Opinion Dynamics (OD) models are a class of agent-based models that describe how opinions evolve within a population. In these models, individual opinions change through interactions governed by an opinion fusion rule that specifies how updates occur. Despite their simplicity, OD models offer interpretable mechanisms for understanding the collective dynamics of opinion formation. However, most existing approaches focus on the emergence of consensus, fragmentation, or polarization, while overlooking real-world scenarios characterized by highly oscillatory trends. This study addresses this limitation by evaluating the ability of several OD models extended with dynamic parameters to reproduce oscillatory dynamics. To this end, we formulate an optimization problem solved via evolutionary algorithms. The methodology is first validated on synthetic target series to assess the intrinsic oscillatory capabilities of the models, and subsequently applied to a real-world dataset of public opinion about immigration, drawn from the monthly barometer of the Spanish Sociological Research Center. Results show that the Agent-independent Time-based Bounded Confidence and Repulsion (ATBCR) model, which combines confidence-based and polarization-based update mechanisms, achieves the best performance. The optimized model closely reproduces historical opinion fluctuations while exhibiting interpretable, human-like patterns of collective evolution.

Opinion Dynamics models in social networks are a valuable tool to study how opinions evolve within a population. However, these models often rely on agent-level parameters that are difficult to measure in a real population. This is the case of the confidence threshold in opinion dynamics models based on bounded confidence, where agents are only influenced by other agents having a similar opinion (given by this confidence threshold). Consequently, a common practice is to apply a universal threshold to the entire population and calibrate its value to match observed real-world data, despite being an unrealistic assumption. In this work, we propose an alternative approach using graph neural networks to infer agent-level confidence thresholds in the opinion dynamics of the Hegselmann-Krause model of bounded confidence. This eliminates the need for additional simulations when faced with new case studies. To this end, we construct a comprehensive synthetic training dataset that includes different network topologies and configurations of thresholds and opinions. Through multiple training runs utilizing different architectures, we identify GraphSAGE as the most effective solution, achieving a coefficient of determination$R^{2}$above 0.7 in test datasets derived from real-world topologies. Remarkably, this performance holds even when the test topologies differ in size from those considered during training.

Agent-based models (ABM) stand as a well-established paradigm in developing computational models. ABM enable the simulation of complex systems by consolidating individual-level interactions through an underlying artificial social network. Upon the accurate construction of a model, experts can employ it as a decision support system for assessing policies in hypothetical what-if scenarios and gaining insights into the operational dynamics of the target system. However, ABM still face diverse challenges such as learning realistic agents' behavioral patterns to model real-world conducts and habits. Existing research shows that machine learning techniques, when used in ABM, can address such challenges. This work focuses on using evolutionary algorithms (EAs) to learn agents' behavior through the definition of their micro-rules, aiming to accurately model them, and thus improving the global performance of the system. To do so, we model the machine learning problem and undertake a comparative analysis of seven EAs, encompassing both classical and recent approaches, to learn agents' micro-rules with a specific focus on consumers' behavior for brand selection. Our investigation involves the assessment of the performance of each EA across problem instances derived from an ABM applied to real-world marketing domains such as dairy products and automakers. The findings of our study reveal a distinct dominance of SHADE-ILS and DECC-g over the remaining algorithms considered. Furthermore, we underline the advantages of these methods to assist modelers in selecting among the best solutions.

One of the most challenging questions in the film industry is to rank superstars, which ultimately affects some performance indicators like movie success. In this work, we address this question by means of opinion dynamics models, where the evolution of opinions in a population is analyzed. We apply a model of this kind to study the evolution of opinions about a set of well-known movie superstars in a real-world population. Also, we use real-world data from a specialized cinema website to model mass communication processes (representing film releases and their related news and marketing campaigns), and to measure the performance of our model. Our results show that the proposed model is able to accurately represent this complex system, where the opinion dynamics of superstars are mostly driven by emotional mechanisms, and reveal that film releases and their corresponding marketing campaigns only have a short term effect on those opinions. To the best of our knowledge, this is the first work that applies opinion dynamics models to the study of opinions about superstars in the film industry.

