Cooperative Clusters Research Articles

Reputation preferences resolve social dilemmas in spatial multigames

Heterogeneity and difference in the dynamics of individual reputation may strongly affect learning behavior, and hence also the evolution of cooperation within a population. Motivated by this, we propose here an evolutionary spatial multigames model, wherein the reputation of an individual increases if they cooperate and decreases if they defect. After the payoffs are determined, individuals with a higher reputation will be more likely to act as strategy sources for other individuals. We perform systematic Monte Carlo simulations to determine the transitions between cooperation and defection, as well as the parameter regions of strategic coexistence. We show that preferential learning, based on dynamic reputation changes, strongly promotes cooperation regardless of the interaction network’s structure. The mechanism responsible for more favorable evolutionary outcomes is enhanced network reciprocity, which leads to more compact cooperator clusters and thus to more robust spatiotemporal dynamics that are resilient to invading defectors. Our research may improve the understanding of selection patterns that favor the emergence and persistence of cooperative behavior.

Cooperative Game Theory Approach for Energy-Efficient Node Clustering in Wireless Sensor Network

Energy consumption is one of the critical challenges in designing wireless sensor network (WSN) since it is typically composed of resource-constrained devices. Many studies have been proposed clustering to deal with energy conservation in WSN. Due to its predominance in coordinating the behaviors of many players, game theory has been considered for improving energy efficiency in WSN. In this paper, we evaluate the performance of cooperative game theoretic clustering (CGC) algorithm which employs cooperative game theory in a form of 3-agent cost sharing game for energy-efficient clustering in WSN. Furthermore, we compared its performance to a well-known traditional clustering method, low-energy adaptive clustering hierarchy (LEACH), in terms of network lifetime and stability, and total residual energy. The simulation results show that CGC has better performance compared to LEACH due to the cooperation among cluster heads in coalition. CGC has higher alive nodes with stability improvement of first node dies (FND) by 65%, and the improvement by 52.4% for half node dies (HND). However, with the increasing of the number of nodes, the performance of LEACH is getting better compared to CGC.

Bridging dynamic regimes of segmental relaxation and center-of-mass diffusion in associative protein hydrogels

Molecular transport in dense, crowded media is often affected by binding and collisions with obstacles, leading to complex spatial distributions and anomalous diffusion on multiple length scales. Here, neutron spin-echo (NSE) spectroscopy and forced Rayleigh scattering (FRS) were used in tandem to study the interplay between segmental and center-of-mass chain dynamics in the presence of strong binding interactions in a model protein hydrogel. The results provide evidence for several dynamic scaling regimes of gel relaxation behavior with varying length scale, including a caging regime bridging submolecular relaxation and center-of-mass diffusion due to transient binding. On mesoscopic length scales larger than the size of the cage, chains undergo two distinct regimes of apparent superdiffusive scaling, with different power-law exponents, before the onset of terminal Fickian diffusion. The combined NSE and FRS results are interpreted in the context of prior Brownian dynamics simulations of associating star polymers, revealing insight into structural length scales and binding kinetics governing the transition from segmental relaxation to self-diffusion in the protein hydrogel. Finally, single-sticker tracer diffusion measurements were performed to directly probe sticker association/dissociation dynamics within the gel, the results suggesting that cooperative cluster motion may play a role in network relaxation on larger length scales.Received 28 February 2020Accepted 30 October 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.043369Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasDiffusionPhysical SystemsPolymer gelsTechniquesLight scatteringNeutron scatteringNeutron spin echo spectroscopyPolymers & Soft Matter

An IGDT-based robust optimization model for optimal operational planning of cooperative microgrid clusters: A normal boundary intersection multi-objective approach

Economical and reliable energy management of isolated networked microgrids (NMGs) is a vital task on operators to optimally preserve system sustainability and security under severe uncertainties. To that end, in this paper, multi-objective risk-averse (RA) and opportunity-seeker (OS) information gap decision theory (IGDT) approaches are proposed to optimize the operational planning of isolated NMGs and assign robustness and opportuneness functions. Three optimization cases are defined as (I) only RES output power, (II) only load consumption, and (III) simultaneous RES and load uncertainties. Furthermore, an effective pricing model based on the microgrid marginal pricing is proposed for the proper management of power exchanges. A normal boundary intersection strategy is applied to generate distributed Pareto Frontier of the optimization model in Case III. The proposed approach is implemented on a test system with five NMGs in a 24-hour scheduling time horizon in which operating constraints are appropriately considered. Evaluating the IGDT-based numerical results verifies the effective performance of the proposed framework in the NMGs compared to the stochastic and Monte Carlo simulation methodologies. According to the results in Cases I and II, by increasing the total operating cost (TOC) up to 23% and 16%, it takes a perfect RA strategy. Also, opportunistic decision makings can be concluded with a 5% decrease in TOC. For Case III, the results with TOCs equal to 19769.19$ in RA strategy and 17612.55$ in OS strategy, are fully conservative and economic, respectively.

