This paper presents a comprehensive review of the Social Laser Theory (SLT) as a natural extension of the broader framework of Quantum-Like Modeling (QLM). While QLMapplies the mathematical formalism of quantum theory—such as Hilbert space representations, interference, and non-commutative observables—to model context-dependent and non-classical phenomena in cognition, decision-making, and social behavior, SLT advances this approach by integrating concepts from quantum field theory. The theory conceptualizes social systems as ensembles of “social atoms” capable of absorbing and emitting quantized units of social energy. Under conditions analogous to population inversion in physical lasers, external informational stimuli (e.g., media signals or mobilizing rhetoric) can trigger coherence across the population, resulting in large-scale, synchronized collective actions such as protests or ideological shifts. SLT thus provides a formal framework for understanding the amplification and coherence of social energy leading to abrupt phase-like transitions in collective behavior. Beyond its metaphorical appeal, the theory proposes measurable quantities and predictive parameters that may support empirical diagnostics of sociopolitical dynamics. By bridging micro-level psychological processes with macro-level sociological phenomena, SLT extends QLM into the domain of complex social systems, offering a mathematically grounded paradigm for interpreting rapid transformations in contemporary societies.

This paper introduces G-Impact, an agent-based model that combines modeling of household consumption and belief diffusion. Household decisions integrate personal impacts (quality, cost), perceived consequences (climate change, human responsibility), and social norms. The evaluation of these different criteria relies on household beliefs, which can be exchanged during social interactions. These beliefs can be used to explain household decisions on a macro and micro scale, and thus to target information or incentive policies. The model is applied to dietary choice in France, among the omnivorous (INCA3), the flexitarian and the vegetarian diets. The costs and greenhouse gases emissions of the different diets are initialized from real data, initial beliefs of households are derived from opinion surveys. We observe after 5 years of simulation a significant increase in the proportion of flexitarians, and a slight increase in the proportion of vegetarians. We use the model to design an information campaign in the aim of making INCA3 dieters switch to the flexitarian diet, and analyze its impact.

In this paper, we use an entropic formalism to compare the temporal strike patterns of two important unions in Córdoba city, Argentina. Despite being uncorrelated, the combined impact of the protests is not simply additive. This is reflected in the form of emerging nonlinear effects. The formalism allows for a study of system’s controllability aspects described in terms of entropic regimes. The analysis enables the characterization of correlations between overlapping periods of protest activity. It makes possible to quantitatively point out how in principle low-intensity seasons of independent strikes can often form a joint pattern of protests with higher social conflict.

Soft set theory offers a systematic method for addressing imprecision and uncertainty by classifying set elements based on specific parameters. In the realm of semigraph theory, soft semigraphs employ this method, providing a parametrized perspective that has greatly enhanced the field through efficient parameter management. Building on this foundation, disemigraphs extend semigraphs by incorporating directional relationships among vertices, making them ideal for modeling situations where the sequence and direction of connections are essential. However, existing models such as fuzzy graphs or soft graphs often fall short in capturing both the directionality and parameter-based uncertainty simultaneously, especially in networks involving multiple interaction types and layered relational structures. This paper introduces the concept of soft disemigraphs, a novel framework that integrates the flexibility of soft set theory with the directional rigor of disemigraphs. This integration enables a more nuanced, structured, and context-aware representation of complex systems, particularly where directionality, hierarchy, and imprecision coexist. We explore various operations on soft disemigraphs, including extended union, restricted union, extended intersection, and restricted intersection, and analyse their structural properties. These operations provide deeper insights into the dynamics of parametrized, directed networks and offer a clear advantage in modeling real-world systems. Furthermore, we demonstrate the practical applicability of this framework through ecological network modeling, particularly in the representation of food webs, where species interactions are inherently directional, hierarchical, and context-sensitive.

The phenomenon of gentrification of an urban area is characterized by the displacement of lower-income residents due to rising living costs and an influx of wealthier individuals. This study presents an agent-based model that simulates urban gentrification through the relocation of three income groups — low, middle, and high — driven by living costs. The model incorporates economic and sociological theories to generate realistic neighborhood transition patterns. We introduce a temporal network-based measure to track the outflow of low-income residents and the inflow of middle- and high-income residents over time. Our experiments reveal that high-income residents trigger gentrification and that our network-based measure consistently detects gentrification patterns earlier than traditional count-based methods, potentially serving as an early detection tool in real-world scenarios. Moreover, the analysis highlights how city density promotes gentrification. This framework offers valuable insights for understanding gentrification dynamics and informing urban planning and policy decisions.

When considering a specific event, news that accurately reflects the ground truth is deemed as real information, while news that deviates from the ground truth is classified as false information. False information often spreads fast due to its novel and attention-grabbing content, threatening our society. By extending the Susceptible-Infected (SI) model, our research offers analytical decision boundaries that enable effective interventions to get desirable results, even when intermediate functions cannot be analytically solved. When assessing intervention costs using the model, the results indicate that the sooner we intervene, the lower the overall intervention cost tends to be.

Financial stock returns correlations have been studied in the prism of random matrix theory to distinguish the signal from the “noise”. Eigenvalues of the matrix that are above the rescaled Marchenko–Pastur distribution can be interpreted as collective modes behavior while the modes under are usually considered as noise. In this analysis, we use complex network analysis to simulate the “noise” and the “market” component of the return correlations, by introducing some meaningful correlations in simulated geometric Brownian motion for the stocks. We find that the returns correlation matrix is dominated by stocks with high eigenvector centrality and clustering found in the network. We then use simulated “market” random walks to build an optimal portfolio and find that the overall return performs better than using the historical mean-variance data, up to [Formula: see text] on short-time scale.

This paper explores the dynamics of an eco-epidemic predator–prey model involving one prey and two competitive predator populations, with infection present in the predator population. The model uses a Holling-type II response function and includes a constant and linear proportion of prey refuge for susceptible and infected predators, respectively. It also accounts the effect of fear of predation and competition among predators for food and shelter. The study formulates the model system, identifies the steady-state points, and analyzes both local and global stability to understand the system’s long-term behavior. A formula for the basic reproduction number is constructed, indicating that controlling this number to be less than 1 can lead to disease eradication. Additionally, Hopf bifurcation in relation to key biological parameters is illustrated. Numerical simulations are conducted to validate the model, revealing diverse dynamic behaviors such as chaos and period-doubling with slight parameter variations.

The Riviera model is a combinatorial model for a settlement along a coastline, introduced recently by the authors. Of most interest are the so-called jammed states, where no more houses can be built without violating the condition that every house needs to have free space to at least one of its sides. In this paper, we introduce new agents (predators and altruists) that want to build houses once the settlement is already in the jammed state. Their behavior is governed by a different set of rules, and this allows them to build new houses even though the settlement is jammed. Our main focus is to detect jammed configurations that are resistant to predators, to altruists, and to both predators and altruists. We provide bivariate generating functions, and complexity functions (configurational entropies) for such jammed configurations. We also discuss this problem in the two-dimensional setting of a combinatorial settlement planning model that was also recently introduced by the authors, and of which the Riviera model is just a special case.