卡洛·金兹伯格微观史学思想述评

在当代西方史学思潮中,微观史学占有重要地位。作为微观史学的代表人物之一,卡洛.金兹伯格的微观史学思想,与西方史学的发展密不可分。卡洛.金兹伯格微观史学的具体内容,是以小群体或个人为研究对象,注重史料的特殊性和多样性,采取以记名法和推测范式为主的研究方法,以及肯定叙述是最好的表现形式等。金兹伯格微观史学研究虽然取得了很大的成绩,却也遭到了诸多的批评和质疑。     

Robustness of Spatial Autocorrelation Specifications: Some Monte Carlo Evidence

Abstract This paper examines the robustness of various models of spatial autocorrelation through a series of Monte Carlo experiments in which each model takes a turn at the data generator. The generated data are then used to estimate all of the models. The estimated models are evaluated primarily on their predictive power.     

Field gamma spectrometry,Monte Carlo simulations and potential of non-invasive measurements

The determination of gamma dose rates is of prior importance in the field of luminescence dating methods. In situ measurements are usually performed by the insertion of dosimeters or a portable gamma spectrometer cell in sediments. In this paper, Monte-Carlo simulations using the Geant4 toolkit allow the development of a new technique of insitu gamma dose rate evaluations: a spectrometer cell is placed on the surface of sediments under excavation to acquire successive spectra as sediments are removed by excavations. The principle of this non-invasive technique is outlined and its potential is discussed, especially in the case of environments in which radioelements are heterogeneously distributed. For such cases, a simple method to reconstruct gamma dose rate values with surface measurements using an attenuator is discussed, and an estimation of errors is given for two simple cases. This technique appears to be applicable, but still needs experimental validation.     

Weighted Spatial Adaptive Filtering: Monte Carlo Studies and Application to Illicit Drug Market Modeling

This paper introduces a pattern recognizer, similar to weighting schemes used in combining time series forecasts, for use in spatial adaptive filtering applied to estimating multivariate cross-sectional models. The pattern recognizer enhances the ability to automatically detect and estimate parameters with discontinuous or sharp gradient changes over geographic contexts. Results from Monte Carlo studies suggest that the weighted spatial adaptive filter is at least as accurate as the unweighted filter for cases having smoothly changing parameters, but superior for cases having discontinuous, step-jump parameters. A case study on illicit drug-market activities using census tract-level data from Pittsburgh, Pennsylvania, further illustrates the advantages of the weighted filter.     

Structural Investigation of 18th-Century Ogival Masonry Domes: From Carlo Fontana to Bernardo Vittone

In this study a structural investigation on the ogival masonry domes is performed with particular attention to the significant debate between mathematicians and architects mainly of the French and Italian schools that characterized the 18th century. In this period, analytical approaches to investigate the static of dome structures were developed concurrently for the affirmation of the proportional methods, as reported in several architectural treatises in which graphical constructions for the design of masonry domes can be found. It is well known that Carlo Fontana, in 1694, was the first to show in detail the geometrical rules for the design of a masonry dome. Later, in 1760, the Italian architect Bernardo Antonio Vittone proposed some variations to the rules written by Fontana in order to increase the height of the dome and the slope of the meridian section at the intersection with the lantern. In this study, Vittone’s dome has been analyzed and compared with Fontana’s dome by means of an analytical approach that also considers the influence of the lantern at the top. The obtained results confirm that Vittone’s rules lead not only to architectural, but also structural improvements.     

Spurios Spatial Regression: Some Monte Carlo Results with a Spatial Unit Root and Spatial Cointegration

In this paper I introduce the concepts of spatial unit roots and spatial cointegration, and via Monte-Carlo simulation I illustrate their implications for spatial regression. It is shown that spatial unit roots lead to spurious (spatial) regression, as in the well-known case involving time-series. Spatial cointegration is similar to its time-series counterpart, although I demonstrate that OLS estimation of spatial error-correction models is not consistent.     

Weighted‐Average Least Squares Applied to Spatial Econometric Models: A Monte Carlo Investigation