Optimization of mobile individuals promotes cooperation in social dilemmas

Abstract We study how mobile individuals affect the evolution of cooperation in social dilemmas. In doing so, we consider two types of players. The traditional type simply copies the most successful strategy in its neighborhood in order to improve its future payoff, while the advantageous type moves away in the hope of settling in a better community. We show that the introduction of the advantageous type leads to larger and more compact cooperative clusters in the prisoner’s dilemma game. This in turn facilitates the evolutionary stability of cooperation even under adverse conditions that are characterized by high temptations to defect. We also verify that the average payoff of a community unit remains proportional to the number of cooperators in this community, which hence indicates that the players pursuing mobility to attain a competitive advantage also foster cooperation in their new communities. Another way to communicate this result in the light of the costs associated with moving is to say that optimal mobility, such that yields higher payoffs to the individual who moved and the community as a whole, is similar to the optimization of the allocation of limited resources. We thus hope that these results will shed new light on how to effectively allocate resources and how to optimize mobility for optimal cooperation.

IOT Energy Consumption Based on PSO-shortest Path Techniques

Background: The Internet of Thing is a network that enables multiple hardware devices, sensors and other home applications from electronically communicate with each other. New era such technology is increasing importance mainly due to the revolutionary development of information technologies. Methods: However, energy efficiency is still a big challenge facing IoT technology. Thus, it becomes an interesting topic for many researchers to investigate. Current work aims to reduce the energy consumption thereby introducing the shortest path technique and another new practicing for Particle Swarm Optimization algorithm in the Internet of Thing cooperative clusters. Results: The main concept is based on cluster heads cooperation with each other known as Cooperative Clusters to transfer information to the base station. The Primary results reveals a 17% and 16% reduction in energy consumption was achieved over the shortest path technique and Particle Swarm Optimization algorithm respectively. Results also show a remarkable improvement in the system lifetime due to the new applied scheme. Conclusion: The other method is by PSO algothrim at beginig it sending Randomly then it will select the path by using the feed back acknowledgment after that it will collect the information by sending it for the Cluster heads by updating the information status automatically. That’s why we discovered the PSO advantages than the others.

Robust cooperation against mutations via costly expulsion

Research has shown that spatial selection can lead to the evolution of cooperation in social dilemmas. However, the effectiveness of spatial reciprocity among cooperative players is often diminished by mutations. Here, we report the evolution of robust cooperation against mutations, which is brought about by costly expulsion in the spatial public goods game. We find that costly expulsion is able to enhance the robustness of positive assortment among cooperators against mutations. Moreover, we also show that moderate mutation rates are beneficial for the expansion of spatial clusters among cooperative individuals once the spontaneous formation of segregation patterns is completed by costly expulsion. Our results thus communicate the counterintuitive conclusions that mutations are not always detrimental for the evolution of cooperation in spatial social dilemma games, and what is more, can even be beneficial for faster spreading of cooperator clusters.

A limited mobility of minorities facilitates cooperation in social dilemmas

Individuals often move to distance themselves from defectors, or to seek better chances for higher payoffs, for example moving from rural to urban areas. Regardless of the reason, however, moving frequently also means alienation, which in turn means bearing costs for seeking new opportunities. With this motivation, we study a prisoner’s dilemma game, where individuals with defectors in their communities either move or update their strategy. We find that the alienation from defectors reinforces larger and more compact cooperative clusters. However, the number of cooperative clusters depends on the viscosity of the interaction network, where network reciprocity still works well. And it is the fine-tuned interplay between the mobility to alienate from defectors and a still functioning network reciprocity that works best in promoting cooperation. Our results suggest that a limited mobility of minorities could spare public resources in social dilemma situations more effectively than reward and punishment.

Mixing protocols in the public goods game.

