1,721,053 research outputs found
On incentivizing innovation diffusion in a network of coordinating agents
Full text linkInnovation diffusion is fundamental for societal growth and development, and understanding how to unlock it is key toward devising policies encouraging the adoption of new practices, e.g., sustainable innovations. Here, we propose a mathematical model to investigate such a problem. Specifically, we consider a coordination game —which is a standard game-theoretic model used to study innovation diffusion—and we embed it on an activity-driven network. Within this model, we integrate three policies to incentivize the adoption of the innovation: i) providing a direct advantage for adopting it, ii) making people sensitive to emerging trends at the population level, and iii) increasing the visibility of adopters of the innovation, respectively. We provide analytical insights to shed light on the effect of the joint use of these three policies on unlocking innovation diffusion, supported by numerical simulations
Data-Informed Modeling of the Formation, Persistence, and Evolution of Social Norms and Conventions
Full text linkSocial norms and conventions are commonly accepted and adopted behaviors and practices within a social group that guide interactions—e.g., how to spell a word or how to greet people—and are central to a group’s culture and identity. Understanding the key mechanisms that govern the formation, persistence, and evolution of social norms and conventions in social communities is a problem of paramount importance for a broad range of real-world applications, spanning from preparedness for future emergencies to promotion of sustainable practices. In the past decades, mathematical modeling has emerged as a powerful tool to reproduce and study the complex dynamics of norm and convention change, gaining insights into their mechanisms, and ultimately deriving tools to predict their evolution. The first goal of this chapter is to introduce some of the main mathematical approaches for modeling social norms and conventions, including population models and agent-based models relying on the theories of dynamical systems, evolutionary dynamics, and game theory. The second goal of the chapter is to illustrate how quantitative observations and empirical data can be incorporated into these mathematical models in a systematic manner, establishing a data-based approach to the mathematical modeling of the formation, persistence, and evolution of social norms and conventions. Finally, current challenges and future opportunities in this growing field of research are discussed
Effect of Network Structure and Committed Minority Placement in Promoting Social Diffusion
Full text linkSocial diffusion is the phenomenon whereby a population collectively adopts a novel (alternative) behavior, opinion, product, or technology to replace an existing status quo. Often the process is driven by a small number of individuals, termed committed minority, who stubbornly promote the alternative. In this work, we use an experimentally proven game-theoretic agent-based model to explore how social diffusion is influenced by the network of social interactions, the placement of committed minority, and the timing that committed minority are introduced into the network. Through a campaign of Monte Carlo simulations, we find that diffusion occurs quicker on sparse and highly clustered networks. In addition, we show that placing the committed minority at nodes with the highest Bonacich centrality with a negative attenuation factor seems to be the best approach for facilitating diffusion. Then, we find that the timing of introducing committed minority has a negligible effect on the diffusion process. Finally, our findings are tested and confirmed on two case studies of real-world networks
An evidence-accumulating drift–diffusion model of competing information spread on networks
Full text linkIn this paper, we propose an agent-based model of information spread, grounded on psychological insights on the formation and spread of beliefs. In our model, we consider a network of individuals who share two opposing types of information on a specific topic (e.g., pro- vs. anti-vaccine stances), and the accumulation of evidence supporting either type of information is modelled by means of a drift–diffusion process. After formalising the model, we put forward a campaign of Monte Carlo simulations to identify population-wide behaviours emerging from agents’ exposure to different sources of information, investigating the impact of the number and persistence of such sources, and the role of the network structure through which the individuals interact. We find similar emergent behaviours for all network structures considered. When there is a single type of information, the main observed emergent behaviour is consensus. When there are opposing information sources, both consensus or polarisation can result; the latter occurs if the number and persistence of the sources exceeds a threshold value identified in the simulations. Importantly, we find the emergent behaviour is mainly influenced by how long the information sources are present for, as opposed to how many sources there are
Control of networked cyber–physical–human systems
