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This paper presents an approach for the modeling and the simulation of the spreading of COVID-19 based on agent-based modeling and simulation (ABMS). Our goal is not only to support large-scale simulations but also to increase the simulation resolution. Moreover, we do not assume an underlying network of contacts, and the person-to-person contacts responsible for the spreading are modeled as a function of the geographical distance among the individuals. In particular, we defined a commuting mechanism combining radiation-based and gravity-based models and we exploited the commuting properties at different resolution levels (municipalities and provinces). Finally, we exploited the high-performance computing (HPC) facilities to simulate millions of concurrent agents, each mapping the individual’s behavior. To do such simulations, we developed a spreading simulator and validated it through the simulation of the spreading in two of the most populated Italian regions: Lombardy and Emilia-Romagna. Our main achievement consists of the effective modeling of 10 million of concurrent agents, each one mapping an individual behavior with a high-resolution in terms of social contacts, mobility and contribution to the virus spreading. Moreover, we analyzed the forecasting ability of our framework to predict the number of infections being initialized with only a few days of real data. We validated our model with the statistical data coming from the serological analysis conducted in Lombardy, and our model makes a smaller error than other state of the art models with a final root mean squared error equal to 56,009 simulating the entire first pandemic wave in spring 2020. On the other hand, for the Emilia-Romagna region, we simulated the second pandemic wave during autumn 2020, and we reached a final RMSE equal to 10,730.11.

This paper addresses the background and current state of the field of agent-based modelling and simulation (ABMS). It revisits the issue of ABMS represents as a new development, considering the extremes of being an overhyped fad, doomed to disappear, or a revolutionary development, shifting fundamental paradigms of how research is conducted. This paper identifies key ABMS resources, publications, and communities. It also proposes several complementary definitions for ABMS, based on practice, intended to establish a common vocabulary for understanding ABMS, which seems to be lacking. It concludes by suggesting research challenges for ABMS to advance and realize its potential in the coming years.

Computational thinking (CT) draws on concepts and practices that are fundamental to computing and computer science. It includes epistemic and representational practices, such as problem representation, abstraction, decomposition, simulation, verification, and prediction. However, these practices are also central to the development of expertise in scientific and mathematical disciplines. Recently, arguments have been made in favour of integrating CT and programming into the K-12 STEM curricula. In this paper, we first present a theoretical investigation of key issues that need to be considered for integrating CT into K-12 science topics by identifying the synergies between CT and scientific expertise using a particular genre of computation: agent-based computation. We then present a critical review of the literature in educational computing, and propose a set of guidelines for designing learning environments on science topics that can jointly foster the development of computational thinking with scientific expertise. This is followed by the description of a learning environment that supports CT through modeling and simulation to help middle school students learn physics and biology. We demonstrate the effectiveness of our system by discussing the results of a small study conducted in a middle school science classroom. Finally, we discuss the implications of our work for future research on developing CT-based science learning environments.

Currently, critical peak load caused by residential customers has attracted utility companies and policymakers to pay more attention to residential demand response (RDR) programs. In typical RDR programs, residential customers react to the price or incentive-based signals, but the actions can fall behind flexible market situations. For those residential customers equipped with smart meters, they may contribute more DR loads if they can participate in DR events in a proactive way. In this paper, we propose a comprehensive market framework in which residential customers can provide proactive RDR actions in a day-ahead market (DAM). We model and evaluate the interactions between generation companies (GenCos), retailers, residential customers, and the independent system operator (ISO) via an agent-based modeling and simulation (ABMS) approach. The simulation framework contains two main procedures—the bottom-up modeling procedure and the reinforcement learning (RL) procedure. The bottom-up modeling procedure models the residential load profiles separately by household types to capture the RDR potential differences in advance so that residential customers may rationally provide automatic DR actions. Retailers and GenCos optimize their bidding strategies via the RL procedure. The modified optimization approach in this procedure can prevent the training results from falling into local optimum solutions. The ISO clears the DAM to maximize social welfare via Karush-Kuhn-Tucker (KKT) conditions. Based on realistic residential data in China, the proposed models and methods are verified and compared in a large multi-scenario test case with 30,000 residential households. Results show that proactive RDR programs and interactions between market entities may yield significant benefits for both the supply and demand sides. The models and methods in this paper may be used by utility companies, electricity retailers, market operators, and policymakers to evaluate the consequences of a ...

