Cooperation-competition Networks Research Articles

Consensus of multi-agent systems in the cooperation–competition network with inherent nonlinear dynamics: A time-delayed control approach

In this paper, the consensus problem is investigated for a group of first-order agents in the cooperation–competition network, where agents can cooperate or even compete with each other, i.e., the elements in the coupling weight matrix of the graph can be either positive or negative. In order to solve this consensus problem, the whole network is firstly divided into two sub-networks, i.e., the cooperation sub-network and the competition sub-network, and then two kinds of time-delayed control schemes are designed in the competition sub-network. By combining the Lyapunov theory together with the synchronization manifold method, several effective sufficient conditions of consensus are provided without assuming that the interaction topology is strongly connected or contains a directed spanning tree, which means that the competition relationships could help the agents achieve consensus under the time-delayed control designed in the competition sub-network. Moreover, the results are also extended to the pure competition networks where all the elements in the weight matrices are either zeros or negative. Finally, some simulation examples are provided to validate the effectiveness of the theoretical analysis.

Second-order consensus of multi-agent systems in the cooperation–competition network with switching topologies: A time-delayed impulsive control approach

This paper investigates the consensus problem of second-order multi-agent systems with switching topologies by designing a time-delayed impulsive consensus control scheme. All the agents are governed by the same nonlinear intrinsic dynamics. In this study, agents can cooperate or compete with each other, i.e., the elements in the weight matrix of the coupling graph can be either positive or negative. By establishing a comparison system, a new comparison principle method is successfully applied to study such consensus problem. Then, several effective sufficient conditions are attained without assuming that the interaction topology is strongly connected or contains a directed spanning tree. Meanwhile, the exponential consensus rate is also obtained. Finally, simulation results are presented to validate the effectiveness of the theoretical analysis.

An extended clique degree distribution and its heterogeneity in cooperation–competition networks

After Xiao et al. [W.-K. Xiao, J. Ren, F. Qi, Z.W. Song, M.X. Zhu, H.F. Yang, H.Y. Jin, B.-H. Wang, Tao Zhou, Empirical study on clique-degree distribution of networks, Phys. Rev. E 76 (2007) 037102], in this article we present an investigation on so-called k-cliques, which are defined as complete subgraphs of k (k>1) nodes, in the cooperation–competition networks described by bipartite graphs. In the networks, the nodes named actors are taking part in events, organizations or activities, named acts. We mainly examine a property of a k-clique called “k-clique act degree”, q, defined as the number of acts, in which the k-clique takes part. Our analytic treatment on a cooperation–competition network evolution model demonstrates that the distribution of k-clique act degrees obeys Mandelbrot distribution, P(q)∝(q+α)−γ. To validate the analytical model, we have further studied 13 different empirical cooperation–competition networks with the clique numbers k=2 and k=3. Empirical investigation results show an agreement with the analytic derivations. We propose a new “heterogeneity index”, H, to describe the heterogeneous degree distributions of k-clique and heuristically derive the correlation between H and α and γ. We argue that the cliques, which take part in the largest number of acts, are the most important subgraphs, which can provide a new criterion to distinguish important cliques in the real world networks.

Competition Ability Dependence on Uniqueness in General Cooperation-Competition Systems

We discuss the dependence of competition ability on uniqueness for the general cooperation-competition networks. An empirical investigation of the 15 real world systems is made. We find that the dependence varies with the change of systems. Although most of the systems show positive tendency, we find that a special system shows negative one. The relationship between the heterogeneity indexes for the distributions of competition ability and uniqueness is also discussed analytically and empirically.

An Investigation into Node Strength Connectivity Correlation

We propose investigating the node strength connectivity correlation by a resource-allocation method and the traditional multiple edge method, respectively. A rough analysis suggests that the resource-allocation node strength connectivity correlation is always negative, which is different from the connectivity correlation of the traditional multiple edge node strength (it can show either positive, negative or no correlation). As examples, empirical investigation results for two real world cooperation-competition networks (the 2004 Athens Olympic Games network and the mixed drink network) are presented. We believe that the resource-allocation node strength connectivity correlation can serve as a description of the relative crackajack distribution, which is a complementarity of the traditional multiple edge one.

