1,720,964 research outputs found
Self-regulation versus social influence for promoting cooperation on networks
Get PDFCooperation is a relevant and controversial phenomenon in human societies. Indeed, although it is widely recognized essential for tackling social dilemmas, finding suitable policies for promoting cooperation can be arduous and expensive. More often, it is driven by pre-established schemas based on norms and punishments. To overcome this paradigm, we highlight the interplay between the influence of social interactions on networks and spontaneous self-regulating mechanisms on individuals behavior. We show that the presence of these mechanisms in a prisoner’s dilemma game, may oppose the willingness of individuals to defect, thus allowing them to behave cooperatively, while interacting with others and taking conflicting decisions over time. These results are obtained by extending the Evolutionary Game Equations over Networks to account for self-regulating mechanisms. Specifically, we prove that players may partially or fully cooperate whether self-regulating mechanisms are sufficiently stronger than social pressure. The proposed model can explain unconditional cooperation (strong self-regulation) and unconditional defection (weak self-regulation). For intermediate selfregulation values, more complex behaviors are observed, such as mutual defection, recruiting (cooperate if others cooperate), exploitation of cooperators (defect if others cooperate) and altruism (cooperate if others defect). These phenomena result from dynamical transitions among different game structures, according to changes of system parameters and cooperation of neighboring players. Interestingly, we show that the topology of the network of connections among players is crucial when self-regulation, and the associated costs, are reasonably low. In particular, a population organized on a random network with a Scale-Free distribution of connections is more cooperative than on a network with an Erdös-Rényi distribution, and, in turn, with a regular one. These results highlight that social diversity, encoded within heterogeneous networks, is more effective for promoting cooperation
Game Interactions and Dynamics on Networked Populations
No full textA new mathematical formulation of evolutionary game dynamics on networked populations is proposed. The model extends the standard replicator equation to a finite set of players organized on an arbitrary network of connections (graph). Classical results of multipopulation evolutionary game theory are used in combination with graph theory to obtain the mathematical model. Specifically, the players, located at the vertices of the graph, are interpreted as subpopulations of a multipopulation dynamical game. The members of each subpopulation are replicators, engaged at each time instant into 2-player games with the members of other connected subpopulations. This idea allows us to write an extended equation describing the game dynamics of a finite set of players connected by a graph. The obtained equation does not require any assumption on the game payoff matrices nor graph topology. Stability of steady states, Nash equilibria and the relationship of the proposed model to the standard replicator equation are discussed. The dynamical behavior of the model over different graphs is also investigated by means of extended simulations
Consensus towards Partially Cooperative Strategies in Self-Regulated Evolutionary Games on Networks
Get PDFCooperation is widely recognized to be fundamental for the well-balanced development of human societies. Several different approaches have been proposed to explain the emergence of cooperation in populations of individuals playing the Prisoner’s Dilemma game, characterized by two concurrent natural mechanisms: the temptation to defect and the fear to be betrayed by others. Few results are available for analyzing situations where only the temptation to defect (Chicken game) or the fear to be betrayed (Stag-Hunt game) is present. In this paper, we analyze the emergence of full and partial cooperation for these classes of games. We find the conditions for which these Nash equilibria are asymptotically stable, and we show that the partial one is also globally stable. Furthermore, in the Chicken and Stag-Hunt games, partial cooperation has been found to be more rewarding than the full one of the Prisoner’s Dilemma game. This result highlights the importance of such games for understanding and sustaining different levels of cooperation in social networks
Evolutionary game for task mapping in resource constrained heterogeneous environments
No full textPower-aware computing is becoming popular using heterogeneous ecosystem. For recent execution units, the heterogeneity is exhibited via the hybrid cores, as well as via virtual and physical asymmetric cores. The inherent performance disparity between different types of execution units at their different clock frequencies offers a great resources scheduling challenge. Multiple metrics (such as throughput, latency, energy cost) are used to decide whether the scheduling is an optimal solution or not. However, in heterogeneous ecosystem, tasks distribution aiming at optimizing costs is not trivial. During the task mapping, one of the primary challenges is to dynamically identify and map the inherent advantages/features of the heterogeneous or hybrid architectures for each individual task. In this work we deal with the task mapping problem using a multi-objective formulation based on evolutionary game theory to optimize a suitable payoff function. This payoff accounts for the power, workload imbalance, task resource affinity and data offloading costs (from host to accelerator). Here, we report that in a very restrictive resource usage scenario (supporting both over and under subscription), the proposed formulation based on Evolutionary Games on Network equation (EGN) can outperform the traditional resource allocation heuristics (such as best-fit, first-fit). Using an extensive set of simulations, we show that our proposed model can outperform first-fit algorithm from more than 5% up to 34.6% and best-fit algorithm from 4% up to 35.7%
A Low-Cost Unmanned Surface Vehicle for Pervasive Water Quality Monitoring
