Individual decision making in task-oriented groups

The strategies adopted by individuals to select relevant information to pass on are central to understanding problem solving by groups. Here we use agent-based simulations to revisit a cooperative problem-solving scenario where the task is to find the common card in decks distributed to the group members. The agents can display only a sample of their cards and we explore different strategies to select those samples based on the confidences assigned to the cards. An agent's confidence that a particular card is the correct one is given by the number of times it observed that card in the decks of the other agents. We use a Gibbs distribution to select the card samples with the temperature measuring the strength of a noise that prevents the agents to correctly rank the cards. The group is guaranteed to find the common card in all runs solely in the infinite temperature limit, where the cards are sampled regardless of their confidences. In this case, we obtain the scaling form of the time constant that characterizes the asymptotic exponential decay of the failure probability. For finite time, however, a finite temperature yields a probability of failure that is several orders of magnitude lower than in the infinite temperature limit. The available experimental results are consistent with the decision-making model for finite temperature only

Policies for allocation of information in task-oriented groups: elitism and egalitarianism outperform welfarism

Communication or influence networks are probably the most controllable of all factors that are known to impact on the problem-solving capability of task-forces. In the case connections are costly, it is necessary to implement a policy to allocate them to the individuals. Here we use an agent-based model to study how distinct allocation policies affect the performance of a group of agents whose task is to find the global maxima of NK fitness landscapes. Agents cooperate by broadcasting messages informing on their fitness and use this information to imitate the fittest agent in their influence neighborhoods. The larger the influence neighborhood of an agent, the more links, and hence information, the agent receives. We find that the elitist policy in which agents with above-average fitness have their influence neighborhoods amplified, whereas agents with below-average fitness have theirs deflated, is optimal for smooth landscapes, provided the group size is not too small. For rugged landscapes, however, the elitist policy can perform very poorly for certain group sizes. In addition, we find that the egalitarian policy, in which the size of the influence neighborhood is the same for all agents, is optimal for both smooth and rugged landscapes in the case of small groups. The welfarist policy, in which the actions of the elitist policy are reversed, is always suboptimal, i.e., depending on the group size it is outperformed by either the elitist or the egalitarian policies

Screening Literacy: Reflecting on Models of Film Education in Europe

Research Project outcomeTomando como base un resumen amplio del “Estudio de Expertos a Escala Europea sobre literacía cinematográfica en Europa 2012” (European-scale Experts’ Study on film literacy in Europe 2012), este artículo examina las dos concepciones diferentes que se tienen de la educación cinematográfica en Europa: (a) como un derecho o un bien social para todos (similar al derecho a la educación universal) y (b) como un medio fundamental para desarrollar consumidores o audiencias de cine. En base al estudio antes mencionado, el cual fue llevado a cabo en 32 países europeos, los autores analizan tres aspectos de la “literacía cinematográfica” (film literacy) en Europa: (1) las prácticas de educadores de cine en los diferentes sectores educativos en los Estados miembros de la Unión Europea; (2) los argumentos más amplios sobre cultura cinematográfica y su importancia; y (3) la relación entre literacía cinematográfica y literacía mediática, especialmente en el contexto de la iniciativa de literacía mediática de la Comisión Europea. Finalmente, el artículo también examina la relación que existe entre la educación cinematográfica y las apropiaciones, recomendaciones, limitaciones y concepciones erróneas que rodean las tecnologías digitales.This article, building on an extensive summary of the European-scale Experts’ Study on film literacy in Europe 2012, draws attention to two different conceptions of film education: as an (a) entitlement for all, a social good (akin to the entitlement to universal literacy) and as (b) an instrumental means of developing film consumers, or audiences. Based upon a large survey of film education across 32 European countries, the authors give the context of film literacy in Europe three perspectives: (1) The established practice of film educators across all sectors in the member states; (2) the wider arguments about film culture and its importance; and (3) the relation between film literacy and media literacy, especially in the context of the EC’s media literacy initiative. We also reflect at the end on the relation between film education and the affordances, recommendations, limitations and misconceptions surrounding digital technologies.European Commissio

Mobility helps problem-solving systems to avoid Groupthink

Groupthink occurs when everyone in a group starts thinking alike, as when people put unlimited faith in a leader. Avoiding this phenomenon is a ubiquitous challenge to problem-solving enterprises and typical countermeasures involve the mobility of group members. Here we use an agent-based model of imitative learning to study the influence of the mobility of the agents on the time they require to find the global maxima of NK-fitness landscapes. The agents cooperate by exchanging information on their fitness and use this information to copy the fittest agent in their influence neighborhoods, which are determined by face-to-face interaction networks. The influence neighborhoods are variable since the agents perform random walks in a two-dimensional space. We find that mobility is slightly harmful for solving easy problems, i.e. problems that do not exhibit suboptimal solutions or local maxima. For difficult problems, however, mobility can prevent the imitative search being trapped in suboptimal solutions and guarantees a better performance than the independent search for any system size