The model explores the emergence of inequality in cognitive and socio-emotional skills at the societal level within and across generations that results from differences in parental investment behavior during childhood and adolescence.

This model simulates a bank - firm credit network.

AMBAWA simulates the flows of biomass between crop and livestock systems at the field, farm, and village scales in order to showcase innovating management practices of soil fertility in West Africa.

A series of studies show the applicability of the NK model in the crowdsourcing research, but it also exposes a problem that the application of the NK model is not tightly integrated with crowdsourcing process, which leads to lack of a basic crowdsourcing simulation model. Accordingly, by introducing interaction relationship among task decisions to define three tasks of different structure: local task, small-world task and random task, and introducing bounded rationality and its two dimensions are taken into account: bounded rationality level that used to distinguish industry types and bounded rationality bias that used to differentiate professional users and ordinary users, an agent-based model that simulates the problem-solving process of tournament-based crowdsourcing is constructed by combining the NK fitness landscapes and the crowdsourcing framework of “Task-Crowd-Process-Evaluation”.

The Regional Security Game is a iterated public goods game with punishement based on based on life sciences work by Boyd et al. (2003 ) and Hintze & Adami (2015 ), with modifications appropriate for an international relations setting. The game models a closed regional system in which states compete over the distribution of common security benefits. Drawing on recent work applying cultural evolutionary paradigms in the social sciences, states learn through imitation of successful strategies rather than making instrumentally rational choices. The model includes the option to fit empirical data to the model, with two case studies included: Europe in 1933 on the verge of war and south-east Asia in 2013.

A series of studies show the applicability of the NK model in the crowdsourcing research, but it also exposes a problem that the application of the NK model is not tightly integrated with crowdsourcing process, which leads to lack of a basic crowdsourcing simulation model. Accordingly, by introducing interaction relationship among task decisions to define three tasks of different structure: local task, small-world task and random task, and introducing bounded rationality and its two dimensions are taken into account: bounded rationality level that used to distinguish industry types and bounded rationality bias that used to differentiate professional users and ordinary users, an agent-based model that simulates the problem-solving process of tournament-based crowdsourcing is constructed by combining the NK fitness landscapes and the crowdsourcing framework of “Task-Crowd-Process-Evaluation”.

Crowd dynamics have important applications in evacuation management systems relevant to organizing safer large scale gatherings. For crowd safety, it is very important to study the evolution of potential crowd behaviours by simulating the crowd evacuation process. Planning crowd control tasks by studying the impact of crowd behaviour evolution towards evacuation could mitigate the possibility of crowd disasters. During a typical emergency evacuation scenario, conflict among agents occurs when agents intend to move to the same location as a result of the interaction with their nearest neighbours. The effect of the agent response towards their neighbourhood is vital in order to understand the effect of variation of crowd behaviour on the whole environment. In this work, we model crowd motion subject to exit congestion under uncertainty conditions in a continuous space via computer simulations. We model best-response, risk-seeking, risk-averse and risk-neutral behaviours of agents via certain game theoretic notions. We perform computer simulations with heterogeneous populations in order to study the effect of the evolution of agent behaviours towards egress flow under threat conditions. Our simulation results show the relation between the local crowd pressure and the number of injured agents. We observe that when the proportion of agents in a population of risk-seeking agents is increased, the average crowd pressure, average local density and the number of injured agents increases. Besides that, based on our simulation results, we can infer that crowd disasters could be prevented if the agent population consists entirely of risk-averse and risk-neutral agents despite circumstances that lead to threats.

This model aims to explore how gambling-like behavior can emerge in loot box spending within gaming communities. A loot box is a purchasable mystery box that randomly awards the player a series of in-game items. Since the contents of the box are largely up to chance, many players can fall into a compulsion loop of purchasing, as the fear of missing out and belief in the gambler’s fallacy allow one to rationalize repeated purchases, especially when one compares their own luck to others. To simulate this behavior, this model generates players in different network structures to observe how factors such as network connectivity, a player’s internal decision making strategy, or even common manipulations games use these days may influence a player’s transactions.

Ge, J., & Polhill, G. (2016). Exploring the Combined Impact of Factors Influencing Commuting Patterns and CO2 Emission in Aberdeen Using an Agent-Based Model. Journal of Artificial Societies and Social Simulation, 19(3). http://jasss.soc.surrey.ac.uk/19/3/11.html
We develop an agent-based transport model using a realistic GIS-enabled road network and the car following method. The model can be used to study the impact of social interventions such as flexi-time and workplace sharing, as well as large infrastructure such as the construction of a bypass or highway. The model is developed in Netlogo version 5 and requires road network data in GIS format to run.

The development and popularisation of new energy vehicles have become a global consensus. The shortage and unreasonable layout of electric vehicle charging infrastructure (EVCI) have severely restricted the development of electric vehicles. In the literature, many methods can be used to optimise the layout of charging stations (CSs) for producing good layout designs. However, more realistic evaluation and validation should be used to assess and validate these layout options. This study suggested an agent-based simulation (ABS) model to evaluate the layout designs of EVCI and simulate the driving and charging behaviours of electric taxis (ETs). In the case study of Shenzhen, China, GPS trajectory data were used to extract the temporal and spatial patterns of ETs, which were then used to calibrate and validate the actions of ETs in the simulation. The ABS model was developed in a GIS context of an urban road network with travelling speeds of 24 h to account for the effects of traffic conditions. After the high-resolution simulation, evaluation results of the performance of EVCI and the behaviours of ETs can be provided in detail and in summary. Sensitivity analysis demonstrates the accuracy of simulation implementation and aids in understanding the effect of model parameters on system performance. Maximising the time satisfaction of ET users and reducing the workload variance of EVCI were the two goals of a multiobjective layout optimisation technique based on the Pareto frontier. The location plans for the new CS based on Pareto analysis can significantly enhance both metrics through simulation evaluation.