Jaakko Kuorikoski

ABSTRACTWe investigate the applicability of Rodrik’s accounts of model selection and horizontal progress to macroeconomic DSGE modelling in both academic and policy-oriented modelling contexts. We argue that the key step of identifying critical assumptions is complicated by the interconnectedness of the common structural core of DSGE models and by the ad hoc modifications introduced to model various rigidities and other market imperfections. We then outline alternative ways in which macroeconomic modelling could become more horizontally progressive.

The recognition that models and simulations play a central role in the epistemology of science is about fifteen years old. Although models had long been discussed as possible foundational units in the logical analysis of scientific knowledge, the philosophical study of modelling as a distinct epistemic practice really got going in the wake of the Models as Mediators anthology edited by Margaret Morrison and Mary Morgan. In spite of the broad agreement that in fact much of science is model-based, however, there is still little agreement on pretty much anything else. What are models? Are they representations or fictions, abstract entities or concrete artifacts? Which functions do they play? Can they explain..

Interdisciplinarity is strongly promoted in science policy across the world. It is seen as a necessary condition for providing practical solutions to many pressing complex problems for which no single disciplinary approach is adequate alone. In this article we model multi- and interdisciplinary research as an instance of collective problem solving. Our goal is to provide a basic representation of this type of problem solving and chart the epistemic benefits and costs of researchers engaging in different forms of cognitive coordination. Our findings suggest that typical forms of interdisciplinary collaboration are unlikely to find optimal solutions to complex problems within short time frames and can lead to methodological conservatism. This provides some grounds for both reflecting on current science policy and envisioning more effective scientific practices with respect to interdisciplinary problem solving.

Mixed methods research - the combination of qualitative and quantitative data within the same design to strengthen causal inference - is gaining prominence in the social sciences but its benefits are contested. There remains confusion over which methods to mix and what is the point of mixing them. We argue that variety of evidence is what matters, not the data or methods, and that distinct epistemic principles underlie its added value for causal inference. The centrality of evidential variety also implies that strong causal pluralism is untenable as a foundation for mixed methods research.

We mobilise contemporary philosophy of science to further clarify observations on economic modelling made by Gilboa et al. (2023). We adopt a normative stance towards these modelling practices to identify the extent to which they are epistemically justified. Our message is simple: many of the distinctions proposed by Gilboa et al. (2023) are useful, but without the proper qualifications, too much flexibility in choosing the right interpretation risks downplaying the crucial role that empirical evidence should play in any modelling endeavour.

According to the diversity-beats-ability theorem, groups of diverse problem solvers can outperform groups of high-ability problem solvers. We argue that the model introduced by Lu Hong and Scott Page is inadequate for exploring the trade-off between diversity and ability. This is because the model employs an impoverished implementation of the problem-solving task. We present a new version of the model that captures the role of ‘ability’ in a meaningful way, and we use it to explore the trade-offs between diversity and ability in scientific problem solving.

Tässä luvussa tarkastelemme hypoteesien testaamista ja kokeellista kausaalista järkeilyä tieteenfilosofisesta näkökulmasta. Arvioimme kokeellisen menetelmän mahdollisuuksia ja rajoituksia yhteiskuntatieteellisen tutkimuksen kontekstissa, jossa luonnontieteille ominaisia yleispäteviä teorioita harvoin on saatavilla ja jossa suoraviivaisiin kausaaliväitteisiin suhtaudutaan usein epäillen. Tämä luku ei siis ole menetelmäopas, joka kädestä pitäen opastaisi, kuinka yhteiskuntatieteellisiä kokeita tulisi rakentaa, vaan katsaus niihin perustaviin metodologisiin kysymyksiin ja periaatteisiin, joihin varsinaiset menetelmät nojaavat.