Master thesis: Migrating a Decision Making Framework to Python
LARA (Lightweight Architecture for boundedly Rational Citizen Agents) is a component-based framework to model agents’ decision making between behavioural options. It includes pre- and postprocessing of options, memory, and option selection. Originally written in Java, it now shall be applied to python models as well.
Ongoing studies in computer science with good programming skills in python and/or Java
Interest in agent-based modelling
Preference to understand, implement and test a decision making modelling component
Proactivity, capacity for teamwork, and good communication skills
Good knowledge of German and/or English (both orally and written)
You may expect
The section “Integrated Energy Systems“ explores the transition of current energy systems with three research foci: energy economics and decision support, coordination and communication, as well as wind energy.
The group Communication and Coordination in the Energy System (COOKIES) develops and applies agent-based models to analyse individual investment decisions. This enables investigations about the impact of changes in the regulatory framework and other measures on dynamics of acceptance and investments of individual actors and their interaction.
Working at the section means profiting from the diverse expertise of an engaged team and access to the Fraunhofer IEE, with which we cooperate.
Introduction to the conception and function of a decision making framework to model agents’ decisions
Developing a concept to migrate and integrate existing Java code to the python-based ABM-framework Mesa and according implementation
Testing of the implementation with existing agent-based models
Adaptation of existing documentation