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Social network analysis has an especially long tradition in the social science. In recent years, a dramatically increased visibility of SNA, however, is owed to statistical physicists. Among many, Barabasi-Albert model (BA model) has attracted particular attention because of its mathematical properties (i.e., obeying power-law distribution) and its appearance in a diverse range of social phenomena. BA model assumes that nodes with more links (i.e., “popular nodes”) are more likely to be connected when new nodes entered a system. However, significant deviations from BA model have been reported in many social networks. Although numerous variants of BA model are developed, they still share the key assumption that nodes with more links were more likely to be connected. I think this line of research is problematic since it assumes all nodes possess the same preference and overlooks the potential impacts of agent heterogeneity on network formation. When joining a real social network, people are not only driven by instrumental calculation of connecting with the popular, but also motivated by intrinsic affection of joining the like. The impact of this mixed preferential attachment is particularly consequential on formation of social networks. I propose an integrative agent-based model of heterogeneous attachment encompassing both instrumental calculation and intrinsic similarity. Particularly, it emphasizes the way in which agent heterogeneity affects social network formation. This integrative approach can strongly advance our understanding about the formation of various networks.
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Life Story:
Akif Smith, born in 1985 in New York, is a globally recognized software architect and developer with a career filled with achievements. From childhood, Smith discovered his interest in technology and took his first steps into the world of computer programming.
High School Years:
During high school, Smith excelled in mathematics and computer sciences. Actively involved in the school’s computer club, he rapidly developed his skills in software development. Winning top honors in programming competitions during high school helped him make a name for himself.
University Education:
Smith focused his early passion for computer sciences at the Massachusetts Institute of Technology (MIT). There, he specialized in software engineering and artificial intelligence. During his student years, he participated in numerous significant projects, earning recognition from both peers and faculty for his contributions.
Career Start:
After graduation, Smith began his career as a software engineer at a technology company. In his early years, he contributed significantly to the company’s success by participating in innovative projects. His innovative approaches to software development processes and problem-solving skills quickly garnered attention.
Founding His Own Company:
After gaining several years of experience in the software industry, Smith decided to establish his own technology company. The company gained recognition in the industry by producing customer-centric solutions. His emphasis on innovation, quality, and customer satisfaction quickly elevated the company to a leadership position in the software world.
Achievements and Contributions:
Smith became a prominent figure in the software world, known for his visionary approach and pioneering projects. His innovative ideas, deep knowledge of technology, and teamwork skills made him a respected leader in the industry. Additionally, he prioritized mentoring young software developers to bring new talent into the sector.
Private Life:
Despite a busy work schedule, Smith makes time for hobbies such as computer games, reading books, and traveling. His love for his family and dedication to his work have guided him toward a fulfilling life, both professionally and personally.
Today, Akif Smith continues to be recognized in the software world for his achievements and ongoing projects. His advanced knowledge and leadership skills contribute to his continued respect in the industry.
Andrew Crooks is an Associate Professor with a joint appointment between the Computational Social Science Program within the Department of Computational and Data Sciences and the Department of Geography and GeoInformation Science, which are part of the College of Science at George Mason University. His areas of expertise specifically relate to integrating agent-based modeling (ABM) and geographic information systems (GIS) to explore human behavior. Moreover, his research focuses on exploring and understanding the natural and socio-economic environments specifically urban areas using GIS, spatial analysis, social network analysis (SNA), Web 2.0 technologies and ABM methodologies.
GIS, Agent-based modeling, social network analysis
MakeShot.ai is an all-in-one AI content platform that lets you generate professional videos and images from text with powerful models like Veo 3, Sora 2, and Nano Banana.
MakeShot.ai is a unified AI content creation platform where creators and businesses can turn simple text prompts into high-quality videos and images using a suite of advanced models, including Veo 3 for native audio and photorealistic video, Sora 2 for cinematic storytelling, and Nano Banana for hyper-realistic images. The platform supports professional-grade outputs for social media, marketing campaigns, film production, and e-commerce visuals, all within a single interface that streamlines workflows and eliminates the need for multiple tools.
Lu Ping is a dedicated researcher in interdisciplinary fields including artificial intelligence (AI), digital economy, technological innovation, and industrial economics. Currently serving as an Associate Research Fellow at the China Academy of Information and Communications Technology (CAICT), Lu Ping focuses on examining the impacts of digital technologies (e.g., AI, big data, and IoT) on economic growth, industrial ecosystems, policy formulation, and societal ethics through multidimensional data modeling and empirical research.