Recently, model averaging techniques have been employed widely in empirical investigations as an alternative to the conventional model selection procedure, a procedure criticized because it disregards a major component of uncertainty, namely, uncertainty regarding the model itself, and, thus, it leads to the underestimation of uncertainty regarding the quantities of interest. Bayesian model averaging (BMA) is one of the most popular model averaging techniques. Some studies indicate that BMA has cumbersome aspects. One of the major practical issues of using BMA is its substantial computational burden, which obstructs the process of obtaining exact estimates. A simulation method, such as Markov chain Monte Carlo (MCMC), is required to resolve this problem. Weighted‐average least squares (WALS) estimation has been proposed as an alternative to BMA. The computational burden of WALS estimation is negligible; therefore, it does not require the MCMC method. Furthermore, WALS estimation has theoretical advantages over BMA estimation. This article presents two contributions to the WALS literature. First, it applies WALS to spatial lag/error models in order to consider spatial dependence. Second, it extends WALS in order to consider explicitly the problem of multicollinearity by employing the technique of principal component regression. The small sample properties of the estimators of the proposed models are examined using Monte Carlo experiments; the results of these experiments suggest that the standard WALS may produce biased estimates when the underlying data‐generating process is a spatial lag process. Results also indicate that when the correlation among the regressors is high, the standard WALS estimators may suffer from large variances and root mean squared errors. Both of these problems are significantly mitigated by using the proposed models. Las técnicas de promediado de modelos (model averaging) vienen siendo empleadas con creciente frecuencia en las investigaciones empíricas como una alternativa a los procedimientos convencionales de selección de modelos estadísticos. Dichos procedimientos convencionales han sido criticados por no tomar en cuenta un componente clave de la incertidumbre: la incertidumbre del modelo en sí, y por lo tanto, conducen a la subestimación de la incertidumbre en la cuantificación de las valores estimados. El promediado bayesiano de modelos (Bayesian Model Averaging‐BMA) es una de las técnicas de promediado más usadas. Algunos estudios indican que BMA tiene aspectos engorrosos: uno de los principales aspectos prácticos a considerar en su uso es su pesada carga computacional, la cual obstruye el proceso de obtención de estimaciones exactas. Esta limitación hace necesario el uso de métodos de simulación, como el de la cadena de Markov de Monte Carlo (Markov Chain Monte Carlo‐MCMC). La estimación de mínimos cuadrados usando un promediado ponderado (Weighted‐Average Least Squares‐WALS) ha sido propuesta como alternativa a BMA. La carga computacional de la estimación WALS es mínima y por lo tanto no requiere del uso de MCMC. Más aun, la estimación WALS posee ventajas teóricas sobre BMA. Este artículo presenta dos contribuciones a la literatura especializada de WALS. En primer lugar, aplica WALS a modelos espaciales tipo lag/error con el fin de incorporar la dependencia espacial. En segundo lugar, modifica el método WALS, a fin de considerar explícitamente el problema de la multicolinealidad entre variables mediante el empleo de la técnica de regresión de componentes principales (Principal Component Regression‐PCR). Luego los autores utilizan experimentos Monte Carlo para examinar las propiedades de tipo “muestra pequeña” (small simple) de los estimadores de los modelos propuestos. Los resultados de los experimentos sugieren que el método WALS estándar puede producir estimaciones sesgadas cuando el proceso generador de datos subyacente (Data Generating Process‐DGP) es un proceso de retardo espacial (Spatial Lag Process‐SLP). Los resultados también indican que cuando la correlación entre las variables es alta, los estimadores estándar de WALS pueden padecer de varianzas y errores cuadráticos medios (root mean squared errors‐RMSEs) atípicamente grandes. Ambos problemas son mitigados significativamente mediante el uso de los modelos propuestos en el presente artículo. 近来,模型平均技术作为与传统模型选择流程可替换的方法,在经验调查中得到广泛应用。传统的模型选择流程忽视了模型本身的不确定性,进而低估了感兴趣样本数量的不确定性而受到批评。贝叶斯模型平均技术(BMA)是最为流行的模型平均技术之一。但已有研究表明,BMA在某些方面较为繁琐复杂,一个最主要的问题是其巨大的计算负荷阻碍模型了精确估计的过程,因此需要利用马尔可夫‐蒙特卡洛(MCMC) 之类的模拟方法进行解决。加权平均最小二乘(WALS)估计可作为BMA的可替换方法,其优点在于计算负荷可以忽略不计,因此不需要采用MCMC方法解决计算负荷问题。此外,WALS估计相比于BMA估计在理论上有一定的优势。本文针对WALS的贡献有两点：将WALS应用于空间滞后/空间误差模型以考虑空间依赖性,并利用主成分回归(PCR)拓展WALS以明确考虑多重共线性问题。本文利用蒙特卡洛实验对所提模型估计的小样本特征进行测试,结果显示当潜在数据生成过程(DGP)是一个空间滞后过程时,标准WALS可能产生有偏估计;此外,当回归量的相关性较高时,标准WALS估计量可能有较大的方差和根均方差(RMSEs).而本文提出的加权平均最小二乘估计模型能很好地缓解这两个问题。     

Stochastic Efficiency of Bayesian Markov Chain Monte Carlo in Spatial Econometric Models: An Empirical Comparison of Exact Sampling Methods

