Real-world Dynamics Research Articles

Opinion Formation in Online Social Networks: Exploiting Predisposition, Interaction, and Credibility

The challenging but intriguing problem of modeling opinion formation dynamics in online social networks (OSNs) has attracted many researchers in recent years because the inherent complexities present in human opinion update process are yet to be clearly understood. Although the existing works adopt the distance-based homophily principle to model the neighbors’ influences on the formation of an agent’s opinion, they ignore several other key factors that govern the update process. Explicitly, we consider two essential aspects of the real-world opinion formation process that were not explored previously. First, we consider the predisposition of agents that leads to selective exposure to information when presented with different opinion sources. Second, we explicitly consider an agent’s past interaction experience with others and how opinions encountered in the past interactions influence future opinion update process of that agent. Although the confidence level of an agent on the expressed opinion was previously used to distinguish an expert, we propose the concept of the relative credibility of the opinion sources for such distinction. For this, we take into account an agent’s perceived credibility about others and the relative nature of human judgment when exposed to many opinion sources with different credibility. In addition, for the first time, the credibility of sources external to an OSN is considered in the opinion formation model proposed in this paper. We validate our model by analyzing its performance in capturing the real-world opinion formation dynamics using traces collected from an OSN, specifically Twitter. On the other hand, through simulation, various scenarios are created to observe the steady-state outcomes of the dynamics under various influences of our model parameters and network characteristics. Finally, different compelling and practical applications with social and economic values can be built based on our model.

Influence of augmented humans in online interactions during voting events.

The advent of the digital era provided a fertile ground for the development of virtual societies, complex systems influencing real-world dynamics. Understanding online human behavior and its relevance beyond the digital boundaries is still an open challenge. Here we show that online social interactions during a massive voting event can be used to build an accurate map of real-world political parties and electoral ranks for Italian elections in 2018. We provide evidence that information flow and collective attention are often driven by a special class of highly influential users, that we name “augmented humans”, who exploit thousands of automated agents, also known as bots, for enhancing their online influence. We show that augmented humans generate deep information cascades, to the same extent of news media and other broadcasters, while they uniformly infiltrate across the full range of identified groups. Digital augmentation represents the cyber-physical counterpart of the human desire to acquire power within social systems.

A Theory of Contestation Space in International Regimes

In a time of uncertainty in the international order, scholars of international relations are directing increased attention to both systemic power transitions and contestation of global norms and institutions. Although similar real-world dynamics are motivating both of these scholarly trends, there has been little cross-pollination among scholars studying power at a systemic and structural level and those studying power at a normative and discursive level. This paper merges insights from these literatures to explain how states’ contestation of the normative components of the international order shapes global politics during times of power transition. It does so by introducing the theoretical concept of contestation space in international regimes. This concept modifies a basic rational choice bargaining model to depict how new equilibria regimes are formed under conditions of contestation over the principles, norms, rules, and decision-making procedures that compose international regimes. The theoretical concept of contestation space shows how ambiguity, omission, and contradiction in complex international agreements make room for contested understandings of bargaining outcomes. This contestation space facilitates the establishment of new equilibria in international regimes, but it can also act as a source of misperception and conflict during power transitions. This theory thus contributes to a burgeoning research program in the study of realpolitik that analyzes the broad range of instruments of power states employ and emphasizes the ways that hierarchy operates in international relations.

Statistical physics models of belief dynamics: Theory and empirical tests

We build simple computational models of belief dynamics within the framework of discrete-spin statistical physics models, and explore how suitable they are for understanding and predicting real-world belief change on both the individual and group levels. We find that accurate modeling of real-world patterns requires attending to social interaction rules that people use, network structures in which they are embedded, distributions of initial beliefs and intrinsic preferences, and the relative importance of social information and intrinsic preferences. We demonstrate that these model parameters can be constrained by empirical measurement, and the resulting models can be used to investigate the mechanisms underlying belief dynamics in actual societies. We use data from two longitudinal studies of belief change, one on 80 individuals living in an MIT dorm during the 2008 presidential election season, and another on 94 participants recruited from Mechanical Turk during the 2016 presidential election primary season. We find that simple statistical physics-based models contain predictive value for real-world belief dynamics and enable empirical tests of different assumptions about the underlying network structure and the social interaction rules.

