11,157 research outputs found
Games on graphs: A minor modification of payoff scheme makes a big difference
Various social dilemma games that follow different strategy updating rules
have been studied on many networks.The reported results span the entire
spectrum, from significantly boosting,to marginally affecting,to seriously
decreasing the level of cooperation.Experimental results that are qualitatively
different from theoretical prediction have also been reported.It is widely
believed that the results are largely determined by three elements,including
payoff matrices of the underlying 2*2 games,the way that the strategic states
of the players are updated and the structure of the networks.Here we discuss
the impact of a seemly non-essential mechanism -- what we refer to as a "payoff
scheme". Specifically, in each round after the states of all of the players are
determined,the payoff scheme is how each player's payoff is calculated.In
addition to the two conventions in which either the accumulated or the averaged
payoff is calculated from playing with all of the neighboring players,we here
study the effects of calculating the payoff from pairing up with one random
player from among the neighboring players. Based on probability theory, in a
situation of uncorrelated events, the average payoff that involves all of the
neighbors should,in principal,be equivalent to the payoff from pairing up with
one neighbor.However,our simulation of games on graphs shows that, in many
cases,the two payoff schemes lead to qualitatively different levels of
cooperation.This finding appears to provide a possible explanation for a wide
spectrum of observed behaviors in the literature.We have also observed that
results from the randomly-pairing-one mechanism are more robust than the
involving-all-neighbours mechanism because,in the former case, neither the
other three main elements nor the initial states of the players have a large
impact on the final level of cooperation compared with in the latter case.Comment: 23 pages,171 figure
Spatial Coordination Strategies in Future Ultra-Dense Wireless Networks
Ultra network densification is considered a major trend in the evolution of
cellular networks, due to its ability to bring the network closer to the user
side and reuse resources to the maximum extent. In this paper we explore
spatial resources coordination as a key empowering technology for next
generation (5G) ultra-dense networks. We propose an optimization framework for
flexibly associating system users with a densely deployed network of access
nodes, opting for the exploitation of densification and the control of overhead
signaling. Combined with spatial precoding processing strategies, we design
network resources management strategies reflecting various features, namely
local vs global channel state information knowledge exploitation, centralized
vs distributed implementation, and non-cooperative vs joint multi-node data
processing. We apply these strategies to future UDN setups, and explore the
impact of critical network parameters, that is, the densification levels of
users and access nodes as well as the power budget constraints, to users
performance. We demonstrate that spatial resources coordination is a key factor
for capitalizing on the gains of ultra dense network deployments.Comment: An extended version of a paper submitted to ISWCS'14, Special Session
on Empowering Technologies of 5G Wireless Communication
Social Dilemmas and Cooperation in Complex Networks
In this paper we extend the investigation of cooperation in some classical
evolutionary games on populations were the network of interactions among
individuals is of the scale-free type. We show that the update rule, the payoff
computation and, to some extent the timing of the operations, have a marked
influence on the transient dynamics and on the amount of cooperation that can
be established at equilibrium. We also study the dynamical behavior of the
populations and their evolutionary stability.Comment: 12 pages, 7 figures. to appea
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The Evolution of Language Groups among Cooperating Digital Predators
Many species of animals have evolved complex means for communicating with one another. Oftentimes, communication is essential for the execution of tasks that require cooperation between individuals, such as group hunting and mate selection. As a result, communication itself becomes essential for survival. While these facts are readily observed, the evolutionary processes underlying them are less understood, in large part because observational - much less controlled - studies of these processes are impossible. Both the timescales and population sizes required for such studies are simply too great.
To address these problems, this thesis uses simulated predators to study the evolution of language in animals. These digital predators evolve to perform two cooperative tasks: hunting and mate selection. After the populations of predators have evolved to perform both tasks successfully, the population is decomposed into both language groups and cooperative groups. Spectral clustering identifies predators that speak similar languages, while merge clustering is used to find those groups of predators that are the most successful when working together.
Analysis of the groups generated by these two different methods shows that the most successful pairings are not necessarily those in which the two individuals are speaking the same language. Rather, organisms can evolve to speak a different language than the one to which they respond. Moreover, even though one task -- mate selection -- evolves earlier in evolutionary history, the language diversity it produces counteracts any head-start provided for the evolution of the second task. Thus, not only is language important for the evolution of cooperative task success, but the appearance of language groups can also play a determinant role in the evolution of cooperation.Computer Science
Image scoring in ad-hoc networks : an investigation on realistic settings
Encouraging cooperation in distributed Multi-Agent Systems (MAS) remains an open problem. Emergent application domains such as Mobile Ad-hoc Networks (MANETs) are characterised by constraints including sparse connectivity and a lack of direct interaction history. Image scoring, a simple model of reputation proposed by Nowak and Sigmund, exhibits low space and time complexity and promotes cooperation through indirect reciprocity, in which an agent can expect cooperation in the future without repeat interactions with the same partners. The low overheads of image scoring make it a promising technique for ad-hoc networking domains. However, the original investigation of Nowak and Sigmund is limited in that it (i) used a simple idealised setting, (ii) did not consider the effects of incomplete information on the mechanism’s efficacy, and (iii) did not consider the impact of the network topology connecting agents. We address these limitations by investigating more realistic values for the number of interactions agents engage in, and show that incomplete information can cause significant errors in decision making. As the proportion of incorrect decisions rises, the efficacy of image scoring falls and selfishness becomes more dominant. We evaluate image scoring on three different connection topologies: (i) completely connected, which closely approximates Nowak and Sigmund’s original setup, (ii) random, with each pair of nodes connected with a constant probability, and (iii) scale-free, which is known to model a number of real world environments including MANETs
Artificial and Natural Genetic Information Processing
Conventional methods of genetic engineering and more recent genome editing techniques focus on identifying genetic target sequences for manipulation. This is a result of historical concept of the gene which was also the main assumption of the ENCODE project designed to identify all functional elements in the human genome sequence.
However, the theoretical core concept changed dramatically. The old concept of genetic sequences which can be assembled and manipulated like molecular bricks has problems in explaining the natural genome-editing competences of viruses and RNA consortia that are able to insert or delete, combine and recombine genetic sequences
more precisely than random-like into cellular host organisms according to adaptational needs or even generate sequences de novo. Increasing knowledge about natural genome editing questions the traditional narrative of mutations (error replications) as essential for generating genetic diversity and genetic content arrangements in biological systems. This may have far-reaching consequences for our understanding
of artificial genome editing
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