If interaction partners in social dilemma games are not selected randomly from the population but are instead determined by a network of contacts, it has far reaching consequences for the evolutionary dynamics. Selecting partners randomly leads to a well-mixed population, where pattern formation is essentially impossible. This rules out important mechanisms that can facilitate cooperation, most notably network reciprocity. In contrast, if interactions are determined by a lattice or a network, then the population is said to be structured, where cooperators can form compact clusters that protect them from invading defectors. Between these two extremes, however, there is ample middle ground that can be brought about by the consideration of temporal networks, mobility, or other coevolutionary processes. The question that we here seek to answer is, when does mixing on a lattice actually lead to well-mixed conditions? To that effect, we use the public goods game on a square lattice, and we consider nearest-neighbor and random mixing with different frequencies, as well as a mix of both mixing protocols. Not surprisingly, we find that nearest-neighbor mixing requires a higher frequency than random mixing to arrive at the well-mixed limit. The differences between the two mixing protocols are most expressed at intermediate mixing frequencies, whilst at very low and very high mixing frequencies the two almost converge. We also find a near universal exponential growth of the average size of cooperator clusters as their fraction increases from zero to one, regardless of whether this increase is due to increasing the multiplication factor of the public goods, decreasing the frequency of mixing, or gradually shifting the mixing from random to nearest neighbors.

Bibliometric and Visualization Analysis of Human Coronaviruses: Prospects and Implications for COVID-19 Research.

Human coronaviruses, which can cause a range of infectious diseases, have been studied for nearly 60 years. The field has gained renewed interest from researchers around the world due to the COVID-19 outbreak in late 2019. Despite a large amount of research, little is known about the knowledge structure and developing trends of this topic. Here, we apply bibliometric analysis along with visualization tools to analyze 15,207 publications related to human coronavirus from the Scopus database, using indicators on publication and citation, journal, country or territory, affiliation and international cooperation, author, and keyword co-occurrence cluster. The results show that research on human coronavirus is dominated by SARS-CoV. Although there have been many publications, only 626 publications (4.1% of total) have more than 100 citations. The top 20 journals with most publications account for 20.6% of total publications and 41% of total citations. In addition to the United States and some European countries, many Asian and African countries are involved in this research, with China holding an important position in this area. Leading researchers from various fields of human coronavirus research are listed to facilitate collaboration and promote effective disease prevention and control. The keywords co-occurrence analysis reveals that the research focus on virology, public health, drugs and other hotspot fields, and uncovers changes in the direction of coronavirus research. The research map on human coronavirus obtained by our analysis are expected to help researchers to efficiently and effectively explore COVID-19.

Reputation-based adjustment of fitness promotes the cooperation under heterogeneous strategy updating rules

Imitation and innovation are the most common ways for people to learn and evolve. However, the combination of imitation and innovation not only inhibits the emergence of cooperation, but also leads to the fragmentation of cooperative clusters. In reality, cooperation exists widely in people's lives. This paper examines how cooperation is maintained in human interaction and what mechanism can eliminate the inhibition of heterogeneous strategy updating rules. To that end, we introduce a reputation mechanism into the traditional prisoner's dilemma model. In our version, the player's reputation is adapted dynamically, and fitness is determined by reputations and strategies. In addition, the players' strategy updating rules are divided into imitation and innovation. The results show that: under the heterogeneous strategy updating rules, the introduction of reputation is beneficial to the emergence of cooperation. Good reputation also enhances the capacity of cooperators to resist temptation. Our research provides insight into the role of reputation under heterogeneous strategy updating rules.

Human social preferences cluster and spread in the field

While it is undeniable that the ability of humans to cooperate in large-scale societies is unique in animal life, it remains open how such a degree of prosociality is possible despite the risks of exploitation. Recent evidence suggests that social networks play a crucial role in the development of prosociality and large-scale cooperation by allowing cooperators to cluster; however, it is not well understood if and how this also applies to real-world social networks in the field. We study intrinsic social preferences alongside emerging friendship patterns in 57 freshly formed school classes (n = 1,217), using incentivized measures. We demonstrate the existence of cooperative clusters in society, examine their emergence, and expand the evidence from controlled experiments to real-world social networks. Our results suggest that being embedded in cooperative environments substantially enhances the social preferences of individuals, thus contributing to the formation of cooperative clusters. Partner choice, in contrast, only marginally contributes to their emergence. We conclude that cooperative preferences are contagious; social and cultural learning plays an important role in the development and evolution of cooperation.

Network Engineering Using Autonomous Agents Increases Cooperation in Human Groups.

