Two Modes of Scheduling in a Simple Economic Agent-Based Model Sarah Wolf 1,2 , Steffen F¨ urst 1,2 , Sophie Knell 2 , Wiebke Lass 2 , Daniel Lincke 1,2 , Antoine Mandel 3 , Jonas Teitge 2 and Carlo Jaeger 1 1 Global Climate Forum, Berlin, Germany 2 Potsdam Institute for Climate Impact Research, Potsdam, Germany 3 Centre d’ ´ Economie de la Sorbonne, Universit´ e 1 Panth´ eon-Sorbonne, Paris, France Keywords: Economic Agent-based Models, Scheduling, Climate Policy, Win-win Strategies. Abstract: Agent-based models (ABMs), and with them simulation, are gaining importance in economics. As they allow to study coordination problems in a dynamic setting, they can be helpful tools for identifying win-win strate- gies for climate policy. This paper argues that strongly simplified models can support a better understanding of economic ABMs. We present work in progress on an example case: while in economic systems in the real world many actions and interactions by various agents take place in parallel, often ABMs use sequential com- putation. With a simple economic agent-based model of firms that trade and produce goods, we explore and discuss two alternative modes of scheduling: the timetable model, where all agents complete one step after the other, and the heliotropic model, where one agent after the other completes steps. We find that the timetable model is better suited for working with data from national statistics, while the heliotropic model dispenses with random shuffling that is often introduced to guarantee symmetric expectations for agents. The latter can be used in a completely deterministic fashion, providing a baseline case for studying the system’s dynamics. 1 INTRODUCTION Simulation plays an ever more important role in eco- nomics as agent-based models (ABMs) are used more frequently. These represent the economy as a com- plex system in which macro-features emerge from the interaction of many heterogeneous agents. Imple- menting a system of agents on a computer and observ- ing simulation runs to study the system’s behaviour poses the question whether some of the observations owe to computational features of the implementation rather than being characteristic of the system under study. For example, in real-world economic systems, many actions take place in parallel, while on the com- puter, parallel actions are often represented by se- quential steps. Some observations might occur due to the sequencing chosen by the modeller 1 . Various platforms for agent-based modelling (such as Swarm, Repast, MASON, Netlogo, etc.) pro- vide tools for representing time and scheduling ac- 1 Parallel computation may provide ways to avoid this problem, but parallelisation is beyond the scope of this short paper: interdependence between the agents makes the model used difficult to parallelise. Also, we aim at a simple model, while parallel computation raises complexity. tions by different agents. ABMs may implement a simple sequence of agents all conducting the same ac- tion one after the other, or complex message passing systems between agents that trigger actions in event- driven simulation. In some cases, randomness needs to be introduced to to guarantee symmetric expecta- tions for agents. For example, in a representation of trade, the first firms buying goods might find full in- ventories of all others, while the last ones may find inventories rather empty. To avoid such a bias, which would be an artefact of computing sequentially, the order in which agents act is often determined by ran- dom shuffling. This means that randomness is intro- duced for computational reasons 2 . Most works related to ABMs provide little detail on how simulations are executed and rather focus on describing agents and their environment, as stated by Mathieu and Secq (2012), who find that the represen- tation of time and scheduling in the simulator used, as well as sequential or parallel execution of actions can have crucial impacts on simulation results. The present paper focuses on the case of sequen- 2 Other sources of randomness, such as random muta- tions to represent innovation, may be essential to the model, but are not of interest here. 303 Wolf S., Fürst S., Knell S., Lass W., Lincke D., Mandel A., Teitge J. and Jaeger C.. Two Modes of Scheduling in a Simple Economic Agent-Based Model. DOI: 10.5220/0004032203030308 In Proceedings of the 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2012), pages 303-308 ISBN: 978-989-8565-20-4 Copyright c  2012 SCITEPRESS (Science and Technology Publications, Lda.)