scholarly journals Exploring S-shape curves and heterogeneity effects of rumor spreading in online collective actions

2022 ◽  
Vol 19 (3) ◽  
pp. 2355-2380
Author(s):  
Peng Lu ◽  
◽  
Rong He ◽  
Dianhan Chen ◽  
Keyword(s):  
Evolutionary Dynamics ◽  
Agent Based Modeling ◽  
Collective Actions ◽  
Key Factors ◽  
Spreading Process ◽  
Rumor Spreading ◽  
Agent Based ◽  
Micro Level ◽  
Heterogeneity Effects ◽  
Shape Curve

<abstract> <p>Nowadays online collective actions are pervasive, such as the rumor spreading on the Internet. The observed curves take on the S-shape, and we focus on evolutionary dynamics for S- shape curves of online rumor spreading. For agents, key factors, such as internal aspects, external aspects, and hearing frequency jointly determine whether to spread it. Agent-based modeling is applied to capture micro-level mechanism of this S-shape curve. We have three findings: (a) Standard S-shape curves of spreading can be obtained if each agent has the zero threshold; (b) Under zero-mean thresholds, as heterogeneity (SD) grows from zero, S-shape curves with longer right tails can be obtained. Generally speaking, stronger heterogeneity comes up with a longer duration; and (c) Under positive mean thresholds, the spreading curve is two-staged, with a linear stage (first) and nonlinear stage (second), but not standard S-shape curves either. From homogeneity to heterogeneity, the spreading S-shaped curves have longer right tail as the heterogeneity grows. For the spreading duration, stronger heterogeneity usually brings a shorter duration. The effects of heterogeneity on spreading curves depend on different situations. Under both zero and positive-mean thresholds, heterogeneity leads to S-shape curves. Hence, heterogeneity enhances the spreading with thresholds, but it may postpone the spreading process with homogeneous thresholds.</p> </abstract>

scholarly journals Agent-based modeling and life cycle dynamics of COVID-19-related online collective actions

2021 ◽  
Author(s):  
Gang Zhang ◽  
Hao Li ◽  
Rong He ◽  
Peng Lu
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Evolutionary Dynamics ◽  
Life Cycles ◽  
Agent Based Modeling ◽  
Macro Level ◽  
Collective Actions ◽  
Agent Based ◽  
Individual Behaviors ◽  
Micro Level

AbstractThe outbreak of COVID-19 has greatly threatened global public health and produced social problems, which includes relative online collective actions. Based on the life cycle law, focusing on the life cycle process of COVID-19 online collective actions, we carried out both macro-level analysis (big data mining) and micro-level behaviors (Agent-Based Modeling) on pandemic-related online collective actions. We collected 138 related online events with macro-level big data characteristics, and used Agent-Based Modeling to capture micro-level individual behaviors of netizens. We set two kinds of movable agents, Hots (events) and Netizens (individuals), which behave smartly and autonomously. Based on multiple simulations and parametric traversal, we obtained the optimal parameter solution. Under the optimal solutions, we repeated simulations by ten times, and took the mean values as robust outcomes. Simulation outcomes well match the real big data of life cycle trends, and validity and robustness can be achieved. According to multiple criteria (spans, peaks, ratios, and distributions), the fitness between simulations and real big data has been substantially supported. Therefore, our Agent-Based Modeling well grasps the micro-level mechanisms of real-world individuals (netizens), based on which we can predict individual behaviors of netizens and big data trends of specific online events. Based on our model, it is feasible to model, calculate, and even predict evolutionary dynamics and life cycles trends of online collective actions. It facilitates public administrations and social governance.

scholarly journals Big data-drive agent-based modeling of online polarized opinions

2021 ◽  
Author(s):  
Peng Lu ◽  
Zhuo Zhang ◽  
Mengdi Li
Keyword(s):  
Big Data ◽  
Life Cycle ◽  
Evolutionary Dynamics ◽  
Life Cycles ◽  
Agent Based Modeling ◽  
Mobile Internet ◽  
Real Case ◽  
Agent Based ◽  
Individual Behaviors ◽  
Life Cycle Process