The B2B Meeting Scheduling Optimization Problem (B2BSP) consists of scheduling a set of meetings between given pairs of participants to an event, minimizing idle time periods in participants' schedules, while taking into account participants’ availability and accommodation capacity. Therefore, it constitutes a challenging combinatorial problem in many real-world B2B events. This work presents a comparative study of several approaches to solve this problem. They are based on Constraint Programming (CP), Mixed Integer Programming (MIP) and Maximum Satisfiability (MaxSAT). The CP approach relies on using global constraints and has been implemented in MiniZinc to be able to compare CP, Lazy Clause Generation and MIP as solving technologies in this setting. A pure MIP encoding is also presented. Finally, an alternative viewpoint is considered under MaxSAT, showing the best performance when considering some implied constraints. Experimental results on real world B2B instances, as well as on crafted ones, show that the MaxSAT approach is the one with the best performance for this problem, exhibiting better solving times, sometimes even orders of magnitude smaller than CP and MIP.

The spreading and evolution of opinions is the key question studied in opinion dynamics. This is especially relevant in applications that depend on (possibly evolving) opinions, such as decision-making (the process of selecting an alternative from a set of possible options). In this work, we extend an existing communication framework of opinion dynamics in order to study the effects of mass communication, such as propaganda or advertisement campaigns. In this framework, fuzzy linguistic 2-tuples are used to represent opinions, which is a realistic representation of this qualitative information, and the communication is divided into three independent sub-processes, which represent the propagation of opinions in a more realistic manner, including a social network to represent agents' interactions and an awareness deactivation mechanism to model the awareness dynamics in the system (i.e., options for which agents have opinions). However, other sources of massive information can also influence opinions. To model them, in this work we present a mass communication mechanism, and integrate it in the previous communication framework. The resulting opinion dynamics model can be useful to analyze real-world scenarios where opinions evolve as a consequence of interactions between agents and also due to mass communication campaigns. In fact, our experimental results show that mass communication can have a major impact on the opinion evolution of the population.

In the bounded confidence framework, agents’ opinions evolve as a result of interactions with other agents having similar opinions. Thus, consensus or fragmentation of opinions can be reached, but not extremization (the evolution of opinions towards an extreme value). In contrast, when repulsion mechanisms are at work, agents with distant opinions interact and repel each other, leading to extremization. This work proposes a general opinion dynamics framework of bounded confidence and repulsion, which includes social network interactions and agent-independent time-varying rationality. We extensively analyze the performance of our model to show that the degree of extremization among a population can be controlled by the repulsion rule, and social networks promote extreme opinions. Agent-based rationality and time-varying adaptation also bear a strong impact on opinion dynamics. The high accuracy of our model is determined in a real-world social network well referenced in the literature, the Zachary Karate Club (with a known ground truth). Finally, we use our model to analyze the extremization of opinions in a real-world scenario, in Spain: a marketing action for the Netflix series “Narcos”.

The Business-to-Business Meeting Scheduling problem consists of scheduling a set of meetings between given pairs of participants to an event, while taking into account participants’ availability and accommodation capacity. A crucial aspect of this problem is that breaks in participants’ schedules should be avoided as much as possible. It constitutes a challenging combinatorial problem that needs to be solved for many real world brokerage events. In this paper we present a comparative study of Constraint Programming (CP), MixedInteger Programming (MIP) and Maximum Satisfiability (MaxSAT) approaches to this problem. The CP approach relies on using global constraints and has been implemented in MiniZinc to be able to compare CP, Lazy Clause Generation and MIP as solving technologies in this setting. We also present a pure MIP encoding. Finally, an alternative viewpoint is considered under MaxSAT, showing best performance when considering some implied constraints. Experiments conducted on real world instances, as well as on crafted ones, show that the MaxSAT approach is the one with the best performance for this problem, exhibiting better solving times, sometimes even orders of magnitude smaller than CP and MIP.

