The Social Time of Organizational Payment Systems

This article explores the relationships between time use and the social construction of meaning surrounding the operation of wage payment systems in three chemical industry organizations. It explores some of the ways that organizational time is subdivided and prioritized, and focuses on the way that patterns of payment for time contribute towards the formation of managerial and shop floor identities. These ideas are developed through the analysis of ethnographic research data, which supports the view that there are multiple kinds of social time in work organizations. It is suggested that much of the management literature concerned with the subject of payment systems treats time as finite, exhaustible and irreversible and this has implications for the way that payment systems are understood by practising managers. Managerial time codes reflect a closely scheduled time culture and reinforce a discourse of progress, whereas employees continue to see work time as something for which they should be compensated

Are models too simple? Arguments for increased parameterization

The idea that models should be as simple as possible is often accepted without question. However, too much simplification and parsimony may degrade a model's utility. Models are often constructed to make predictions; yet, they are commonly parameterized with a focus on calibration, regardless of whether (1) the calibration data can constrain simulated predictions or (2) the number and type of calibration parameters are commensurate with the hydraulic property details on which key predictions may depend. Parameterization estimated through the calibration process is commonly limited by the necessity that the number of calibration parameters be smaller than the number of observations. This limitation largely stems from historical restrictions in calibration and computing capability; we argue here that better methods and computing capabilities are now available and should become more widely used. To make this case, two approaches to model calibration are contrasted: (1) a traditional approach based on a small number of homogeneous parameter zones defined by the modeler a priori and (2) regularized inversion, which includes many more parameters than the traditional approach. We discuss some advantages of regularized inversion, focusing on the increased insight that can be gained from calibration data. We present these issues using reasoning that we believe has a common sense appeal to modelers; knowledge of mathematics is not required to follow our arguments. We present equations in an Appendix, however, to illustrate the fundamental differences between traditional model calibration and a regularized inversion approach