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

Opinion dynamics: models, extensions and external effects

Recently, social phenomena have received a lot of attention not only from social scientists, but also from physicists, mathematicians and computer scientists, in the emerging interdisciplinary field of complex system science. Opinion dynamics is one of the processes studied, since opinions are the drivers of human behaviour, and play a crucial role in many global challenges that our complex world and societies are facing: global financial crises, global pandemics, growth of cities, urbanisation and migration patterns, and last but not least important, climate change and environmental sustainability and protection. Opinion formation is a complex process affected by the interplay of different elements, including the individual predisposition, the influence of positive and negative peer interaction (social networks playing a crucial role in this respect), the information each individual is exposed to, and many others. Several models inspired from those in use in physics have been developed to encompass many of these elements, and to allow for the identification of the mechanisms involved in the opinion formation process and the understanding of their role, with the practical aim of simulating opinion formation and spreading under various conditions. These modelling schemes range from binary simple models such as the voter model, to multi-dimensional continuous approaches. Here, we provide a review of recent methods, focusing on models employing both peer interaction and external information, and emphasising the role that less studied mechanisms, such as disagreement, has in driving the opinion dynamics. [...]Comment: 42 pages, 6 figure

Wealth distribution and collective knowledge. A Boltzmann approach

We introduce and discuss a nonlinear kinetic equation of Boltzmann type which describes the influence of knowledge in the evolution of wealth in a system of agents which interact through the binary trades introduced in Cordier, Pareschi, Toscani, J. Stat. Phys. 2005. The trades, which include both saving propensity and the risks of the market, are here modified in the risk and saving parameters, which now are assumed to depend on the personal degree of knowledge. The numerical simulations show that the presence of knowledge has the potential to produce a class of wealthy agents and to account for a larger proportion of wealth inequality.Comment: 21 pages, 10 figures. arXiv admin note: text overlap with arXiv:q-bio/0312018 by other author

Modelling Opinion Formation with Physics Tools: Call for Closer Link with Reality

The growing field of studies of opinion formation using physical formalisms and computer simulation based tools suffers from relative lack of connection to the 'real world' societal behaviour. Such sociophysics research should aim at explaining observations or at proposing new ones. Unfortunately, this is not always the case, as many works concentrate more on the models themselves than on the social phenomena. Moreover, the simplifications proposed in simulations often sacrifice realism on the altar of computability. There are several ways to improve the value of the research, the most important by promoting truly multidisciplinary cooperation between physicists aiming to describe social phenomena and sociologists studying the phenomena in the field. In the specific case of modelling of opinion formation there are a few technical ideas which might bring the computer models much closer to reality, and therefore to improve the predictive value of the sociophysics approach.Methodology, Agent Based Social Simulation, Qualitative Analysis; Evidence; Conditions of Application

Early Warning Analysis for Social Diffusion Events

There is considerable interest in developing predictive capabilities for social diffusion processes, for instance to permit early identification of emerging contentious situations, rapid detection of disease outbreaks, or accurate forecasting of the ultimate reach of potentially viral ideas or behaviors. This paper proposes a new approach to this predictive analytics problem, in which analysis of meso-scale network dynamics is leveraged to generate useful predictions for complex social phenomena. We begin by deriving a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies; this modeling approach is inspired by recent work in biology demonstrating that S-HDS offer a useful mathematical formalism with which to represent complex, multi-scale biological network dynamics. We then perform formal stochastic reachability analysis with this S-HDS model and conclude that the outcomes of social diffusion processes may depend crucially upon the way the early dynamics of the process interacts with the underlying network's community structure and core-periphery structure. This theoretical finding provides the foundations for developing a machine learning algorithm that enables accurate early warning analysis for social diffusion events. The utility of the warning algorithm, and the power of network-based predictive metrics, are demonstrated through an empirical investigation of the propagation of political memes over social media networks. Additionally, we illustrate the potential of the approach for security informatics applications through case studies involving early warning analysis of large-scale protests events and politically-motivated cyber attacks

