Requirements and Approaches for a Complexity Science-based Modelling of International Supply Networks - Lessons Learned from Financial Market Multi-agent Models for the Simulation of Complex Adaptive Logistics Systems

International Supply Networks have to cope with changing customer demands, innovative technologies, and increasingly ecological awareness in a complex context, whereas the challenges’ consequences are hardly predictable. Hence, a modelling concept might be useful to analyse and develop system designs and deduce design options, which can applied to the real world in order to enable the system to react to these challenges by adapting its behaviour. In order to identify a catalogue of requirements for the modelling of logistics systems, the paper intends to analyse the application of LeBaron’s stock market model to logistics systems from a complexity theoretical perspective. Therefore, the feasibility and possible contributions of applying the introduced financial stock market model to logistics systems in order to learn from an existing modelling approach will be examined. INTRODUCTION Globalization combined with accelerating production times, new communication technologies and increasing ecological awareness forces companies to establish global alliances in order to develop and maintain sustainable competitiveness (Klaus & Kille 2006). These Global networks, having interconnections among their actors, work together to reach their goals and are therefore one approach to face these challenges (Hülsmann & Windt 2007). Therewith, the understanding of logistics systems has changed from linear structures to complex systems (Bowersox et al. 2002; Hülsmann & Grapp 2005). Hülsmann and Grapp (2005) describe the resulting logistics structures as International Supply Networks (ISN). ISN consist of heterogeneous agents (e.g. suppliers and manufacturers on macro-level and containers and cars on micro-level), which are to a certain extent autonomous, interactive and able to learn. In addition, the system as a whole co-evolves with other systems and its environment over time (Wycisk, McKelvey & Hülsmann 2008). These are the constitutive characteristics of the concept of Complex Adaptive Systems, which originates in complexity-science (Holland 2002). Hence, ISN can be described as Complex Adaptive Logistics Systems (CALS) (Wycisk, McKelvey & Hülsmann 2008). CALS are emerging (e.g. new kinds of orders evolve), evolving (e.g. the behaviour of the whole system adapts to environmental changes), self-organising (e.g. self-initiated changes of the system’s structure and behaviour), dynamic (e.g. the system and its inter-relations change over time) and behave like living systems to a certain degree (e.g. CALS interact and act together to reach their goals) (McKelvey, Wycisk & Hülsmann 2009). Since complexity theory-based modelling considers these characteristics, it can be used for the designing of CALS (Choi et al. 2001). Financial markets feature parallel characteristics with CALS (McKelvey, Wycisk & Hülsmann 2009). These markets also consist of heterogeneous agents (e.g. traders having different information), which are to a certain extent autonomous, able to learn, and interacting. These markets as well co-evolve as a whole with their environments over time (LeBaron 2001a). Considering the parallels in the characteristics of ISN and financial markets, a model approach for modelling financial markets could be applied to the modelling of ISN. As LeBaron’s model has already shown its capabilities in representing and predicting complex market structures and dynamic market behaviour, it shall be used as a starting