126,754 research outputs found
Producer biases and kin selection in the evolution of communication
The evolution of communication requires the co-evolution of two abilities: the ability of sending useful signals and the ability of reacting appropriately to perceived signals. This fact poses two related but distinct problems, which are often confused the one with the other: (1) the phylogenetic problem regarding how can communication evolve if the two traits that are necessary for its emergence are complementary and seem to require each other for providing reproductive advantages; (2) the adaptive problem regarding how can communication systems that do not advantage both signallers and receivers in the same way emerge, given their altruistic character. Here we clarify the distinction, and provide some insights on how these problems can be solved in both real and artificial systems by reporting experiments on the evolution of artificial agents that have to evolve a simple food-call communication system. Our experiments show that (1) the phylogenetic problem can be solved thanks to the presence of producer biases that make agents spontaneously produce useful signals, an idea that is complementary to the well-known ?receiver bias? hyopthesis found in the biological literature, and (2) the adaptive problem can be solved by having agents communicate preferentially among kin, as predicted by kin selection theory. We discuss these results with respect both to the scientific understanding of the evolution of communication and to the design of embodied and communicating artificial agents
Consensus formation on adaptive networks
The structure of a network can significantly influence the properties of the
dynamical processes which take place on them. While many studies have been
devoted to this influence, much less attention has been devoted to the
interplay and feedback mechanisms between dynamical processes and network
topology on adaptive networks. Adaptive rewiring of links can happen in real
life systems such as acquaintance networks where people are more likely to
maintain a social connection if their views and values are similar. In our
study, we consider different variants of a model for consensus formation. Our
investigations reveal that the adaptation of the network topology fosters
cluster formation by enhancing communication between agents of similar opinion,
though it also promotes the division of these clusters. The temporal behavior
is also strongly affected by adaptivity: while, on static networks, it is
influenced by percolation properties, on adaptive networks, both the early and
late time evolution of the system are determined by the rewiring process. The
investigation of a variant of the model reveals that the scenarios of
transitions between consensus and polarized states are more robust on adaptive
networks.Comment: 11 pages, 14 figure
Naming Game on Adaptive Weighted Networks
We examine a naming game on an adaptive weighted network. A weight of
connection for a given pair of agents depends on their communication success
rate and determines the probability with which the agents communicate. In some
cases, depending on the parameters of the model, the preference toward
successfully communicating agents is basically negligible and the model behaves
similarly to the naming game on a complete graph. In particular, it quickly
reaches a single-language state, albeit some details of the dynamics are
different from the complete-graph version. In some other cases, the preference
toward successfully communicating agents becomes much more relevant and the
model gets trapped in a multi-language regime. In this case gradual coarsening
and extinction of languages lead to the emergence of a dominant language,
albeit with some other languages still being present. A comparison of
distribution of languages in our model and in the human population is
discussed.Comment: 22 pages, accepted in Artificial Lif
Controllability of Social Networks and the Strategic Use of Random Information
This work is aimed at studying realistic social control strategies for social
networks based on the introduction of random information into the state of
selected driver agents. Deliberately exposing selected agents to random
information is a technique already experimented in recommender systems or
search engines, and represents one of the few options for influencing the
behavior of a social context that could be accepted as ethical, could be fully
disclosed to members, and does not involve the use of force or of deception.
Our research is based on a model of knowledge diffusion applied to a
time-varying adaptive network, and considers two well-known strategies for
influencing social contexts. One is the selection of few influencers for
manipulating their actions in order to drive the whole network to a certain
behavior; the other, instead, drives the network behavior acting on the state
of a large subset of ordinary, scarcely influencing users. The two approaches
have been studied in terms of network and diffusion effects. The network effect
is analyzed through the changes induced on network average degree and
clustering coefficient, while the diffusion effect is based on two ad-hoc
metrics defined to measure the degree of knowledge diffusion and skill level,
as well as the polarization of agent interests. The results, obtained through
simulations on synthetic networks, show a rich dynamics and strong effects on
the communication structure and on the distribution of knowledge and skills,
supporting our hypothesis that the strategic use of random information could
represent a realistic approach to social network controllability, and that with
both strategies, in principle, the control effect could be remarkable
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