The my-side bias is a well-documented cognitive bias in the evaluation of arguments, in which reasoners in a discussion tend to overvalue arguments that confirm their prior beliefs, while undervaluing arguments that attack their prior beliefs. This agent-based model in Netlogo simulates a group discussion among myside-biased agents, within a Bayesian setting. This model is designed to investigate the effects of the myside bias on the ability of groups to reach a consensus or collectively track the correct answer to a given binary issue.

In this agent-based model, agents decide to adopt a new product according to a utility function that depends on two kinds of social influences. First, there is a local influence exerted on an agent by her closest neighbors that have already adopted, and also by herself if she feels the product suits her personal needs. Second, there is a global influence which leads agents to adopt when they become aware of emerging trends happening in the system. For this, we endow agents with a reflexive capacity that allows them to recognize a trend, even if they can not perceive a significant change in their neighborhood.

Results reveal the appearance of slowdown periods along the adoption rate curve, in contrast with the classic stylized bell-shaped behavior. Results also show that network structure plays an important role in the effect of reflexivity: while some structures (e.g., scale-free networks) may amplify it, others (e.g., small-world structure) weaken such an effect.

Agents are linked in a social-network and make decisions on which of 2 types of behavior to adopt. We explore consequences of different information feedback and providing targeted feedback to individuals.

Indirect reciprocity can be evolved by the shared information among the people of small subgroups in the population.

This model expands approaches from social practice theories and is used to investigate the ability of the underlying conceptual model to explain the emergence of social practices, defined as routine behaviour that is similar amoung peers.

This is a model of a community of online communities. Using mechanisms such as win-stay, lose-shift, and preferential attachment the model can reproduce similar patterns to those of the Stack Exchange network.

An Agent-Based Model to simulate agent reactions to threatening information based on the anxiety-to-approach framework of Jonas et al. (2014).
The model showcases the framework of BIS/BAS (inhibitory and approach motivated behavior) for the case of climate information, including parameters for anxiety, environmental awareness, climate scepticism and pro-environmental behavior intention.

Agents receive external information according to threat-level and information frequency. The population dynamic is based on the learning from that information as well as social contagion mechanisms through a scale-free network topology.

The model uses Netlogo 6.2 and the network extension.
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Due to the large extent of the Harz National Park, an accurate measurement of visitor numbers and their spatiotemporal distribution is not feasible. This model demonstrates the possibility to simulate the streams of visitors with ABM methodology.

Swidden Farming is designed to explore the dynamics of agricultural land management strategies.

We used a computer simulation to measure how well different network structures (fully connected, small world, lattice, and random) find and exploit resource peaks in a variable environment.