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.

Examples of models built with the SOSIEL algorithm may be found here, while examples of systems that the SOSIEL algorithm is coupled with may be found here.