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Following the paradigm set by attraction-repulsion-alignment schemes, a myriad of individual based models have been proposed to calculate the evolution of abstract agents. While the emergent features of many agent systems have been described astonishingly well with force-based models, this is not the case for pedestrians. Many of the classical schemes have failed to capture the fine detail of crowd dynamics, and it is unlikely that a purely mechanical model will succeed. As a response to the mechanistic literature, we will consider a model for pedestrian dynamics that attempts to reproduce the rational behaviour of individual agents through the means of anticipation. Each pedestrian undergoes a two-step time evolution based on a perception stage and a decision stage. We will discuss the validity of this game theoretical based model in regimes with varying degrees of congestion, ultimately presenting a correction to the mechanistic model in order to achieve realistic high-density dynamics.
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Traditional opinion dynamics models are simple and yet, enough to explore the consequences in basic scenarios. But, to better describe problems such as polarization and extremism, we might need to include details about human biases and other cognitive characteristics. In this paper, I explain how we can describe and use mental models and assumptions of the agents using Bayesian-inspired model building. The relationship between human rationality and Bayesian methods will be explored, and we will see that Bayesian ideas can indeed be used to explain how humans reason. We will see how to use Bayesian-inspired rules using the simplest version of the Continuous Opinions and Discrete Actions (CODA) model. From that, we will explore how we can obtain update rules that include human behavioral characteristics such as confirmation bias, motivated reasoning, or our tendency to change opinions much less than we should. Keywords: Opinion dynamics, Bayesian methods, Cognition, CODA, Agent-based models
It is known that individual opinions on different policy issues often align to a dominant ideological dimension (e.g. left vs. right) and become increasingly polarized. We provide an agent-based model that reproduces these two stylized facts as emergent properties of an opinion dynamics in a multi-dimensional space of continuous opinions. The mechanisms for the change of agents opinions in this multi-dimensional space are derived from cognitive dissonance theory and structural balance theory. We test assumptions from proximity voting and from directional voting regarding their ability to reproduce the expected emerging properties. We further study how the emotional involvement of agents, i.e. their individual resistance to change opinions, impacts the dynamics. We identify two regimes for the global and the individual alignment of opinions. If the affective involvement is high and shows a large variance across agents, this fosters the emergence of a dominant ideological dimension. Agents align their opinions along this dimension in opposite directions, i.e. create a state of polarization.
We propose an agent-based model of collective opinion formation to study the wisdom of crowds under social influence. The opinion of an agent is a continuous positive value, denoting its subjective answer to a factual question. The wisdom of crowds states that the average of all opinions is close to the truth, i.e. the correct answer. But if agents have the chance to adjust their opinion in response to the opinions of others, this effect can be destroyed. Our model investigates this scenario by evaluating two competing effects: (i) agents tend to keep their own opinion (individual conviction $beta$), (ii) they tend to adjust their opinion if they have information about the opinions of others (social influence $alpha$). For the latter, two different regimes (full information vs. aggregated information) are compared. Our simulations show that social influence only in rare cases enhances the wisdom of crowds. Most often, we find that agents converge to a collective opinion that is even farther away from the true answer. So, under social influence the wisdom of crowds can be systematically wrong.
Pedestrians are often encountered walking in the company of some social relations, rather than alone. The social groups thus formed, in variable proportions depending on the context, are not randomly organised but exhibit distinct features, such as the well-known tendency of 3-member groups to be arranged in a V-shape. The existence of group structures is thus likely to impact the collective dynamics of the crowd, possibly in a critical way when emergency situations are considered. After turning a blind eye to these group aspects for years, endeavours to model groups in crowd simulation software have thrived in the past decades. This fairly short review opens on a description of their empirical characteristics and their impact on the global flow. Then, it aims to offer a pedagogical discussion of the main strategies to model such groups, within different types of models, in order to provide guidance for prospective modellers.
(shortened version) Religions and languages are social variables, like age, sex, wealth or political opinions, to be studied like any other organizational parameter. In fact, religiosity is one of the most important sociological aspects of populations. Languages are also a characteristics of the human kind. New religions, new languages appear though others disappear. All religions and languages evolve when they adapt to the society developments. On the other hand, the number of adherents of a given religion, the number of persons speaking a language is not fixed. Several questions can be raised. E.g. from a macroscopic point of view : How many religions/languages exist at a given time? What is their distribution? What is their life time? How do they evolve?. From a microscopic view point: can one invent agent based models to describe macroscopic aspects? Does it exist simple evolution equations? It is intuitively accepted, but also found through from statistical analysis of the frequency distribution that an attachment process is the primary cause of the distribution evolution : usually the initial religion/language is that of the mother. Later on, changes can occur either due to heterogeneous agent interaction processes or due to external field constraints, - or both. Such cases can be illustrated with historical facts and data. It is stressed that characteristic time scales are different, and recalled that external fields are very relevant in the case of religions, rending the study more interesting within a mechanistic approach
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