.
Whilst AI is very powerful it can often be limited to predicting the future based on past datasets which are not always great at dealing with future uncertainty, and which can at times exacerbate inequalities.
Our interest is in specific AI technologies that can help augment human decision-making, particularly in groups, to produce better results than people alone or AIs alone can generate, leading to better policy and execution.
The tools our partners are developing have wide application in boardrooms, government, and communities, and lead to greater levels of satisfaction in the process than other forms of decision making including voting.