Vehicular Networks Simulation With Realistic Physics

Evaluation of cooperative automated driving applications requires the capability of simulating the vehicle and traffic dynamics as well as the communications with a level of accuracy that most of the current tools still lack. In this paper, we explore the use of game engines in hybrid traffic-network simulators. We describe and validate a novel framework based on this approach: Veneris. Our framework is made of a traffic simulator, implemented on the top of the Unity game engine, which includes a realistic vehicle model and a set of driving and lane change behaviors adapted to a 3D environment that reproduces real-world traffic dynamics; a ray-launching propagation simulator on graphics-processing-unit (GPU), called Opal, and a set of modules, which enable bidirectional coupling with the OMNET++ network simulator. The more relevant and novel mechanisms of Veneris are introduced, but further implementation details can be checked on the source code provided in our repository. We discuss the validation tests we have performed and show how it provides accurate results in three key areas: 1) the fidelity of the vehicle dynamics; 2) the recreation of realistic traffic flows, and; 3) the accuracy of the propagation simulation. In addition, the general results of the expected performance are provided.

A two–step radial basis function-based CFD mesh displacement tool

Mesh displacement based on Radial Basis Functions (RBF) interpolation is known for its ability to preserve the validity and quality of the mesh, even for large displacements, without being affected by mesh connectivity. However, in the case of large meshes, such as those used in real-world Computational Fluid Dynamics (CFD) applications, RBF interpolation, in its standard formulation, becomes excessively expensive. This paper proposes a cost reduction technique for mesh displacement based on RBF, by splitting the process into two steps. In the first step, named predictor, a data reduction algorithm that adaptively agglomerates mesh boundary nodes by reducing the RBF interpolation problem size is used. Upon completion of the first step, due to the agglomeration and the fact that the RBF interpolation is applied to the boundary nodes too, the so-displaced boundaries do not match the given displacements; thus, the position of the boundary nodes must be corrected during the second step, named corrector. The latter performs a local deformation based on RBF kernels with local support, to make the boundary conform to the known displacements of its nodes. The proposed method is accelerated by employing the Sparse Approximate Inverse preconditioner based on geometrical considerations and the Fast Multipole Method. The method and the programmed software are validated on three test cases related to the deformation of CFD meshes inside a duct and a turbine stator row as well as around a car model.

Treatment Patterns Among Adults with Newly Diagnosed Primary Immune Thrombocytopenia in the United States

