Panagiotis Karadimas

The ontic conception of explanation is predicated on the proposition that “explanation is a relation between real objects in the world” and hence, according to this approach, scientific explanation cannot take place absent such a premise. Despite the fact that critics have emphasized several drawbacks of the ontic conception, as for example its inability to address the so-called “abstract explanations”, the debate is not settled and the ontic view can claim to capture cases of explanation that are non-abstract, such as causal relations between events. However, by eliminating the distinction between abstract and non-abstract explanations, it follows that ontic and epistemic proposals can no longer contend to capture different cases of explanation and either all are captured by the ontic view or all are captured by the epistemic view. On closer inspection, it turns out that the ontic view deals with events that fall outside the scientists’ scope of observation and that it does not accommodate common instances of explanation such as explanations from false propositions and hence it cannot establish itself as the dominant philosophical stance with respect to explanation. On the contrary, the epistemic conception does account for almost all episodes of explanation and can be described as a relation between representations, whereby the explanans transmit information to the explanandum and that this information can come, dependent on context, in the form of any of the available theories of explanation (law-like, unificatory, causal and non-causal). The range of application of the ontic view thus is severely restricted to trivial cases of explanation that come through direct observation of the events involved in an explanation and explanation is to be mostly conceived epistemically.

The ontic conception of explanation is predicated on the proposition that “explanation is a relation between real objects in the world” and hence, according to this approach, scientific explanation cannot take place absent such a premise. Despite the fact that critics have emphasized several drawbacks of the ontic conception, as for example its inability to address the so-called “abstract explanations”, the debate is not settled and the ontic view can claim to capture cases of explanation that are non-abstract, such as causal relations between events. However, by eliminating the distinction between abstract and non-abstract explanations, it follows that ontic and epistemic proposals can no longer contend to capture different cases of explanation and either all are captured by the ontic view or all are captured by the epistemic view. On closer inspection, it turns out that the ontic view deals with events that fall outside the scientists’ scope of observation and that it does not accommodate common instances of explanation such as explanations from false propositions and hence it cannot establish itself as the dominant philosophical stance with respect to explanation. On the contrary, the epistemic conception does account for almost all episodes of explanation and can be described as a relation between representations, whereby the explanans transmit information to the explanandum and that this information can come, dependent on context, in the form of any of the available theories of explanation (law-like, unificatory, causal and non-causal). The range of application of the ontic view thus is severely restricted to trivial cases of explanation that come through direct observation of the events involved in an explanation and explanation is to be mostly conceived epistemically.

Events regarding individuals’ preferences that do not always follow from standard measures such as “value of statistical life” or “quality-adjusted life years” as well as events that occur in some market-related settings which distort the information conveyed by price mechanisms, suggest that a notable chunk of what Hayek called “local knowledge” remains inaccessible by scientific tools and that only the individuals who interact in these local frameworks can have access to it. This casts serious doubt on the epistemic possibility of economic calculation for if events in the local world do not observe the economic models, it is hard to see how central planners can have knowledge on them. Refined modeling strategies and cutting-edge Artificial Intelligence systems do not manage to overcome this hurdle. They may provide huge datasets in a data model, but since not all events represented in the data model are embeddable in known economic models, the knowledge problem is not evaded. Even if a theory ever manages to capture all possible events as they appear in the data model then we are left with a relationship between models (the theoretical model and the data model) with no direct access to the local world and thus the problem of accessibility of local knowledge remains unresolved.

Climate-econometric models postulate that increased CO2 levels cause the earth’s average temperature to increase and that the increases in the average temperature will result in huge costs in GDP. However, this causal relation is at odds with datasets that tell a different story: that the causal relationship is inversed and the variable once thought to be the effect may turn out to be the cause and vice versa. Moreover, when the Earth’s temperature is measured, it appears that changes in the measurement set up can lead to changes in the measurement outcomes thereby skewing the relevant estimates. These results reveal two sources of endogeneity in climate-econometrics: simultaneity whereby X causes Y and Y causes X and also measurement error thus making the relevant causal inferences weak from an epistemological viewpoint. Moreover, since climate-econometric models are used to advice decision-making there are further connotations: first, climate policies can hardly be evaluated through common economic methods and, second, different model-based estimates could lead to different policy-prescriptions dependent on whether endogeneity is taken into account or not. And if different policies are tied to different sets of value judgments, then it can be said that different model-based projections carry different normative implications.

This monograph evaluates public policy responses to the Covid-19 pandemic through a public choice lens. The book compares two prominent, albeit mutually exclusive, theories in social sciences—public interest theory and public choice theory—and explores how their predictions perform within the framework of the Covid-19 pandemic. The chapters present different pandemic policies alongside empirical data in order to draw conclusions about their efficacy, and, in turn, draw conclusions about the veracity of each theory. By the end of the volume, the reader will be able to draw their own conclusions about whether the pandemic policy responses served the public interest, as public interest theory suggests, or the personal interests of the politicians who implemented them, as public choice theory holds.

Different why-questions emerge under different contexts and require different information in order to be addressed. Hence a relevance relation can hardly be invariant across contexts. However, what is indeed common under any possible context is that all explananda require scientific information in order to be explained. So no scientific information is in principle explanatorily irrelevant, it only becomes so under certain contexts. In view of this, scientific thought experiments can offer explanations, should we analyze their representational strategies. Their representations involve empirical as well as hypothetical statements. I call this the “representational mingling” which bears scientific information that can explain events. Buchanan’s thought experiment from constitutional economics is examined to show how mingled representations explain.