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The largely dominant meritocratic paradigm of highly competitive Western cultures is rooted on the belief that success is mainly due, if not exclusively, to personal qualities such as talent, intelligence, skills, smartness, efforts, willfulness, hard work or risk taking. Sometimes, we are willing to admit that a certain degree of luck could also play a role in achieving significant success. But, as a matter of fact, it is rather common to underestimate the importance of external forces in individual successful stories. It is very well known that intelligence (or, more in general, talent and personal qualities) exhibits a Gaussian distribution among the population, whereas the distribution of wealth — often considered as a proxy of success — follows typically a power law (Pareto law), with a large majority of poor people and a very small number of billionaires. Such a discrepancy between a Normal distribution of inputs, with a typical scale (the average talent or intelligence), and the scale-invariant distribution of outputs, suggests that some hidden ingredient is at work behind the scenes. In this paper, we suggest that such an ingredient is just randomness. In particular, our simple agent-based model shows that, if it is true that some degree of talent is necessary to be successful in life, almost never the most talented people reach the highest peaks of success, being overtaken by averagely talented but sensibly luckier individuals. As far as we know, this counterintuitive result — although implicitly suggested between the lines in a vast literature — is quantified here for the first time. It sheds new light on the effectiveness of assessing merit on the basis of the reached level of success and underlines the risks of distributing excessive honors or resources to people who, at the end of the day, could have been simply luckier than others. We also compare several policy hypotheses to show the most efficient strategies for public funding of research, aiming to improve meritocracy, diversity of ideas and innovation.

Categories or tags that appear in faceted search interfaces which are representative of an information item, rarely convey unexpected or non-obvious associated concepts buried within search results. No prior research has been identified which assesses the usefulness of discriminative search term word co-occurrence to generate facets to act as catalysts to facilitate insightful and serendipitous encounters during exploratory search. In this study, 53 scientists from two organisations interacted with semi-interactive stimuli, 74% expressing a large/moderate desire to use such techniques within their workplace. Preferences were shown for certain algorithms and colour coding. Insightful and serendipitous encounters were identified. These techniques appear to offer a significant improvement over existing approaches used within the study organisations, providing further evidence that insightful and serendipitous encounters can be facilitated in the search user interface. This research has implications for organisational learning, knowledge discovery and exploratory search interface design.

Few reports discuss R&D divisions in manufacturers adapting a management system for promoting serendipity or creativity. We focused on a highly competitive and growing market, in which serendipity is expected to play an important role: the functional food market. We conducted survey analysis of 114 R&D researchers and engineers at 74 companies in the sector using a questionnaire on idea generation. Through factorial analysis, we extracted the novel factors underlying creativity and serendipity, which highly correlated with personal and environmental conditions in R&D worksites.

With the traditional similarity-based approaches to recommender systems, it is unlikely to discover truly novel things. Users no longer find the outputs interesting or surprising since the outputs are locked into clusters of similarities on single domain. To make recommendations more attractive to the users, the system must provide a serendipitous recommendation list which is new, exciting and unexpectable. Serendipitous recommendation increases the chance of discovering music that is truly novel and unexpectedly useful leading to better performance and higher efficiency. In this paper, we propose a bisociative based approach to automatically generate a serendipitous recommendation list for particular users. The unexpected interesting links crossing different context domains are found by applying bisociative knowledge discovery concept and inducting the rules for generating the serendipitous list using probabilistic logic framework. For the music recommendation on subset of Amazon product dataset including users’ music and movie preferences, we are able to achieve a recommendation list with 60% accuracy. The list includes recommendations which are not found in the single-domain based systems.