The Importance of Representing Cognitive Processes in Multi-Agent
CPM Report No.: 01-81
By: Bruce Edmonds and
Date: 5th March 2001
Published as: Edmonds, B. and Moss, S. (2001). The importance of
representing cognitive processes in multi-agent models. In: Dorffner, G.,
Bischof, H. and Hornik, K. (eds.) Artificial Neural Networks - ICANN 2001,
Lecture Notes in Computer Science, 2130:759-766.
We distinguish between two main types of model: predictive and explanatory.
It is argued (in the absence of models that predict on unseen data) that
in order for a model to increase our understanding of the target system
the model must credibly represent the structure of that system, including
the relevant aspects of agent cognition. Merely “plugging in” an existing
algorithm for the agent cognition will not help in such understanding.
In order to demonstrate that the cognitive model matters, we compare two
multi-agent stock market models that differ only in the type of algorithm
used by the agents to learn. We also present a positive example where a
neural net is used to model an aspect of agent behaviour in a more descriptive
Keywords: modelling, methodology, agent, economics, neural net,
genetic programming, representation, prediction, explanation, cognition,
stock market, negotiation.