Model Building in the Social Sciences.

1970 ◽  
Vol 133 (3) ◽  
pp. 488
Author(s):  
G. R. Fisher ◽  
Roger Peltier
Keyword(s):  
Social Sciences ◽  
Model Building ◽  
The Social

Social Ontology and Model-Building: A Response to Epstein

2021 ◽  
Vol 51 (2) ◽  
pp. 176-192
Author(s):  
Nadia Ruiz
Keyword(s):  
Social Sciences ◽  
Social Science ◽  
Model Building ◽  
Social Ontology ◽  
Model Construction ◽  
Ontological Commitments ◽  
Starting Point ◽  
The Social

Brian Epstein has recently argued that a thoroughly microfoundationalist approach towards economics is unconvincing for metaphysical reasons. Generally, Epstein argues that for an improvement in the methodology of social science we must adopt social ontology as the foundation of social sciences; that is, the standing microfoundationalist debate could be solved by fixing economics’ ontology. However, as I show in this paper, fixing the social ontology prior to the process of model construction is optional instead of necessary and that metaphysical-ontological commitments are often the outcome of model construction, not its starting point. By focusing on the practice of modeling in economics the paper provides a useful inroad into the debate about the role of metaphysics in the natural and social sciences more generally.

Stochastic Actor-Oriented Models for the Co-Evolution of Networks and Behavior: An Introduction and Tutorial

2019 ◽  
Vol 23 (3) ◽  
pp. 511-534 ◽  
Author(s):  
Yuval Kalish
Keyword(s):  
Social Sciences ◽  
Model Building ◽  
Network Evolution ◽  
Network Ties ◽  
Network Change ◽  
Step Model ◽  
The Social ◽  
Research Questions ◽  
Organizational Scholarship ◽  
And Behavior

Stochastic actor-oriented (SAO) models are a family of models for network dynamics that enable researchers to test multiple, often competing explanations for network change and estimate the extent and relative power of various influences on network evolution. SAO models for the co-evolution of network ties and actor behavior, the most comprehensive category of SAO models, examine how networks and actor attributes—their behavior, performance, or attitudes—influence each other over time. While these models have been widely used in the social sciences, and particularly in educational settings, their use in organizational scholarship has been extremely limited. This paper provides a layperson introduction to SAO models for the co-evolution of networks and behavior and the types of research questions they can address. The models and their underpinnings are explained in nonmathematical terms, and theoretical explanations are supported by a concrete, detailed example that includes step-by-step model building and hypothesis testing, alongside an R script.

scholarly journals Supporting theoretically-grounded model building in the social sciences through interactive visualisation

2017 ◽  
Vol 268 ◽  
pp. 153-163 ◽  
Author(s):  
Cagatay Turkay ◽  
Aidan Slingsby ◽  
Kaisa Lahtinen ◽  
Sarah Butt ◽  
Jason Dykes
Keyword(s):  
Social Sciences ◽  
Model Building ◽  
The Social ◽  
Interactive Visualisation

scholarly journals The Ethics of Automated Vehicles: Why Self-driving Cars Should not Swerve in Dilemma Cases

Res Publica ◽  
2021 ◽  
Author(s):  
Rob Lawlor
Keyword(s):  
Social Sciences ◽  
Model Building ◽  
Trolley Problem ◽  
Automated Vehicles ◽  
Safe Spaces ◽  
The Social ◽  
Legal Responsibilities ◽  
Self Driving Cars ◽  
Trolley Cases ◽  
Do So

AbstractIn this paper, I will argue that automated vehicles should not swerve to avoid a person or vehicle in its path, unless they can do so without imposing risks onto others. I will argue that this is the conclusion that we should reach even if we start by assuming that we should divert the trolley in the standard trolley case (in which the trolley will hit and kill five people on the track, unless it is diverted onto a different track, where it will hit and kill just one person). In defence of this claim, I appeal to the distribution of moral and legal responsibilities, highlighting the importance of safe spaces, and arguing in favour of constraints on what can be done to minimise casualties. My arguments draw on the methodology associated with the trolley problem. As such, this paper also defends this methodology, highlighting a number of ways in which authors misunderstand and misrepresent the trolley problem. For example, the ‘trolley problem’ is not the ‘name given by philosophers to classic examples of unavoidable crash scenarios, historically involving runaway trolleys’, as Millar suggests, and trolley cases should not be compared with ‘model building in the (social) sciences’, as Gogoll and Müller suggest. Trolley cases have more in common with lab experiments than model building, and the problem referred to in the trolley problem is not the problem of deciding what to do in any one case. Rather, it refers to the problem of explaining what appear to be conflicting intuitions when we consider two cases together. The problem, for example, could be: how do we justify the claim that automated vehicles should not swerve even if we accept the claim that we should divert the trolley in an apparently similar trolley case?

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