LIONESS Lab is a free web-based platform for interactive online experiments. An intuitive, user-friendly graphical interface enables researchers to develop, test, and share experiments online, with minimal need for programming experience. LIONESS Lab provides solutions for the methodological challenges of interactive online experimentation, including ways to reduce waiting time, form groups on-the-fly, and deal with participant dropout. We highlight key features of the software, and show how it meets the challenges of conducting interactive experiments online.

In many social systems, groups of individuals can find remarkably efficient solutions to complex cognitive problems, sometimes even outperforming a single expert. The success of the group, however, crucially depends on how the judgments of the group members are aggregated to produce the collective answer. A large variety of such aggregation methods have been described in the literature, such as averaging the independent judgments, relying on the majority or setting up a group discussion. In the present work, we introduce a novel approach for aggregating judgments—the transmission chain—which has not yet been consistently evaluated in the context of collective intelligence. In a transmission chain, all group members have access to a unique collective solution and can improve it sequentially. Over repeated improvements, the collective solution that emerges reflects the judgments of every group members. We address the question of whether such a transmission chain can foster collective intelligence for binary-choice problems. In a series of numerical simulations, we explore the impact of various factors on the performance of the transmission chain, such as the group size, the model parameters, and the structure of the population. The performance of this method is compared to those of the majority rule and the confidence-weighted majority. Finally, we rely on two existing datasets of individuals performing a series of binary decisions to evaluate the expected performances of the three methods empirically. We find that the parameter space where the transmission chain has the best performance rarely appears in real datasets. We conclude that the transmission chain is best suited for other types of problems, such as those that have cumulative properties.

In many daily life situations, people face decisions involving a trade-off between exploring new options and exploiting known ones. In these situations, observing the decisions of others can influence people’s decisions. Whereas social information often helps making better decisions, research has suggested that under certain conditions it can be detrimental. How precisely social information influences decision strategies and impacts performance is, however, disputed. Here we study how social information influences individuals’ exploration-exploitation trade-off and show that this adaptation can undermine their performance. Using a minimal experimental paradigm, we find that participants tend to copy the solution of other individuals too rapidly, thus decreasing the likelihood of discovering a better solution. Approximating this behavior with a simple model suggests, that individuals’ willingness to explore only depends on the value of known existing solutions. Our results allow for a better understanding of the interplay between social and individual factors in individual decision-making.

Groups can be very successful problem-solvers. this collective achievement crucially depends on how the group is structured, that is, how information flows between members and how individual contributions are merged. numerous methods have been proposed, which can be divided into two major categories: those that involve an exchange of information between the group members, and those that do not. Here we compare two instances of such methods for solving multi-dimensional problems: (1) transmission chains, where individuals tackle the problem one after the other, each one building on the solution of the predecessor and (2) groups of independent solvers, where individuals tackle the problem independently, and the best solution found in the group is selected afterwards. By means of numerical simulations and experimental observations, we show that the best performing method is determined by the interplay between two key factors: the individual's degrees of freedom as an aspect of skill and the complexity of the problem. We find that transmission chains are superior either when the problem is rather smooth, or when the group is composed of rather unskilled individuals with a low degree of freedom. on the contrary, groups of independent solvers are preferable for rugged problems or for groups of rather skillful individuals with a high degree of freedom. Finally, we deepen the comparison by studying the impact of the group size and diversity. our research stresses that efficient collective problem-solving requires a good matching between the nature of the problem and the structure of the group. Collective problem-solving and the related concepts of swarm intelligence and collective intelligence have been studied in a wide variety of domains. In biological systems, examples include the nest construction in eusocial insects 1,2 or collective foraging in group-living species 3. In robotics and artificial intelligence, swarms of relatively simple agents can explore and solve optimization problems efficiently 4,5. Likewise, humans can solve problems in groups during discussions 6 , by means of wisdom of crowds procedures 7 , or when creating Wikipedia articles 8,9. Despite this considerable diversity of examples and application domains, many instances of collective problem-solving come down to one central challenge: When given a specific number of individuals with a certain skill set, how should a group be structured to produce the best possible collective output? Numerous procedures have been proposed to that end. These can be divided into two major categories 10,11 : (1) those that involve an exchange of information between the group members, and (2) those that do not. In the first category, direct or indirect interactions among individuals can lead to the emergence of a collective solution 12-14. With direct interactions, group members exchange information directly via physical signals. Group-living animals, for example, communicate by means of acoustic and visual cues to detect and avoid predators 15-17. In human groups, t...

LIONESS Lab is a free web-based platform for interactive online experiments. It provides solutions for the methodological challenges of interactive online experimentation, including ways to reduce waiting time, form groups on-the-fly and deal with participant dropout. The intuitive, user-friendly graphical interface enables researchers to develop, test and share experiments online, with minimal need for programming experience. We highlight key features of the software and show how it meets the challenges of conducting interactive experiments online.