Opinion Dynamics with Memory Loss and Communication Delays

Opinion Dynamics for Multidimensional Friedkin–Johnsen Model With Issue Sequences and Switching Topologies

Belief Coevolution in a Social Network of Generalist and Specialist Large Language Models

It is shown that simple persistence-based opinion-dynamics models reproduce collective outcomes in all-generalist LLM populations, whereas heterogeneous LLM populations require population-level belief composition to reproduce consensus and agent identity to predict individual belief transitions.

A model of opinion dynamics evolving via a preferential attachment mechanism involving multiple extractions

We study a model of opinion dynamics / social learning / peer-review-based market economics on an evolving network, wherein i) each of the first $N$ agents adopts one of two available opinions arbitrarily, and ii) the $(n+1)$-st agent, for $n\geqslant N$, upon arrival, draws a sample of size $k_{n}$, with replacement, from the past agents, such that the $i$-th agent (for $i\leqslant n$) is included in the sample with probability proportional to the number of times they were previously sampled and agreed with. The $(n+1)$-st agent then decides which opinion to adopt i) based on the proportion of sampled agents conforming to each of the two opinions, and ii) according to a stochastic update rule that involves a memory parameter and a rather general reinforcement function. We study both i) the scenario where $k_{n}=k$ remains fixed with $n$, and ii) the scenario where $k_{n}$ grows at a suitable rate with $n$. This model can be represented as an evolving preferential attachment network wherein each vertex is endowed with one of two possible states, and all edges are directed. It can also be framed as a variant of the celebrated elephant random walk. We study the asymptotics of this stochastic process -- in particular, the almost sure convergence, and in case of fixed sample sizes, second order fluctuations, of the relative dominance of each opinion, the influence capital and overall network activity.

Metastability and phase transition in a social network model with multiple opinions

We consider a stochastic opinion dynamics model on a fully connected social network with $N$ actors interacting by expressing opinions from a set of $M$ opinions. At any time $t\geq 0$, each actor is associated to an $M$-tuple representing the social pressure exerted on this actor for each opinion. The evolution of the matrix containing the social pressure of all actors for all opinions is a Markov jump process. Each actor tends to express opinions according to their social pressure vector and this tendency is modulated by a polarization coefficient. When an actor expresses an opinion $o$, its social pressure for all opinions is reset to zero, while for other actors the social pressure for $o$ increases by 1 and the social pressure for other opinions decreases by $1/(M-1)$. In this setting, we prove fast consensus formation, existence of a unique invariant measure and metastability in a highly polarized network. Moreover, by considering a communication bias parameter, the system exhibits a phase transition described as follows. With a negative communication bias parameter, all actors except one stop expressing in a finite time almost surely. Otherwise, no actor stops expressing opinions.

Mobility, Memory, and Network Structure in Agent-Based Models of Convention Tipping and Convergence

A unified predictive model is introduced that accurately estimates how structural and behavioral parameters determine the time required for complete adoption, showing that mobility is the dominant accelerator while memory and connectivity modulate convergence in systematic ways.

Bridging Semantics and Behavior: An Agent-Based Evolutionary Model of Topic Competition with BERTopic

Understanding how information spreads and opinions evolve on social media is a core challenge in computational social science. Users constantly face competing topics, yet their decisions to engage are shaped by social learning, perceived utility, and cognitive limits. Traditional models often separate content from behavior, missing how individual cognition and social interaction jointly drive large-scale discourse. We propose a language-independent computational framework that bridges semantic topic modeling with evolutionary game theory and agent-based simulation. Using BERTopic, we extract coherent topics from a large Weibo dataset. We then quantify engagement through a unified metric based on likes, comments, and reposts. In NetLogo, agents update topic preferences via local imitation, guided by payoff comparisons and behavioral noise---mirroring real-world bounded rationality. Tests on two real-world datasets show that our model successfully replicates observed topic diffusion trends. It highlights how social learning frequency and payoff sensitivity shape public attention. Our results demonstrate that micro-level mechanisms---imitation and noise-tolerant choice---can produce emergent macro-level patterns. This work contributes to human-computer interaction by linking semantic analysis with behavioral simulation, offering insights into the cognitive and social foundations of online collective behavior. The framework also has practical potential for misinformation resilience, opinion forecasting, and adaptive content curation.