Optimizing learning in associative networks and gamification: Lessons learnt and implications

Technological trends have evolved, extended and converged in the 21^st century. Much of these evolutions are incremental, differential and the aim is to sustain or rise above others amidst a dynamic landscape. As data and knowledge bases extend, the fitness of features in each evolution requires systemic modelling targeting multi-objectives. Identification of the local regions of interest and significant features enable more efficient optimization. The information core, current or new invariant features which proceed to the next iteration coupled with gamification in certain domains, can lead to varying implications. This paper briefly reviews literature on associative networks, case-based reasoning, combinations and permutations, optimization, and gamification and conclude with implications and opportunities for symbiotic networks.