No full textCyber–physical–human systems (CPHSs) — characterized by the organic integration of physical components, a computation and communication cyber layer, and humans — are becoming an integral part of daily life, with applications ranging from assistive robots to smart buildings and modern logistics systems. The key role of humans in these complex systems calls for a paradigm shift, from classical machine-oriented control methods to approaches that focus on the explicit modelling and control of the human layer of a CPHS. In this Review, we showcase state-of-the-art research in mathematical modelling and control of CPHSs. We discuss established control-theoretic approaches to model and control cyber–physical systems, explore two mathematical approaches to modelling human behaviour and social influence, utilizing tools from game theory and opinion dynamics, and show how these models are integrated into CPHSs. Moving to control, we focus on the potential and the challenges that are associated with the human layer. We present approaches for control on different layers, paying attention to the fact that humans can typically only be guided or nudged, and then discuss control across layers. Finally, we describe some major open questions in controlling CPHSs, including integrating data-driven approaches and bridging the gap between theoretical and experimental studies
On Adaptive-Gain Control of Replicator Dynamics in Population Games
Full text linkControlling evolutionary game-theoretic dynamics is a problem of paramount importance for the systems and control community, with several applications spanning from social science to engineering. Here, we study a population of individuals who play a generic 2-action matrix game, and whose actions evolve according to a replicator equation -- a nonlinear ordinary differential equation that captures salient features of the collective behavior of the population. Our objective is to steer such a population to a specified equilibrium that represents a desired collective behavior -- e.g., to promote cooperation in the prisoner\u27s dilemma. To this aim, we devise an adaptive-gain controller, which regulates the system dynamics by adaptively changing the entries of the payoff matrix of the game. The adaptive-gain controller is tailored according to distinctive features of the game, and conditions to guarantee global convergence to the desired equilibrium are established.6 pages, Accepted for presentation at the 2023 IEEE CD
On Controlling a Coevolutionary Model of Actions and Opinions
Full text linkWe deal with a control problem for a complex social network in which each agent has an action and an opinion, evolving according to a coevolutionary model. In particular, we consider a scenario in which a committed minority -a set of stubborn nodes- aims to steer a population, initially at a consensus, to a different consensus state. Our study focuses on determining the conditions under which such a goal is reached, and ultimately, how to optimally define a minimal committed minority. First, we derive a general monotone convergence result for the controlled coevolutionary model, under mild and general assumptions on the agents revision sequence. Then, we build on our theoretical result to propose a systematic approach to investigate the research problem
Coevolution of actions and opinions in networks of coordinating and anti-coordinating agents
No full textIn this paper, we investigate the dynamics of coordinating and anti-coordinating agents in a coevolutionary model for actions and opinions. In the model, individuals in a population interact on a two-layer network, sharing their opinions and observing others’ actions, while revising their own opinions and actions according to a game-theoretic mechanism grounded in social psychology literature. First, we consider the scenario of coordinating agents, where convergence to a Nash equilibrium (NE) is guaranteed. We identify conditions for reaching consensus configurations and establish regions of attraction for these equilibria. Second, we study networks of anti-coordinating agents. Here, we prove that all trajectories converge to an NE by leveraging potential game theory. Then, we establish analytical conditions on the network structure and model parameters to guarantee the existence of consensus and polarized equilibria, characterizing their regions of attraction
On a bi-virus epidemic model with partial and waning immunity
Full text linkWe propose a deterministic compartmental model to study the impact of partial and waning immunity on the spread of two competitive epidemic diseases, hereafter termed viruses. Building on a standard bi-virus SIS model, we introduce additional compartments to account for individuals who recovered from each virus, and tunable parameters to capture the level of virus-specific and cross protection acquired after recovery from a specific virus, and the rate at which such immunity could wane. We formalise the model as a system of nonlinear ordinary differential equations, which is amenable to analytical treatment, and we focus our analysis on two specialisations of the model. First, in the absence of waning immunity, we establish a global convergence result showing that, above the epidemic threshold, only the “fittest” virus becomes endemic. Second, in the absence of cross-immunity, we demonstrate instead that long-lasting co-existence of the two viruses may emerge, depending on the model parameters
Modeling and analyzing competitive epidemic diseases with partial and waning virus-specific and cross-immunity
Full text linkIn this paper, we consider a novel mathematical modeling framework for the spread of two competitive diseases in a well-mixed population. The proposed framework, which we term a bivirus SIRIS model, encapsulates key real-world features of natural immunity, accounting for different levels of (partial and waning) virus-specific and cross protection acquired after recovery. Formally, the proposed framework consists of a system of coupled nonlinear ordinary differential equations that builds on a classical bivirus susceptible-infected-susceptible model by means of the addition of further states to account for (temporarily) protected individuals. Through the analysis of the proposed framework and of two specializations, we offer analytical insight into how natural immunity can shape a wide range of complex emergent behaviors, including eradication of both diseases, survival of the fittest one, or even steady-state co-existence of the two diseases
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