Near is beautiful’ was argued by Miller (2004, p. 248) in his essay on “Tobler’s First Law and Spatial Analysis”. The awareness has also grown that relations among things that are near can generate complex spatio-temporal phenomena. The simplicity of Tobler’s law invokes reflections on the complexity of interacting phenomena and the ‘simple’ laws which have been articulated in the scientific literature when attempting to ‘decode’ these phenomena. Certainly, from a spatial economic viewpoint, Tobler’s law is consistent with the minimum cost-distance principle. In addition, Miller sheds light on the meaning of ‘near’ and ‘distant’: near is central to the space-economy, it is a more flexible and powerful concept than is often appreciated, and it could be expanded to include both space and time. Thus, not only (near or distant) space, but also the time component is fundamental in the analysis of the interacting economic phenomena. In parallel with Tobler, Hägerstrand (1967) pointed to the relevance of joint space-time diffusion processes, and Wilson (1967) linked spatial interaction with statistical information principles and entropy laws. An associated microeconomic foundation of spatial interaction modelling was subsequently developed by Anas (1983) on the basis of random utility theory (McFadden, 1974). Later on, Nijkamp and Reggiani (1992) linked dynamic entropy with (dynamic) spatial interaction models. The clear methodological interrelationships between the above-mentioned theories and models call for further reflections on the complexity of space-time phenomena and the simplicity of the laws describing these phenomena. The primary idea of complexity concerns the mapping of a system’s non-intuitive behaviour, particularly the evolutionary patterns of connections among interacting components of a system whose long-run behaviour is hard to predict. But, particularly at a dynamic level, it is noteworthy that May’s law (May, 1976) – describing the evolution of a population in discrete terms by means of a simple logistic equation – shows irregular and chaotic (and thus unpredictable) characteristics for certain values of the parameters and initial conditions. Also the ‘complex’ interacting evolution of two species can be described by the ‘simple’ Lotka-Volterra equations, whose analytical form is based on two interrelated logistic equations, and, surprisingly, the dynamic logistic equation turns out to be to be the dynamic form (under a certain condition of the utility function) of the associated logit model, and hence of the related spatial interaction model of the Wilson type (Reggiani, 2004). The recent enormous interdisciplinary interest in network concepts, analysis, and modelling – arising from the study of complex interconnected dynamic systems – again underlines the ‘simplicity law’. Networks often show common behaviour, based on their topological characteristics, and this behaviour is mainly derived from exponential/power forms, which are strongly related to the equations that govern spatial interaction. In other words, the topological properties of a network can give useful insights into: how the network is structured; which are the most ‘important’ nodes/agents; and how network topology can influence the conventional spatial economic laws (such as equilibrium theory, spatial interaction theory, etc.). However, this topology structure is again expressed by very simple laws, and in most cases these laws can be interpreted in a spatial economic framework. In this framework, it is still an open research issue which specific and novel contributions network analysis can offer to spatial economic analysis, and – vice versa – whether the solidity of spatial economic laws needs to be reconsidered in the light of recent advances in complexity and network theory. Hence, a dual analysis is necessary, in order to explore potential connections between these two approaches. In this respect, a synthesis of prelimin...

Titolo: | From Complexity to Simplicity |

Autore/i: | REGGIANI, AURA |

Autore/i Unibo: | |

Anno: | 2009 |

Titolo del libro: | Complexity and Spatial Networks |

Pagina iniziale: | 275 |

Pagina finale: | 284 |

Abstract: | Near is beautiful’ was argued by Miller (2004, p. 248) in his essay on “Tobler’s First Law and Spatial Analysis”. The awareness has also grown that relations among things that are near can generate complex spatio-temporal phenomena. The simplicity of Tobler’s law invokes reflections on the complexity of interacting phenomena and the ‘simple’ laws which have been articulated in the scientific literature when attempting to ‘decode’ these phenomena. Certainly, from a spatial economic viewpoint, Tobler’s law is consistent with the minimum cost-distance principle. In addition, Miller sheds light on the meaning of ‘near’ and ‘distant’: near is central to the space-economy, it is a more flexible and powerful concept than is often appreciated, and it could be expanded to include both space and time. Thus, not only (near or distant) space, but also the time component is fundamental in the analysis of the interacting economic phenomena. In parallel with Tobler, Hägerstrand (1967) pointed to the relevance of joint space-time diffusion processes, and Wilson (1967) linked spatial interaction with statistical information principles and entropy laws. An associated microeconomic foundation of spatial interaction modelling was subsequently developed by Anas (1983) on the basis of random utility theory (McFadden, 1974). Later on, Nijkamp and Reggiani (1992) linked dynamic entropy with (dynamic) spatial interaction models. The clear methodological interrelationships between the above-mentioned theories and models call for further reflections on the complexity of space-time phenomena and the simplicity of the laws describing these phenomena. The primary idea of complexity concerns the mapping of a system’s non-intuitive behaviour, particularly the evolutionary patterns of connections among interacting components of a system whose long-run behaviour is hard to predict. But, particularly at a dynamic level, it is noteworthy that May’s law (May, 1976) – describing the evolution of a population in discrete terms by means of a simple logistic equation – shows irregular and chaotic (and thus unpredictable) characteristics for certain values of the parameters and initial conditions. Also the ‘complex’ interacting evolution of two species can be described by the ‘simple’ Lotka-Volterra equations, whose analytical form is based on two interrelated logistic equations, and, surprisingly, the dynamic logistic equation turns out to be to be the dynamic form (under a certain condition of the utility function) of the associated logit model, and hence of the related spatial interaction model of the Wilson type (Reggiani, 2004). The recent enormous interdisciplinary interest in network concepts, analysis, and modelling – arising from the study of complex interconnected dynamic systems – again underlines the ‘simplicity law’. Networks often show common behaviour, based on their topological characteristics, and this behaviour is mainly derived from exponential/power forms, which are strongly related to the equations that govern spatial interaction. In other words, the topological properties of a network can give useful insights into: how the network is structured; which are the most ‘important’ nodes/agents; and how network topology can influence the conventional spatial economic laws (such as equilibrium theory, spatial interaction theory, etc.). However, this topology structure is again expressed by very simple laws, and in most cases these laws can be interpreted in a spatial economic framework. In this framework, it is still an open research issue which specific and novel contributions network analysis can offer to spatial economic analysis, and – vice versa – whether the solidity of spatial economic laws needs to be reconsidered in the light of recent advances in complexity and network theory. Hence, a dual analysis is necessary, in order to explore potential connections between these two approaches. In this respect, a synthesis of prelimin... |

Data prodotto definitivo in UGOV: | 4-gen-2010 |

Appare nelle tipologie: | 2.01 Capitolo / saggio in libro |