No full textThis article discusses the architecture of a low-cost unmanned surface vehicle (USV) to be employed for the collection of crucial parameters about water quality in rivers, lakes, or sea. The vehicle, called water environmental mobile observer (WeMo), has been realized exploiting off-the-shelf components and is provided with a modular array of sensors to measure chemical and physical parameters as well as to perform bathymetry. The low-cost requirement is crucial since the vehicle is expected to be replicated in large quantities and then used for pervasive monitoring operations by providing it to local communities, administrations, or even private stakeholders, in order to set up a sort of 'social sensor network.' In this sense, data analytics tools have also been introduced in order to automatically drive the vehicle along desired and suitable trajectories and to process the collected data. These data can be used to estimate the parameters of a mathematical model describing the ecological status of the monitored system. In particular, we apply an estimation procedure to a simple mathematical model of oxygen concentration in the water with explicit dependence on biophysical inputs. The estimation provides very satisfying performances, indeed the relative square error is less than 4 \cdot 10^{-2}. Moreover, once the vehicle is moving along a given trajectory, the status in the spatial domain can be reconstructed also in nonmonitored locations. The whole article aims then at developing a complete monitoring ecosystem covering all the tasks of data collection, storage, and analysis. © 1963-2012 IEEE
Hypnotic assessment based on the Recurrence Quantification Analysis of EEG recorded in the ordinary state of consciousness
No full textThe cerebral cortical correlates of the susceptibility to hypnosis in the ordinary states of consciousness have not been clarified. Aim of the study was to characterize the EEG dynamics of subjects with high (highs) and low hypnotisability (lows) through the non-linear method of Recurrence Quantification Analysis (RQA). The EEG of 16 males - 8 highs and 8 lows - was monitored for 1 min without instructions other than keeping the eyes closed, being silent and avoiding movements (short resting), and during 15 min of simple relaxation, that is with the instruction to relax at their best. Highs and lows were compared on the RQA measures of Determinism (DET) and Entropy (ENT), which are related to the signal determinism and complexity. In the short resting condition discriminant analysis could classify highs and lows on the basis of DET and ENT values at temporo-parietal sites. Many differences in DET and all differences in ENT disappeared during simple relaxation, although DET still separated the two groups in the earliest 6 min of relaxation at temporo-parietal sites. Our RQA based approach allows to develop computer-based methods of hypnotic assessment using short-lasting, single channel EEG recordings analyzed through standard mathematical methods. (C) 2013 Elsevier Inc. All rights reserved
Emergence of microbial networks as response to hostile environments
Get PDFThe majority of microorganisms live in complex communities under varying conditions. One pivotal question in evolutionary biology is the emergence of cooperative traits and their sustainment in altered environments or in the presence of free-riders. Co-occurrence patterns in the spatial distribution of biofilms can help define species' identities, and systems biology tools are revealing networks of interacting microorganisms. However, networks of inter-dependencies involving micro-organisms in the planktonic phase may be just as important, with the added complexity that they are not bounded in space. An integrated approach linking imaging, ``Omics'' and modeling has the potential to enable new hypothesis and working models. In order to understand how cooperation can emerge and be maintained without abilities like memory or recognition we use evolutionary game theory as the natural framework to model cell-cell interactions arising from evolutive decisions. We consider a finite population distributed in a spatial domain (biofilm), and divided into two interacting classes with different traits. This interaction can be weighted by distance, and produces physical connections between two elements allowing them to exchange finite amounts of energy and matter. Available strategies to each individual of one class in the population are the propensities or ``willingness'' to connect any individual of the other class. Following evolutionary game theory, we propose a mathematical model which explains the patterns of connections which emerge when individuals are able to find connection strategies that asymptotically optimize their fitness. The process explains the formation of a network for efficiently exchanging energy and matter among individuals and thus ensuring their survival in hostile environments
Pain perception and EEG dynamics: does hypnotisability account for the efficacy of the suggestions of analgesia?
No full textWe report novel findings concerning the role of hypnotisability, suggestions of analgesia and the activity of the Behavioral Inhibition/Activation System (BISBAS) in the modulation of the subjective experience of pain and of the associated EEG dynamics. The EEG of high (highs) and low hypnotisable participants (lows) who completed the BISBAS questionnaire was recorded during basal conditions, tonic nociceptive stimulation without (PAIN) and with suggestions for analgesia (AN). Participants scored the perceived pain intensity at the end of PAIN and AN. The EEG midline dynamics was characterized by indices indicating the signal predictability (Determinism) and complexity (Entropy) obtained through the Recurrence Quantification Analysis. The reduced pain intensity reported by highs during AN was partially accounted for by the activity of the Behavioral Activation System. The decreased midline cortical Determinism observed during nociceptive stimulation in both groups independently of suggestions remained significantly reduced only in lows after controlling for the activity of the Behavioural Activation System . Finally, controlling for the activity of the Behavioral Inhibition System abolished stimulation, suggestions and hypnotisability- related differences. Results indicate that the BISBAS activity may be more important than hypnotizability itself in pain modulation and in the associated EEG dynamics
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