Introduction: Immune thrombocytopenia (ITP) is a rare platelet disorder that can lead to an increased tendency to bleed. Recommended first-line therapies include corticosteroids, intravenous immunoglobulin (IVIg) and intravenous (IV) anti-D. An estimated two-thirds of adult patients with ITP will develop persistent or chronic disease (ITP lasting 3-12 months or >12 months, respectively). Several evidence-based options for second-line treatment exist, but no randomized trials have directly compared one therapy to another. Patterns of treatment in routine clinical practice therefore vary. There is a paucity of data on current real-world treatment dynamics in ITP, and such data could help identify gaps in care and inform future studies of real-world comparative effectiveness and safety. We described the types of treatments administered following an ITP diagnosis, as well as the subsequent occurrence of bleeding and requirement for rescue therapy among adults being managed in routine practice in the United States (US).Methods: We used electronic health record data from hematology clinics across the US (Flatiron Health, Inc.) linked to MarketScan® employer-based and Medicare Supplemental administrative health insurance claims databases (Truven Health Analytics, Inc.). We included patients aged 18 years or older with a new ITP diagnosis from January 1, 2011 through June 30, 2016, continuous enrollment in MarketScan prior to diagnosis, and no previous diagnosis of a secondary cause of thrombocytopenia. The cumulative incidence of each ITP treatment after diagnosis was estimated using competing risk models to account for deaths occurring before initiation. Estimates were provided specifically for 90 days and 1 year following diagnosis to describe treatment uptake in the newly diagnosed and persistent phases, respectively. The incidence of bleeding events and rescue therapy was quantified after the start of the more prevalent second-line therapies: rituximab, splenectomy, and thrombopoietin receptor agonists (TPO RAs) - eltrombopag and romiplostim. Rescue therapies (those that rapidly increase platelet counts in the setting of severe thrombocytopenia or active bleeding) included IV anti-D, IVIg, IV steroids, and platelet transfusions.Results: Among the cohort of 447 adults diagnosed with primary ITP, 47% were male, 61% were white, 32% were 65 years or older, and the median lowest platelet count in the 60 days prior to diagnosis was 85x109/L (IQR: 39, 125). Use of each ITP therapy by 90 days and 12 months post-diagnosis are provided in the Table. Oral corticosteroids were the most commonly used; the cumulative incidence of initiation was 41% by 90 days and 50% by 1 year following ITP diagnosis. IV steroids and rituximab were the next most frequently used medications (16% and 11% at 90 days; and 26% and 16% by 1 year, respectively). The cumulative incidence of the TPO RAs, eltrombopag and romiplostim, by 90 days was 3% and 7%, respectively, and by 1 year was 5% and 9%, respectively. Splenectomy was relatively rare (<4% by 1 year) as was use of all other non-rescue ITP medications (≤1% by 1 year). At 180 days post-ITP treatment initiation, rituximab initiators (N = 84) had a slightly lower incidence of bleeding overall (12% [6, 20]) than the other treatment groups (17% [6, 33] among 31 eltrombopag initiators; 19% [9, 31] among 49 romiplostim initiators; and 19% [6, 38] among 21 splenectomized patients). However, rituximab initiators had the highest cumulative incidence of rescue therapy use (48% [36, 58] compared with 29% [14, 46] for eltrombopag, 26% [14, 39] for romiplostim, and 19% [6, 39] for splenectomized patients). Subsequent oral steroid use was less frequent among TPO RA initiators than rituximab initiators or patients who underwent splenectomy.Conclusions: In this descriptive study of patients with primary ITP receiving care in the US, oral steroids were the most commonly used medication after diagnosis, reflecting their continued role as a frontline therapy. By 1 year after diagnosis, approximately 15% received rituximab, nearly 10% received romiplostim, and 5% received eltrombopag. Splenectomy was less common. Among the medical treatments, although bleeding risk overall appeared lowest in rituximab patients, oral steroid and rescue therapy use were lowest among the patients who initiated TPO RAs. DisclosuresCetin:Amgen Inc: Employment, Equity Ownership. Sharma:Amgen: Employment, Equity Ownership. Brookhart:Amgen Inc: Consultancy, Research Funding; NoviSci: Equity Ownership; Union Chimique Belge: Consultancy; GlaxoSmithKline: Consultancy; Merck: Consultancy; Genentech: Consultancy; TargetPharma: Consultancy; RxAnte: Consultancy; AstraZeneca: Research Funding. Altomare:Genentech: Consultancy; Ipsen: Other: Advisory Board Member; Amgen: Consultancy; Celgene: Other: Advisory Board Member; Novartis: Consultancy; Bayer: Consultancy; Incyte: Consultancy. Wasser:Amgen Inc: Consultancy; Novartis: Consultancy; Becton Dickinson: Equity Ownership; Abbott Labs: Equity Ownership; Biogen: Equity Ownership; Allergan: Equity Ownership; Eli Lilly: Equity Ownership; Incyte: Research Funding; Merck: Equity Ownership, Research Funding; Pfizer: Equity Ownership, Research Funding; Guardant: Research Funding.

Time-Delay Estimation in State and Output Equations of Nonlinear Systems Using Optimal Computational Approach

Many real-world dynamics can be modeled as nonlinear time-delay systems. In order to capture a more realistic model for system dynamics, the exact values of time-delay should be taken into account. For nonlinear time-delay systems, the estimation of delays in both state and output equations is discussed. A cost function is defined based on least-square error between actual and estimated values of the output measurement. The value of time-delays in the nonlinear system are then derived using a gradient-based optimization method. Because of the implicit description of the cost function with respect to the delay value, its gradients cannot be obtained by standard analytical differentiation rules. In this case, the optimal computational methods are utilized to derive two formulas for computing the gradient. An optimization scheme is then formulated to estimate both state and output delays. The effectiveness of the proposed estimation method is finally demonstrated using the simulation results on a benchmark chemical process.

