The SOSIEL algorithm
SOSIEL (Self-Organizing Social & Inductive Evolutionary Learning) is an open-source multi-agent algorithm that was developed for capturing the spatio-temporal complexity of social contexts in which the heterogeneity of knowledge, the need for learning, and the potential for collective action play a significant role. The algorithm can simulate the cross-generational progression of one or a large number of boundedly-rational agents, each of which is represented by a cognitive architecture that consists of theoretically-grounded cognitive processes and agent-specific and empirically-grounded knowledge. The agents can interact among themselves and/or with coupled natural (e.g., LANDIS-II) and/or technical systems, learn from their and each other’s experience, create new practices, and make decisions about taking and then take (potentially collective) actions.
A multi-agent algorithm simulating social learning, collective action, cross-generational population dynamics, and the self-organization of multi-layered social network structures.
Agent cognition is represented with a general cognitive architecture that consists of a memory component, a learning component, and a decision-making component and that can be set to one of four cognitive levels.
Agents can be empowered with place-based and/or hypothetical knowledge that is organized, updated, modified, and/or utilized by the cognitive architecture.