629 research outputs found
Measles-containing vaccines in Brazil: Coverage, homogeneity of coverage and associations with contextual factors at municipal level
We aimed to (i) describe both the coverage and the homogeneity of coverage of the first and second doses of measles-containing vaccines (MCV) in Brazil in 2017, and (ii) to investigate the potential influence of contextual factors at municipal level. All 5570 Brazilian municipalities were included. The North and Center-West regions presented the lowest coverages of the first and second doses of MCV, respectively. We found significant associations of both first and second doses of MCV with population size, coverage of Family Health Strategy (FHS) and other indicators of living conditions and inequalities. Monitoring the homogeneity of MCV coverage at national, regional and state levels is essential, as it allows identifying areas at higher risk of measles spread that should be targeted for vaccination. Targeting large cities i.e. 100,000 or more inhabitants, especially poor neighborhoods and areas with low FHS coverage, could lead to improvements in coverage homogeneity
Dynamic selective switching in antiferromagnetically-coupled bilayers close to the spin reorientation transition
We have designed a bilayer synthetic antiferromagnet where the order of layer reversal can be selected by varying the sweep rate of the applied magnetic field. The system is formed by two ultra-thin ferromagnetic layers with different proximities to the spin reorientation transition, coupled antiferromagnetically using Ruderman-Kittel-Kasuya-Yosida interactions. The different dynamic magnetic reversal behavior of both layers produces a crossover in their switching fields for field rates in the kOe/s range. This effect is due to the different effective anisotropy of both layers, added to an appropriate asymmetric antiferromagnetic coupling between them. Field-rate controlled selective switching of perpendicular magnetic anisotropy layers as shown here can be exploited in sensing and memory applications.Copyright (2014) American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in Applied Physics Letters (volume 105: 092408) and may be found at http://scitation.aip.org/content/aip/journal/apl/105/9/10.1063/1.4895032
Counterfactual thinking in cooperation dynamics
Counterfactual Thinking is a human cognitive ability studied in a wide
variety of domains. It captures the process of reasoning about a past event
that did not occur, namely what would have happened had this event occurred,
or, otherwise, to reason about an event that did occur but what would ensue had
it not. Given the wide cognitive empowerment of counterfactual reasoning in the
human individual, the question arises of how the presence of individuals with
this capability may improve cooperation in populations of self-regarding
individuals. Here we propose a mathematical model, grounded on Evolutionary
Game Theory, to examine the population dynamics emerging from the interplay
between counterfactual thinking and social learning (i.e., individuals that
learn from the actions and success of others) whenever the individuals in the
population face a collective dilemma. Our results suggest that counterfactual
reasoning fosters coordination in collective action problems occurring in large
populations, and has a limited impact on cooperation dilemmas in which
coordination is not required. Moreover, we show that a small prevalence of
individuals resorting to counterfactual thinking is enough to nudge an entire
population towards highly cooperative standards.Comment: 18 page
Interdependent network reciprocity in evolutionary games
Besides the structure of interactions within networks, also the interactions between networks are of the outmost
importance. We therefore study the outcome of the public goods game on two interdependent networks that are
connected by means of a utility function, which determines how payoffs on both networks jointly influence the
success of players in each individual network. We show that an unbiased coupling allows the spontaneous
emergence of interdependent network reciprocity, which is capable to maintain healthy levels of public
cooperation even in extremely adverse conditions. The mechanism, however, requires simultaneous formation of
correlated cooperator clusters on both networks. If this does not emerge or if the coordination process is
disturbed, network reciprocity fails, resulting in the total collapse of cooperation. Network interdependence can
thus be exploited effectively to promote cooperation past the limits imposed by isolated networks, but only if the
coordination between the interdependent networks is not disturbe
If players are sparse social dilemmas are too: Importance of percolation for evolution of cooperation
Spatial reciprocity is a well known tour de force of cooperation promotion. A
thorough understanding of the effects of different population densities is
therefore crucial. Here we study the evolution of cooperation in social
dilemmas on different interaction graphs with a certain fraction of vacant
nodes. We find that sparsity may favor the resolution of social dilemmas,
especially if the population density is close to the percolation threshold of
the underlying graph. Regardless of the type of the governing social dilemma as
well as particularities of the interaction graph, we show that under pairwise
imitation the percolation threshold is a universal indicator of how dense the
occupancy ought to be for cooperation to be optimally promoted. We also
demonstrate that myopic updating, due to the lack of efficient spread of
information via imitation, renders the reported mechanism dysfunctional, which
in turn further strengthens its foundations.Comment: 6 two-column pages, 5 figures; accepted for publication in Scientific
Reports [related work available at http://arxiv.org/abs/1205.0541
Different reactions to adverse neighborhoods in games of cooperation
In social dilemmas, cooperation among randomly interacting individuals is
often difficult to achieve. The situation changes if interactions take place in
a network where the network structure jointly evolves with the behavioral
strategies of the interacting individuals. In particular, cooperation can be
stabilized if individuals tend to cut interaction links when facing adverse
neighborhoods. Here we consider two different types of reaction to adverse
neighborhoods, and all possible mixtures between these reactions. When faced
with a gloomy outlook, players can either choose to cut and rewire some of
their links to other individuals, or they can migrate to another location and
establish new links in the new local neighborhood. We find that in general
local rewiring is more favorable for the evolution of cooperation than
emigration from adverse neighborhoods. Rewiring helps to maintain the diversity
in the degree distribution of players and favors the spontaneous emergence of
cooperative clusters. Both properties are known to favor the evolution of
cooperation on networks. Interestingly, a mixture of migration and rewiring is
even more favorable for the evolution of cooperation than rewiring on its own.