Modelling and prediction of resources and services state evolvement for efficient runtime adaptations

Regardless of their implementation aspects and distribution elements, i.e. centralized or distributed, service-based environments such as cloud computing and edge/fog infrastructures, enable the provisioning of services addressing a wide range of application domains. The key requirement for users and consumers of such services refers to the corresponding levels of quality, which is affected both by the real-world dynamics – given the non-deterministic use of services, and by the underlying resources state – given the typically virtualized sharing nature of the resources. In this paper, an approach is presented that aims at estimating the evolvement of services and resources state in order to provide insights for runtime adaptations, as required to ensure services quality. The state refers to different metrics/parameters such as memory, number of users, throughput, etc, and can be extended and applied to different ones. The proposed approach exploits polynomial regression and prediction to identify the aforementioned state evolvement by mapping the two first monitoring data points for each metric/parameter to the corresponding function that depicts their evolvement. The latter provides added value in different cases, including among others the adaptation of monitoring time intervals, the estimation of the potential breach of quality thresholds, and the prediction of the time for runtime adaptations and scaling decisions. The effectiveness of the implemented approach is demonstrated and evaluated through a set of different scenarios.

The effect of cooperation among shipping lines on transport costs and pollutant emissions

This study explores the effect of cooperation among shipping lines on transport costs and pollutant emissions. The quantitative benefits of the cooperation were measured via a simulation-based model that (i) optimized inland empty container reuse and (ii) considered a two-dimensional capacity (weight and size) for vehicle types and demands. Inland empty container optimization was integrated with a dynamic vehicle allocation and routing problem with time-window constraints, while the two-dimensional capacity considered minimising total transport costs in a time-varying network with road segment usage constraints by truck type. The simulation model was used to evaluate the status quo and the cooperation scenarios by analysing two weeks of import and export container movements for the port of Brisbane (Australia). The major findings from the study are: (i) the cooperation among shipping companies avoids a significant number of unnecessary truck movements and of storage days for empty containers; (ii) the cooperation translates into truck-sharing and utilisation of larger trucks, which are more environmentally friendly and cost-efficient choices when compared to smaller trucks; (iii) the introduction of a decision support system provides solutions to the freight actors regarding optimal routing and vehicle allocation, based on real-world constraints and dynamics. Remarkably, the savings in the cooperation scenario are substantial, yielding a 40% reduction of fuel consumption and pollutant emissions.

Real-world diffusion dynamics based on point process approaches: a review

Bursts in human and natural activities are highly clustered in time, suggesting that these activities are influenced by previous events within the social or natural system. Bursty behavior in the real world conveys information of underlying diffusion processes, which have been the focus of diverse scientific communities from online social media to criminology and epidemiology. However, universal components of real-world diffusion dynamics that cut across disciplines remain unexplored. Here, we introduce a wide range of diffusion processes across disciplines and propose universal components of diffusion frameworks. We apply these components to diffusion-based studies of human disease spread, through a case study of the vector-borne disease dengue. The proposed universality of diffusion can motivate transdisciplinary research and provide a fundamental framework for diffusion models.

Micro-Simulation of Truck Platooning with Cooperative Adaptive Cruise Control: Model Development and a Case Study

Cooperative adaptive cruise control (CACC) systems have the potential to improve traffic flow and fuel efficiency, but these effects are challenging to estimate. This paper reports the development of a micro-simulation model to represent these impacts for heavy trucks using CACC when they share a freeway with manually driven passenger cars. The simulation incorporates automated truck-following models that have been derived from experimental data recorded on heavy trucks driven under CACC, adaptive cruise control (ACC), and conventional cruise control (CC). The simulation includes other behavioral models for lane changing, lane change cooperation and lane use restrictions for trucks to better capture real-world traffic dynamics. The paper explains the calibration of the simulation method for a 15-mile urban freeway corridor with heavy truck traffic and significant congestion. Simulation results for different market penetration rates show that truck CACC improved traffic operations for trucks in terms of vehicle miles traveled, average speed, and flow rate. In addition, truck CACC did not adversely affect passenger car operations and in some locations it even produced considerable improvements in the general traffic conditions.

Policy experiments in an agent-based model with credit networks

Abstract In this paper the authors extend the macroeconomic agent-based model described in Delli Gatti, D., Desiderio, S., Gaffeo, E., Cirillo, P. and Gallegati, M. (Macroeconomics from the bottom-up, 2011) with the inclusion of a bank-bank network that mimics real-world dynamics of interbank markets. They also introduce a public sector and other modifications in order to carry out different policy experiments. More specifically, they test the implementation of a monetary policy by means of a standard Taylor rule, an unconventional monetary policy (i.e. helicopter drop) and a set of macroprudential regulations. The authors explore the properties of the model for such different scenarios. The results shed some light on the effectiveness of monetary and macroprudential policies in an economy with an interbank market during times of crises.