Two's Company, Three's a Crowd? Triads in Cooperative-Competitive Networks

Triadic structure is an important, but neglected, aspect of interfirm networks. We developed the constructs clustering and countering as potential drivers of triadic structure and combined them with the recently developmed p* network model to demonstrate the value and feasibility of triadic analysis. Exploratory analysis of data from the global steel industry revealed firms' tendency to form transitive triads, in which three firms all have direct ties with each other, especially within blocks defined by geography or technology.

The stability of sustained oscillations in symmetric cooperative-competitive networks

Cohen and Grossberg (1983) proved that a large class of neural networks can function as stable content addressable memories. These Cohen-Grossberg networks were designed to include additive neural networks as studied by Hopfield (1984), and shunting neural networks. The shunting networks included cooperative-competitive networks, also called on-center off-surround feedback networks. The coefficients of the negative feedback signals between populations were drawn from a symmetric matrix. The positive feedback signals were self-excitatory only; that is, no node took excitation from any of its neighbors. The question was next raised concerning how much the Cohen-Grossberg form for a content addressable memory could be generalized. Cohen (1988) showed that if the excitatory on-center was broadened to admit positive feedback from neighboring populations, then persistent oscillations were possible. In this work, the stability of some of these oscillations is shown by studying the Hopf bifurcation constructed in Cohen (1988). These neural networks are transformed to a standard coordinate system and the computation of a coefficient which determines the stability of the oscillations near the Hopf bifurcation is carried out. The negativity of this coefficient implies the bifurcating periodic orbits are stable.

Motions in orthogonal directions are mutually suppressive

Models of motion-sensing units that are local or nearly local in their measurements suffer from a problem that the actual meaning of a unit’s firing is ambiguous. In order for this ambiguity to be resolved, the outputs of many units must be combined and compared. In the experiments described here the predictions of one such model of this process were tested. The effect of vertical motion was measured for patterns moving horizontally. It was found that there was a strong suppression from the vertical motion that could not be accounted for by the simple addition of noise to the motion signal. Further, this suppressive effect was found to be asymmetric in time, in that motions occurring for as much as 300 msec after the vertical motion were affected. whereas those almost immediately before the motion were unaffected. I suggest that such results are predicted by a cooperative–competitive network of interactions between local motion measurements.

Sustained oscillations in a symmetric cooperative-competitive neural network: Disproof of a conjecture about content addressable memory

Cohen and Grossberg proved that a large class of neural networks with symmetric interaction coefficients admit a global Liapunov function guaranteeing that their trajectories approach equilibrium points. Such networks function as content-addressable memories, and the equilibria are the stored memories. Cohen and Grossberg also conjectured, based upon substantial computational evidence, that networks within a class of mixed cooperative-competitive networks with symmetric interaction coefficients also have this property. This conjecture is here disproved. In particular, a class of homogeneous, distance-dependent, on-center off-surround neural networks are constructed which supports persistent oscillations for appropriate initial data. Such a class is constructed in each even dimension. Similar systems, which have been used to model the dynamics of the hippocampus, are compared to this class of networks to clarify the origins of oscillatory class of behavior in this class of systems.

Parallel model of the kinetic depth effect using local computations.

This paper defines a new model for the kinetic depth effect for multidot stimuli. The calculation is performed in a cooperative-competitive network, described as a relaxation labeling process. The process involves a local iterative computation to meet best the constraints indicated by image cues to depth. Given a constraint that prefers interdot distances in three dimensions to remain constant (local rigidity), the model becomes a local parallel computation of the Ullman incremental-rigidity scheme. Several simulations of the model are described, including some in which additional cues are combined with the changing-dot-position cue.

