Social Science

Atoosa Kasirzadeh (University of Toronto)
The question of whether and if so how, computer simulations are engaged in the production of new knowledge has been subject to an on-going debate in the philosophy of science. This question has been mainly investigated concerning the connection between data and knowledge. The main idea is that because data is the building block of knowledge, it is new data which is transformed into new knowledge. Therefore, to understand whether computer simulation is engaged in the generation of new knowledge, we need to understand whether it generates genuinely new data. In this paper, I argue that the emphasis on the production of new data does not exhaust all the ways in which computer simulations play a role in the production of new knowledge. In particular, I show that in certain social scientific inquiries such as health economics, computer simulations engage in the production of new pragmatic knowledge for action.

Luis Mireles-Flores (TINT, University of Helsinki)
How is it possible to infer reliable token-level policy interventions from scientific causal generalisations? Causal accounts make a number of assumptions to define truth conditions for causal generalisations. Deciding on using one set of assumptions rather than another determines different conceptions of causality. Empirical methods of causal inference have thus distinct causal concepts already built into them a priori. When a causal generalisation is assessed and accepted as genuine, the specific assumptions used strongly determine the inferences that can be made about potential interventions on concrete policy targets. I analyse the potential outcomes framework to illustrate the argument.

Maria Jimenez (UNED)
The upsurge in social science experimentation that has taken place in the last two decades is partly (if not mainly) based on the idea that experiments have a privileged access to causal identification and inference. In the case of laboratory experiments with humans, though, a pervasive potential threat to intelligibility of results for inferential purposes comes in the form of demand effects of experimentation. This way, reactivity, or the phenomenon by which subjects tend to modify their behavior in virtue of their being studied upon, is often cited as one of the most important difficulties involved in social scientific experiments, and yet, there is to date a persistent conceptual muddle when dealing with the many dimensions of reactivity. This paper offers a conceptual framework to reactivity that draws on an interventionist approach to causality. The approach allows us to offer an unambiguous definition of reactivity and distinguishes it from placebo effects. Further, it allows us to distinguish between benign and malign forms of the phenomenon, depending on whether reactivity constitutes a danger to the validity of the causal inferences drawn from experimental data.