What is it?

This model demonstrates a very simple bidding market where buyers try to acquire a desired item at the best price in a competitive environment

…

The “Descriptive Norm and Fraud Dynamics” model demonstrates how fraudulent behavior can either proliferate or be contained within non-hierarchical organizations, such as peer networks, through social influence taking the form of a descriptive norm. This model expands on the fraud triangle theory, which posits that an individual must concurrently possess a financial motive, perceive an opportunity, and hold a pro-fraud attitude to engage in fraudulent activities (red agent). In the absence of any of these elements, the individual will act honestly (green agent).

The model explores variations in a descriptive norm mechanism, ranging from local distorted knowledge to global perfect knowledge. In the case of local distorted knowledge, agents primarily rely on information from their first-degree colleagues. This knowledge is often distorted because agents are slow to update their empirical expectations, which are only partially revised after one-to-one interactions. On the other end of the spectrum, local perfect knowledge is achieved by incorporating a secondary source of information into the agents’ decision-making process. Here, accurate information provided by an observer is used to update empirical expectations.

The model shows that the same variation of the descriptive norm mechanism could lead to varying aggregate fraud levels across different fraud categories. Two empirically measured norm sensitivity distributions associated with different fraud categories can be selected into the model to see the different aggregate outcomes.

The model combines the two elements of disorganization and motivation to explore their impact on teams. Effects of disorganization on team task performance (problem solving)

Model on the use of shared renewable resources including impact of imitation via success-bias and altruistic punishment.
The model is discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/

The purpose of this agent-based model is to simulate the behaviors of small farming households in the Amazon estuary region and evaluate their resilience to external shocks with the presence of several government cash transfer programs.

This proof-of-concept model explores the effects of how social and natural factors are incorporated (factor configuration) in environmentally induced migration. It is built in a conceptual environment where five regions are located in a row.

This is model that explores how a few farmers in a Chinese village, where all farmers are smallholders originally, reach optimal farming scale by transferring in farmland from other farmers in the context of urbanization and aging.

Exploring learned cooperation, coevolution and free-riding. Learning is achieved through Multi-Agent Deep Reinforcement Learning (MADRL) in an ecological environment. The environment emits no other than sparse reproduction rewards. No reward shaping, no explicit cooperation signal.

CREativity from a SYstems perspective, Model II.

This model inspects the performance of firms as the product attribute space changes, which evolves as a consequence of firms’ actions. Firms may create new product variants by dragging demand from other existing variants. Firms decide whether to open new product variants, to invade existing ones, or to keep their variant portfolio. At each variant there is a Cournot competition each round. Competition is nested since many firms compete at many variants simultaneously, affecting firm composition at each location (variant).

After the Cournot outcomes, at each round firms decide whether to (i) keep their existing product variant niche, (ii) invade an existing variant, (iii) create a new variant, or (iv) abandon a variant. Firms’ profits across their niche take into consideration the niche-width cost and the cost of opening a new variant.