Efficient team structures in an open-ended cooperative creativity experiment
- aSony Computer Science Laboratories, 75005 Paris, France;
- bSorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique, F-75012 Paris, France;
- cSociology and Economics of Networks and Services lab at Orange Experience Design Lab (SENSE/XDLab) Chatillion, 92320 Paris, France;
- dPhysics Department, Sapienza University of Rome, 00185 Rome, Italy;
- eComplexity Science Hub Vienna, A-1080 Vienna, Austria
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Edited by Vito Latora, Queen Mary University of London, London, United Kingdom, and accepted by Editorial Board Member Jennifer A. Richeson September 19, 2019 (received for review June 7, 2019)

Significance
Understanding how to best form teams to perform creative tasks is a fascinating although elusive problem. Here we propose an experimental setting for studying the performances of a population of individuals committed to an open-ended cooperative creativity task, namely the construction of LEGO artworks. The real-time parallel monitoring of the growth of the artworks and the structure and composition of the dynamically working teams allow identifying the key ingredients of successful teams. Large teams composed of committed and influential people are more effectively building. Also, there exists an optimal fraction of weak ties in the working teams, i.e., an optimal ratio exploit/explore that maximizes the building efficiency.
Abstract
Creativity is progressively acknowledged as the main driver for progress in all sectors of humankind’s activities: arts, science, technology, business, and social policies. Nowadays, many creative processes rely on many actors collectively contributing to an outcome. The same is true when groups of people collaborate in the solution of a complex problem. Despite the critical importance of collective actions in human endeavors, few works have tackled this topic extensively and quantitatively. Here we report about an experimental setting to single out some of the key determinants of efficient teams committed to an open-ended creative task. In this experiment, dynamically forming teams were challenged to create several artworks using LEGO bricks. The growth rate of the artworks, the dynamical network of social interactions, and the interaction patterns between the participants and the artworks were monitored in parallel. The experiment revealed that larger working teams are building at faster rates and that higher commitment leads to higher growth rates. Even more importantly, there exists an optimal number of weak ties in the social network of creators that maximizes the growth rate. Finally, the presence of influencers within the working team dramatically enhances the building efficiency. The generality of the approach makes it suitable for application in very different settings, both physical and online, whenever a creative collective outcome is required.
Creativity is one of the most distinctive features of human beings. The ability to conceive new ideas, objects, technologies, and business is one of the founding factors of our societies in their quest for progress and better living conditions. As such, creativity is a powerful engine behind innovation, be it artistic, scientific, technological, or social.
The investigation of the very nature of creativity has a very long history, from the philosophy of creativity to aesthetics to experimental approaches to crack its very nature (1⇓–3). For centuries scholars have raised questions about the emergence of creativity, its determinants, and the differences of creativity in very different domains. More recently, heralded by pioneering works of Shaw, Simon, and Newell (4) and due to the terrific progress made in complexity science (5⇓–7) and artificial intelligence, the new field of computational creativity (for instance, ref. 8) emerged with the scope of defining and understanding creativity through the concrete implementation of artificial creative systems. This way, the set of questions about creativity expanded to include what makes a system creative, whether technology can enhance human creativity, and what the best environments are to foster and nurture creativity.
One of the most intriguing aspects of creativity concerns the interplay between individual exploits and collective achievements. Unlike earlier times, modern creative industries (music, games, cinema, publishing, computer programs, etc.) rely on complex creation processes, where many actors (from dozens to thousands) contribute little chunks of content in a complex process eventually converging to a final product. Final products can be songs, interactive scripts, video games, screenplays, computer codes (9), or texts (10, 11).
Recent studies have investigated collective creative processes leading to innovation from very different perspectives, e.g., the creation of knowledge on a Q&A website (12); the impact of mobility of scientists on scientific research (13); the design of frameworks to speed up research discoveries (14); and the role of serendipity in creative processes (6). Finally, some of us recently proposed a modeling framework for the emergence of novelties (5, 7, 15, 16), based on the notion of “adjacent possible expansion” theorized by Stuart Kauffman (17) and capable of reproducing many statistical patterns linked to the emergence of novelties in a very general way.