In pharmaceutical manufacturing, the utmost aim is reliably producing high quality products. Simulation approaches allow virtual experiments of processes in the planning phase and the implementation of digital twins in operation. The industrial processing of active pharmaceutical ingredients (APIs) into tablets requires the combination of discrete and continuous sub-processes with complex interdependencies regarding the material structures and characteristics. The API and excipients are mixed, granulated if required, and subsequently tableted. Thereby, the structure as well as the properties of the intermediate and final product are influenced by the raw materials, the parametrized processes and environmental conditions, which are subject to certain fluctuations. In this study, for the first time, an agent-based simulation model is presented, which enables the prediction, tracking, and tracing of resulting structures and properties of the intermediates of an industrial tableting process. Therefore, the methodology for the identification and development of product and process agents in an agent-based simulation is shown. Implemented physical models describe the impact of process parameters on material structures. The tablet production with a pilot scale rotary press is experimentally characterized to provide calibration and validation data. Finally, the simulation results, predicting the final structures, are compared to the experimental data.

Previous research on organizations often focuses on either the individual, team, or organizational level. There is a lack of multidimensional research on emergent phenomena and interactions between the mechanisms at different levels. This paper takes a multifaceted perspective on individual learning and autonomous group formation. To analyze interactions between the two levels, we introduce an agent-based model that captures an organization with a population of heterogeneous agents who learn and are limited in their rationality. To solve a task, agents form a group which experiences turnover from time to time, i.e., its composition changes periodically. We explore organizations that promote learning and changes in group composition either simultaneously or sequentially and analyze the interactions between the activities and the effects on performance. We observe underproportional interactions when tasks are interdependent and show that pushing learning and group turnover too far might backfire and decrease performance significantly.

Kumbh-Mela of Prayagraj, India, a festival of faith and belief, is one of the many significant gathering events worldwide. Pilgrims arrive from different places to take a holy bath at the confluence point of 3-rivers the Ganges, Yamuna, and Sarasvati. The police department is assigned a major role of handling and managing the dense traffic of pilgrims in this event to avoid unwanted situations. The primary surveillance points are the intersecting junctions and bathing zones. In addition, the authorities make crowd movement plans with different route diversion schemes and sets bathing time intervals to maintain crowd density at the Kumbh Mela site. Significantly, we must test these crowd management plans for a realistic assessment of population count, density maintenance, and time management. In this paper, we have created a model utilizing a micro-modeling agent-based approach that incorporates the virtual environment of the site. We have used AnyLogic, a ABMS tool, to incorporate social forces and stochastic behavior among the synthetic agents. The model simulates different crowd movement plans according to real behavioral scenarios. In the simulation, we have considered the whole bathing procedure as a halt time in the area. We have utilized our model to evaluate the time consumed by the pilgrims to reach “Ghat”. Also, to count the number of pilgrims that took a bath in 12 hours on different time intervals set for bathing. The significance of performing these evaluations is to assess the effect on density at the site during the whole arrival, bathing, and departure process.

The purpose of this study is to review existing research on organization management that applied agent-based modeling and simulation (ABMS). First, we systematically identified 133 relevant articles published between 1998 and 2022 using the Web of Science (WoS) and EBSCOhost database. Second, we analyzed the characteristics of ABMS reported in the 133 articles. The results illustrated that the focal articles made extensive use of ABMS as a means of theory development and the enhancement of transparency was demanded. Third, we used a bibliometric mapping approach to analyze the 133 articles visually. The results identified 36 key terms and four clusters: team behaviors under complex environments, organizational structure and design, knowledge management in organizations, and organizational decision-making. The analysis also showed which key terms are used as research fronts and which terms are emerging. Lastly, we suggest five promising research opportunities that should either be continued or be addressed in organization management.

•Seeding increases the NPV of profits under various market conditions.•Targeting social hubs produces higher profits than seeding other targets.•The degree of consumer homophily affects the NPV of profits that seeding generates.•Consumer homophily negatively affects the profit impact of seeding early adopters.•The effect follows a U-shaped function for random seeding and seeding social hubs.•Optimal seeding sizes are explored for alternative targets and various conditions. This study explores the profit impact of seeding programs—giving away free new products to enhance new product diffusion. We conducted extensive agent-based simulation experiments using empirical social connectivity data from five consumer social networks. The findings suggest that the effect of consumer homophily—the similarity of adjacent consumers in a social network—on the profit impact of seeding depends on the seeding target. Consumer homophily negatively affects the profit impact of seeding early adopters but it exhibits a U-shaped relationship with the profit impact of seeding social hubs and random seeding. The right side of the U-shaped curve (high homophily) reflects a higher profit impact when compared to the left side (low homophily). We integrate literature from sociology, social networks, and marketing to explain this finding. The results also suggest that seeding social hubs generates the greatest NPV (net present value), followed by seeding randomly chosen targets, and early adopters, in that order. Finally, we explore the optimal seeding size—the percentage of the market to seed—and discuss managerial implications for seeding strategies.