While most models only consider a single type of reaction to adverse
neighborhoods, the coexistence of several such reactions may actually be an
optimal setting for the evolution of cooperation.Comment: 12 pages, 5 figures; accepted for publication in PLoS ON
Optimal interdependence between networks for the evolution of cooperation
Recent research has identified interactions between networks as crucial for the outcome of evolutionary
games taking place on them. While the consensus is that interdependence does promote cooperation by
means of organizational complexity and enhanced reciprocity that is out of reach on isolated networks, we
here address the question just how much interdependence there should be. Intuitively, one might assume
the more the better. However, we show that in fact only an intermediate density of sufficiently strong
interactions between networks warrants an optimal resolution of social dilemmas. This is due to an intricate
interplay between the heterogeneity that causes an asymmetric strategy flow because of the additional links
between the networks, and the independent formation of cooperative patterns on each individual network.
Presented results are robust to variations of the strategy updating rule, the topology of interdependent
networks, and the governing social dilemma, thus suggesting a high degree of universality
Learning and innovative elements of strategy adoption rules expand cooperative network topologies
Cooperation plays a key role in the evolution of complex systems. However,
the level of cooperation extensively varies with the topology of agent networks
in the widely used models of repeated games. Here we show that cooperation
remains rather stable by applying the reinforcement learning strategy adoption
rule, Q-learning on a variety of random, regular, small-word, scale-free and
modular network models in repeated, multi-agent Prisoners Dilemma and Hawk-Dove
games. Furthermore, we found that using the above model systems other long-term
learning strategy adoption rules also promote cooperation, while introducing a
low level of noise (as a model of innovation) to the strategy adoption rules
makes the level of cooperation less dependent on the actual network topology.
Our results demonstrate that long-term learning and random elements in the
strategy adoption rules, when acting together, extend the range of network
topologies enabling the development of cooperation at a wider range of costs
and temptations. These results suggest that a balanced duo of learning and
innovation may help to preserve cooperation during the re-organization of
real-world networks, and may play a prominent role in the evolution of
self-organizing, complex systems.Comment: 14 pages, 3 Figures + a Supplementary Material with 25 pages, 3
Tables, 12 Figures and 116 reference
From Local to Global Dilemmas in Social Networks
Social networks affect in such a fundamental way the dynamics of the population they support that the global, population-wide behavior that one observes often bears no relation to the individual processes it stems from. Up to now, linking the global networked dynamics to such individual mechanisms has remained elusive. Here we study the evolution of cooperation in networked populations and let individuals interact via a 2-person Prisoner's Dilemma â a characteristic defection dominant social dilemma of cooperation. We show how homogeneous networks transform a Prisoner's Dilemma into a population-wide evolutionary dynamics that promotes the coexistence between cooperators and defectors, while heterogeneous networks promote their coordination. To this end, we define a dynamic variable that allows us to track the self-organization of cooperators when co-evolving with defectors in networked populations. Using the same variable, we show how the global dynamics â and effective dilemma â co-evolves with the motifs of cooperators in the population, the overall emergence of cooperation depending sensitively on this co-evolution
Reward and punishment in climate change dilemmas
Mitigating climate change effects involves strategic decisions by individuals that may choose to limit their emissions at a cost. Everyone shares the ensuing benefits and thereby individuals can free ride on the effort of others, which may lead to the tragedy of the commons. For this reason, climate action can be conveniently formulated in terms of Public Goods Dilemmas often assuming that a minimum collective effort is required to ensure any benefit, and that decision-making may be contingent on the risk associated with future losses. Here we investigate the impact of reward and punishment in this type of collective endeavors - coined as collective-risk dilemmas - by means of a dynamic, evolutionary approach. We show that rewards (positive incentives) are essential to initiate cooperation, mostly when the perception of risk is low. On the other hand, we find that sanctions (negative incentives) are instrumental to maintain cooperation. Altogether, our results are gratifying, given the a-priori limitations of effectively implementing sanctions in international agreements. Finally, we show that whenever collective action is most challenging to succeed, the best results are obtained when both rewards and sanctions are synergistically combined into a single policy.This research was supported by Fundacao para a Ciencia e Tecnologia (FCT) through grants PTDC/EEISII/5081/2014 and PTDC/MAT/STA/3358/2014 and by multiannual funding of INESC-ID and CBMA (under the projects UID/CEC/50021/2019 and UID/BIA/04050/2013). F.P.S. acknowledges support from the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Dynamic and Multi-scale Systems Postdoctoral Fellowship Award. All authors declare no competing financial or non-financial interests in relation to the work described
- âŚ