One of the open critical questions concerning collective creative processes concerns the efficient team structure. Many efforts have been devoted to identifying the conditions favorable for creativity to emerge (18⇓–20). In real social systems, agents have strong ties defined as frequently repeated connections and weak ties indicating sporadic interactions. Following Granovetter (21), the presence of “weak ties” in a social network might be one of the most critical drivers of collective creativity, allowing for the flow of new information and eventually leading to the development of new creative ideas. However, although few weak ties within a social group indicate the absence of new information circulating between the members of the groups, too many weak ties might prevent an efficient communication between the individuals. In refs. 18 and 19 it is reported that a more significant number of weak ties should correspond with higher creativity at work, up to a point; beyond this point, there is less benefit realized from more substantial amounts of weak ties, and they may constrain creativity at work. Several models have been designed for studying the interplay between social relations and the emerging dynamics of information and cooperation (22⇓–24). In ref. 25, it has been reported that in heterogeneous groups a vast number of weak ties can negatively affect the spreading velocity of information in a social network.
Despite the abundant literature on the subject, the problem of how to best assemble a group of people with a specific creative task is wide open. Here we address this problem by proposing an experimental setting where a population of participants is called to develop 3D artworks in open-ended environments using LEGO bricks collectively. Here open-endedness refers both to the dynamical composition of the working teams (participants were free to join and leave teams at any time) and to the loose constraints on the outcome. Participants were challenged to contribute to the emerging artworks by either building new parts or removing even substantial parts of them.
Through our experimental setup, we were able to record social interactions among the participants using Radio Frequency Identification (RFID) sensors developed within the SocioPatterns project (26⇓–28). The same sensors were adopted to monitor which artwork a specific participant contributed to and for how long, among the several evolving in parallel. The networks of interactions of users and users–artworks are intrinsically dynamical (29, 30). The RFIDs allowed us to monitor the dynamics of the social bonds which are continuously formed and broken and to follow the constant restructuring of the working teams (31⇓⇓–34).
In parallel with the social interactions, we monitored the growth of each artwork through infrared depth sensors that provide in real time an accurate 3D reconstruction of the artworks. This way, we could relate the dynamical composition of teams, as mirrored through several observables, to the efficiency of the building activity, measured through the growth rate.
The analysis of the ensemble of the data allowed us to draw several important conclusions. First, faster growth of the artworks is more likely to occur when the working teams have specific topological features, namely an optimal balance between weak and strong ties in a preferably large group. Also, the presence of influencers within the working teams greatly enhances the building efficiency. Finally, a high level of commitment, i.e., focusing on only one artwork, improves building efficiency. The combination of all these circumstances emerges as a sufficient condition to enhance the effectiveness of the collective creative task.
Results
The experiments took place in the framework of an open event dubbed KREYON Days that took place in Rome, Italy, from 2015 to 2017. On each day, for 3 d, from 9 AM until 7 PM, participants were allowed to join and leave the experimental hall at any time freely. Upon entrances, participants were registered and provided with RFID sensors (shown in Fig. 1A) to be returned upon leaving the hall. Entrances and exits were recorded. The RFID sensors allowed us to map face-to-face interactions between individuals with a temporal resolution of 20 s. In other words, 2 participants are considered as interacting if they stand in front of each other within a radius of 1 m for at least 20 s. Through the RFID sensors, we were able to record the total activity time of each user as well as of his/her social interactions (see SI Appendix, section S1 for more information). In the experimental hall, we installed three 1-m square large LEGO building platforms, and a large stash of LEGO bricks was available. We chose LEGO bricks due to their popularity and the immediacy of interaction they allow, independently of age, gender, manipulation, and artistic skills. Fig. 1B shows a picture of one of these platforms. Each building platform had, at any given time, an assigned topic (e.g., “spring,” “Halloween”) and each participant was free to decide to which building platforms to contribute, not necessarily following the topic. The presence of various artworks was necessary to study whether participants focused their efforts on a single artwork or multiple ones and how this affected the growth of the artworks. RFID sensors were also positioned all around the building platforms to monitor who contributed at which side of the artworks, when, and for how long. Through the RFID sensors, we reconstructed the dynamical networks of the participants as well as those of participants and building platforms, with a 20-s time resolution. Fig. 1C shows the aggregated networks obtained by the 3 dynamical networks in the 3 distinct days of activity. These networks were constructed by considering all of the links that were active at least once, weighting each link with the total activation time. The application of a community detection algorithm (35) to these networks reveals a clear community structure organized around the artworks (larger nodes in Fig. 1C), so that each artwork belongs to a different community. All of the interactions recorded by the RFIDs during the experiment and the times series of the artwork volumes are provided in Datasets S1–S9 and described in SI Appendix, section S9.
