This model simulates a bank - firm credit network.

The Emergent Firm (EF) model is based on the premise that firms arise out of individuals choosing to work together to advantage themselves of the benefits of returns-to-scale and coordination. The Emergent Firm (EF) model is a new implementation and extension of Rob Axtell’s Endogenous Dynamics of Multi-Agent Firms model. Like the Axtell model, the EF model describes how economies, composed of firms, form and evolve out of the utility maximizing activity on the part of individual agents. The EF model includes a cash-in-advance constraint on agents changing employment, as well as a universal credit-creating lender to explore how costs and access to capital affect the emergent economy and its macroeconomic characteristics such as firm size distributions, wealth, debt, wages and productivity.

Signaling chains are a special case of Lewis’ signaling games on networks. In a signaling chain, a sender tries to send a single unit of information to a receiver through a chain of players that do not share a common signaling system.

The modeling includes citizens, bounded into families; firms and governments; all of them interacting in markets for goods, labor and real estate. The model is spatial and dynamic.

The purpose of the model presented by Glance et al is to study the ‘contribute vs. free-ride’ dilemma present in organizations.

This is an adaptation and extension of Robert Axtell’s model (2013) of endogenous firms, in Python 3.4

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.

Default Initial skill, read ODD for more info. The purpose of the model presented by Salau is to study the ’player profit vs. club benefit’ dilemma present in professional soccer organizations.

This agent-based model represents a stylized inter-organizational innovation network where firms collaborate with each other in order to generate novel organizational knowledge.

The purpose of this model is to analyze the dynamics of endogenously created oscillations in housing prices using a system dynamics simulation model, built from the perspective of construction companies.