The society of mind
The New Science of Management Decision
The New Science of Management Decision
Game Theory and Decision Theory in Multi-Agent Systems
Autonomous Agents and Multi-Agent Systems
Agent-mediated electronic commerce: a survey
The Knowledge Engineering Review
Folk Psychology for Human Modelling: Extending the BDI Paradigm
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
The theory of social functions: challenges for computational social science and multi-agent learning
Cognitive Systems Research
A multi-agent simulation for social agents
Proceedings of the 2008 Spring simulation multiconference
E Pluribus Unum: Polyagent and Delegate MAS Architectures
Multi-Agent-Based Simulation VIII
Simulating human intuitive decisions by Q-learning
Proceedings of the 2009 ACM symposium on Applied Computing
Agent Computing and Multi-Agent Systems
Hybrid multi-agent systems: integrating swarming and BDI agents
ESOA'06 Proceedings of the 4th international conference on Engineering self-organising systems
Simulating human-like decisions in a memory-based agent model
Computational & Mathematical Organization Theory
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In this paper, we propose an agent-based model of human decision making: CODAGE (Cognitive Decision AGEnt). Classical Decision Theories have been widely used in multi-agent systems, but imply a too rational behavior when faced with real-world human data. Moreover, classical model usually exceeds human capabilities. Therefore, we derived our decision model from several cognitive psychological theories (e.g. Simon's decision theory, Montgomery's search of dominance structure, etc.) to take human bounded rationality into account. While most of existing cognitive agents use the BDI framework, we propose a new kind of architecture. In the CODAGE model, the decision maker is modeled by an entire multi-agent system, where each agent is in charge a particular sub-process of the whole decision. The architecture is intended to be as generic as possible. It could be viewed as an agent-based decision framework, in which different decision heuristics and biases could be implemented. We illustrate this approach with a simulation of a small experimental financial market, for which our model was able to replicate some human decision behaviors.