Enabling Better Decisions with Multi-Agent Systems
There is an acceptance that we cannot stop widespread population transmission immediately, but by flattening the curve of peak infections we can reduce the burden on health services and ultimately decrease the death rate.
However, creating predictive models to make actionable insights from is difficult with today’s technology. As the COVID-19 outbreak unfolds, new data will continually emerge and it’s vital that governments and businesses can rapidly and effectively respond.
Governments must be decisive in order to distribute resources and save lives. Accurate modelling has helped South Korea achieve the lowest death rate of all countries with significant numbers of patients.
Enabled by early testing, South Korea was then able to map the potential spread and implement the most effective solutions quickly – “deploying a range of legal, medical, technological and public communication efforts.” As such they were able to achieve inspiring results without implementing draconian measures such as cordoning off entire cities, like neighbouring China.
How Can Governments Better Advise The Public In The Event Of A Health Crisis?
Modelling different scenarios via a multi-agent system simulation can highlight behaviour patterns that emerge over time.
Multi-agent systems consist of autonomous entities known as agents. Working collaboratively, agents solve tasks by learning and acting on interactions with one another and the environment they’re in. These entities could represent anything, including different computers in a network, different pieces of software or even a person.
When multi-agent systems are used to process lots of real-world data from an emerging situation such as an outbreak, it can allow civil servants, doctors, and organisations to create malleable models of different potential scenarios. These simulations reveal non-obvious outcomes, which otherwise may have gone unnoticed allowing leaders to make informed decisions.
UK Multi-Agent Systems Symposium At The Turing Institute
From Autonomous Vehicles to Healthcare, Industry 4.0 to Gaming, multi-agent systems are commonplace. Researchers from both industry and academia are currently investigating the topic including: Microsoft, Deepmind, the University of Oxford, University of Edinburgh.
However, as access to accurate data and the availability of computing power continues to rise we expect a proliferation of multi-agent systems entering society. For example, as artificial intelligence becomes more integrated into our healthcare system, it’s integral that these systems work together to help improve the efficiency of our services.
Hadean and Multi-Agent System Modelling
Last week, we published our work modelling the spread of COVID-19, illustrating the effect of combining multi-agent systems and spatial simulations. In the space of 48 hours, a model of 100,000 entities was created using Aether Engine, mapping the potential spread of the pathogen throughout the country.
But this is only a prototype. The complexity of the model can grow infinitely, layering in additional spatial data – including point-based (e.g. individual people), graph-based (e.g. transport networks), or grid-based (e.g. transmission/spread of droplets) – to refine the accuracy of the simulation.
These models can be manipulated to simulate “what ifs” quickly. Traditionally these models require significant computing power and time to run the simulations – but, by contrast, the Aether Engine simulation can be up in a matter of minutes, and scale into the cloud with no additional effort from developers.
More eventualities are mapped and the probability of potential outcomes is accurately predicted, allowing governments to make more informed decisions, quickly.
Hadean and Aether Engine is allowing more data to be processed far more efficiently and effectively making games more fun, models more accurate, and work more rewarding.
If you are struggling to simulate thousands of entities in a single space, get in touch today.