Lightweight Architecture for boundedly Rational Agents


For the purpose of policy simulation in coupled social-ecological systems (e.g. energy supply), a credible modelling of actors – especially citizens – and their decision processes is needed. This requires a framework capable of handling high numbers of heterogeneous agents (several hundreds of thousands). LARA (Lightweight Architecture for boundedly Rational Agents) meets these requirements and fills the gap between frameworks without built-in psychological foundations and full-fledged cognitive architectures which are both not viable options in this context. LARA provides prefabricated components of an agent’s decision process like perception, memory, and different modes of decision making. These components are psychologically plausible, i.e. based on appropriate psychological results and theories. Moreover, interfaces for basic learning and social influence are available.

Next Steps

  1. To get an introduction to LARA: download and read a copy of "Social-ecological modelling with LARA: A psychologically well-founded lightweight agent architecture" (PDF-Download). The article helps you to understand the core concepts of LARA by presenting a simple example model (LARA_Model_Houseplant).
  2. Read the about concept the components.
  3. Download the simple example model LARA_Model_Houseplant and do a code-walkthrough. The code includes many comments.
  4. Download the template model LARA_Model_Template and adapt it to your needs. If it's more convenient for you to fork on bitbucket, see Subprojects for particular links. The Usage Manual includes How-Tos for essential implementation tasks.
  5. Have a look at the Toolbox to find useful additional code. e.g. adapter classes to Repast Simphony models (see RS Models Manual).
  6. Use the forums or mailing list to ask questions if you get stuck. If you encounter any bugs, please create a ticket.