Abstract
This paper presents a set of guidelines, imported from the field of forecasting, that can help social simulation and, more specifically, agent-based modelling practitioners to improve the predictive performance and the robustness of their models. The presentation starts with a discussion on the current debate on prediction in social processes, followed by an overview of the recent experience and lessons learnt from the field of forecasting. This is the basis to define standard practices when developing agent-based models under the perspective of forecasting experimentation. In this context, the guidelines are structured in six categories that correspond to key issues that should be taken into account when building a predictor agent-based model: the modelling process, the data adequacy, the space of solutions, the expert involvement, the validation, and the dissemination and replication. The application of these guidelines is illustrated with an existing agent-based model. We conclude by tackling some intrinsic difficulties that agent-based modelling often faces when dealing with prediction models.
Highlights
1.1 The technique of Agent-Based Modelling (ABM) has become widely used for research in Social Sciences ( Gilbert & Troitzsch, 1999), especially for understanding social phenomena or to validate social theories
Given its ability to show the evolution of complex systems, one question arises: can ABM support forecasting? This issue has risen lively discussions, until a point where many prefer to avoid dealing with this hornets' nest
6.1 The aim of this paper has been to identify those lessons from the forecasting field that can be useful to the ABM community in order to improve the models from a prediction perspective
Summary
1.1 The technique of Agent-Based Modelling (ABM) has become widely used for research in Social Sciences ( Gilbert & Troitzsch, 1999), especially for understanding social phenomena or to validate social theories. 1.3 Current agent-based models do not reach the prediction capabilities that stake-holders would desire, and this fact feeds the continuous need of ABM to 'defend' and justify its existence. In this context, the role that prediction should play in ABM can be very different depending on the researcher. Many researchers, such as Joshua Epstein (Epstein, 2008), place prediction as a secondary objective, arguing that there are many other possible reasons to build models He lists 16 of them, including explanation, guiding data collection, raising new questions or suggesting analogies. He stresses his point stating that 'Explanation does not imply Prediction', the same way as Tectonics explains earthquakes but cannot predict them
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