Simulation in the City
In a city context, if you are lucky, we find simulations of water supplies, of electricity lines, of transportation modes, of human behaviour, economic state, climate impacts, buildings and zoning, food and supply chains, and geographic context. These, and any other models that may exist for any given city, sit as representations of real world vertical domains: happily chugging away without reference to each other.
This is not how a city works.
When we consider a city as a system-of-systems – a layered environment pervaded by complex interdependencies and cascading effects - simulation in the city is ripe for innovation. There is a very real need to be able to leverage existing investments in simulation, regardless of the underlying methodologies or temporal constraints of each, as components in a holistic, intra- and intersystem representation. Part of this is the requirement to be able to integrate hard (ie: physical systems) models with soft (ie: socio-cultural, behavioural, and influence). There is an equal need, given the rise of so-called smart city services and the general availability of useful data, to be able to drive such a holistic representation with near real time[1] data in order to obtain actionable insight.
Most importantly, however, there is an underlying requirement to democratise simulation: to demystify and make more intuitive the somewhat arcane specialisations that simulation practitioners have created around their art. We need to allow domain experts to dynamically create, execute, and evaluate bespoke solutions that answer their questions when required rather than forcing them to wait for the development, testing, and release of such solutions. If a solar flare is imminent, for example, we want to know the impact of that on hospital intensive care – via electricity, water, communications, supply chain, transportation, and personnel layers – as soon as possible: not in six months time when the flare is a distant memory, apart from those pesky deaths it caused.
There is, in the United Kingdom alone, a lot of current attention – not to mention money – focused on the stimulation of Future, Smart, or Integrated Cities. I do not expect real innovations to arise as a result. When operating within the administrative entity of a city, politics and economics very quickly raise their heads and, in general, the result is a much-depleted outcome from what may have originally been intended. Moreover, given the same actors, what is developed and demonstrated for one city often fails to transition to another with the result that multiple cities have multiple solutions that operate and have benefit, of whatever scale, only within the boundaries of each city.
Instead of cities, therefore, I see the potential for real innovation arising from those who operate or are responsible for city-scale environments. Consider, for example, the United Kingdom rail network, Canary Wharf, a global multinational financial group, or a major international retailer. Such entities embrace – at a minimum - a collection of buildings, infrastructure, supply chains, humans, operational systems, and transportation; with a wealth of connected interdependency across all layers.
They are, in short, just like a city: only geographically dispersed.
Indeed, it is arguable that city-scale environments like this – spanning jurisdictional and geographic boundaries – are more complex than the average geospatially bounded city. Different localisations, applied across time zones in many cases, create non-uniform complexity that must be understood, managed, and interacted with if the operation of the environment is to be successful.
It is here that the opportunity arises. City-scale environments are, normally, commercial entities driven commercial imperatives. In the case of a global financial institution, for example, the commercial imperative to make money relies on a relatively simply set of functional applications that, in turn, depend on a complex, interdependent causal mesh that follows the sun across national borders, regulatory jurisdictions, cultures, and environmental contexts. A seemingly minor event at any point in any layer of that mesh can – and will – trigger cascading effects that have the potential to shut down or degrade any given functional application. And, of course, such events must be detected, analysed, and handled in near real time as the risk, threat, and opportunity context of the operation changes.
It is these considerations that raise the bar for simulation in city-scale environments.
At city-scale, simulation must evolve away from current siloed niches and embrace integration with both other dissimilar models and with near real time stimuli. Simulation must move away from the use of static data, other than for historical references, and towards an event-driven mode of operation. Beyond that, simulation must become both accessible to and intuitive enough for layperson use.
This change means rethinking simulation and its role in city-scale contexts. Instead of being a safe place in which to test theories and strategies, simulation becomes a form of digital imagination: testing the potential outcomes and ramifications of intervention options arising from the taking of a near real time pulse across all layers in a city-scale environment.
In this approach, simulation no longer ‘just’ tells you where the vulnerabilities are in any given vertical but, instead, is part of a tool chain that provides actionable insight to city-scale operations. Instead of providing insight into cyber assets and the impact of potential risks, as just one example, this new model actively and dynamically calculates the risk and threat impact of cyber effects on functional applications and, through simulation, then explores the potential ramifications of any of a relevant set of interventions that might be made. And all this is recalculated and reimagined every time there is a change in any real-world layer.
With Resilio, we have been exploring the viability of this approach and - while I cannot yet tell you that it will prevent the next Detroit, Yebes, or Pripyat - I can already tell you that it gets us a lot closer to being able to sense, model, optimise, and take action, in near real time, across the entire scope of complex interdependencies, both hard and soft, at city-scale.
[1] There is always some network latency when acquiring data.
