This model is an agent-based simulation written in Python 2.7, which simulates the cost of social care in an ageing UK population. The simulation incorporates processes of population change which affect the demand for and supply of social care, including health status, partnership formation, fertility and mortality. Fertility and mortality rates are drawn from UK population data, then projected forward to 2050 using the methods developed by Lee and Carter 1992.

The model demonstrates that rising life expectancy combined with lower birthrates leads to growing social care costs across the population. More surprisingly, the model shows that the oft-proposed intervention of raising the retirement age has limited utility; some reductions in costs are attained initially, but these reductions taper off beyond age 70. Subsequent work has enhanced and extended this model by adding more detail to agent behaviours and familial relationships.

The version of the model provided here produces outputs in a format compatible with the GEM-SA uncertainty quantification software by Kennedy and O’Hagan. This allows sensitivity analyses to be performed using Gaussian Process Emulation.

The purpose of the Digital Mobility Model (DMM) is to explore how a society’s adoption of digital technologies can impact people’s mobilities and immobilities within an urban environment. Thus, the model contains dynamic agents with different levels of digital technology skills, which can affect their ability to access urban services using digital systems (e.g., healthcare or municipal public administration with online appointment systems). In addition, the dynamic agents move within the model and interact with static agents (i.e., places) that represent locations with different levels of digitalization, such as restaurants with online reservation systems that can be considered as a place with a high level of digitalization. This indicates that places with a higher level of digitalization are more digitally accessible and easier to reach by individuals with higher levels of digital skills. The model simulates the interaction between dynamic agents and static agents (i.e., places), which captures how the gap between an individual’s digital skills and a place’s digitalization level can lead to the mobility or immobility of people to access different locations and services.

MicroAnts 2.5 is a general-purpose agent-based model designed as a flexible workhorse for simulating ecological and evolutionary dynamics in artificial populations, as well as, potentially, the emergence of political institutions and economic regimes. It builds on and extends Stephen Wright’s original MicroAnts 2.0 by introducing configurable predators, inequality tracking, and other options.
Ant agents are of two tyes/casts and controlled by 16-bit chromosomes encoding traits such as vision, movement, mating thresholds, sensing, and combat strength. Predators (anteaters) operate in static, random, or targeted predatory modes. Ants reproduce, mutate, cooperate, fight, and die based on their traits and interactions. Environmental pressures (poison and predators) and social dynamics (sharing, mating, combat) drive emergent behavior across red and black ant populations.
The model supports insertion of custom agents at runtime, configurable mutation/inversion rates, and exports detailed statistics, including inequality metrics (e.g., Gini coefficients), trait frequencies, predator kills, and lineage data. Intended for rapid testing and educational experimentation, MicroAnts 2.5 serves as a modular base for more complex ecological and social simulations.

In his 2003 book, Historical Dynamics (ch. 4), Turchin describes and briefly analyzes a spatial ABM of his metaethnic frontier theory, which is essentially a formalization of a theory by Ibn Khaldun in the 14th century. In the model, polities compete with neighboring polities and can absorb them into an empire. Groups possess “asabiya”, a measure of social solidarity and a sense of shared purpose. Regions that share borders with other groups will have increased asabiya do to salient us vs. them competition. High asabiya enhances the ability to grow, work together, and hence wage war on neighboring groups and assimilate them into an empire. The larger the frontier, the higher the empire’s asabiya.
As an empire expands, (1) increased access to resources drives further growth; (2) internal conflict decreases asabiya among those who live far from the frontier; and (3) expanded size of the frontier decreases ability to wage war along all frontiers. When an empire’s asabiya decreases too much, it collapses.  Another group with more compelling asabiya eventually helps establish a new empire.

The purpose of this agent-based model is to explore the emergent phenomena associated with scientific publication, including quantity and quality, from different academic types based on their publication strategies.

Reconstruction of the original code M. Cohen, J. March, and J. Olsen garbage can model, realized by means of Microsoft Office Excel 2010

This model simulates the lithic raw material use and provisioning behavior of a group that inhabits a permanent base camp, and uses stone tools.

This model simulates the emergence of a dual market structure from firm-level interaction. Firms are profit-seeking, and demand is represented by a unimodal distribution of consumers along a set of taste positions.

This model explores a social mechanism that links the reversal of the gender gap in education with changing patterns in relative divorce risks in 12 European countries.

The intention of this model is to create an universal basis on how to model change in value prioritizations within social simulation. This model illustrates the designing of heterogeneous populations within agent-based social simulations by equipping agents with Dynamic Value-based Cognitive Architectures (DVCA-model). The DVCA-model uses the psychological theories on values by Schwartz (2012) and character traits by McCrae and Costa (2008) to create an unique trait- and value prioritization system for each individual. Furthermore, the DVCA-model simulates the impact of both social persuasion and life-events (e.g. information, experience) on the value systems of individuals by introducing the innovative concept of perception thermometers. Perception thermometers, controlled by the character traits, operate as buffers between the internal value prioritizations of agents and their external interactions. By introducing the concept of perception thermometers, the DVCA-model allows to study the dynamics of individual value prioritizations under a variety of internal and external perturbations over extensive time periods. Possible applications are the use of the DVCA-model within artificial sociality, opinion dynamics, social learning modelling, behavior selection algorithms and social-economic modelling.