Biology subjects

Moussaid, M.

Publications and source records attributed to Moussaid, M..

4 recordsLinked to original sources

bioRxiv2019

Transmission Chains or Independent Solvers? A Comparative Study of Two Collective Problem-Solving Methods

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 complex 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.\n\nBy means of numerical simulations and experimental observations, we show that the best performing method is determined by the interplay between two key factors: the skills of the individuals and the difficulty of the problem. We find that transmission chains are superior either when the problem is rather easy, or when the group is composed of rather unskilled individuals. On the contrary, groups of independent solvers are preferable for harder problems or for groups of rather skillful individuals. 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.

animal behavior and cognition↗

bioRxiv2019

The social dynamics of collective problem-solving

When searching for solutions to a problem, people often rely on the observation of their peers. How does this process of social learning impact the individual and the groups performance? On the one hand, research has shown that individuals benefit from social learning in numerous situations and across many domains. Through social learning, individuals can access good solutions found by others, improve them, and share them in turn. On the other hand, this individual benefit may come at a cost: An excessive tendency to copy others often decreases the overall exploration volume of the group, thus reducing the diversity of discovered solutions, and eventually impairing the collective performance.\n\nHere we investigate the conditions under which social learning can be beneficial or detrimental to individuals and to the group. For that, we model problem-solving as a search task and simulate various amounts of social learning. We avoid model specific considerations by relying on a simple framework whereby individuals gradually explore the search environment - a two-dimensional landscape of solutions - while being attracted to the best solution of the group.\n\nOur results highlight a collective search dilemma: When group members learn from one another, they tend to improve their own individual performance at the expense of the collective performance. How is this dilemma affected by the structure of the search environment? By varying two structural aspects of the search environment, our results reveal that the negative effect of the dilemma is mitigated in more difficult environments.\n\nFinally, we show that single individuals can profit from a high propensity of social learning, which in turn is damaging for the other group members. As a consequence, if individuals continually adapt their behavior to maximize their own payoff, groups converge to a sub-optimal level of social learning. Unraveling these intricate social dynamics helps to understand the complex picture of collective problem-solving.

animal behavior and cognition↗

bioRxiv2019

Search as a simple take-the-best heuristic

Humans commonly engage in a variety of search behaviours, for example when looking for an object, a partner, information, or a solution to a complex problem. The success or failure of a search strategy crucially depends on the structure of the environment and the constraints it imposes on the individuals. Here we focus on environments in which individuals have to explore the solution space gradually and where their reward is determined by one unique solution they choose to exploit. This type of environment has been relatively overlooked in the past despite being relevant to numerous real-life situations, such as spatial search and various problem-solving tasks.\n\nBy means of a dedicated experimental design, we show that the search behaviour of experimental participants can be well described by a simple heuristic model. Both in rich and poor solution spaces, a take-the-best procedure that ignores all but one cue at a time is capable of reproducing a diversity of observed behavioural patterns. Our approach, therefore, sheds lights on the possible cognitive mechanisms involved in human search.

animal behavior and cognition↗

bioRxiv2017

Forecasting crowd dynamics through coarse-grained data analysis

Understanding and predicting the collective behaviour of crowds is essential to improve the efficiency of pedestrian flows in urban areas and minimize the risks of accidents at mass events. We advocate for the development of a & \"crowd forecasting system\"whereby real-time observations of crowds are coupled to fast and reliable models to produce rapid predictions of the crowd movement and eventually help crowd managers choose between tailored optimization strategies. Here, we propose a Bi-directional Macroscopic (BM) model as the core of such a system. Its key input is the fundamental diagram for bi-directional flows, i.e. the relation between the pedestrian fluxes and densities. We design and run a laboratory experiments involving a total of 119 participants walking in opposite directions in a circular corridor and show that the model is able to accurately capture the experimental data in a typical crowd forecasting situation. Finally, we propose a simple segregation strategy for enhancing the traffic efficiency, and use the BM model to determine the conditions under which this strategy would be beneficial. The BM model, therefore, could serve as a building block to develop on the fly prediction of crowd movements and help deploying real-time crowd optimization strategies.

animal behavior and cognition↗