Spatial econometric specifications pose unique computational challenges to Bayesian analysis, making it difficult to estimate models efficiently. In the literature, the main focus has been on extending Bayesian analysis to increasingly complex spatial models. The stochastic efficiency of commonly used Markov Chain Monte Carlo (MCMC) samplers has received less attention by comparison. Specifically, Bayesian methods to analyze effective sample size and samplers that provide large effective size have not been thoroughly considered in the literature. Thus, we compare three MCMC techniques: the familiar Metropolis‐within‐Gibbs sampling, Slice‐within‐Gibbs sampling, and Hamiltonian Monte Carlo. The latter two methods, while common in other domains, are not as widely encountered in Bayesian spatial econometrics. We assess these methods across four different scenarios in which we estimate the spatial autoregressive parameter in a mixed regressive, spatial autoregressive specification (or, spatial lag model). We find that off‐the‐shelf implementations of the newer high‐yield simulation techniques require significant adaptation to be viable. We further find that the effective sizes are often significantly smaller than nominal sizes. In addition, we find that stopping simulation early may understate posterior credible interval widths when effective sample size is small. More broadly, we suggest that sample information and stopping rules deserve more attention in both applied and basic Bayesian spatial econometric research.     

'Liberty and truth' or 'the sovereignty of reason': Carlo Cattaneo and the place of politics in the modern world

My purpose here is to place the thought of Carlo Cattaneo in relation to French Restoration liberalism, and I therefore consider the doctrinaire school (among others, Guizot and Cousin) and the Coppet Circle (notably, Constant, StaËl and Sismondi). A concern to render reason sovereign was perhaps shared by all in post-revolutionary Europe who were cultivating human or social science, yet there was in Guizot's concept of 'the sovereignty of reason' a spiritualist, authoritarian and anti-individualist implication. By contrast, Cattaneo wished to honour 'the truth of local facts', that is the specific attributes of all the parts of which a state consisted, and in this regard his thought is descended not from the doctrinaires but from the individualist liberalism of Constant. I also remark upon Cattaneo's debt, openly declared, to the earlier historical writings of Thierry,and therefore to the political precepts of the late id é ologue tradition. Il mio scopo è quello di porre il pensiero di Carlo Cattaneo in relazione al liberalismo francese durante il periodo della Restaurazione, e confrontarlo in particolare con l'Ecole Doctrinaire (tra gli altri, Guizot e Cousin) ed il Circolo Coppet (principalmente, Constant, StaËl e Sismondi). L'idea di rendere la ragione sovrana era forse un sentimento condiviso da molti intellettuali nell'Europa post-rivoluzionaria, specialmente in coloro che dedicavano attenzione ed interesse alle scienze umane e sociali; tuttavia, nel concetto di Guizot sulla 'sovranitÀ della ragione' era insita una componente spiritualista, autoritaria ed antindividualista. Cattaneo, diversamente, tendeva ad onorare 'la veritÀ delle realtÀ locali', ossia di tutte le caratteristiche insite negli innumerevoli elementi di cui lo stato è composto; ed in questo aspetto, il suo pensiero traeva ispirazione dal liberalismo individualista di Constant piuttosto che dai doctrinaires. Credo anche che il liberalismo di Cattaneo sia dichiaratamente debitore verso gli scritti di Thierry ed inoltre nei confronti della filosofia politica appartenente alla tradizione degli idéologues.     

Using Monte Carlo Simulation for the Environmental Analysis of Small Archaeologic Datasets,with the Mesolithic in Northeast Belgium as a Case Study

In Europe often only small archaeological databases are available due to a lack of extensively prospected areas and the disturbance of the soils. Traditional statistical techniques do not allow location analyses on small archaeological databases, composed of dependent site data. Several authors have therefore developed alternative techniques, in which observed weight factors for the sample of the sites were compared with a distribution of weight factors obtained by simulating a randomly distributed site population of the same size. However, the Monte Carlo simulation does not require a prior defined weight factors. With this simplified technique, it becomes possible to use small archaeological datasets for demonstrating significant relations between environmental data and location behaviour in the past. An application of the technique to the Mesolithic in the northeast of Belgium has demonstrated that the proximity to water played a major role in the location behaviour. Small evolutions in the location choice could be linked with climate fluctuations in the Early Holocene.     

Modelling of hydrology and potential population levels at Bronze Age Jawa,Northern Jordan: a Monte Carlo approach to cope with uncertainty

An annual water balance model for Wadi Rajil, in Northern Jordan, is used to simulate the ancient water supply system for the Early Bronze Age site of Jawa. The model includes: water delivery from the catchment; local pond storage; and water demand for people, animals and irrigation. A Monte Carlo approach is used to incorporate the uncertainty associated with a range of factors including rainfall, evaporation, water losses and use. The stochastic simulation provides estimates of potential population levels sustainable by the water supply system. Historical precipitation estimates from a Global Circulation Model, with uncertainty bounds, are used to reconstruct the climate at Jawa in the Early Bronze Age (EBA). Model results indicate that the population levels in the predicted wetter conditions during parts of the EBA could have risen to ∼6000 and may have been higher in wet years. However, palaeoclimatic proxies also suggest prolonged droughts in the EBA; and during these periods the water management system was unable to provide adequate supply for a population of 6000. The utility of Monte Carlo based hydrological modelling as a tool within archaeological science is discussed.