(A) Scheme of the experiment. Participants were asked to wear RFID sensors allowing for the recording of their face-to-face interactions with time steps of 20 s. We deployed 3 building areas into the experimental hall, equipped with RFIDs to map the amount of time spent in the surroundings of each one. Each participant could freely join and leave the activity at any time. (B) Picture of one of the building areas. RFIDs can be seen right below the base. Participants were asked to wear RFIDs at the waist level to allow the RFIDs of the building areas to detect them when they were close. (C) The aggregated network obtained from the RFID data in 3 different days of the experiment. Larger nodes represent the building areas, while different colors represent communities detected through the Louvain method for community detection, which assigns each node in the network to a unique community of highly interconnected nodes (35).
To understand how the evolution of the team composition (revealed by the dynamical networks of interactions) affects the efficiency of the collective creative task, it is essential to monitor the development of the artworks in time. Thus, we placed infrared depth sensors on top of each building platform, with their axes orthogonal to the plane of the platforms. These sensors allowed us to monitor the growth, in time, of the volume of each artwork. The depth sensors adopted have a sample rate, which is higher than that of the RFID sensors. Hence, we aggregated the volume measurements in time steps of 20 s starting from the beginning of the experiment. Measuring time t in steps of 20 s, we denote with
SI Appendix, Fig. S7 shows examples of the time evolution of
(A–E) Time evolution of growth speed (A), weak ties (B), commitment (C), social influence (D), and team size (E) on the third day of activity of a specific artwork and for
Having identified the working teams, we can now proceed to investigate their properties, in terms of their dynamical composition and their topological structures. We investigate in particular 4 observables susceptible to play an essential role in the efficiency of the collective building activity.
Weak Ties.
A first significant quantity to look at is the fraction of weak ties in a working team
Commitment.
Another important element of the collective building activity what is we define as commitment. The commitment h quantifies how evenly is distributed the total effort of an individual over the 3 artworks. h is based on the total interaction time between an individual and the artworks. h will be 1 if all such time has been spent on a single artwork and 0 if it is evenly distributed among the three (see Materials and Methods for details). Fig. 3C reports the distribution of the commitment h, which has a peak at
(A) Inverse cumulative distributions of the interaction times between participants, conditioned on the total activity time of 1 of the 2 participants. (B) Same distributions as in A scaled by the average interaction time,
Social Influence.
We now focus our attention on the role of socially influential individuals in working teams, identified as very effective information spreaders in the social network (30). To identify influential individuals in our dynamical interaction networks, we adopted the SI (susceptible-infected) model of epidemic spreading (30). In this way, every individual is associated with an observable,
Team Size.
Finally, one natural measure we take into account is the size of each working team. Since the size of each working team depends on the length of the interval
(A–D) Binned scatter plots of the growth speeds of the artworks vs. the fraction of weak ties (A), the commitment (B), the social influence (C), and the team size (D). All of the observables have been computed for different time windows
Discussion
The understanding of collective creative processes has been boosted by the fast-growing relevance of creative processes, where an open-ended multitude of contributors cooperate toward an important collective outcome. The question of what is the best way to organize the interactions within the community of creators and what features a working team should have to get the best out of the collective process is wide open.
