Models of representation of social mobility and inequality systems. A neural network approach

This paper reports on the results of the application of an innovative technique, i.e. neural network models, to mobility data. Our primary aim is to show that the technique is more flexible than traditional statistical modeling, and that it entails less strong methodological assumptions concerning the phenomenon which they are intended to represent. Two kinds of networks have been applied: heteroassociative networks, used for prevision and class membership recognition; and autoassociative networks, used for simulation tasks. Results obtained from experiments with neural networks on Italian data are highly consistent with the body of knowledge derived from previous classical analysis. The explicative power of neural network models proved to be higher than that of path analysis given their capacity to uncover any kind or relation between variables, whether linear or nonlinear. When compared to log-linear models, they enable the reconstruction of mobility processes within a global frame, controlling all relevant variables at once.

[1]  E. Garnsey Women's Work and Theories of Class Stratification , 1978 .

[2]  David M. Skapura,et al.  Neural networks - algorithms, applications, and programming techniques , 1991, Computation and neural systems series.

[3]  O. D. Duncan,et al.  The American Occupational Structure , 1967 .

[4]  Murray Smith,et al.  Neural Networks for Statistical Modeling , 1993 .

[5]  W. T. Illingworth,et al.  Practical guide to neural nets , 1991 .

[6]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[7]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[8]  A. Schizzerotto,et al.  La mobilità sociale in Italia , 1994 .

[9]  Bart Kosko,et al.  Neural networks and fuzzy systems , 1998 .

[10]  M. Stanworth Women and Class Analysis: A Reply to John Goldthorpe , 1984 .

[11]  J. Goldthorpe Women and Class Analysis: In Defence of the Conventional View , 1983 .

[12]  J. Goldthorpe Women and Class Analysis: A Reply to the Replies , 1984 .

[13]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Geoffrey E. Hinton,et al.  Schemata and Sequential Thought Processes in PDP Models , 1986 .

[15]  J. Goldthorpe,et al.  The Constant Flux: A Study of Class Mobility in Industrial Societies , 1993 .

[16]  Terrence J. Sejnowski,et al.  The Computational Brain , 1996, Artif. Intell..

[17]  A. Dale,et al.  Integrating Women into Class Theory , 1985 .

[18]  G. O. Stone,et al.  An analysis of the delta rule and the learning of statistical associations , 1986 .

[19]  R. Erikson Social Class of Men, Women and Families , 1984 .

[20]  David Zipser,et al.  Feature Discovery by Competive Learning , 1986, Cogn. Sci..

[21]  N. Britten,et al.  Women's Jobs do Make a Difference: A Reply to Goldthorpe , 1984 .