[Do we need to include the partial repetition below or is the above formal enough? Could these bulleted ideas be condensed into one or two paragraphs]

Following Section \ref{specs-overview}, the 13 criteria can be used to evaluate the serendipity potential of existing systems, which we discuss first. The 13 criteria could also be used to guide design of future systems to maximise potential for serendipity; we explore this in the thought experiment outlined in Section \ref{sec:ww}.

Key condition for serendipity

• Focus shift: A focus shift is linked to re-evaluation of data, processes, or products. It may precipitate changes in the entire framework of evaluation or its effects may be more contained. Such reevaluation could be modelled using a multi-agent architecture, in which each agent has a goal and evaluates generated products relative this goal, but in which agents also share their products with other, who then evaluate them against their own metrics.

Components of serendipity

• Prepared mind: This comprises the background knowledge, unsolved problems, current goal, programming, and operating environment of a computational system.

• Serendipity trigger: The generation or observation of a potentially novel example, concept, or conjecture, etc., which precedes a discovery in a computational system.¹ The trigger is outside of the direct control of the system components responsible for evaluations.

• Bridge: Reasoning and/or programmatic interaction brings about a focus shift at an opportune juncture, building on prior preparation and on the serendipity trigger. The bridge may be constructed on the basis of logical methods, analogies, conceptual blending, evolutionary search, automated theory formation and may draw on interactions with other systems.

• Result: The discovery itself may be a new product, artefact, process, hypothesis, use for an object, etc., generated by computational means, which may influence the future operations of the system.

Dimensions of serendipity

• Chance: Controlled randomness in AI systems is well-established, e.g. in Genetic Algorithms and search. Chance also applies in connection with an under-determined outside world (see below).

• Curiosity: The system needs to expend discretionary computational effort on the serendipity trigger. This may be accompanied by system features that an observer would describe as playfulness, inventiveness, and the drive to experiment or understand.

• Sagacity: Sagacity be modelled by employing reasoning over multiple application domains simultaneously; or, again, with a social analogue in cases where the system does not know, but “knows who to ask.”

• Value: The result should be interesting or useful, as judged by the system, the programmer, the user, or another party (potentially another system).

Environmental factors

• Dynamic world: Connections with other systems, data sources, or user input, e.g., via the web, which is highly dynamic – or in the context of a larger simulation.

• Multiple contexts: Reasoning which operates across domains, such as analogical reasoning, or that considers multiple perspectives, as in systems with social awareness.

• Multiple tasks: Multiple goals or targets that compete for resources. The system may be implemented using a multithreaded, parallel processing design.

• Multiple influences: This may again be modelled as a multi-agent systems, as or multiple interacting systems, each with different knowledge and goals. The source of unexpectedness may be arise on various levels, and a system may bring this to bear using techniques of reflection.