Agent-based modeling (ABM) is a simulation paradigm to model complex systems by defining heterogeneous individual-level behaviors in a bottom-up approach. ABM is typically employed to simulate markets to study consumer decisions and to see how consumers make their purchase decisions. In this work, we present a marketing ABM where consumer perceptions are modeled using 2-tuple fuzzy linguistic variables. These variables represent the opinions the consumers have on the different features of every product, which drive their decisions (e.g., price or quality). In contrast to numerical or crisp values, fuzzy linguistic variables are a realistic representation of these qualitative aspects. In our ABM, agents use a decision-making heuristic to select a product, which is based on those perceptions and a probabilistic utility maximization rule. This process requires a fuzzy aggregation of the perceptions of every product, based on an ordered weighted average (OWA). In addition, consumers can be aware or unaware of each product in the market. In our ABM, we model this information by introducing a brand awareness filter when applying the decision-making heuristic. Thus, consumer agents can only select those products they are aware of. Our experimental results show that our realistic representation of the consumer preferences is more accurate than other existing approaches.

Understanding consumer behaviors and how consumers react to marketing campaigns and viral word-of-mouth processes is crucial for marketers. Classical approaches try to infer this information from a global top-down perspective. However, a more suitable and natural approach is to model consumer behaviors in a heterogeneous and decentralized bottom-up approach. In this case, each virtual consumer has her own mental state and decision-making strategies to simulate her purchase decisions. The system of virtual consumers generates the global sales and a marketer can understand the rules that govern the market. A well-known paradigm to model these systems is agent-based modeling (ABM). In this manuscript we present an ABM where the brand preferences of the consumer agents are modeled using 2-tuple fuzzy linguistic variables. These variables represent the perceptions these consumers have on the different aspects or drivers every product available in the market has (e.g., price or quality). The product selection process of the agents is based on those perceptions and a utility maximization rule. This rule requires a fuzzy aggregation of the fuzzy linguistic perceptions about the products. Our proposal employs an ordered weighted average (OWA) to aggregate them. Our experiments show this approach does not suffer any loss of information when applied on data from real markets. Hence it is a suitable representation of the products preferences, normally represented by qualitative values in marketing surveys. To the best of our knowledge, this is the first work integrating a marketing ABM with fuzzy linguistic modeling.

Modern SAT solvers have experienced a remarkable progress on solving industrial instances. It is believed that most of these successful techniques exploit the underlying structure of industrial instances. Recently, there have been some attempts to analyze the structure of industrial SAT instances in terms of complex networks, with the aim of explaining the success of SAT solving techniques, and possibly improving them. In this paper, we study the community structure, or modularity, of industrial SAT instances. In a graph with clear community structure, or high modularity, we can find a partition of its nodes into communities such that most edges connect variables of the same community. Representing SAT instances as graphs, we show that most application benchmarks are characterized by a high modularity. On the contrary, random SAT instances are closer to the classical Erdös-Rényi random graph model, where no structure can be observed. We also analyze how this structure evolves by the effects of the execution of a CDCL SAT solver, and observe that new clauses learned by the solver during the search contribute to destroy the original structure of the formula. Motivated by this observation, we finally present an application that exploits the community structure to detect relevant learned clauses, and we show that detecting these clauses results in an improvement on the performance of the SAT solver. Empirically, we observe that this improves the performance of several SAT solvers on industrial SAT formulas, especially on satisfiable instances.

Over the last decades Boolean satisfiability (SAT) solvers based on conflict-driven clause learning (CDCL) have developed to the point where they can handle formulas with millions of variables. Yet a deeper understanding of how these solvers can be so successful has remained elusive. In this work we shed light on CDCL performance by using theoretical benchmarks, which have the attractive features of being a) scalable, b) extremal with respect to different proof search parameters, and c) theoretically easy in the sense of having short proofs in the resolution proof system underlying CDCL. This allows for a systematic study of solver heuristics and how efficiently they search for proofs. We report results from extensive experiments on a wide range of benchmarks. Our findings include several examples where theory predicts and explains CDCL behaviour, but also raise a number of intriguing questions for further study.

Despite the success of CDCL SAT solvers solving industrial problems, there are still many open questions to explain such success. In this context, the generation of random SAT instances having computational properties more similar to real-world problems becomes crucial. Such generators are possibly the best tool to analyze families of instances and solvers behaviors on them. In this paper, we present a random SAT instances generator based on the notion of locality. We show that this is a decisive dimension of attractiveness among the variables of a formula, and how CDCL SAT solvers take advantage of it. To the best of our knowledge, this is the first random SAT model that generates both scale-free structure and community structure at once.