SummaryCooperation in human groups is challenging, and various mechanisms are required to sustain it, although it nevertheless usually decays over time. Here, we perform theoretically informed experiments involving networks of humans (1,024 subjects in 64 networks) playing a public-goods game to which we sometimes added autonomous agents (bots) programmed to use only local knowledge. We show that cooperation can not only be stabilized, but even promoted, when the bots intervene in the partner selections made by the humans, re-shaping social connections locally within a larger group. Cooperation rates increased from 60.4% at baseline to 79.4% at the end. This network-intervention strategy outperformed other strategies, such as adding bots playing tit-for-tat. We also confirm that even a single bot can foster cooperation in human groups by using a mixed strategy designed to support the development of cooperative clusters. Simple artificial intelligence can increase the cooperation of groups.

Evolutionary game dynamics of combining the payoff-driven and conformity-driven update rules

Abstract Conformity has been found to play a significant role in promoting cooperation. In this paper, we propose a spatial prisoner’s dilemma game with a novel strategy updating rule, combining the payoff-driven and conformity-driven update rules. For the noise strengths κ 1 = κ 2 = 0.1 , our results reveal that the dynamics of this model is always absorbed into the frozen state. Three phases are appeared in the order of full cooperators, bistability and full defectors with increasing temptation to defect b. In the bistability phase, the dynamics can evolve into either a full-cooperator state or full-defector state, and the final steady states of the model are mainly determined by the synchronous expanding along the smooth boundaries of the cooperator clusters. Moreover, the full κ − b phase diagram is illustrated to explore the effects of noise on the evolution of cooperation in the proposed model, and the bell-shape phase diagrams indicate that there exists an optimal uncertainty for the emergence and maintenance of cooperation. Furthermore, the effects of distinct κ1 and κ2 on the evolution of cooperation have also been investigated. Interestingly, the coexistence of cooperators and defectors could be found in appropriate parameter regions. In addition, we also consider another way to integrate the payoff-driven and conformity-driven strategy updating rules, and the outcomes clearly show that the evolution of cooperation can be significantly enhanced within an optimal region of conformity strength. These observations enrich our knowledge of the evolution of cooperation in the games when the payoff-driven and conformity-driven update rules are combined.

How does conformity promote the enhancement of cooperation in the network reciprocity in spatial prisoner's dilemma games?

A new spatial prisoner's dilemma game model considering the conformity is proposed. A series of simulations confirms the present model bringing more enhanced network reciprocity than the conventional model. The observed enhancement is mechanically justified by relying the concept of END and EXP period. In line with more enhanced network reciprocity, we built a new spatial prisoner's dilemma (SPD) game considering conformity with presumption of lattice for underlying network and pairwise Fermi (PW-Fermi) for update rule. The key protocol is that a neighbor with more weight resulting from the conformity would be more likely selected as a pairwise opponent in the PW-Fermi updating process instead of random selection the conventional model presuming. A series of systematic simulations confirms that our model realizes more enhanced network reciprocity than the conventional SPD model. We elucidated its mechanism by means of considering on the concept of END period (meaning an initial period in which the global-cooperation fraction decreases from its initial value) and EXP periods (meaning the period following to END period in which global-cooperation increases) that substantially explaining how cooperative clusters survive in initial period and extend to defective zones afterwards.

Scalable Cell-Free Massive MIMO Systems

Imagine a coverage area with many wireless access points that cooperate to jointly serve the users, instead of creating autonomous cells. Such a cell-free network operation can potentially resolve many of the interference issues that appear in current cellular networks. This ambition was previously called Network MIMO (multiple-input multiple-output) and has recently reappeared under the name Cell-Free Massive MIMO. The main challenge is to achieve the benefits of cell-free operation in a practically feasible way, with computational complexity and fronthaul requirements that are scalable to large networks with many users. We propose a new framework for scalable Cell-Free Massive MIMO systems by exploiting the dynamic cooperation cluster concept from the Network MIMO literature. We provide a novel algorithm for joint initial access, pilot assignment, and cluster formation that is proved to be scalable. Moreover, we adapt the standard channel estimation, precoding, and combining methods to become scalable. A new uplink and downlink duality is proved and used to heuristically design the precoding vectors on the basis of the combining vectors. Interestingly, the proposed scalable precoding and combining outperform conventional maximum ratio processing and also performs closely to the best unscalable alternatives.

