Mechanistic Explanations in the Social Sciences

Ríos, Diego Martín

doi:10.1177/0011392104039315

Cited by 8 publications

(6 citation statements)

References 10 publications

(8 reference statements)

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“…(21) Much literature on this subject has been written since the early 1990s: see Blossfeld (1996); Boudon (1998); Bunge (1998Bunge ( , 2004; Cherkaoui (2005, ch. 4); Elster (1989Elster ( , 1998Elster ( , 2003; Erikson (1998); Fararo (1989); Hechter (1998); Hedstrom (2003); Pawson (1989); Rios (2004); Schelling (1998); Sorensen (1998); Stinchcome (1991); Van den Berg (1998). The idea of generative mechanism has been the acknowledged core of such disciplines as physics, biology and physiology since they became modern sciences (Machamer, Darden and Craver, 2000).…”

Section: The "Language Of Variables" and The "Language Of Mechanisms"mentioning

confidence: 99%

Variables, Mechanisms, and Simulations : Can the Three Methods Be Synthesized ?

Manzo

2007

Revue Française De Sociologie

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This article offers a critical analysis of four contemporary sociological debates. Systematic analysis of the relevant literature suggests the existence of a project for reformulating the methodological foundations of empirical quantitative sociology. This reconstruction shows the emergence of the following idea: an alliance of variable analysis, mechanism methodology, and simulation techniques would be of great help in resolving some of the impasses that "standard" empirical quantitative sociology encounters. This thesis is then tested for quantitative sociology of social stratification: where there are "irrefutable signs" that the idea is becoming acceptable. Lastly, a link is established between these recent debates and older, similar proposals for resolving the problem, and the question of why the soundness of these proposals has only recently been recognized is examined.

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Section: The "Language Of Variables" and The "Language Of Mechanisms"mentioning

confidence: 99%

Variables, Mechanisms, and Simulations : Can the Three Methods Be Synthesized ?

Manzo

2007

Revue Française De Sociologie

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“…As mentioned in the introduction, in this paper, explicit incorporation of organizational factors refers to the model-based or mechanistic (e.g., (Rios, 2004)) integration of organizational performance with PRA. There is a reasonable consensus in the research community that the current generation of PRA does not include an explicit representation of organizational factors (Ghosh & Apostolakis, 2005;Modarres, Mosleh, & Wreathall, 1992;Mohaghegh & Mosleh, 2009a;Renn, 1998).…”

Section: Groupsmentioning

confidence: 99%

“…Rasmussen, 1997). Because the language of PRA is built on systematic, scenario-based, functional logic based on "scientific, mechanistic calculations" (Bley, Kaplan, & Johnson, 1992), theoretical bases for organizational factors should also move toward mechanistic theorization (i.e., descriptions of the rules that govern the production of the dependent variable) (Rios, 2004). Through mechanistic theoretical bases, which can depict those underlying mechanisms of actual organizational behavior, models can become more than abstractions or metaphors that contribute to parsimoniousness and simplicity by "postulating very few elements… to account for largescale, complex phenomena" (Rios, 2004).…”

Section: Vroom's Expectancy Theorymentioning

confidence: 99%

“…This paper is the product of a line of research by the authors on the advancement of a socio-technical risk analysis (Mohaghegh, 2007;Mohaghegh, Kazemi, & Mosleh, 2009;Mohaghegh & Mosleh, 2009a, 2009bJustin Pence et al, 2015;Justin Pence et al, 2014;Justin Pence et al, 2019;, which explicitly incorporates organizational factors into PRA. In this paper, "explicit" incorporation of organizational factors refers to the model-based or mechanistic (e.g., (Rios, 2004)) integration of organizational performance with PRA. The ideal goals of explicit incorporation of organizational factors into PRA are to (a) make risk assessments more accurate by considering the effects of organizational factors in the estimation of human error and equipment failure probabilities, and (b) improve risk management and prevention strategies by identifying and ranking critical organizational factors based on their influences on the technical system (e.g., Core Damage Frequency (CDF) in NPPs) and their impacts on Risk-Informed Performance-Based Applications (RIPBAs).…”

Section: Introductionmentioning

confidence: 99%

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A Discourse on the Incorporation of Organizational Factors into Probabilistic Risk Assessment: Key Questions and Categorical Review

Pence

Mohaghegh

2020

Risk Analysis

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This paper presents a discourse on the incorporation of organizational factors into Probabilistic Risk Assessment (PRA)/Probabilistic Safety Assessment (PSA), a topic of debate since the 1980s that has spurred discussions among industry, regulatory agencies, and the research community. The main contributions of this paper include (1) identifying the four key open questions associated with this topic; (2) framing ongoing debates by considering differing perspectives around each question; (3) offering a categorical review of existing studies on this topic to justify the selection of each question and to analyze the challenges related to each perspective; and (4) highlighting the directions of research required to reach a final resolution for each question. The four key questions are: (I) how significant is the contribution of organizational factors to accidents and incidents? (II) how critical, with respect to improving risk assessment, is the explicit incorporation of organizational factors into PRA? (III) what theoretical bases are needed for explicit This article is protected by copyright. All rights reserved. incorporation of organizational factors into PRA? and (IV) what methodological bases are needed for the explicit incorporation of organizational factors into PRA? Questions I and II mainly analyze PRA literature from the nuclear domain. For Questions III and IV, a broader review and categorization is conducted of those existing cross-disciplinary studies which have evaluated the effects of organizational factors on safety (not solely PRA-based) to shed more light on future research needs.

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“…In cellular biology, for example, a large number of mechanisms have been proposed in order to explain phenomena like fermentation, protein synthesis and secretion. Another field that has developed a tradition of mechanistic explanations is the one of the social sciences (Ylikovski 1995;Elster 1998Elster , 1989Rios 2004), in which scientific laws generally do not have any explicit explanatory function (Hempel 1965).…”

Section: Introductionmentioning

confidence: 99%

Mechanistic Information as Evidence in Decision-Oriented Science

2015

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Mechanistic information is used in the field of risk assessment in order to clarify two controversial methodological issues, the selection of inference guides and the definition of standards of evidence. In this paper we present an analysis of the concept of mechanistic information in risk assessment by recurring to previous philosophical analyses of mechanistic explanation. Our conclusion is that the conceptual analysis of mechanistic explanation facilitates a better characterization of the concept of mechanistic information. However, it also shows that the use of this kind of information in risk assessment is heavily influenced by pragmatic factors, which have not been sufficiently taken into account in philosophical analysis. Mechanistic models are like hypothesis that have to be validated empirically. Due to their dependence on the standards of evidence, they are subject to the same pragmatic factors. Therefore, recurring to mechanistic information does not lead to closure of the methodological controversies in risk assessment.

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Mechanistic Explanations in the Social Sciences

Cited by 8 publications

References 10 publications

Variables, Mechanisms, and Simulations : Can the Three Methods Be Synthesized ?

Variables, Mechanisms, and Simulations : Can the Three Methods Be Synthesized ?

A Discourse on the Incorporation of Organizational Factors into Probabilistic Risk Assessment: Key Questions and Categorical Review

Mechanistic Information as Evidence in Decision-Oriented Science

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