The wisdom of the crowd refers to the phenomenon in which a group of individuals, each making independent decisions, can collectively arrive at highly accurate solutions—often more accurate than any individual within the group. This principle relies heavily on independence: if individual opinions are unbiased and uncorrelated, their errors tend to cancel out when averaged, reducing overall bias. However, in real-world social networks, individuals are often influenced by their neighbors, introducing correlations between decisions. Such social influence can amplify biases, disrupting the benefits of independent voting. This trade-off between independence and interdependence has striking parallels to ensemble learning methods in machine learning. Bagging (bootstrap aggregating) improves classification performance by combining independently trained weak learners, reducing bias. Boosting, on the other hand, explicitly introduces sequential dependence among learners, where each learner focuses on correcting the errors of its predecessors. This process can reinforce biases present in the data even if it reduces variance. Here, we introduce a new meta-algorithm, casting, which captures this biological and computational trade-off. Casting forms partially connected groups (“castes”) of weak learners that are internally linked through boosting, while the castes themselves remain independent and are aggregated using bagging. This creates a continuum between full independence (i.e., bagging) and full dependence (i.e., boosting). This method allows for the testing of model capabilities across values of the hyperparameter which controls connectedness. We specifically investigate classification tasks, but the method can be used for regression tasks as well. Ultimately, casting can provide insights for how real systems contend with classification problems.

An empirical ABM of smallholder decisions in times of drought stress.

Digital social networks facilitate the opinion dynamics and idea flow and also provide reliable data to understand these dynamics. Public opinion and cooperation behavior are the key factors to determine the capacity of a successful and effective public policy. In particular, during the crises, such as the Corona virus pandemic, it is necessary to understand the people’s opinion toward a policy and the performance of the governance institutions. The problem of the mathematical explanation of the human behaviors is to simplify and bypass some of the essential process. To tackle this problem, we adopted a data-driven strategy to extract opinion and behavioral patterns from social media content to reflect the dynamics of society’s average beliefs toward different topics. We extracted important subtopics from social media contents and analyze the sentiments of users at each subtopic. Subsequently, we structured a Bayesian belief network to demonstrate the macro patters of the beliefs, opinions, information and emotions which trigger the response toward a prospective policy. We aim to understand the factors and latent factors which influence the opinion formation in the society. Our goal is to enhance the reality of the simulations. To capture the dynamics of opinions at an artificial society we apply agent-based opinion dynamics modeling. We intended to investigate practical implementation scenarios of this framework for policy analysis during Corona Virus Pandemic Crisis. The implemented modular modeling approach could be used as a flexible data-driven policy making tools to investigate public opinion in social media. The core idea is to put the opinion dynamics in the wider contexts of the collective decision-making, data-driven policy-modeling and digital democracy. We intended to use data-driven agent-based modeling as a comprehensive analysis tools to understand the collective opinion dynamics and decision making process on the social networks and uses this knowledge to utilize network-enabled policy modeling and collective intelligence platforms.

An agent-based simulation of a game of basketball. The model implements most components of a standard game of basketball. Additionally, the model allows the user to test for the effect of two separate cognitive biases – the hot-hand effect and a belief in the team’s franchise player.

Abstract: The notion of physical space has long been central in geographical theories. However, the widespread adoption of information and communication technologies (ICTs) has freed human dynamics from purely physical to also relational and cyber spaces. While researchers increasingly recognize such shifts, rarely have studies examined how the information propagates in these hybrid spaces (i.e., physical, relational, and cyber). By exploring the vaccine opinion dynamics through agent-based modeling, this study is the first that combines all hybrid spaces and explores their distinct impacts on human dynamics from an individual’s perspective. Our model captures the temporal dynamics of vaccination progress with small errors (MAE=2.45). Our results suggest that all hybrid spaces are indispensable in vaccination decision making. However, in our model, most of the agents tend to give more emphasis to the information that is spread in the physical instead of other hybrid spaces. Our study not only sheds light on human dynamics research but also offers a new lens to identifying vaccinated individuals which has long been challenging in disease-spread models. Furthermore, our study also provides responses for practitioners to develop vaccination outreach policies and plan for future outbreaks.

This is an agent-based model of a population of scientists alternatively authoring or reviewing manuscripts submitted to a scholarly journal for peer review. Peer-review evaluation can be either ‘confidential’, i.e. the identity of authors and reviewers is not disclosed, or ‘open’, i.e. authors’ identity is disclosed to reviewers. The quality of the submitted manuscripts vary according to their authors’ resources, which vary according to the number of publications. Reviewers can assess the assigned manuscript’s quality either reliably of unreliably according to varying behavioural assumptions, i.e. direct/indirect reciprocation of past outcome as authors, or deference towards higher-status authors.

A demonstration model showing how modellers can create a multi regional tram network with commuters, destinations and houses. The model offers options to create a random tram network made from modeller input or to load shapefiles for the Greater Manchester Metrolink.

The model uses NetLogo with gis, nw an csv extensions.

This NetLogo model simulates the spread of climate change beliefs within a population of individuals. Each believer has an initial belief level, which changes over time due to interactions with other individuals and exposure to media. The aim of the model is to identify possible methods for reducing climate change denial.

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.

Innovation a byproduct of the intellectual capital, requires a new paradigm for the production constituents. Human Capital HC,Structural capital SC and relational capital RC become key for intellectual capital and consequently for innovation.