The impact of agent density on scalability in collective systems : noise-induced versus majority-based bistability

In this paper, we show that non-uniform distributions in swarms of agents have an impact on the scalability of collective decision-making. In particular, we highlight the relevance of noise-induced bistability in very sparse swarm systems and the failure of these systems to scale. Our work is based on three decision models. In the first model, each agent can change its decision after being recruited by a nearby agent. The second model captures the dynamics of dense swarms controlled by the majority rule (i.e., agents switch their opinion to comply with that of the majority of their neighbors). The third model combines the first two, with the aim of studying the role of non-uniform swarm density in the performance of collective decision-making. Based on the three models, we formulate a set of requirements for convergence and scalability in collective decision-making

Kinetic models for goods exchange in a multi-agent market

We introduce a system of kinetic equations describing an exchange market consisting of two populations of agents (dealers and speculators) expressing the same preferences for two goods, but applying different strategies in their exchanges. We describe the trading of the goods by means of some fundamental rules in price theory, in particular by using Cobb-Douglas utility functions for the exchange. The strategy of the speculators is to recover maximal utility from the trade by suitably acting on the percentage of goods which are exchanged. This microscopic description leads to a system of linear Boltzmann-type equations for the probability distributions of the goods on the two populations, in which the post-interaction variables depend from the pre-interaction ones in terms of the mean quantities of the goods present in the market. In this case, it is shown analytically that the strategy of the speculators can drive the price of the two goods towards a zone in which there is a marked utility for their group. Also, the general system of nonlinear kinetic equations of Boltzmann type for the probability distributions of the goods on the two populations is described in details. Numerical experiments then show how the policy of speculators can modify the final price of goods in this nonlinear setting

Towards a decision-support procedure to foster stakeholder involvement and acceptability of urban freight transport policies

Purpose: This paper addresses the complex problem of multi-stakeholder decisions in urban freight transport policy-making from a public authority perspective, by proposing a procedure based on a modelling approach to support stakeholder involvement in the decision-making process. The paper analyses the existing methods that can be used for participatory decision-support, with the intent of contextualizing and introducing the innovative modelling approach. Methods: The modelling approach consists of a well-thought integration of discrete choice models (DCM) with agent-based models (ABM) as an effective way to account for stakeholders’ opinions in the policy-making process, while mimicking their interaction to find a shared policy package. The integrated modelling approach is able to combine the advantages of the two methods while overcoming their respective weaknesses. Since it is well grounded on sound microeconomic theory, it provides a detailed (static) stakeholders’ behavioural knowledge, but it is also capable of reproducing agents’ (dynamic) interaction during the decision-making process. The integration allows performing an ex-ante behavioural analysis, with the aim of testing the potential acceptability of the solutions proposed. The methodology is applied in a real case study to prove its feasibility and usefulness for participatory decision-making. Results: The integrated modelling approach can be used for participatory decision-support and it can be casted in the overall UFT policy-making process. The results of the behavioural analysis, in terms of ranking of potentially accepted policies, linked with the technical evaluations from transport network modelling tools, provide a sound basis for active participation and deliberation with stakeholders and policy-makers. The aim is to guide an effective participation process aimed at consensus building among stakeholders, by proposing them a subset of policies that, as a result of a preliminary analysis, are likely to be accepted while performing well in terms of technical results. Conclusions: This approach, integrating DCM and ABM, represents a promising way to tackle the complexity of multi-stakeholder involvement in UFT policy-making and to support an efficient and effective decision-making process. It produces an added value for UFT policy-making and it can be framed in the overall context of transport planning. In fact, together with technical and economic analyses, the stakeholder behavioural analysis proposed contributes to the ex-ante policy assessment needed to support decision-makers in taking well-thought decisions. Keywords: stakeholder behavioural analysis, discrete choice models, agent-based models, city logistics, participatory policy-makingpublishedVersio