45 research outputs found
A tool for parameter-space explorations
A software for managing simulation jobs and results, named "OACIS", is
presented. It controls a large number of simulation jobs executed in various
remote servers, keeps these results in an organized way, and manages the
analyses on these results. The software has a web browser front end, and users
can submit various jobs to appropriate remote hosts from a web browser easily.
After these jobs are finished, all the result files are automatically
downloaded from the computational hosts and stored in a traceable way together
with the logs of the date, host, and elapsed time of the jobs. Some
visualization functions are also provided so that users can easily grasp the
overview of the results distributed in a high-dimensional parameter space.
Thus, OACIS is especially beneficial for the complex simulation models having
many parameters for which a lot of parameter searches are required. By using
API of OACIS, it is easy to write a code that automates parameter selection
depending on the previous simulation results. A few examples of the automated
parameter selection are also demonstrated.Comment: 4 pages, 5 figures, CSP 2014 conferenc
Friendly-rivalry solution to the iterated -person public-goods game
Repeated interaction promotes cooperation among rational individuals under
the shadow of future, but it is hard to maintain cooperation when a large
number of error-prone individuals are involved. One way to construct a
cooperative Nash equilibrium is to find a `friendly-rivalry' strategy, which
aims at full cooperation but never allows the co-players to be better off.
Recently it has been shown that for the iterated Prisoner's Dilemma in the
presence of error, a friendly rival can be designed with the following five
rules: Cooperate if everyone did, accept punishment for your own mistake,
punish defection, recover cooperation if you find a chance, and defect in all
the other circumstances. In this work, we construct such a friendly-rivalry
strategy for the iterated -person public-goods game by generalizing those
five rules. The resulting strategy makes a decision with referring to the
previous rounds. A friendly-rivalry strategy for inherently has
evolutionary robustness in the sense that no mutant strategy has higher
fixation probability in this population than that of a neutral mutant. Our
evolutionary simulation indeed shows excellent performance of the proposed
strategy in a broad range of environmental conditions when and .Comment: 19 pages, 6 figure
Grouping promotes both partnership and rivalry with long memory in direct reciprocity
Biological and social scientists have long been interested in understanding
how to reconcile individual and collective interests in iterated Prisoner's
Dilemma. Many effective strategies have been proposed, and they are often
categorized into one of two classes, `partners' and `rivals.' More recently,
another class, `friendly rivals,' has been identified in longer-memory strategy
spaces. Friendly rivals qualify as both partners and rivals: They fully
cooperate with themselves, like partners, but never allow their co-players to
earn higher payoffs, like rivals. Although they have appealing theoretical
properties, it is unclear whether they would emerge in evolving population
because most previous works focus on memory-one strategy space, where no
friendly rival strategy exists. To investigate this issue, we have conducted
large-scale evolutionary simulations in well-mixed and group-structured
populations and compared the evolutionary dynamics between memory-one and
memory-three strategy spaces. In a well-mixed population, the memory length
does not make a major difference, and the key factors are the population size
and the benefit of cooperation. Friendly rivals play a minor role because being
a partner or a rival is often good enough in a given environment. It is in a
group-structured population that memory length makes a stark difference: When
memory-three strategies are available, friendly rivals become dominant, and the
cooperation level nearly reaches a maximum, even when the benefit of
cooperation is so low that cooperation would not be achieved in a well-mixed
population. This result highlights the important interaction between group
structure and memory lengths that drive the evolution of cooperation.Comment: 18 pages, 11 figure
Multilayer weighted social network model
Recent empirical studies using large-scale data sets have validated the
Granovetter hypothesis on the structure of the society in that there are
strongly wired communities connected by weak ties. However, as interaction
between individuals takes place in diverse contexts, these communities turn out
to be overlapping. This implies that the society has a multilayered structure,
where the layers represent the different contexts. To model this structure we
begin with a single-layer weighted social network (WSN) model showing the
Granovetterian structure. We find that when merging such WSN models, a
sufficient amount of interlayer correlation is needed to maintain the
relationship between topology and link weights, while these correlations
destroy the enhancement in the community overlap due to multiple layers. To
resolve this, we devise a geographic multilayer WSN model, where the indirect
interlayer correlations due to the geographic constraints of individuals
enhance the overlaps between the communities and, at the same time, the
Granovetterian structure is preserved.Comment: 9 pages, 9 figure
Sampling networks by nodal attributes
In a social network individuals or nodes connect to other nodes by choosing
one of the channels of communication at a time to re-establish the existing
social links. Since available data sets are usually restricted to a limited
number of channels or layers, these autonomous decision making processes by the
nodes constitute the sampling of a multiplex network leading to just one
(though very important) example of sampling bias caused by the behavior of the
nodes. We develop a general setting to get insight and understand the class of
network sampling models, where the probability of sampling a link in the
original network depends on the attributes of its adjacent nodes. Assuming
that the nodal attributes are independently drawn from an arbitrary
distribution and that the sampling probability for a
link of nodal attributes and is also arbitrary, we derive
exact analytic expressions of the sampled network for such network
characteristics as the degree distribution, degree correlation, and clustering
spectrum. The properties of the sampled network turn out to be sums of
quantities for the original network topology weighted by the factors stemming
from the sampling. Based on our analysis, we find that the sampled network may
have sampling-induced network properties that are absent in the original
network, which implies the potential risk of a naive generalization of the
results of the sample to the entire original network. We also consider the
case, when neighboring nodes have correlated attributes to show how to
generalize our formalism for such sampling bias and we get good agreement
between the analytic results and the numerical simulations.Comment: 11 pages, 5 figure