In the consumer advice network, users with connections can interact with each other, and the network topology will change during the opinion interaction. When the opinion distance from i to j is greater than the confidence threshold, the two consumers cannot exchange opinions, and the link between them will disconnect with probability DE. Then, a link from node i to node k is established with probability CE and node i learning opinion from node k.

This simulation model is to simulate the emergence of technological innovation processes from the hypercycles perspective.

This model is to explore the changes of paddy field landscape and household livelihood structure in the village under different policy scenarios, evaluate the eco-social effects of different policies, and provide decision support tools for proposing effective and feasible policies.

Interest-based compound economies generate monotonically increasing wealth inequality through multiplicative accumulation dynamics, yet the conditions under which gift-based reciprocal exchange outperforms such systems in collective well-being remain unquantified. We present Zensei Wago (全生和合), a seven-layer agent-based model comparing a Gift Resource Circulation (GRC) economy with a Compound Interest Circulation (CIC) economy under identical initial conditions. Across N = 5000 Monte Carlo replications (T = 700 ticks, N = 100 agents), GRC produced significantly higher collective resonance than CIC (p < 0.001, Cohen’s d = +0.171), above a critical prosocial threshold pm ≈ 0.698. Cohen’s d grows monotonically with duration — d = +1.943 at T = 1500 and d = +4.126 at T = 3000 — driven primarily by structural collapse of CIC resonance as inequality exceeds a critical Gini threshold (G > 0.333), while GRC resonance remains stable. The gift mechanism further decouples collective well-being from distributional outcomes, generating resonance through relational quality rather than material redistribution. Network topology analysis across seven configurations — combining a Watts-Strogatz rewiring sweep and a T = 1500 longitudinal replication — reveals that ring topology maximises GRC advantage (d = +1.17), that most topology-dependent reversals are transient (sparse and small-world both transition to significantly positive by T = 1500), and that a critical rewiring threshold of p ≈ 0.10–0.20 separates GRC-advantaged from GRC-disadvantaged network configurations. Scale-free networks remain persistently adverse (d = -7.24*), requiring structural redesign for gift-economy viability.

The model that simulates the dynamic creation and maintenance of knowledge-based formations such as communities of scientists, fashion movements, and subcultures. The model’s environment is a spatial one, representing not geographical space, but a “knowledge space” in which each point is a different collection of knowledge elements. Agents moving through this space represent people’s differing and changing knowledge and beliefs. The agents have only very simple behaviors: If they are “lonely,” that is, far from a local concentration of agents, they move toward the crowd; if they are crowded, they move away.

Running the model shows that the initial uniform random distribution of agents separates into “clumps,” in which some agents are central and others are distributed around them. The central agents are crowded, and so move. In doing so, they shift the centroid of the clump slightly and may make other agents either crowded or lonely, and they too will move. Thus, the clump of agents, although remaining together for long durations (as measured in time steps), drifts across the view. Lonely agents move toward the clump, sometimes joining it and sometimes continuing to trail behind it. The clumps never merge.

The model is written in NetLogo (v6). It is used as a demonstration of agent-based modelling in Gilbert, N. (2008) Agent-Based Models (Quantitative Applications in the Social Sciences). Sage Publications, Inc. and described in detail in Gilbert, N. (2007) “A generic model of collectivities,” Cybernetics and Systems. European Meeting on Cybernetic Science and Systems Research, 38(7), pp. 695–706.

How do bots influence beliefs on social media? Why do beliefs propagated by social bots spread far and wide, yet does their direct influence appear to be limited?

This model extends Axelrod’s model for the dissemination of culture (1997), with a social bot agent–an agent who only sends information and cannot be influenced themselves. The basic network is a ring network with N agents connected to k nearest neighbors. The agents have a cultural profile with F features and Q traits per feature. When two agents interact, the sending agent sends the trait of a randomly chosen feature to the receiving agent, who adopts this trait with a probability equal to their similarity. To this network, we add a bot agents who is given a unique trait on the first feature and is connected to a proportion of the agents in the model equal to ‘bot-connectedness’. At each timestep, the bot is chosen to spread one of its traits to its neighbors with a probility equal to ‘bot-activity’.

The main finding in this model is that, generally, bot activity and bot connectedness are both negatively related to the success of the bot in spreading its unique message, in equilibrium. The mechanism is that very active and well connected bots quickly influence their direct contacts, who then grow too dissimilar from the bot’s indirect contacts to quickly, preventing indirect influence. A less active and less connected bot leaves more space for indirect influence to occur, and is therefore more successful in the long run.

A reimplementation of the Wedding Ring model by Francesco Billari. We investigate partnership formation in an agent-based framework, and combine this with statistical demographic projections using real empirical data.

The purpose of the model is to study the dynamical relationship between individual needs and group performance when focusing on self-organizing task allocation. For this, we develop a model that formalizes Deci & Ryan’s self-determination theory (SDT) theory into an ABM creating a framework to study the social dynamics that pertain to the mutual relations between the individual and group level of team performance. Specifically, it aims to answer how the three individual motivations of autonomy, competence, and belonging affect team performance.

This model simulates the mechanisms of evolution, or how allele frequencies change in a population over time.

This model system aims to simulate the whole process of task allocation, task execution and evaluation in the team system through a feasible method. On the basis of Complex Adaptive Systems (CAS) theory and Agent-based Modelling (ABM) technologies and tools, this simulation system attempts to abstract real-world teams into MAS models. The author designs various task allocation strategies according to different perspectives, and the interaction among members is concerned during the task-performing process. Additionally, knowledge can be acquired by such an interaction process if members encounter tasks they cannot handle directly. An artificial computational team is constructed through ABM in this simulation system, to replace real teams and carry out computational experiments. In all, this model system has great potential for studying team dynamics, and model explorers are encouraged to expand on this to develop richer models for research.