The Carington model is designed to provide insights into the factors affecting informal health care for older adults. It encompasses older adults, caregivers, and factors affecting informal health care. The Carington model includes no submodels.

The model formalizes a situation where agents embedded in different types of networks (random, small world and scale free networks) interact with their neighbors and express an opinion that is the result of different mechanisms: a coherence mechanism, in which agents try to stick to their previously expressed opinions; an assessment mechanism, in which agents consider available external information on the topic; and a social influence mechanism, in which agents tend to approach their neighbor’s opinions.

Our aim is to show effects of group living when only low-level cognition is assumed, such as pattern recognition needed for normal functioning, without assuming individuals have knowledge about others around them or warn them actively.
The model is of a group of vigilant foragers staying within a patch, under attack by a predator. The foragers use attentional scanning for predator detection, and flee after detection. This fleeing action constitutes a visual cue to danger, and can be received non-attentionally by others if it occurs within their limited visual field. The focus of this model is on the effectiveness of this non-attentional visual information reception.
A blind angle obstructing cue reception caused by behaviour can exist in front, morphology causes a blind angle in the back. These limitations are represented by two visual field shapes. The scan for predators is all-around, with distance-dependent detection; reception of flight cues is limited by visual field shape.
Initial parameters for instance: group sizes, movement, vision characteristics for predator detection and for cue reception. Captures (failure), number of times the information reached all individuals at the same time (All-fled, success), and several other effects of the visual settings are recorded.

This model allows for analyzing the most efficient levers for enhancing the use of recycled construction materials, and the role of empirically based decision parameters.

This model simulates networking mechanisms of an empirical social network. It correlates event determinants with place-based geography and social capital production.

Due to the role of education in promoting social status and facilitating upward social mobility, individuals and their families spare no effort to pursue better educational opportunities, especially in countries where education is highly competitive.

In China, the enrollment of senior high schools and universities mainly follows a ranking system based on students’ scores in national entrance exams (Zhongkao and Gaokao). Typically, students with higher scores have priority in choosing schools and endeavor to get into better senior high schools to increase their chances of entering a prestigious university.

However, students can only select “better” senior high schools based on their average Gaokao grades, which are strongly influenced by the initial performance (Zhongkao grades) of enrolled students. The true quality indicator of school education (schooling effect, defined as the grade improvement achieved through education at the senior high school) is unknowable. This raises the first question: will school rankings reflect the real educational quality of schools over decades of educational competition, or merely the initial quality of the students they enroll?

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Confirmation Bias is usually seen as a flaw of the human mind. However, in some tasks, it may also increase performance. Here, agents are confronted with a number of binary Signals (A, or B). They have a base detection rate, e.g. 50%, and after they detected one signal, they get biased towards this type of signal. This means, that they observe that kind of signal a bit better, and the other signal a bit worse. This is moderated by a variable called “bias_effect”, e.g. 10%. So an agent who detects A first, gets biased towards A and then improves its chance to detect A-signals by 10%. Thus, this agent detects A-Signals with the probability of 50%+10% = 60% and detects B-Signals with the probability of 50%-10% = 40%.
Given such a framework, agents that have the ability to be biased have better results in most of the scenarios.

This model is used to investigate the role of opinion leader. More specifically: the influence of ‘innovative behavior’, ‘weigth of normative influence’, ‘better product judgment’, ‘number of opinion

The SWE models firms search behaviour as the performance landscape shifts. The shift represents society’s pricing of negative externalities, and the performance landscape is an NK structure. The model is written in NetLogo.

The purpose of this model is to explore the effects of different power structures on a cross-functional team’s prosocial decision making. Are certain power distributions more conducive to the team making prosocial decisions?