Smart systems are based on models of their environments. Even a bacterium, as it swims towards its food, has an implicit model of its environment – its model says that a certain chemical gradient implies that food is probably available in that direction. The smart energy grid is based on a model of generation, transmission, distribution, and consumption of electricity.
MODELS AND REALITY
Smart systems must use models because no human-designed system can have a complete representation of its environment; the model may be implicit or explicit, but every smart system is based on a model. Designers of smart systems must acknowledge that their models may misrepresent or ignore critical features of reality. Whether smart systems deal with options trading, baggage handling, medical alerts or earthquake response, smart system vendors and their customers should be aware of the premises upon which a smart system is based.
IMPACT OF MODELS
The model determines what sensors are used and where they are placed, how measured data is analyzed to determine appropriate responses, and the types and locations of responders. When an organization implements a smart system or acquires one from a vendor, the organization uses (possibly implicit) cost-benefit analyses based on models of the system and its environment. Models, implicit or explicit, have a deep impact because they influence whether a smart system is implemented or not. But, most people are not aware of the pervasive role of models in smart systems.
PROBABILISTIC MODELS AND RARE EVENTS
In many cases, models of the environment are probabilistic. Smart systems help organizations exploit opportunities and protect against threats; they are most useful when they help exploit unusually good opportunities and protect against unusually severe threats. But, unusual events are rare and generally don’t occur in a predetermined way. So, most models of smart systems and their environments are fundamentally probabilistic, and some models deal with probabilities of rare events. Decision-making under uncertainty, when the uncertainty is about rare events, is difficult; perforce, analyses and models of smart systems that respond to rare, but very critical events, are complex.
Designers may change models on which their systems are predicated, and so smart systems may be changed as well. Some systems may use machine learning to adapt to changes in their environments automatically; but even so, the process of machine learning is itself based on a model. As in all human endeavors based on models of reality, users of smart systems should check whether the assumptions upon which smart-system models are predicated are likely to remain valid in the future.
MODELS ARE GOOD; SMART SYSTEMS ARE GOOD
None of the ideas on this page are new – they have been propounded for decades by control theorists, and operations researchers studying decision making under uncertainty. What is new, however, is the variety of applications of smart systems ranging from smart roads to ensuring sustainable fish habitats. Smart systems amplify our human ability to sense and respond effectively and intelligently to our world. The use of models is necessary and good. The homo sapiens species is the modeling species – to be sapient is to build abstractions, i.e., models. Vendors and users of smart systems should remain aware of the differences between models and reality, and should continue to verify that their models retain fidelity.