The PLS agent



Agent-based modeling (ABM) is widely applied in the social sciences. However, the validation of agent behavior is challenging and identified as one of the shortcomings in the field. Methods are required to establish empirical links and support the implementation of valid agent models. The PLS agent concept contributes to this by showing a way to transfer results from empirical surveys into an agent-based decision model, through processing the output of a PLS-SEM model. This should simplify and foster the use of empirical results in ABM and support collaborative studies over the disciplines.


Schubring, S., Lorscheid, I., Meyer, M., & Ringle, C. M. (2016). The PLS agent: Predictive modeling with PLS-SEM and agent-based simulation. Journal of Business Research, 69(10), 4604-4612.

Lorscheid, Meyer, Pakur, Ringle (2014). The PLS Agent – Agent Behavior Validation by Partial Least Squares. In Miguel, Amblard, Barceló & Madella (eds.) Advances in Computational Social Science and Social Simulation, Proceedings of Social Simulation Conference 2014 in Barcelona, Spain.

Iffländer, Klaus; Levsen, Nils; Lorscheid, Iris; Pakur, Sandra; Wellner, Konstantin; Herstatt, Cornelius et al. (2012): InnoAge - Innovation and Product Development for Aging Users. Hamburg University of Technology (TUHH). Hamburg (Management@TUHH, 006).


Learning agent modeling



Learning agents are a useful concept for agent-based simulation. The learning ability increases the autonomy of agents, which may lead them to unforeseen results on the individual (micro) and group (macro) level. Learning is even required to be successful in human complex systems, for which an adaptation to environmental changes is necessary. However, learning agents are considered as complex and not easy to understand. We provide an overview of the learning agent concept by (1) putting learning agents in the context of the research field machine learning, (2) clarifying the basic learning agent decision process, and (3) providing a systematic overview of the learning agent properties as model guideline and communication scheme. Overall, we aim to encourage researchers in the field to apply learning agents by supporting the understanding and communication of the concept.


Lorscheid, I. (2014). Learning Agents for Human Complex Systems. In Computer Software and Applications Conference Workshops (COMPSACW), 2014 IEEE 38th International (pp. 432-437). IEEE.

Lorscheid, I. and Troitzsch, K. G. (2009) How do agents learn to behave normatively? Machine learning concepts for norm learning in the EMIL project. In Proceedings of the 6th Annual Conference of the European Social Simulation Association, Guildford, UK, September 2009.