\label{sec:recommendations}
Viewing the concepts in Section \ref{sec:by-example} through the practice scenarios we have discussed, we can describe the following challenges for research in computational serendipity.
Autonomy: Our case studies in Section \ref{sec:computational-serendipity} highlight the potential value of increased autonomy on the system side. The search for connections that make raw data into “strategic data” is an appropriate theme for research in computational intelligence and machine learning to grapple with. In the standard cybernetic model, we control computers, and we also control the computer’s operating context. There is little room for serendipity if there is nothing outside of our direct control. In contrast with the mainstream model, von Foerster advocated a second-order cybernetics in which “the observer who enters the system shall be allowed to stipulate his own purpose.” Accordingly, a primary challenge to the serendipitous operation of computers is developing computational agents that specify their own problems.
Learning: Each of the case studies considered in Section \ref{sec:computational-serendipity} is able to learn from experience. As we considered ways to enhance measures of serendipity in these examples, we were led to consider computational agents that participate meaningfully in “our world” rather than in a circumscribed microdomain. A second challenge is for computational agents to learn more and more about the world we live in.
Sociality: We may be aided in our pursuit of the “smart mind” required for serendipity by recalling Turing’s proposal that computers should “be able to converse with each other to sharpen their wits” \cite{turing-intelligent}. Turing recognised that computers would have to be coached in the direction of social learning, but that once they attain that standard they will learn much more quickly. The four supportive factors for serendipity described in this paper resemble nothing more than our experience of social reality. A third challenge is for computational agents to interact in a recognisably social way with us and with each other, resulting in emergent effects.
Embedded evaluation: outline a general programme for computational creativity, and examined perceptions of creativity in computational systems found among members of the general public, Computational Creativity researchers, and existing creative communities. We should now add a fourth important “stakeholder” group in computational creativity research: computer systems themselves. System designers need to teach their systems how to make evaluations in way that is both reasonable and ethical. This condition is exemplified by the preference for a “non-zero sum” criterion for value introduced in Section \ref{sec:by-example}. A fourth challenge is for computational agents to evaluate their own creative process and products.