Surspécification

Surspécification is a term in the fields of artificial intelligence, computer science, and decision theory, referring to the process of specifying a requirement or constraint in such a way that is either too vague, too broad, or too complex to be actionable.

The concept is closely related to the idea of under-or over-specification, where an initial requirement specifies too much or too little information for a particular task. Surspecification often arises when attempting to formalize a concept or intent that is inherently ambiguous or context-dependent.

In the AI and decision-making context, surspécification can lead to arbitrary or incorrect solutions, as the system may choose an approach that satisfies the vague constraints in a manner that is not intended or optimal by the system designers.

Several factors can contribute to surspécification, including:

* Ambiguous language or unclear requirements

* Lack of specific context or domain knowledge

* Oversimplification or abstraction of complex systems

* Difficulty in quantifying or measuring certain properties or criteria

Mitigating surspécification involves providing clear, concise, and context-specific requirements that are grounded in the available knowledge and relevant constraints of the problem at hand.

Examples of surspécification can be seen in various real-world applications, such as machine learning, decision support systems, and optimisation algorithms, where any degree of vagueness in the input or objective function can lead to outputs that are far from the expected results.

By being aware of the limitations and pitfalls associated with surspécification, system designers can take steps to refine and narrow down requirements to ensure that results and outcomes align with the initially intended goals and intentions.