2,729 research outputs found
Emergent Behaviors over Signed Random Networks in Dynamical Environments
We study asymptotic dynamical patterns that emerge among a set of nodes that
interact in a dynamically evolving signed random network. Node interactions
take place at random on a sequence of deterministic signed graphs. Each node
receives positive or negative recommendations from its neighbors depending on
the sign of the interaction arcs, and updates its state accordingly. Positive
recommendations follow the standard consensus update while two types of
negative recommendations, each modeling a different type of antagonistic or
malicious interaction, are considered. Nodes may weigh positive and negative
recommendations differently, and random processes are introduced to model the
time-varying attention that nodes pay to the positive and negative
recommendations. Various conditions for almost sure convergence, divergence,
and clustering of the node states are established. Some fundamental
similarities and differences are established for the two notions of negative
recommendations
Dynamics over Signed Networks
A signed network is a network with each link associated with a positive or
negative sign. Models for nodes interacting over such signed networks, where
two different types of interactions take place along the positive and negative
links, respectively, arise from various biological, social, political, and
economic systems. As modifications to the conventional DeGroot dynamics for
positive links, two basic types of negative interactions along negative links,
namely the opposing rule and the repelling rule, have been proposed and studied
in the literature. This paper reviews a few fundamental convergence results for
such dynamics over deterministic or random signed networks under a unified
algebraic-graphical method. We show that a systematic tool of studying node
state evolution over signed networks can be obtained utilizing generalized
Perron-Frobenius theory, graph theory, and elementary algebraic recursions.Comment: In press, SIAM Revie
Emergent Behaviors over Signed Random Dynamical Networks: State-Flipping Model
Recent studies from social, biological, and engineering network systems have
drawn attention to the dynamics over signed networks, where each link is
associated with a positive/negative sign indicating trustful/mistrustful,
activator/inhibitor, or secure/malicious interactions. We study asymptotic
dynamical patterns that emerge among a set of nodes that interact in a
dynamically evolving signed random network. Node interactions take place at
random on a sequence of deterministic signed graphs. Each node receives
positive or negative recommendations from its neighbors depending on the sign
of the interaction arcs, and updates its state accordingly. Recommendations
along a positive arc follow the standard consensus update. As in the work by
Altafini, negative recommendations use an update where the sign of the neighbor
state is flipped. Nodes may weight positive and negative recommendations
differently, and random processes are introduced to model the time-varying
attention that nodes pay to these recommendations. Conditions for almost sure
convergence and divergence of the node states are established. We show that
under this so-called state-flipping model, all links contribute to a consensus
of the absolute values of the nodes, even under switching sign patterns and
dynamically changing environment. A no-survivor property is established,
indicating that every node state diverges almost surely if the maximum network
state diverges.Comment: IEEE Transactions on Control of Network Systems, in press. arXiv
admin note: substantial text overlap with arXiv:1309.548
Cultural Evolution of Sustainable Behaviors: Pro-environmental Tipping Points in an Agent-Based Model
To reach sustainability transitions, we must learn to leverage social systems into tipping points, where societies exhibit positive-feedback loops in the adoption of sustainable behavioral and cultural traits. However, much less is known about the most efficient ways to reach such transitions or how self-reinforcing systemic transformations might be instigated through policy. We employ an agent-based model to study the emergence of social tipping points through various feedback loops that have been previously identified to constitute an ecological approach to human behavior. Our model suggests that even a linear introduction of pro-environmental affordances (action opportunities) to a social system can have non-linear positive effects on the emergence of collective pro-environmental behavior patterns. We validate the model against data on the evolution of cycling and driving behaviors in Copenhagen. Our model gives further evidence and justification for policies that make pro-environmental behavior psychologically salient, easy, and the path of least resistance.Peer reviewe
Methodological Implications of Nonlinear Dynamical Systems Models for Whole Systems of Complementary and Alternative Medicine
This paper focuses on the worldview hypotheses and research design approaches from nonlinear dynamical complex systems (NDS) science that can inform future studies of whole systems of complementary and alternative medicine (WS-CAM), e.g., Ayurveda, traditional Chinese medicine, and homeopathy. The worldview hypotheses that underlie NDS and WS-CAM (contextual, organismic, interactive-integrative - Pepper, 1942) overlap with each other, but differ fundamentally from those of biomedicine (formistic, mechanistic). Differing views on the nature of causality itself lead to different types of study designs. Biomedical efficacy studies assume a simple direct mechanistic cause-effect relationship between a specific intervention and a specific bodily outcome, an assumption less relevant to WS-CAM outcomes. WS-CAM practitioners do not necessarily treat a symptom directly. Rather, they intervene to modulate an intrinsic central imbalance of the person as a system and to create a more favorable environmental context for the emergence of health, e. g., with dietary changes compatible with the constitutional type. The rebalancing of the system thereby fosters the emergence of indirect, diffuse, complex effects throughout the person and the person\u27s interactions with his/her environment. NDS theory-driven study designs thus have the potential for greater external and model validity than biomedically driven efficacy studies (e. g., clinical trials) for evaluating the indirect effects of WS-CAM practices. Potential applications of NDS analytic techniques to WS-CAM include characterizing different constitutional types and documenting the evolution and dynamics of whole-person healing and well-being over time. Furthermore, NDS provides models and methods for examining interactions across organizational scales, from genomic/proteomic/metabolomic networks to individuals and social groups
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