Two Modes of Scheduling in a Simple Economic Agent-Based Model

Agent-based models (ABMs), and with them simulation, are gaining importance in economics. As ABMs offer the possibility to consider several equilibria, they can be helpful tools in the attempt at identifying win- win strategies for climate policy. Aiming at a better understanding of economic ABMs (also, but not only as dynamical systems in the mathematical sense), first steps can be made by studying strongly simplified economic ABMs. This paper presents 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 computation. With a simple economic agent-based model of firms that trade and produce goods, we explore and discuss two alternative modes of scheduling. While one is better suited for working with data from national statistics, the other one dispenses with random shuffling that is often introduced to avoid a bias between agents which arises from sequential computations.

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