AbstractUnder the mobile internet and big data era, more and more people are discussing and interacting online with each other. The forming process and evolutionary dynamics of public opinions online have been heavily investigated. Using agent-based modeling, we expand the Ising model to explore how individuals behave and the evolutionary mechanism of the life cycles. The big data platform of Douban.com is selected as the data source, and the online case “NeiYuanWaiFang” is applied as the real target, for our modeling and simulations to match. We run 10,000 simulations to find possible optimal solutions, and we run 10,000 times again to check the robustness and adaptability. The optimal solution simulations can reflect the whole life cycle process. In terms of different levels and indicators, the fitting or matching degrees achieve the highest levels. At the micro-level, the distributions of individual behaviors under real case and simulations are similar to each other, and they all follow normal distributions; at the middle-level, both discrete and continuous distributions of supportive and oppositive online comments are matched between real case and simulations; at the macro-level, the life cycle process (outbreak, rising, peak, and vanish) and durations can be well matched. Therefore, our model has properly seized the core mechanism of individual behaviors, and precisely simulated the evolutionary dynamics of online cases in reality.

scholarly journals Exploring the dynamics of neighborhood ethnic segregation with agent-based modelling: an empirical application to Bradford

2021 ◽  
Author(s):  
Carolina Zuccotti ◽  
Jan Lorenz ◽  
Rocco Paolillo ◽  
Alejandra Rodríguez Sánchez ◽  
Selamavit Serka
Keyword(s):  
Real Data ◽  
Agent Based Modeling ◽  
Residential Choice ◽  
Ethnic Segregation ◽  
Agent Based ◽  
Schelling Model ◽  
Residential Moves ◽  
Micro Level ◽  
The Uk

How individuals’ residential moves in space—derived from their varied preferences and constraints—translate into the overall segregation patterns that we observe, remains a key challenge in neighborhood ethnic segregation research. In this paper we use agent-based modeling to explore this concern, focusing on the interactive role of ethnic and socio-economic homophily preferences and housing constraints as determinants of residential choice. Specifically, we extend the notorious Schelling’s model to a random utility discrete choice approach to simulate the relocation decision of people (micro level) and how they translate into spatial segregation outcomes (macro level). We model different weights for preferences of ethnic and socioeconomic similarity in neighborhood composition over random relocations, in addition to housing constraints. We formalize how different combinations of these variables could replicate real segregation scenarios in Bradford, a substantially segregated local authority in the UK. We initialize our model with geo-referenced data from the 2011 Census and use Dissimilarity and the Average Local Simpson Indices as measures of segregation. As in the original Schelling model, the simulation shows that even mild preferences to reside close to co-ethnics can lead to high segregation levels. Nevertheless, ethnic over-segregation decreases, and results come close to real data, when preferences for socioeconomic similarity are slightly above preferences for ethnic similarity, and even more so when housing constraints are considered in relocation moves of agents. We discuss the theoretical and policy contributions of our work.

Agent-Based Modeling Bridges Theory of Behavioral Finance and Financial Markets

2010 ◽  
pp. 134-155
Author(s):  
Hiroshi Takahashi ◽  
Takao Terano
Keyword(s):  
Decision Making ◽  
Asset Pricing ◽  
Financial Market ◽  
Behavioral Finance ◽  
Investment Strategy ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based ◽  
Micro Level ◽  
Fundamental Values

This chapter describes advances of agent-based models to financial market analyses based on our recent research. We have developed several agent-based models to analyze microscopic and macroscopic links between investor behaviors and price fluctuations in a financial market. The models are characterized by the methodology that analyzes the relations among micro-level decision making rules of the agents and macro-level social behaviors via computer simulations. In this chapter, we report the outline of recent results of our analysis. From the extensive analyses, we have found that (1) investors’ overconfidence behaviors plays various roles in a financial market, (2) overconfident investors emerge in a bottom-up fashion in the market, (3) they contribute to the efficient trades in the market, which adequately reflects fundamental values, (4) the passive investment strategy is valid in a realistic efficient market, however, it could have bad influences such as instability of market and inadequate asset pricing deviations, and (5) under certain assumptions, the passive investment strategy and active investment strategy could coexist in a financial market.

