Epistemic systems are social processes generating judgments of truth and falsity. I outline a mathematical theory of epistemic systems that applies widely. Areas of application include pure science, torture, police forensics, espionage, auditing, clinical medical testing, democratic procedure, and the market economy. I examine torture and police forensics in relative detail. This paper is an exercise in comparative institutional epistemics, which considers how the institutions of an epistemic system influence its performance as measured by such things as error rates and the volume of judgments generated.

Contemporary liberal democracy employs a conception of legitimacy according to which political decisions and institutions must be at least in principle justifiable to all citizens. This conception of legitimacy is difficult to satisfy when citizens are deeply divided at the level of fundamental moral, religious, and philosophical commitments. Many have followed the later Rawls in holding that where a reasonable pluralism of such commitments persists, political justification must eschew appeal to any controversial moral, religious, or philosophical premises. In this way, the Rawlsian account of public political justification involves a politics of omission, where citizens are expected to bracket off their most fundamental commitments and seek justifications that draw only from uncontroversial premises. This politics of omission is necessary, Rawls argues, for political stability. But there is good social epistemic evidence for the view that the politics of omission encourages insularity among like-minded groups, and that this insularity in turn generates extremism. So omission is likely to lead to instability, not stability.

The Ostrogorski paradox and the discursive dilemma are seemingly unrelated paradoxes of aggregation. The former is discussed in traditional social choice theory, while the latter is at the core of the new literature on judgment aggregation. Both paradoxes arise when, in a group, each individual consistently makes a judgment, or expresses a preference, (in the form of yes or no) over specific propositions, and the collective outcome is in some respect inconsistent. While the result is logically inconsistent in the case of the discursive paradox, it is not stable with respect to the level of aggregation in the case of the Ostrogorski paradox. In the following I argue that, despite these differences, the two problems have a similar structure. My conclusion will be twofold: on the one hand, the similarities between the paradoxes support the claim that these problems should be tackled using the same aggregation procedure; on the other hand, applying the same procedure to these paradoxes will help clarify the strengths and weaknesses of the aggregation method itself. More specifically, I will show that an operator defined in artificial intelligence to merge belief bases can deal with both paradoxes.

Most epistemologists of testimony assume that testifying requires that the beliefs to which speakers attest are identical to the beliefs that hearers accept. I argue that this characterization of testimony is misleading. Characterizing testimony in terms of duplicating speaker/hearer belief unduly resticts the variety of beliefs that might be accepted from speaker testimony.