Summary: The paper reviews dynamic models of decentralised economies assuming spatially distributed agents who interact directly and locally. Basically, this means that: (i) agents are located in a space such as an integer lattice or a graph; (ii) the current choice of each agent is influenced by past choices of his neighbours, that is agents who are spatially closest to him. It is argued that a key feature concerns the properties displayed by the underlying ‘interaction structure’, that is assumptions about space locations and neighbourhood sets. In particular, different sub-classes of models are singled out according to their relative ability to depict evolving, time-dependent patterns of interactions (‘flexibility’). Markov random fields, cellular automata, stochastic graphs, ‘artificial’ economies and many other classes of spatial models are discussed in detail and their main drawbacks are put forth. Finally, it is pointed out that a stimulating path to follow in the future could be that of modelling spatial ‘open-ended’ economies characterised by an endogenous emergence of novelty.
For the entire collection see [Zbl 0922.00013].