Traffic state estimation using stochastic Lagrangian dynamics

This paper proposes a new stochastic model of traffic dynamics in Lagrangian coordinates. The source of uncertainty is heterogeneity in driving behavior, captured using driver-specific speed-spacing relations, i.e., parametric uncertainty. It also results in smooth vehicle trajectories in a stochastic context, which is in agreement with real-world traffic dynamics and, thereby, overcoming issues with aggressive oscillation typically observed in sample paths of stochastic traffic flow models. We utilize ensemble filtering techniques for data assimilation (traffic state estimation), but derive the mean and covariance dynamics as the ensemble sizes go to infinity, thereby bypassing the need to sample from the parameter distributions while estimating the traffic states. As a result, the estimation algorithm is just a standard Kalman–Bucy algorithm, which renders the proposed approach amenable to real-time applications using recursive data. Data assimilation examples are performed and our results indicate good agreement with out-of-sample data.

Prediction and analysis of compression behaviour of warp-knitted spacer fabric with cylindrical surface

Nowadays, applications of spacer fabric cover wider areas of technical textile. It is used in the automotive textile, personal protective clothing, sports textile, foundation garments, pads for swimwear, buffer clothing, medical textile etc. It does possess good recovery to compression, high bulk with relatively lightweight and very good moisture permeability. Almost in all applications, spacer fabrics are compressed by different parts of human body. Body parts have different shapes and curvatures. In all standard methods, spacer fabric compressibility is measured by a pair of flat circular plate which cannot represent a human body. The contour of body can be assumed as cylindrical with varying radius of curvature. So, it is necessary to understand the mechanism of compression of spacer fabric with cylindrical surface in order to understand the performance of the fabric under real-world dynamics. In this research, an effort is being made to predict the compression behaviour of warp-knitted spacer fabric by flat as well as cylindrical surface. Finite Element Models were designed on Abaqus/CAE platform to meet above requirement with variable circumstances. Experimental setup was also made to analyse cylindrical and flat compression at different circumstances. Results show that flat compression and cylindrical compression are largely deferred in terms of shape of load-deformation curve and compressional energy. Effect of variables on compression behaviour was also analysed. Model results were validated with experimental values. It is found that the proposed model has got a good agreement with the experimental results.

Optimal Energy Procurement for Geo-distributed Data Centers in Multi-timescale Electricity Markets

Multi-timescale electricity markets augment the traditional electricity market by enabling consumers to procure electricity in a futures market. Heavy power consumers, such as cloud providers and data center operators, can significantly benefit from multi-timescale electricity markets by purchasing some of the needed electricity ahead of time at cheaper rates. However, the energy procurement strategy for data centers in multi-timescale markets becomes a challenging problem when real world dynamics, such as spatial diversity of data centers and uncertainties of renewable energy, IT workload, and electricity price, are taken into account. In this paper, we develop energy procurement algorithms for geo-distributed data centers that utilize multitimescale markets to minimize the electricity procurement cost. We propose two algorithms. The first algorithm provides provably optimal cost minimization while the other achieves near-optimal cost at a much lower computational cost. We empirically evaluate our energy procurement algorithms using real-world traces of renewable energy, electricity prices, and workload demand. Our empirical evaluations show that our proposed energy procurement algorithms save up to 44% of the total cost compared to traditional algorithms that do not use multi-timescale electricity markets or geographical load balancing.

