Multi-agent decision-making dynamics inspired by honeybees

When choosing between candidate nest sites, a honeybee swarm reliably chooses the most valuable site and even when faced with the choice between near-equal value sites, it makes highly efficient decisions. Value-sensitive decision-making is enabled by a distributed social effort among the honeybees, and it leads to decision-making dynamics of the swarm that are remarkably robust to perturbation and adaptive to change. To explore and generalize these features to other networks, we design distributed multi-agent network dynamics that exhibit a pitchfork bifurcation, ubiquitous in biological models of decision-making. Using tools of nonlinear dynamics we show how the designed agent-based dynamics recover the high performing value-sensitive decision-making of the honeybees and rigorously connect investigation of mechanisms of animal group decision-making to systematic, bio-inspired control of multi-agent network systems. We further present a distributed adaptive bifurcation control law and prove how it enhances the network decision-making performance beyond that observed in swarms

Collective decision-making on triadic graphs

Many real-world networks exhibit community structures and non-trivial clustering associated with the occurrence of a considerable number of triangular subgraphs known as triadic motifs. Triads are a set of distinct triangles that do not share an edge with any other triangle in the network. Network motifs are subgraphs that occur significantly more often compared to random topologies. Two prominent examples, the feedforward loop and the feedback loop, occur in various real-world networks such as gene-regulatory networks, food webs or neuronal networks. However, as triangular connections are also prevalent in communication topologies of complex collective systems, it is worthwhile investigating the influence of triadic motifs on the collective decision-making dynamics. To this end, we generate networks called Triadic Graphs (TGs) exclusively from distinct triadic motifs. We then apply TGs as underlying topologies of systems with collective dynamics inspired from locust marching bands. We demonstrate that the motif type constituting the networks can have a paramount influence on group decision-making that cannot be explained solely in terms of the degree distribution. We find that, in contrast to the feedback loop, when the feedforward loop is the dominant subgraph, the resulting network is hierarchical and inhibits coherent behavior

Heterogeneity Induced Order in Globally Coupled Chaotic Systems

Collective behavior is studied in globally coupled maps with distributed nonlinearity. It is shown that the heterogeneity enhances regularity in the collective dynamics. Low-dimensional quasiperiodic motion is often found for the mean-field, even if each element shows chaotic dynamics. The mechanism of this order is due to the formation of an internal bifurcation structure, and the self-consistent dynamics between the structures and the mean-field. Keywords: Globally Coupled Map with heterogeneity, Collective behaviorComment: 11 pages (Revtex) + 4 figures (PostScript,tar+gzip

Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda

Emergence of Leadership in Communication

We study a neuro-inspired model that mimics a discussion (or information dissemination) process in a network of agents. During their interaction, agents redistribute activity and network weights, resulting in emergence of leader(s). The model is able to reproduce the basic scenarios of leadership known in nature and society: laissez-faire (irregular activity, weak leadership, sizable inter-follower interaction, autonomous sub-leaders); participative or democratic (strong leadership, but with feedback from followers); and autocratic (no feedback, one-way influence). Several pertinent aspects of these scenarios are found as well---e.g., hidden leadership (a hidden clique of agents driving the official autocratic leader), and successive leadership (two leaders influence followers by turns). We study how these scenarios emerge from inter-agent dynamics and how they depend on behavior rules of agents---in particular, on their inertia against state changes.Comment: 17 pages, 11 figure

An Agent-Based Model of Collective Emotions in Online Communities

We develop a agent-based framework to model the emergence of collective emotions, which is applied to online communities. Agents individual emotions are described by their valence and arousal. Using the concept of Brownian agents, these variables change according to a stochastic dynamics, which also considers the feedback from online communication. Agents generate emotional information, which is stored and distributed in a field modeling the online medium. This field affects the emotional states of agents in a non-linear manner. We derive conditions for the emergence of collective emotions, observable in a bimodal valence distribution. Dependent on a saturated or a superlinear feedback between the information field and the agent's arousal, we further identify scenarios where collective emotions only appear once or in a repeated manner. The analytical results are illustrated by agent-based computer simulations. Our framework provides testable hypotheses about the emergence of collective emotions, which can be verified by data from online communities.Comment: European Physical Journal B (in press), version 2 with extended introduction, clarification

The Hopfield model and its role in the development of synthetic biology

Neural network models make extensive use of concepts coming from physics and engineering. How do scientists justify the use of these concepts in the representation of biological systems? How is evidence for or against the use of these concepts produced in the application and manipulation of the models? It will be shown in this article that neural network models are evaluated differently depending on the scientific context and its modeling practice. In the case of the Hopfield model, the different modeling practices related to theoretical physics and neurobiology played a central role for how the model was received and used in the different scientific communities. In theoretical physics, where the Hopfield model has its roots, mathematical modeling is much more common and established than in neurobiology which is strongly experiment driven. These differences in modeling practice contributed to the development of the new field of synthetic biology which introduced a third type of model which combines mathematical modeling and experimenting on biological systems and by doing so mediates between the different modeling practices

Setting the Agenda: Different strategies of a Mass Media in a model of cultural dissemination

Day by day, people exchange opinions about a given new with relatives, friends, and coworkers. In most cases, they get informed about a given issue by reading newspapers, listening to the radio, or watching TV, i.e., through a Mass Media (MM). However, the importance of a given new can be stimulated by the Media by assigning newspaper's pages or time in TV programs. In this sense, we say that the Media has the power to "set the agenda", i.e., it decides which new is important and which is not. On the other hand, the Media can know people's concerns through, for instance, websites or blogs where they express their opinions, and then it can use this information in order to be more appealing to an increasing number of people. In this work, we study different scenarios in an agent-based model of cultural dissemination, in which a given Mass Media has a specific purpose: To set a particular topic of discussion and impose its point of view to as many social agents as it can. We model this by making the Media has a fixed feature, representing its point of view in the topic of discussion, while it tries to attract new consumers, by taking advantage of feedback mechanisms, represented by adaptive features. We explore different strategies that the Media can adopt in order to increase the affinity with potential consumers and then the probability to be successful in imposing this particular topic.Comment: 23 pages, 7 figure