Energy Efficient Distributed Cooperative Cluster Based Communication Protocol in Wireless Sensor Networks

Theoretical Energy imperative in remote sensor systems has gotten an expanding research enthusiasm for late years. Radio abnormality, channel blurring and obstruction brings about bigger vitality utilization and inertness for packets transmission over remote channel. One late innovation that can possibly drastically increment the channel limit and lessen transmission vitality utilization in blurring channels is helpful correspondence. The expansion in the direct limit brings about diminished blunder rate. In this paper, one agreeable correspondence method is proposed by developing vitality effective sending and getting bunches for each jump. It comprises of two stages to be specific routing stage, selecting and-transmitting stage. In the routing stage, the underlying way between the source and the sink hubs is found. In the second stage, the hubs on the underlying way progress toward becoming group heads, which select extra contiguous hubs with most minimal vitality cost from their neighborhood then the bundle is transmitted from the sending bunch to the recently settled accepting bunch. The recreation comes about demonstrate that the decrease in mistake rate and the vitality funds convert into expanded lifetime of helpful systems.

Exit Option Induced by Win-Stay-Lose-Leave Rule Provides Another Route to Solve the Social Dilemma in Structured Populations

Theoretical have shown that cooperation can be promoted by the so-called reciprocity, where cooperation can coexist with defection via the compact cooperative clusters. However, this mechanism has one assumption interpreted as players have no chance to exit from the game even the situation is extremely bad, which is in sharp contrast with real-life situations. Here, we relax this assumption in the spatial prisoner's dilemma game by giving players the right to choose one of their state between active (participate in the game) and inactive (exit from the game) in each round of the game determined by win-stay-lose-leave rule. This new rule attracts a winner whose payoff is larger than the average payoff of its neighbors to stay in its current state, thus retains its current advantage to its neighbors. Conversely, a loser is pushed to leave from its current state and thus increase its chance to obtain a higher payoff. Specifically, we incorporate exit costs into consideration by assuming anyone who decides to exit from the game must pay a cost $\gamma$. Extensively numerical simulation shows that for the requirement of exit cost is neither too high or too small, a full cooperation plateau is achieved, where cooperation evolves with the support of enhanced network reciprocity. In fact, inactive players can only exist at the boundary of cooperative clusters during its evolution dynamics, which create a crucial buffer area for the endangered cooperators. Finally, the joint effect of this protective film composed of inactive players and cooperative clusters forming the foundation of enhanced network reciprocity. Our findings may provide new insight into network reciprocity.

Evolutionary public goods game on the birandom geometric graph.

To investigate the evolution of cooperation in spatial public goods games, this paper establishes a birandom geometric graph, in which two types of nodes, representing players and public goods respectively, are placed at random locations in the unit square. Each public good has a limit influence range and the individuals that fall into the same range engage in a public good game. In contrast to the classical network models consisting of only one type of nodes, the birandom geometric graph provides a natural way to describe the scenarios where individuals and public resources are independent of each other. Numerical simulations reveal that cooperation can be significantly promoted when the group size and the average number of groups that each player participates in are relatively small, which is at odds with the results on the square lattice, but is consistent with a body of empirical evidence reported by Ostrom and Olson etal. Analysis of the evolutionary process suggests that the facilitation of cooperation is due primarily to the formation of the cooperative clusters, which can effectively resist the invasion of the defectors.

Joint Unmanned Aerial Vehicle (UAV) Deployment and Power Control for Internet of Things Networks

In this paper, we study unmanned aerial vehicle (UAV) aided internet of things (IoT) networks, where UAVs facilitate data transmission of IoT devices. We focus on uplink transmission from IoT devices to base station (BS). Multiple UAVs are employed as UAV relays between IoT devices and BS to enhance received signal strength at BS. Specifically, IoT devices periodically detect wireless channel quality between IoT devices and BS, as well as that among IoT devices. Based on the wireless channel quality, we propose a distributed user cluster (UC) algorithm to cluster IoT devices as multiple UCs. One IoT device in a UC, which is named cluster head (CH), is selected to connect to the BS and gather uplink signals of IoT devices. If the wireless channel quality between CH and BS is good, a direct connection between CH and the BS can be built. Otherwise, UAVs are divided into multiple UAV cooperative relay clusters (CRCs). The UAVs in a CRC are located between a specific CH and BS to relay uplink signals. We then formulate a system optimization model to minimize system power consumption, where UAV deployment and transmission power of UAV are jointly optimized. We solve this optimization problem by dual decomposition method. By extensive simulations, we demonstrate the effectiveness of the proposed algorithm. We also reveal several interesting insights for practical UAV aided IoT networks.