The traditional sciences often bracket away ambiguity through the imposition of “enabling constraints”—making a set of assumptions and then declaring ceteris paribus. These enabling constraints take the form of uncritically examined presuppositions or “uceps.” Second order science reveals hidden issues, problems and assumptions which all too often escape the attention of the practicing scientist. These hidden values—precisely because they are hidden and not made explicit—can get in the way of the public’s acceptance of a scientific claim. A conflict in understood (...) meaning—between the scientist’s restricted claims and the public’s broader understanding of those same claims can result in cognitive dissonance or the equivalent of the Mori Uncanny Valley. Scientists often react to these differences by claiming “incommensurability” between their restricted claim and the public’s understanding. Second order science, by explicating the effects of variations in values assumed for these uceps and associated impacts on related scientific claims, can often moot these assertions of incommensurability and thereby promote greater scientific understanding. This article explores how second order science can address issues of public doubt regarding the scientific enterprise using examples from medicine, diet and climate science. (shrink)

The traditional sciences have always had trouble with ambiguity. To overcome this barrier, ‘science’ has imposed “enabling constraints”—hidden assumptions which are given the status of ceteris paribus. Such assumptions allow ambiguity to be bracketed away at the expense of transparency. These enabling constraints take the form of uncritically examined presuppositions, which we refer to throughout the article as “uceps.” The meanings of the various uceps are shown via their applicability to the science of climate change. Second order science examines variations (...) in values assumed for these uceps and looks at the resulting impacts on related scientific claims. Second order science reveals hidden issues, problems and assumptions which all too often escape the attention of the practicing scientist. This article lays out initial foundations for second order science, its ontology, methodology, and implications. (shrink)

Syntactic and structural models specify relationships between their constituents but cannot show what outcomes their interaction would produce over time in the world. Simulation consists in iterating the states of a model, so as to produce behaviour over a period of simulated time. Iteration enables us to trace the implications and outcomes of inference rules and other assumptions implemented in the models that make up a theory. We apply this method to experiments which we treat as models of the particular (...) aspects of reality they are designed to investigate. Scientific experiments are constantly designed and re-designed in the context of implementation and use. They mediate between theoretical understanding and the practicalities of engaging with the empirical and social world. In order to model experiments we need to identify and represent features that all experiments have in common. We treat these features as parameters of a general model of experiment so that by varying these parameters different types of experiment can be modelled. (shrink)

In this paper we describe in some detail a formal computer model of inferential discourse based on a belief system. The key issue is that a logical model in a computer, based on rational sets, can usefully model a human situation based on irrational sets. The background of this work is explained elsewhere, as is the issue of rational and irrational sets (Billinge and Addis, in: Magnani and Dossena (eds.), Computing, philosophy and cognition, 2004; Stepney et al., Journey: Non-classical philosophy—socially (...) sensitive computing in journeys non-classical computation: A grand challenge for computing research, 2004). The model is based on the Belief System (Addis and Gooding, Proceedings of the AISB’99 Symposium on Scientific Creativity, 1999) and it provides a mechanism for choosing queries based on a range of belief. We explain how it provides a way to update the belief based on query results, thus modelling others’ experience by inference. We also demonstrate that for the same internal experience, different models can be built for different actors. (shrink)

In this paper we want to examine how the mutual understanding of speakers is reached during a conversation through collaborative processes, and what role is played by abductive inference (in the Peircean sense) in these processes. We do this by bringing together contributions coming from a variety of disciplines, such as logic, philosophy of language and psychology. When speakers are engaged in a conversation, they refer to a supposed common ground: every participant ascribes to the others some knowledge, belief, opinion (...) etc. on which to rely in order to reach mutual understanding. As the conversation unfolds, this common ground is continually corrected and reshaped by the interchanges. An abductive reasoning takes place, in a collaborative setting, in order to build new possible theories about the common ground. In reconstructing this process through the use of a working example, we argue that the integration of a collaborative perspective within the Peircean theory of abduction can help to solve some of the drawbacks that the critics of the latter have outlined, for example its permissivity and non generativity. (shrink)

We argue that abduction does not work in isolation from other inference mechanisms and illustrate this through an inference scheme designed to evaluate multiple hypotheses. We use game theory to relate the abductive system to actions that produce new information. To enable evaluation of the implications of this approach we have implemented the procedures used to calculate the impact of new information in a computer model. Experiments with this model display a number of features of collective belief-revision leading to consensus-formation, (...) such as the influence of bias and prejudice. The scheme of inferential calculations invokes a Peircian concept of ‘belief’ as the propensity to choose a particular course of action. (shrink)