Errors and Artefacts in Agent-Based Modelling

The objectives of this paper are to define and classify different types of errors and artefacts that can appear in the process of developing an agent-based model, and to propose activities aimed at avoiding them during the model construction and testing phases. To do this in a structured way, we review the main concepts of the process of developing such a model – establishing a general framework that summarises the process of designing, implementing, and using agent-based models. Within this framework we identify the various stages where different types of errors and artefacts may appear. Finally we propose activities that could be used to detect (and hence eliminate) each type of error or artefact.Verification, Replication, Artefact, Error, Agent-Based Modelling, Modelling Roles

Modelling Learning and R&D in Innovative Environments: a Cognitive Multi-Agent Approach

Evolutionary arguments are an appropriate approach to the analysis of industry dynamics in a knowledge-based economy, because they can deal properly with innovation processes, technological change, path-dependence and knowledge. But in order to formalise all of this verbal accounting, researchers need methodological tools which support their theoretical analysis. In this paper we suggest some of the main requirements for computer simulation to have the same standing as the traditional tools used by neoclassical economists. Among others, aggregated behaviour should “emerge†from micro-foundations, economic agents should exhibit bounded rational behaviour, learning must be endogenous and human learning should be in agreement with some stylised facts from cognitive science and psychology. We argue that multi-agent systems is a methodology which fulfills some of the requirements above. We also propose an alternative way for modelling cognitive learning in evolutionary environments, which is in agreement with some basic concepts from cognitive science. Agents are endowed with both declarative and procedural knowledge. We have used our approach to build evolutionary models of innovative industries, where firms learn how to change their decisions about R&D budget, production, technology, etc. We refer as well to some applications using the same framework to model behavioural financial markets, economic geography and water resource management.Evolutionary economics, agent-based, muti-agent systems, innovation, research and development, industry evolution, knowledge-based economy