Sven Ove Hansson

One of the major problems in formal epistemology is the difficulty of combining probabilistic and dichotomous (all-or-nothing) beliefs in one and the same formal framework. Based on the properties of actual human belief systems, a set of ten desiderata is proposed for belief system models that contain both probabilistic and full beliefs. Previously proposed combined belief systems are shown to fall short of these desiderata. A proof of concept is provided to show that they can be satisfied in a formal model. In the proposed model, the amnesia problem of probabilistic updating is solved by introducing infinitesimal probabilities as carriers of memories of beliefs that have been given up. In the resulting system of probability revision, contingent propositions can be transferred in both directions between full beliefs and beliefs held to a lower degree, in a way that mirrors real-life acquisitions and losses of full beliefs. The subsystem consisting of full beliefs has a pattern of change that constitutes a credible system of dichotomous belief change.

Primitive dyadic (two-place) probability functions have been proposed both as representations of conditional probability and as tools for modelling iterated probability revision. However, the usefulness of dyadic probability functions for these purposes is limited by the problems involved in updating with propositions whose prior probability is zero. These limitations are investigated and specified. They turn out to be more serious for the use of dyadic probability functions to represent probability revision than for their use as representations of conditional probability.

One of the major problems in formal epistemology is the difficulty of combining probabilistic and dichotomous (all-or-nothing) beliefs in one and the same formal framework. Based on the properties of actual human belief systems, a set of ten desiderata is proposed for belief system models that contain both probabilistic and full beliefs. Previously proposed combined belief systems are shown to fall short of these desiderata. A proof of concept is provided to show that they can be satisfied in a formal model. In the proposed model, the amnesia problem of probabilistic updating is solved by introducing infinitesimal probabilities as carriers of memories of beliefs that have been given up. In the resulting system of probability revision, contingent propositions can be transferred in both directions between full beliefs and beliefs held to a lower degree, in a way that mirrors real-life acquisitions and losses of full beliefs. The subsystem consisting of full beliefs has a pattern of change that constitutes a credible system of dichotomous belief change.

After a review of previous uses of the term “pseudotechnology”, a definition is proposed: “A pseudotechnology is an alleged technology that is irreparably dysfunctional for its intended purpose since it is based on construction principles that cannot be made to work”. The relationship between pseudotechnology and pseudoscience is discussed, and so is the relationship between pseudotechnology and the much weaker concept of technological malfunction. An explanation is offered of why pseudotechnology is much more seldom referred to than pseudoscience: dysfunctional technology usually reveals itself when put to use, whereas dysfunctional science tends to be more difficult to disclose.

The relatively new discipline of risk analysis promises to provide objective guidance in some of the most controversial issues in modern high‐technology societies. Four conditions are discussed that must be satisfied for this promise to be fulfilled. Since none of these conditions is satisfied, risk analysis does not keep its promise. In its attempts to reduce genuinely political issues to technocratic calculations, it neglects many of the factors that should influence decisions on risk acceptance. A list of tentative guidelines is given for numerical decision support that encourages democratic decision‐making instead of trying to evade it.