18 research outputs found
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
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
Persistent agents in Axelrod's social dynamics model
Axelrod's model of social dynamics has been studied under the effect of external media. Here we study the formation of cultural domains in the model by introducing persistent agents. These are agents whose cultural traits are not allowed to change but may be spread through local neighborhood. In the absence of persistent agents, the system is known to present a transition from a monocultural to a multicultural regime at some critical Q (number of traits). Our results reveal a dependence of critical Q on the occupation probability p of persistent agents and we obtain the phase diagram of the model in the -plane. The critical locus is explained by the competition of two opposite forces named here barrier and bonding effects. Such forces are verified to be caused by non-persistent agents which adhere (adherent agents) to the set of traits of persistent ones. The adherence (concentration of adherent agents) as a function of p is found to decay for constant Q. Furthermore, adherence as a function of Q is found to decay as a power law with constant p