Virtuous Distortion

Woods, John; Rosales, Alirio

doi:10.1007/978-3-642-15223-8_1

Cited by 22 publications

(11 citation statements)

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“…This entails abstracting and generalizing patterns identified from data analytics to support theory development and refinement. Abstraction (Woods & Rosales, 2010) involves hiding noise and details to focus on higher-level theoretical connections. Generalization (Parsons & Wand, 2013) involves the hierarchical organization of constructs to express levels of theoretical knowledge by making explicit subclass and superclass connections.…”

Section: Resolution By Abstraction and Generalizationmentioning

confidence: 99%

Data-Driven Meets Theory-Driven Research in the Era of Big Data: Opportunities and Challenges for Information Systems Research

Maaß¹,

Parsons²,

Purao³

et al. 2018

JAIS

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The era of big data provides many opportunities for conducting impactful research from both datadriven and theory-driven perspectives. However, data-driven and theory-driven research have progressed somewhat independently. In this paper, we develop a framework that articulates important differences between these two perspectives and propose a role for information systems research at their intersection. The framework presents a set of pathways that combine the datadriven and theory-driven perspectives. From these pathways, we derive a set of challenges, and show how they can be addressed by research in information systems. By doing so, we identify an important role that information systems research can play in advancing both data-driven and theory-driven research in the era of big data. Medicine. His research investigates the relationship between conceptual modeling and AI/ML, AI in distributed environments (EdgeAI), and the design of smart services. Additionally he has a strong focus on knowledge transfer to industry through funded projects and spin-offs. 1273Carson Woo is Stanley Kwok Professor of Business, Sauder School of Business, University of British Columbia. His research interests include conceptual modeling, systems analysis and design, and requirements engineering. In particular, he is interested in using conceptual models to acquire relevant contextual information (e.g., business goals) and utilizing it to design new information systems, or aligning it to existing information systems design, so that changes can be more appropriate to business needs. At the University of British Columbia, he is a member of two research clusters: (1) Artificial Intelligence, and (2) Blockchain. Dr. Woo is editor of Information Technology and Systems Abstracts Journal at the Social Science Research Network, and serves or has served on several editorial boards, including ACM Transactions on Management Information Systems, Business & Information Systems Engineering Journal, Information and Management, and Requirements Engineering. He currently serves as the chair (2019-2020) of the Conceptual Modeling Conference steering committee and has served as the president of the

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Section: Resolution By Abstraction and Generalizationmentioning

confidence: 99%

Data-Driven Meets Theory-Driven Research in the Era of Big Data: Opportunities and Challenges for Information Systems Research

Maaß¹,

Parsons²,

Purao³

et al. 2018

JAIS

View full text Add to dashboard Cite

show abstract

“…While 'abstraction' is often framed in the current literature as the process of neglecting or omitting representational detail, 'idealization' is typically seen as a "departure from complete, veridical representation of real-world phenomena" through the addition of details known to be false (Weisberg 2012, p. 98; see also, e.g., Woods and Rosales 2010). But the same move toward action-relevant similarities seems to be available here.…”

Section: Materials Engagement and The Enactive Logic Of Science: A Ra...mentioning

confidence: 92%

Radical artifactualism

Oliveira

2022

Euro Jnl Phil Sci

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A powerful idea put forward in the recent philosophy of science literature is that scientific models are best understood as instruments, tools or, more generally, artifacts. This idea has thus far been developed in combination with the more traditional representational approach: accordingly, current artifactualist accounts treat models as representational tools. But artifactualism and representationalism are independent views, and adopting one does not require acceptance of the other. This paper argues that a leaner version of artifactualism, free of representationalist assumptions, is both desirable and viable. Taking seriously the idea that models are artifacts can help us philosophically to make sense of how and why scientific modeling works even without reference to representation.

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“…A scientist who imagines the world, theorizes about it, generates or tests hypotheses, designs and conducts a study, or performs a statistical analysis is running a model regardless of their assumed scientific paradigm. A major problem with result-centric 2 Abstractions are simplifications and subtractions we make to suppress aspects of the target system while idealizations involve smoothing out certain features to create a more perfect, albeit intentionally false version of the system (Woods & Rosales, 2010).…”

Section: Paradigm Shift To a Model-centric Sciencementioning

confidence: 99%

Rigorous exploration in a model-centric science via epistemic iteration

Devezer¹,

Buzbas²

2023

Preprint

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Urgent attention is needed to address generalizability problems in psychology. However, the current dominant paradigm, which centers dichotomous results and rapid discoveries, may not provide the solution. Instead, we propose a paradigm shift towards a model-centric approach, which can aid in understanding the sources of generalizability and promote systematic exploration. In a model-centric paradigm, scientific activity involves iteratively building and refining theoretical, empirical, and statistical models that communicate with each other. This approach is organic, transparent, and efficient in addressing generalizability issues. We illustrate the nature of scientific activity in a model-centric system and its potential for advancing the field of psychology.

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Virtuous Distortion

Cited by 22 publications

References 0 publications

Data-Driven Meets Theory-Driven Research in the Era of Big Data: Opportunities and Challenges for Information Systems Research

Data-Driven Meets Theory-Driven Research in the Era of Big Data: Opportunities and Challenges for Information Systems Research

Radical artifactualism

Rigorous exploration in a model-centric science via epistemic iteration

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