A Dynamic Solution to the Problem of Logical Omniscience

The traditional possible-worlds model of belief describes agents as ‘logically omniscient’ in the sense that they believe all logical consequences of what they believe, including all logical truths. This is widely considered a problem if we want to reason about the epistemic lives of non-ideal agents who—much like ordinary human beings—are logically competent, but not logically omniscient. A popular strategy for avoiding logical omniscience centers around the use of impossible worlds: worlds that, in one way or another, violate the laws of logic. In this paper, we argue that existing impossible-worlds models of belief fail to describe agents who are both logically non-omniscient and logically competent. To model such agents, we argue, we need to ‘dynamize’ the impossible-worlds framework in a way that allows us to capture not only what agents believe, but also what they are able to infer from what they believe. In light of this diagnosis, we go on to develop the formal details of a dynamic impossible-worlds framework, and show that it successfully models agents who are both logically non-omniscient and logically competent

Economic Cooperation and Social Identity: Towards a Model of Economic Cross-Cultural Integration

In arguing that borders not only should be understood as economic barriers to trade, but also as cultural barriers to interaction, this paper attempts to operationalize a broader interpretation of borders with regards to economic cross-cultural integration. Thus, by formalizing the cultural effects of borders as mental distances (interpreted as social identities), and by using an agentbased simulation model, I analyze how the border affects, and is itself affected by, economic integration. The model is based on two regions separated by a border. Based on expected payoffs and mental distance, agents first choose whether to interact at home or to cross the border. Then, agents choose their action in a simple PD game based on a general disposition of trust, as well as the mental distance should the interaction partner be from across the border. The agent’s mental distance and trust level are then updated according to the agent’s experience of the interaction (positive or negative). The model generally reveals that underlying cultural processes may affect the success of economic integration considerably, and suggests that the success of the integration depends significantly (and in asymmetric ways) on mental distances between regions, on economically vs. culturally motivated behavior, and on collectivistic vs. individualistic characters of the regional cultures.Agent-Based Modeling; Cooperation; Culture; Social Identity; Economic Integration

Bayesianism for Non-ideal Agents

Orthodox Bayesianism is a highly idealized theory of how we ought to live our epistemic lives. One of the most widely discussed idealizations is that of logical omniscience: the assumption that an agent’s degrees of belief must be probabilistically coherent to be rational. It is widely agreed that this assumption is problematic if we want to reason about bounded rationality, logical learning, or other aspects of non-ideal epistemic agency. Yet, we still lack a satisfying way to avoid logical omniscience within a Bayesian framework. Some proposals merely replace logical omniscience with a different logical idealization; others sacrifice all traits of logical competence on the altar of logical non-omniscience. We think a better strategy is available: by enriching the Bayesian framework with tools that allow us to capture what agents can and cannot infer given their limited cognitive resources, we can avoid logical omniscience while retaining the idea that rational degrees of belief are in an important way constrained by the laws of probability. In this paper, we offer a formal implementation of this strategy, show how the resulting framework solves the problem of logical omniscience, and compare it to orthodox Bayesianism as we know it

Artificial Intelligence and Patient-Centered Decision-Making

Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, and procedures cannot be meaningfully understood by human practitioners. When AI systems reach this level of complexity, we can also speak of black-box medicine. In this paper, we want to argue that black-box medicine conflicts with core ideals of patient-centered medicine. In particular, we claim, black-box medicine is not conducive for supporting informed decision-making based on shared information, shared deliberation, and shared mind between practitioner and patient

Hyperintensional semantics: a Fregean approach

In this paper, we present a new semantic framework designed to capture a distinctly cognitive or epistemic notion of meaning akin to Fregean senses. Traditional Carnapian intensions are too coarse-grained for this purpose: they fail to draw semantic distinctions between sentences that, from a Fregean perspective, differ in meaning. This has led some philosophers to introduce more fine-grained hyperintensions that allow us to draw semantic distinctions among co-intensional sentences. But the hyperintensional strategy has a flip-side: it risks drawing semantic distinctions between sentences that, from a Fregean perspective, do not differ in meaning. This is what we call the ‘new problem’ of hyperintensionality to distinguish it from the ‘old problem’ that faced the intensional theory. We show that our semantic framework offers a joint solution to both these problems by virtue of satisfying a version of Frege’s so-called ‘equipollence principle’ for sense individuation. Frege’s principle, we argue, not only captures the semantic intuitions that give rise to the old and the new problem of hyperintensionality, but also points the way to an independently motivated solution to both problems

Higher-order knowledge and sensitivity

It has recently been argued that a sensitivity theory of knowledge cannot account for intuitively appealing instances of higher-order knowledge. In this paper, we argue that it can once careful attention is paid to the methods or processes by which we typically form higher-order beliefs. We base our argument on what we take to be a well-motivated and commonsensical view on how higher-order knowledge is typically acquired, and we show how higher-order knowledge is possible in a sensitivity theory once this view is adopted

Experiences in Multi-domain Management Service Development

The developers of management systems and the management services that operate over them will be faced with increasing complexity as services are developed for the open service market. This paper presents experiences in the development of management services that span several administrative domains and which are therefore representative of the complexities of the open service market. The work described involved the development of TMN based management systems that provided management services in support of multimedia teleservices operating over broadband networks

Bedre mælkekvalitet fra økologiske køer

En stor undersøgelse har vi påvist, at indholdet af frie fedtsyrer (FFA) i økologisk mælk er markant lavere end i konventionel mælk. Årsagen til den bedre mælkekvalitet fra økologiske besætninger kan ikke pålægges malketeknikker, men formodes at skyldes fodringsstrategi eller management