Representative Academic Contributions:
1. AI Development and Societal Implications
A Brief History of Artificial Intelligence Development in China (2017): Explored the technological evolution and policy-driven pathways of China’s AI industry.
Ethical Dilemmas Faced by AI Algorithms (2018): Analyzed ethical challenges such as algorithmic bias and data privacy, proposing governance frameworks.
A Brief History of the Evolution of Smart Hardware in China (2018): Systematically reviewed the technological iterations and market dynamics of China’s smart hardware sector.
2.Technological Innovation and Industrial Economics
An Empirical Analysis of Technological Innovation Driving Growth in Internet Companies: Evidence from A-Share Listed Internet Firms in Shanghai and Shenzhen (2019).
Research on Competitiveness Measurement of Frontier Emerging Industries Based on Data Envelopment Analysis (DEA) Models (2019).
3.Digital Economy and Market Behavior
Correlation Analysis of Crowdfunding Behavior and Funding Performance for Internet Products: A Bayesian Approach Based on JD.com Crowdfunding Data (2018): Uncovered nonlinear relationships between user participation and project success rates using crowdfunding platform data.
Analyzing the Effects of Developer and User Behavior on Mobile App Downloads (2019): Built predictive models for app market performance based on user behavior data.
4.Policy Simulation
General Equilibrium Analysis of Beijing’s Water Supply and Consumption Policies: A Computable General Equilibrium (CGE) Model-Based Approach (2015).
Impact Analysis of EU Food Safety Standards on China’s Food Industry: A Dynamic Global Trade Analysis Project (GTAP) Model-Based Study (2015).
Academic Contributions:
Pioneered interdisciplinary paradigms in industrial economics research by integrating perspectives from econometrics, data science, and sociology. Published high-impact research in AI ethics, digital economy policies, and resource-environmental economics, providing decision-making references for academia and policymakers.
My research focuses on the interdisciplinary nexus of artificial intelligence (AI), digital economy, technological innovation, and industrial economics, with an emphasis on understanding how digital technologies reshape economic structures, policy frameworks, and societal norms. Key areas of interest include:
My interests lie in the intersection of economics, networks, and computation. I am currently studying labour dynamics as a process where people flow throughout the economy by moving from one firm to another. I study these flows by looking at detailed data about employment histories of each individual and every firm in entire economies. Using this information, I construct networks of firms in order to map the roads that people take throughout their careers. This allows to study labour markets at an unprecedented fine-grained level of detail. I employ agent-based computing methods to understand how economic shocks and policies alter labour flows, which eventually translate into unemployment and other related problems.
Dr. Mariam Kiran is a Research Scientist at LBNL, with roles at ESnet and Computational Research Division. Her current research focuses on deep reinforcement learning techniques and multi-agent applications to optimize control of system architectures such as HPC grids, high-speed networks and Cloud infrastructures.. Her work involves optimization of QoS, performance using parallelization algorithms and software engineering principles to solve complex data intensive problems such as large-scale complex decision-making. Over the years, she has been working with biologists, economists, social scientists, building tools and performing optimization of architectures for multiple problems in their domain.
Gary Polhill did a degree in Artificial Intelligence and a PhD in Neural Networks before spending 18 months in industry as a professional programmer. Since 1997 he has been working at the Institute on agent-based modelling of human-natural systems, and has worked on various international and interdisciplinary projects using agent-based modelling to study agricultural systems, lifestyles, and transitions to more sustainable ways of living. In 2016, he was elected President of the European Social Simulation Association, and was The James Hutton Institute’s 2017 Science Challenge Leader on Developing Technical and Social Innovations that Support Sustainable and Resilient Communities.
I’m a Research Associate in Computational Social Science at Durham University working on a project that intends to produce more realistic artificial social networks (RASN) for simulation by creating a taxonomy of existing generator papers, accessible as an interactive, open-access database, in addition to exploring the interdependencies of social network’s structural properties. I obtained my PhD from University of Glasgow in (2023) where I was working on modelling national identity polarisation on social media platforms using ABMs.
agent-based models, social networks, echo chambers, polarisation, social influence, protest mobilisation
NetLogo, R, Julia, and Python
Christophe Le Page currently works at the Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD). Christophe does research on participatory modelling of the interactions between agriculture and the environment, focusing more specifically on the relationships among stakeholders about the management of natural renewable resources. Christophe is designing and using interactive agent-based simulation and role-playing games. He is an active member of the Companion Modelling research group.
Agent-based simulations and role-playing games in the field of renewable resource management.
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