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Evolutionary systems such as GAmprovising necessarily operate in a \textbf{dynamic world} which is evolving continuously and may also be affected by changes in user tastes as they evaluate musical output from Improvisors. The \textbf{multiple contexts} arise from the possibility of having multiple users evaluate the musical output (though this is as yet not implemented formally) or through the user changing their preferences over time. \textbf{Multiple tasks} are carried out by the system including evolution of Improvisors, generation of music by individual Improvisors, capturing of user ratings of a sample of the Improvisors' output, and fitness calculations. \textbf{Multiple influences} are captured through the various combinations of parameters that could be set and the potential range of values for each parameter.  \paragraph{Minimally serendipitous systems}  If applied to a system which could be described as minimally serendipitous at best, and perhaps not at all serendipitous, does our model identify the lack of serendipity?  As example, a spellchecker program identifies spelling errors in text input and optionally can correct spelling automatically. The only situation we can conceive of where serendipity could possibly occur is tenuous; perhaps a suggested correction may be incorrect, but may lead the user to interpret the correction in an unexpected way. In all other aspects that we have considered, spellchecker software would be a decidedly unlikely candidate for harbouring serendipitous opportunities.  The system *** %AARGH how do we begin to do this...! to try and apply our model to a spell checker?Giving up for now. I do think it's worth it, perhaps I'm just a bit tired at this point! Feel free to have a go if you fancy the challenge! AJ  % \paragraph{{[}To add: HR.{]}}  \paragraph{Recommender systems.}  
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% As a general comment, we would say that this is largely how  % \emph{research and development} of recommender systems works, but  % without the same levels of system automony envisioned here.  \small  \begin{table}[ht]%dp]  \caption{Summary of case studies as viewed with our \caption{Summary: applying  computational serendipity model} model to positive case studies}  \begin{center}  \footnotesize  \begin{tabular}{|c|l|l|}  \hline  {\textbf Model part}  & Evolutionary music systems & Recommender systems \\ \hline  \hline  %{\em Key Condition} && \\ 
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{\em Components} && \\  \hline  \hline  Serendipity trigger && & Evolutionary operations and user input &  Input from user behaviour \\ \hline  Prepared mind & Musical knowledge, evolution mechanisms  & Through user model/domain model \\ \hline  Bridge & Creation of newly-evolved Improvisors  & Elements identified outside clusters \\ \hline  Result & & Music generated by fittest Improvisors&  Dependent on organisation goals \\ \hline  \hline  {\em Dimensions} && \\  \hline  \hline  Chance & If discovered in huge search space  & If learning from user behaviour \\ \hline  Curiosity & If a particular user notes an Improvisor  & Making unusual recommendations \\ \hline  Sagacity & User appreciation of Improvisor over time  & Updating models after user behaviour \\ \hline  Value & Via fitness function (proxy of creativity)  & As per business metrics/objectives \\ \hline  \hline  {\em Environmental} && \\  {\em Factors} && \\  \hline  \hline  Dynamic && & Continuous computational evolution&  As precondition for testing system's \\ world && & \hspace{3mm}and changes in user tastes &  \hspace{3mm} influences on user behaviour\\ \hline  Multiple && & Multiple &  User model and domain model\\ contexts && & \hspace{3mm}users opinions? &  \\ \hline  Multiple && & Evolving Improvisors, generating music, &  Making recommendations, learning\\ tasks && & \hspace{3mm}collecting user input, fitness calculations&  \hspace{3mm}from users, updating models \\ \hline  Multiple && & Through various musical&  Experimental design, psychology, \\ influences && &\hspace{3mm}parameter combinations&  \hspace{3mm} domain understanding\\ \hline  \end{tabular}  \end{center}