4,716 research outputs found
Rewiring strategies for changing environments
A typical pervasive application executes in a changing environment: people, computing resources, software services and network connections come and go continuously. A robust pervasive application needs adapt to this changing context as long as there is an appropriate rewiring strategy that guarantees correct behavior. We combine the MERODE modeling methodology with the ReWiRe framework for creating interactive pervasive applications that can cope with changing environments. The core of our approach is a consistent environment model, which is essential to create (re)configurable context-aware pervasive applications. We aggregate different ontologies that provide the required semantics to describe almost any target environment. We present a case study that shows a interactive pervasive application for media access that incorporates parental control on media content and can migrate between devices. The application builds upon models of the run-time environment represented as system states for dedicated rewiring strategies
Degree Variance and Emotional Strategies Catalyze Cooperation in Dynamic Signed Networks
We study the problem of the emergence of cooperation in dynamic signed
networks where agent strategies coevolve with relational signs and network
topology. Running simulations based on an agent-based model, we compare results
obtained in a regular lattice initialization with those obtained on a
comparable random network initialization. We show that the increased degree
heterogeneity at the outset enlarges the parametric conditions in which
cooperation survives in the long run. Furthermore, we show how the presence of
sign-dependent emotional strategies catalyze the evolution of cooperation with
both network topology initializations.Comment: 16 Pages, Proceeding of the European Conference on Modelling and
Simumatio
Social learning strategies modify the effect of network structure on group performance
The structure of communication networks is an important determinant of the
capacity of teams, organizations and societies to solve policy, business and
science problems. Yet, previous studies reached contradictory results about the
relationship between network structure and performance, finding support for the
superiority of both well-connected efficient and poorly connected inefficient
network structures. Here we argue that understanding how communication networks
affect group performance requires taking into consideration the social learning
strategies of individual team members. We show that efficient networks
outperform inefficient networks when individuals rely on conformity by copying
the most frequent solution among their contacts. However, inefficient networks
are superior when individuals follow the best member by copying the group
member with the highest payoff. In addition, groups relying on conformity based
on a small sample of others excel at complex tasks, while groups following the
best member achieve greatest performance for simple tasks. Our findings
reconcile contradictory results in the literature and have broad implications
for the study of social learning across disciplines
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
Emotional Strategies as Catalysts for Cooperation in Signed Networks
The evolution of unconditional cooperation is one of the fundamental problems
in science. A new solution is proposed to solve this puzzle. We treat this
issue with an evolutionary model in which agents play the Prisoner's Dilemma on
signed networks. The topology is allowed to co-evolve with relational signs as
well as with agent strategies. We introduce a strategy that is conditional on
the emotional content embedded in network signs. We show that this strategy
acts as a catalyst and creates favorable conditions for the spread of
unconditional cooperation. In line with the literature, we found evidence that
the evolution of cooperation most likely occurs in networks with relatively
high chances of rewiring and with low likelihood of strategy adoption. While a
low likelihood of rewiring enhances cooperation, a very high likelihood seems
to limit its diffusion. Furthermore, unlike in non-signed networks, cooperation
becomes more prevalent in denser topologies.Comment: 24 pages, Accepted for publication in Advances in Complex System
Programmable biomaterials for dynamic and responsive drug delivery
Biomaterials are continually being designed that enable new methods for interacting dynamically with cell and tissues, in turn unlocking new capabilities in areas ranging from drug delivery to regenerative medicine. In this review, we explore some of the recent advances being made in regards to programming biomaterials for improved drug delivery, with a focus on cancer and infection. We begin by explaining several of the underlying concepts that are being used to design this new wave of drug delivery vehicles, followed by examining recent materials systems that are able to coordinate the temporal delivery of multiple therapeutics, dynamically respond to changing tissue environments, and reprogram their bioactivity over time
Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments
One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store 'good behaviour' and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment
Shift of percolation thresholds for epidemic spread between static and dynamic small-world networks
The aim of the study was to compare the epidemic spread on static and dynamic
small-world networks. The network was constructed as a 2-dimensional
Watts-Strogatz model (500x500 square lattice with additional shortcuts), and
the dynamics involved rewiring shortcuts in every time step of the epidemic
spread. The model of the epidemic is SIR with latency time of 3 time steps. The
behaviour of the epidemic was checked over the range of shortcut probability
per underlying bond 0-0.5. The quantity of interest was percolation threshold
for the epidemic spread, for which numerical results were checked against an
approximate analytical model. We find a significant lowering of percolation
thresholds for the dynamic network in the parameter range given. The result
shows that the behaviour of the epidemic on dynamic network is that of a static
small world with the number of shortcuts increased by 20.7 +/- 1.4%, while the
overall qualitative behaviour stays the same. We derive corrections to the
analytical model which account for the effect. For both dynamic and static
small-world we observe suppression of the average epidemic size dependence on
network size in comparison with finite-size scaling known for regular lattice.
We also study the effect of dynamics for several rewiring rates relative to
latency time of the disease.Comment: 13 pages, 6 figure
Early fragmentation in the adaptive voter model on directed networks
We consider voter dynamics on a directed adaptive network with fixed
out-degree distribution. A transition between an active phase and a fragmented
phase is observed. This transition is similar to the undirected case if the
networks are sufficiently dense and have a narrow out-degree distribution.
However, if a significant number of nodes with low out degree is present, then
fragmentation can occur even far below the estimated critical point due to the
formation of self-stabilizing structures that nucleate fragmentation. This
process may be relevant for fragmentation in current political opinion
formation processes.Comment: 9 pages, 8 figures as published in Phys. Rev.
Clustering Algorithms for Scale-free Networks and Applications to Cloud Resource Management
In this paper we introduce algorithms for the construction of scale-free
networks and for clustering around the nerve centers, nodes with a high
connectivity in a scale-free networks. We argue that such overlay networks
could support self-organization in a complex system like a cloud computing
infrastructure and allow the implementation of optimal resource management
policies.Comment: 14 pages, 8 Figurs, Journa
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