Here we provided experimental pieces of evidence of the critical ingredients underlying efficient, creative cooperation. To this end, we conceived and deployed a real-world open-ended experiment in which teams of participants were committed to the realization of artworks using LEGO bricks.
The growth of the artworks was monitored through depth sensors, while the interactions between the participants were monitored employing RFID sensors worn by each of them. In this way, we obtained a real-time parallel picture of the growth of the artworks and the evolution of social interactions. Through this comprehensive monitoring, we were able to correlate the dynamical evolution of a working team, along with its features, to the growth speed of the emerging artworks.
We have identified 4 key determinants underlying a faster growth of the artworks. In particular, we discovered the following: 1) There exists an optimal value of the fraction of weak ties within the working teams that makes their outcome particularly efficient. This result implies that the self-organized working teams greatly benefited from a balance between weak and strong ties in the network of interactions. In turn, this highlights the relevance of a subtle equilibrium between exploit and explore strategies for creative purposes. 2) Working teams with more committed individuals perform much better. 3) Influential individuals, i.e., individuals with a more significant potential to spread their ideas within the team, greatly enhance the performances of the team itself. 4) Finally, larger teams tend to perform better.
Despite the interest of these results, the experiment could be improved to allow the study of collective creativity in a more general sense. Growth speed is a simple observable measuring the level of coordination of team members, but the experimental setting allows for the monitoring of other quantities related to the popularity of the artworks. In this sense, we can monitor the ability of an artwork to attract new contributors and, at the cost of more complexity in the setup, it is possible to directly ask the participants their opinion about the originality and creativity of the artworks.
The generality and the effectiveness of the proposed experimental framework make it suitable to be extended to other kinds of joint activities both in real and in virtual worlds: for instance, the realization of collective works like texts, screenplays, music, video games, free software, or situations where work division is relevant, as in large institutions or corporations.
Materials and Methods
Experimental Protocols Approval and Participants’ Consent.
All methods in experiments E1 to E3 were carried out in accordance with relevant guidelines and regulations. The experimental protocols used have been approved by the General Data Protection Regulation (EU) 2016/679. Informed consent was obtained from all subjects. For participants below 18 y old, informed consent was obtained from parents or legal tutors.
Weak Ties.
Given 2 participants i and j at time t, we indicate the weight of the link connecting them in
Given a working team
Commitment.
Here we give the proper definition of commitment. Similarly to the case of contacts between participants, it is possible to define the total interaction time between the individual i and 1 of the 3 artworks s (with
Social Influence.
The importance of an individual within a social network is usually quantified with simple metrics such as the degree, the closeness centrality, and the betweenness centrality (38). Although initially defined for static networks, these observables naturally extend to dynamical networks (39, 40). We refer to SI Appendix, section S5 for a brief investigation on the correlations between centrality metrics for static networks and growth speed. A common way in which importance can be assessed is through information diffusion models, which are typically used also in epidemic spreading simulations (30). Hence, we used a simple SI model of epidemic spreading (30) running on the global dynamic interaction network of participants’ interactions. In turn, every individual i, i.e., every node of the network, acts as the starting seed of a virtual infection and we measure the fraction of nodes,
Team Size.
The limited size of the building supports allowed only a limited number of participants to work together at the construction of each artwork at a time. Even though the RFID sensors attached to each artwork might have recorded the presence of participants just around it and not participating to the construction, it is reasonable to assume that a working team with a large number of members will also be one in which many different people have given their contribution to the artwork. Obviously, as
Acknowledgments
We thank Alain Barrat and Ciro Cattuto for the kind offer to use the sensors developed in the framework of the SocioPatterns project, http://www.sociopatterns.org/.
Footnotes
- ↵1To whom correspondence may be addressed. Email: bernardo.monechi{at}sony.com.
Author contributions: B.M. and V.L. designed research; B.M. performed research; B.M. and G.P. analyzed data; and B.M., G.P., and V.L. wrote the paper.
The authors declare no competing interest.
This article is a PNAS Direct Submission. V.L. is a guest editor invited by the Editorial Board.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1909827116/-/DCSupplemental.
Published under the PNAS license.
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