Charting the Emergence of the Cultural from the Cognitive with Agent-Based Modeling

2019 ◽  
pp. 402-421
Author(s):  
Lynette Shaw
Keyword(s):  
Social Theory ◽  
Cognitive Processing ◽  
Historical Context ◽  
Agent Based Modeling ◽  
Computational Method ◽  
Macro Level ◽  
Cognitive Sociology ◽  
Agent Based ◽  
Micro Level ◽  
Made In

This chapter provides an overview of agent-based modeling (ABM), a computational method that allows researchers to simulate how macro-level phenomena spontaneously arise from micro-level interactions, and examines how sociologists might apply it to chart the emergence of cultural phenomena from individual cognitive processing. After providing some historical context for the concepts of “emergence” and the “micro-to-macro” transition in social theory and summarizing contributions ABM has already made in this arena, this work makes a case for how cognitive sociology might employ ABM toward the end of developing new, nonrational microfoundations for social theory and lays out the argument for why it should. The chapter concludes by offering a brief introduction to the basics of ABM design along with an overview of resources available to researchers interested in getting started with it.

scholarly journals Agent-Based Modeling of Rumor Propagation Using Expected Integrated Mean Squared Error Optimal Design

2020 ◽  
Vol 3 (4) ◽  
pp. 48
Author(s):  
Shih-Hsien Tseng ◽  
Tien Son Nguyen
Keyword(s):  
Social Impact ◽  
Mean Squared Error ◽  
Agent Based Modeling ◽  
Rumor Spreading ◽  
Agent Based ◽  
Social Lives ◽  
Rumor Propagation ◽  
Impact Theory ◽  
Simple Simulation ◽  
Management Positions

In the “Age of the Internet”, fake news and rumor-mongering have emerged as some of the most critical factors that affect our online social lives. For example, in the workplace, rumor spreading runs rampant during times when employees may be plagued with uncertainty about the nature and consequences of major changes. Positive information should be widely propagated as much as possible; however, we must limit the spread of rumors in an effort to reduce their inherently harmful effects. The purpose of this research is to explain the mechanisms for controlling rumors and suggest an approach for dispelling the rumor effect in the workplace. In this study, we will present a simple simulation framework of agent-based modeling and apply Social Impact Theory to explain rumor propagation within social networks. Based on our results, we have found that organizations can significantly reduce the spread of the rumors by improving the workplace environment and instituting counseling for those in management positions.

Putting Social Sciences Together Again; An Introduction to the Volume

2000 ◽  
Author(s):  
Timothy A. Kohler
Keyword(s):  
Evolutionary Dynamics ◽  
Social Research ◽  
Agent Based Modeling ◽  
Distributed Artificial Intelligence ◽  
Agent Based ◽  
Distributed Processes ◽  
Child's Play ◽  
Recent Developments ◽  
Principal Tool ◽  
Definition Of

We accept many definitions for games, most not so grandiose as those of Napoleon treated by Byron. Often when I demonstrate the simulation of Anasazi settlement discussed in chapter 7 of this volume someone will say, "This is just a game isn't it?" I'm happy to admit that it is, so long as our definition of games encompasses child's play—which teaches about and prepares for reality—and not just those frivolous pastimes of adults, which release them from it. This volume is based on and made possible by recent developments in the field of agent-based simulation. More than some dry computer science technology or another corporate software gambit, this technology is in fact provoking great interest in the possibilities of simulating social, spatial, and evolutionary dynamics in human and primate societies in ways that have not previously been possible. What is agent-based modeling? Models of this sort are sometimes also called individual-oriented, or distributed artificial intelligence- based. Action in such models takes place through agents, which are processes, however simple, that collect information about their environment, make decisions about actions based on that information, and act (Doran et al. 1994:200). Artificial societies composed of interacting collections of such agents allow controlled experiments (of the sort impossible in traditional social research) on the effects of tuning one behavioral or environmental parameter at a time (Epstein and Axtell 1996:1-20). Research using these models emphasizes dynamics rather than equilibria, distributed processes rather than systems-level phenomena, and patterns of relationships among agents rather than relationships among variables. As a result visualization is an important part of analysis, affording these approaches a sometimes gamelike and often immediately engaging quality. OK, I admit it—they're fun. Despite our emphasis on agent-based modeling, we do not mean to imply that it should displace, or is always superior to, systems-level models based on, for example, differential equations. On the contrary: te Boekhorst and Hemelrijk nicely demonstrate how these approaches may be complementary. Even more strongly, we do not argue that these activities should become, ahead of empirical research, the principal tool of social science.

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