SOSIEL Models

Below are models powered by the SOSIEL Platform in an alphabetical order.

The CEMMA Game

Developers: Garry Sotnik & Egor Fedorenko

Expected release: September, 2019

Description: Journal Article, User Guide, Specs

Requirements: Windows 7 or higher; .NET Framework 4.6.1

Description: The CEMMA (Co-Evolutionary, Multilevel, & Multi-Agent) game.

The CHAD Model

Developers: Garry Sotnik, Morey Burnham, & Alejandro Flores

Expected release: July, 2019 (Download)

Description: Journal Article, User Guide, Specs

Requirements: Windows 7 or higher; .NET Framework 4.6.1

Description: The CHAD (Climate, Hydrology, Agriculture, & Decision-making) model.

Co-evolution of mental models among socially learning agents

Developer: Garry Sotnik

Latest release: September, 2018 (Download)

Description: Journal Article, User Guide, Specs

Requirements: Windows 7 or higher; .NET Framework 4.6.1

Description: The model simulates seven agents engaging in collective action and inter-network social learning. The objective of the model is to demonstrate how mental models of agents can co-evolve through a complex relationship among factors influencing decision-making, such as access to knowledge and personal- and group-level constraints.

The SOSIEL Human Extension (SHE) for LANDIS-II

Developers: Garry Sotnik & Robert M. Scheller

Latest release: September, 2018 (Download)

Description: Journal Article, User Guide, Specs

Requirements: Windows 7 or higher; .NET Framework 4.6.1

Description: The SOSIEL Platform was coupled with LANDIS-II as its SOSIEL Human Extension (SHE). The model simulates one forest management agent directing its attention from one forest site to another and each time making a harvesting decision and potentially taking a harvesting action. The model demonstrates the complex relationships among factors influencing decision-making, including conflicting goals, site-specific expectations, known decision options, and dynamic macro- and micro-level landscape conditions. These complex relations make it difficult for a decision-maker to accurately anticipate the levels of influences of specific decision options and, in turn, accurately select decision options that are most appropriate for achieving their goals. Recognizing and analyzing these complex relationships has the potential of improving our understanding of forest management and, in turn, our ability to manage forests.