Suppes' Methodology of Economics

Aunque las contribuciones de Patrick Suppes en el ámbito de la lógica, la metodología y la losofía de la economía (por no mencionar a la propia economía) hayan sido importantes, los metodólogos de la economía apenas las conocen. El propósito de este artículo es cubrir este vacío. Presentaremos su concepción general de la losofía de la ciencia y analizaremos después con detalle su noción de modelos de datos, surgida de sus famosos experimentos sobre aprendizaje. Discutiremos también su concepción de la medición fundamental en relación con tales experimentos. El artículo incluye además una amplia bibliografía de los trabajos de Suppes sobre el tema

What Distinguishes Data from Models?

I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs

What Distinguishes Data from Models?

This is the final version. Available on open access from Springer Verlag via the DOI in this recordI propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs.European CommissionAustralian Research Counci

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ABSTRACT: Even though Patrick Suppes has many important contributions in the logic, methodology, and philosophy of economics (not to mention economics itself) his work in this area is, surprisingly, not well known within the community of economic methodologists. The aim of the present paper is to contribute to ll this lacuna. After presenting his general views on philosophy of science, the paper discusses at length his important notion of model of data, in connection with his famous learning experiments. His views on fundamental measurement are also discussed in connection with such experiments. The article contains a comprehensive bibliography of Suppes on the subject

What Distinguishes Data from Models?

I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs

Understanding scientific study via process modeling

This paper argues that scientific studies distinguish themselves from other studies by a combination of their processes, their (knowledge) elements and the roles of these elements. This is supported by constructing a process model. An illustrative example based on Newtonian mechanics shows how scientific knowledge is structured according to the process model. To distinguish scientific studies from research and scientific research, two additional process models are built for such processes. We apply these process models: (1) to argue that scientific progress should emphasize both the process of change and the content of change; (2) to chart the major stages of scientific study development; and (3) to define “science”