On Theories of Flash Synchronization in Fireflies

Previous articleNext article No AccessNotes and CommentsOn Theories of Flash Synchronization in FirefliesDaniel OtteDaniel Otte Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by The American Naturalist Volume 116, Number 4Oct., 1980 Published for The American Society of Naturalists Article DOIhttps://doi.org/10.1086/283648 Views: 9Total views on this site Citations: 16Citations are reported from Crossref Copyright 1980 The University of ChicagoPDF download Crossref reports the following articles citing this article:Vlad Demartsev, Michal Haddas‐Sasson, Amiyaal Ilany, Lee Koren, Eli Geffen Male rock hyraxes that maintain an isochronous song rhythm achieve higher reproductive success, Journal of Animal Ecology 23 (Sep 2022).https://doi.org/10.1111/1365-2656.13801Sun-Ho Choi, Hyowon Seo Asymptotic behavior of the Kuramoto system with periodic natural frequency, Journal of Differential Equations 308 (Jan 2022): 160–187.https://doi.org/10.1016/j.jde.2021.11.007Lei Shi, Yuhua Cheng, Jinliang Shao, Hanmin Sheng, Qingchen Liu Cucker-Smale flocking over cooperation-competition networks, Automatica 135 (Jan 2022): 109988.https://doi.org/10.1016/j.automatica.2021.109988Andrew Moiseff, Jonathan Copeland Behavioral Consequences of Sensory System Constraints in the Firefly Photinus carolinus, Ecological Psychology 32, no.44 (Nov 2020): 143–152.https://doi.org/10.1080/10407413.2020.1846455Jianwen Feng, Le Zhang, Jingyi Wang, Yi Zhao The synchronization of complex dynamical networks with discontinuous dynamics and exogenous disturbances, Asian Journal of Control 6 (Sep 2020).https://doi.org/10.1002/asjc.2414Pawan Kumar, P. Parmananda Dependence of synchronization on frequency mismatch and network configuration in chemo-mechanical oscillators, EPL (Europhysics Letters) 122, no.22 (Jun 2018): 20002.https://doi.org/10.1209/0295-5075/122/20002Nicolas Rehberg-Besler, Stéphanie M. Doucet, Daniel J. Mennill Overlapping vocalizations produce far-reaching choruses: a test of the signal enhancement hypothesis, Behavioral Ecology 52 (Dec 2016): arw176.https://doi.org/10.1093/beheco/arw176Qi Xuan, Vladimir Filkov Synchrony in Social Groups and Its Benefits, (Nov 2013): 791–802.https://doi.org/10.1007/978-1-4614-8806-4_64Leandro dos Santos Coelho, Diego Luis de Andrade Bernert, Viviana Cocco Mariani A chaotic firefly algorithm applied to reliability-redundancy optimization, (Jun 2011): 517–521.https://doi.org/10.1109/CEC.2011.5949662Andrew Moiseff, Jonathan Copeland Firefly Synchrony: A Behavioral Strategy to Minimize Visual Clutter, Science 329, no.59885988 (Jul 2010): 181–181.https://doi.org/10.1126/science.1190421Z.-H. Liu, P.M. Hui Collective signaling behavior in a networked-oscillator model, Physica A: Statistical Mechanics and its Applications 383, no.22 (Sep 2007): 714–724.https://doi.org/10.1016/j.physa.2007.04.042Michael D. Greenfield Mechanisms and Evolution of Communal Sexual Displays in Arthropods and Anurans, (Jan 2005): 1–62.https://doi.org/10.1016/S0065-3454(05)35001-7Peter J Herring Bioluminescent signals and the role of reflectors, Journal of Optics A: Pure and Applied Optics 2, no.66 (Oct 2000): R29–R38.https://doi.org/10.1088/1464-4258/2/6/202 John Buck Synchronous Rhythmic Flashing of Fireflies. II., The Quarterly Review of Biology 63, no.33 (Oct 2015): 265–289.https://doi.org/10.1086/415929 Albert D. Carlson , and Jonathan Copeland Flash Communication in Fireflies, The Quarterly Review of Biology 60, no.44 (Oct 2015): 415–436.https://doi.org/10.1086/414564James A. Serpell Reply to Wickler & Seibt, Animal Behaviour 30, no.33 (Aug 1982): 944–945.https://doi.org/10.1016/S0003-3472(82)80179-6