 Miriam Solomon’s social empiricism is focused on scientific rationality (Solomon 2001). It, too, involves denying a universal principled distinction among the causes of belief. Solomon draws on contemporary cognitive science literature to argue that what are traditionally called biases are simply among the kinds of “decision vector” that influence belief. They are not necessarily undesirable elements from which science needs to be protected, and can be productive of insight and rational belief. Salience and availability (of data, of measurement technologies), also called cold biases, are decision vectors as much as social ideologies or other motivational factors, “hot biases.” The distinctive feature of Solomon’s social empiricism is her contrast between individual and community rationality. Her (2001) urges the pluralistic view that a community is rational when the theories it accepts are those that have unique empirical successes. Individuals can persist in beliefs that are (from a panoptic perspective) less well supported than others on this view, if the totality of available evidence (or empirical data) is not available to them, or when their favored theory accounts for phenomena not accounted for other theories, even when those may have a greater quantity of empirical successes. What matters to science, however, is that the aggregated judgments of a community be rational. A community is rational when the theories it accepts are those with all or with unique empirical successes. It is collectively irrational to jettison a theory with unique empirical successes. Thus, the community can be rational even when its members are, as judged by traditional epistemic standards, individually irrational. Indeed, individual irrationality can contribute to community rationality in that individuals committed to a theory that accounts for their data keep that data in the range of phenomena any theory accepted by the entire community must eventually explain. In addition to empirical success, Solomon proposes an additional normative criterion. In order to secure appropriate distribution of scientific effort, biases must be appropriately distributed in the community. Solomon proposes a scheme for ascertaining when a distribution is normatively appropriate. Thus, for Solomon, a scientific community is rational when biases are appropriately distributed and it accepts only a theory with all or theories with unique empirical successes as the normative epistemological condition. Rationality accrues only to a community, and not to the individuals constituting the community. As in Zollman’s network models, consensus just is all members of the community assigning the same value (T/F) to a hypothesis or theory.