Role of simulation as a teaching-learning tool for interns

Background: Simulation is a realistic representation of real-world dynamics or processes that reflect or parallel patient scenarios. Simulation-based medical education enables knowledge, skills and attitudes to be acquired by all health-care professionals. It has been widely adopted as a training and assessment tool in medical education. The aim of the study was to compare simulation-based versus apprenticeship-based teaching-learning tool on patient. Patients and Methods: In this prospective randomised interventional study, 30 interns were randomly assigned to perform laryngoscopy and endotracheal intubations (L and EI) on patients (Group 1) or on an airway mannequin (Group 2) up to 5 times over 3 successive days. Following this, all interns regardless of their group allocation performed up to 10 intubations on patients. They were assessed and scored by two observers using a rating scale and every attempt scored as 'Success' (1 point) or 'Failure' (0 point). Average scores of intubations of two groups were compared using the paired t -test. Results: Average scores of intubations on 10 patients by two independent observers were 6.220 (standard deviation [SD] 1.1428) in Group 1 and 7.147 (SD 0.6520) in Group 2 and 6.307 (SD 1.1317) in Group 1 and 7.193 (SD 0.6170) in Group 2, respectively ( P = 0.012 and 0.014 for Observers A and B, respectively). The score of attempt 1 and attempt 10 was observed by a single observer between groups, and significant difference ( P = 0.021) was found at attempt 1 between groups with the mannequin group scoring better. At attempt 10, there was no significant difference between the groups. Conclusion: The mannequin group acquired better skills in airway management with respect to L and EI.

Selective copying of the majority suggests children are broadly "optimal-" rather than "over-" imitators.

Human children, in contrast to other species, are frequently cast as prolific "over-imitators". However, previous studies of "over-imitation" have overlooked many important real-world social dynamics, and may thus provide an inaccurate account of this seemingly puzzling and potentially maladaptive phenomenon. Here we investigate this topic using a cultural evolutionary approach, focusing particularly on the key adaptive learning strategy of majority-biased copying. Most "over-imitation" research has been conducted using consistent demonstrations to the observer, but we systematically varied the frequency of demonstrators that 4- to 6-year-old children observed performing a causally irrelevant action. Children who "over-imitate" inflexibly should copy the majority regardless of whether the majority solution omits or includes a causally irrelevant action. However, we found that children calibrated their tendency to acquire the majority behavior, such that copying did not extend to majorities that performed irrelevant actions. These results are consistent with a highly functional, adaptive integration of social and causal information, rather than explanations implying unselective copying or causal misunderstanding. This suggests that our species might be better characterized as broadly "optimal-" rather than "over-" imitators.

The robustness of Brownie tag return models to complex spatiotemporal dynamics evaluated through simulation analysis

The development of a reliable tagging program requires simulation testing the experimental design. However, the potential for model misspecification, particularly in the underlying spatiotemporal dynamics, is often ignored. A continuous time, spatially explicit, age-structured, capture–recapture operating model was developed to better emulate real-world population dynamics typically overlooked in spatially aggregated or discrete time tagging models. Various spatiotemporal model parametrizations, including case studies with Atlantic bluefin tuna (Thunnus thynnus) and yellowfin tuna (Thunnus albacares), were explored to evaluate the bias associated with Brownie tag return estimation models. Simulations demonstrated that accounting for connectivity was essential for obtaining unbiased parameter estimates and that migration rates could be reliably estimated without the correlation associated with other parameters (e.g., between tag reporting and mortality). Mortality parameter estimates were particularly sensitive to the temporal dynamics of the tagging and fishing seasons, but accounting for the seasonality in tag releases and fishery recaptures allowed for relatively unbiased estimation. Our results indicate that parameter bias and uncertainty can be severely underestimated when discrete time or spatially aggregated operating models are used to determine optimal experimental design of tagging studies.

Optimal Energy Procurement for Geo-distributed Data Centers in Multi-timescale Electricity Markets

Heavy power consumers, such as cloud providers and data center operators, can significantly benefit from multi-timescale electricity markets by purchasing some of the needed electricity ahead of time at cheaper rates. However, the energy procurement strategy for data centers in multi-timescale markets becomes a challenging problem when real world dynamics, such as the spatial diversity of data centers and the uncertainty of renewable energy, IT workload, and electricity price, are taken into account. In this paper, we develop energy procurement algorithms for geo-distributed data centers that utilize multi-timescale markets to minimize the electricity procurement cost. We propose two algorithms. The first algorithm provides provably optimal cost minimization while the other achieves near-optimal cost at a much lower computational cost. We empirically evaluate our energy procurement algorithms using real-world traces of renewable energy, electricity prices, and the workload demand. Our empirical evaluations show that our proposed energy procurement algorithms save up to 44% of the total cost compared to traditional algorithms that do not use multi-timescale electricity markets or geographical load balancing.