- New Horizons in Regional Science series
Edited by Aura Reggiani and Peter Nijkamp
Chapter 4: Spatial Data Clustering and Self-Organized Criticality: Empirical Experiments on Regional Labour Market Dynamics
4. Spatial Data Clustering and Self-Organized Criticality: Empirical Experiments on Regional Labour Market Dynamics Aura Reggiani, Christian Ventrucci, Peter Nijkamp and Giovanni Russo 4.1 THE SELF-ORGANIZED CRITICALITY ISSUE 4.1.1 The SOC Principle: Prefatory Remarks The analysis of dynamic systems and of critical paths has inspired much research on complex phenomena. Self-organized criticality (SOC) is based on the idea that complex behavioural systems can spontaneously develop, that is, they auto-organize themselves into a state characterized by a complex structure, where small shocks can provoke chain reactions in all constituting elements (Bak et al. 1987; Bak and Chen 1991). In other words, SOC shows that several dynamic systems may evolve, in a natural way, into a ‘critical state’ without any spatial or temporal influences (Jensen 1998), according to a self-organized process (Nicolis and Prigogine 1977) and without any adjustments of the systems’ parameters. Thus, the SOC concept can capture phenomena like catastrophes and avalanches1 – either positive or negative – which are usually considered to be the result of aggregate shocks. Such critical shocks in a complex system emerge from the interactive behaviour of small units, but may alter the constellation of the entire complex system. It should be noted here that in the scientific discussion centring around the SOC concept, there is a clear lack of a well-defined theoretical and operational testing procedure. Despite this general drawback, the generally accepted feature of a critical SOC state is a statistical feature, namely, the existence of a power-law distribution of the ‘avalanches’ (Bak 1996; Jensen...
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