Wlodek Rabinowicz, seniorprofessor i praktisk filosofi vid Lunds universitet och Centennial Professor vid London School of Economics
In this talk I focus on a contrast between aggregation of individual preferences and aggregation of individual value judgments. In the former case the objective is to form a collective preference, while in the latter we are after a collective value judgment. On some normative models of democracy, in popular democracy the goal is to aggregate the voters’ preferences, while in ‘committee democracy’ what is being aggregated are the committee members’ judgments, which quite often concern the value of the alternatives that are being considered.
The case targeted in the talk is the one in which the two aggregation scenarios exhibit close structural similarities: more precisely, in which the individual value judgments that are to be aggregated consist in value rankings of alternatives, while individual preferences are represented as preference rankings. I will suggest that, despite of their formal similarity as rankings, the difference in the nature of individual inputs in two aggregation scenarios has important implications: the kind of procedure that looks fine for aggregation of judgments turns out to be inappropriate for aggregation of preferences. The procedure I have in mind consists in similarity maximization, or – more precisely – in minimization of average distance from individual inputs. It can be shown that, whatever measure of distance is chosen, distance-based procedures violate the (strong) Pareto condition. This is plausible as far as value judgment aggregation goes, but would be unacceptable for preference aggregation.
When applied to judgment aggregation, aggregation procedures might also be approached from the epistemic perspective: questions might be posed concerning their advantages as truth-trackers. From that perspective, what matters is not only the probability of the aggregation outcome being true, but also – in case of distance-based procedures – the expected verisimilitude of the outcome: its expected distance from truth. The epistemic status of distance-based aggregation procedures still remains to be investigated.
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