Social-ecological modelling with LARA: A psychologically well-founded lightweight agent architecture
Keywords
agent-based modelling, social-ecological modelling, decision modelling, agent architecture, bounded rationality
Start Date
1-7-2012 12:00 AM
Abstract
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). In our presentation we describe a framework called LARA (Lightweight Architecture for boundedly Rational Agents) which 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. To demonstrate the flexibility of LARA, we present an exemplary application model
Social-ecological modelling with LARA: A psychologically well-founded lightweight agent architecture
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). In our presentation we describe a framework called LARA (Lightweight Architecture for boundedly Rational Agents) which 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. To demonstrate the flexibility of LARA, we present an exemplary application model
