The ironies of automation | David Slater
Which of these functions should be performed by human operators and which by machine elements? (Fitts)
David Slater
The Functional Resonance Analysis Method (FRAM) Hollnagel (2012), offers a unique lens to examine the complexity and variability of socio-technical systems, making it a powerful tool to analyze both human and machine interactions within such systems. Among its many insights, the description of a FRAM function shares intriguing parallels with the formal definition of an automaton, particularly the classical Turing machine, Turing (1936). This analogy bridges concepts from systems engineering and computational theory to deepen our understanding of system behaviour and emergent outcomes.
An automaton can be formally described as a quintuple, Hollnagel (2024):
A = (I, O, S, λ, δ)
where:
- I is the set of inputs,
- O is the set of outputs,
- S is the set of internal states,
- λ: S x I → S is the set of rules governing state transitions, and
- δ: S x I → O is the set of rules for determining the output based on the current state and input.
This foundational structure has long been used to describe computational systems, such as finite automata and Turing machines, which operate by transitioning between discrete states in response to inputs, producing outputs based on predefined rules. Interestingly, this description can also encapsulate the essence of a FRAM function.
In FRAM, a function is defined by six aspects: Input, Output, Precondition, Resource, Time, and Control. These aspects form a network of interdependencies that govern the variability and interactions within a system. Conceptually, this aligns closely with the automaton structure. The Input in FRAM corresponds to the automaton’s set of inputs (I), while the Output parallels the automaton’s outputs (O). The states (S) of the automaton can be likened to the function’s endogenous processing of the inputs to produce the outputs in the operational context, shaped by Precondition, Resource, Time, and Control; information transmitted in the metadata of the functions and processed by the algorithms used. The transitions between states (λ) they produce, and the outputs derived (δ) mirror the dynamic coupling and emergent behaviour captured in FRAM models.
This analogy is particularly compelling because it frames a FRAM function as not merely a static representation, but an active element in the computational fabric of a system. Each function, influenced by variability, transitions dynamically between states, producing outcomes contingent on its interactions with other functions and the broader system context. This perspective elevates the role of a FRAM function to that of an automaton-like entity, capable of representing and interpreting the complexity inherent in socio-technical systems.