We compare different forms of communication in the context of cheap talk sender-receiver games. While previous experiments find evidence supporting the comparative statics prediction that more preference divergence leads to less information transmission, there is also a consistent pattern of overcommunication and exaggeration, not predicted by theory, in which subjects convey more information than predicted in equilibrium. The latter of these findings may be due to the restricted nature of the message space in most experimental cheap talk games, encouraging subjects to engage in exaggeration artificially, rather than allowing it to emerge naturally. We tested this hypothesis with an incentivized lab experiment, and found evidence both phenomena persist with natural language (text-based) communication. Moreover, we probe the consequences of this expanded message space for outcomes, showing that senders benefit more than receivers, but that the most notable effect is that text messages improve efficiency.

The role of natural language communication in economic exchange has been the focus of substantial experimental analysis. Recently, scholars have taken the important step of investigating whether certain types of communication (e.g., promises) might affect decisions differently than other types of communication. This requires classifying natural language messages. Unfortunately, no broadly-accepted method is available for this purpose. We here describe a coordination game for classification of natural language messages. The game is similar in spirit to the “ESP” game that has proven successful for the classification of tens of millions of internet images. We compare our approach to self-classification as well as to classifications based on a standard content analysis. We argue that our classification game has advantages over those alternative approaches, and that these advantages might stem from the salient rewards earned by our game's participants.

Nancy Cartwright's 1983 book How the Laws of Physics Lie argued that theories of physics often make use of idealisations, and that as a result many of these theories were not true. The present paper looks at idealisation in logic and argues that, at least sometimes, the laws of logic fail to be true. That might be taken as a kind of skepticism, but I argue rather that idealisation is a legitimate tool in logic, just as in physics, and recognising this frees logicians up to use false laws where these are helpful.

I argue that Aristotle takes a ‘natural language semantics’ approach to logic, which is consistent with the general attitudes one finds in informal logic today. Although his position is complex, Aristotle emphasizes the intensional rather than the extensional side of argument evaluation. He does not take a truth-functional approach to semantics, but an approach that elucidates the illative mechanism through an understanding of natures. This comes close to what informal logicians insist on. The informal logic movement was, to a very large extent, a Canadian initiative, prominently featuring authors such as Johnson, Blair, Govier, and many others.

Thomas Reid distinguished between natural and artificial language and argued that natural language has a very specific sort of priority over artificial language. This paper critically interprets Reid's discussion, extracts a Reidian explanatory argument for the priority of natural language, and places Reid's thought in the broad tradition of Cartesian linguistics.

Four on-road studies were conducted in the Clifton area of Nottingham, UK, aiming to explore the relationships between driver workload and environmental engagement associated with ‘active’ and ‘passive’ navigation systems. In a between-subjects design, a total of 61 experienced drivers completed two experimental drives comprising the same three routes (with overlapping sections), staged one week apart. Drivers were provided with the navigational support of a commercially-available navigation device (‘satnav’), an informed passenger (a stranger with expert route knowledge), a collaborative passenger (an individual with whom they had a close, personal relationship) or a novel interface employing a conversational natural language ‘NAV-NLI’ (Navigation Natural Language Interface). The NAV-NLI was created by curating linguistic intercourse extracted from the earlier conditions and delivering this using a ‘Wizard-of-Oz’ technique. This term describes a research experiment in which subjects interact with a computer system that they believe to be autonomous, but which is actually being operated or partially operated by an unseen human being. The different navigational methods were notable for their varying interactivity and the preponderance of environmental landmark information within route directions. Participants experienced the same guidance on each of the two drives to explore changes in reported and observed behaviour. Results show that participants who were more active in the navigation task (collaborative passenger or NAV-NLI) demonstrated enhanced environmental engagement (landmark recognition, route-learning and survey knowledge) allowing them to reconstruct the route more accurately post-drive, compared to drivers using more passive forms of navigational support (SatNav or informed passenger). Workload measures (the Tactile Detection Task (TDT) and the National Aeronautical and Space Administration Task Load Index (NASA-TLX)) indicated no differences between conditions, although SatNav users and collaborative passenger drivers reported lower workload during their second drive. The research demonstrates clear benefits and potential for a navigation system employing two-way conversational language to deliver instructions. This could help support a long-term perspective in the development of spatial knowledge, enabling drivers to become less reliant on the technology and begin to re-establish associations between viewing an environmental feature and the related navigational manoeuvre.

This paper investigates formal logics for reasoning about determinacy and independence. Propositional Dependence Logic ${\cal D}$ and Propositional Independence Logic ${\cal I}$ are recently developed logical systems, based on team semantics, that provide a framework for such reasoning tasks. We introduce two new logics ${{\cal L}_D}$ and ${{\cal L}_{\,I\,}}$, based on Kripke semantics, and propose them as alternatives for ${\cal D}$ and ${\cal I}$, respectively. We analyse the relative expressive powers of these four logics and discuss the way these systems relate to natural language. We argue that ${{\cal L}_D}$ and ${{\cal L}_{\,I\,}}$ naturally resolve a range of interpretational problems that arise in ${\cal D}$ and ${\cal I}$. We also obtain sound and complete axiomatizations for ${{\cal L}_D}$ and ${{\cal L}_{\,I\,}}$.

This paper presents an extended version of the Quantified Argument Calculus (Quarc). Quarc is a logic comparable to the first-order predicate calculus. It employs several nonstandard syntactic and semantic devices, which bring it closer to natural language in several respects. Most notably, quantifiers in this logic are attached to one-place predicates; the resulting quantified constructions are then allowed to occupy the argument places of predicates. The version presented here is capable of straightforwardly translating natural-language sentences involving defining clauses. A three-valued, model-theoretic semantics for Quarc is presented. Interpretations in this semantics are not equipped with domains of quantification: they are just interpretation functions. This reflects the analysis of natural-language quantification on which Quarc is based. A proof system is presented, and a completeness result is obtained. The logic presented here is capable of straightforward translation of the classical first-order predicate calculus, the translation preserving truth values as well as entailment. The first-order predicate calculus and its devices of quantification can be seen as resulting from Quarc on certain semantic and syntactic restrictions, akin to simplifying assumptions. An analogous, straightforward translation of Quarc into the first-order predicate calculus is impossible.

This paper explores the contributions of Answer Set Programming (ASP) to the study of an established theory from the field of Second Language Acquisition: Input Processing. The theory describes default strategies that learners of a second language use in extracting meaning out of a text based on their knowledge of the second language and their background knowledge about the world. We formalized this theory in ASP, and as a result we were able to determine opportunities for refining its natural language description, as well as directions for future theory development. We applied our model to automating the prediction of how learners of English would interpret sentences containing the passive voice. We present a system, PIas, that uses these predictions to assist language instructors in designing teaching materials.

For every problem mentioned by crew members in an aircraft log book, an associated repair action note is entered in the same log book by a maintenance technician after the problem has been handled. These hand-written repair notes, subsequently transcribed into a database, give an account of the actions undertaken by the technicians to fix the problems. Written in a free-text format with peculiar linguistic characteristics, including many arbitrary abbreviations and missing auxiliaries, they contain valuable information that can be used for decision support methods such as case-based reasoning. We use natural language techniques in our information extraction system to analyze the structure and contents of these notes in order to determine the pieces of equipment involved in a repair and what was done to them. Lexical information and domain knowledge are extracted from an electronic version of the illustrated parts catalog for the particular airplane, and are used at different stages of the process, from the morpholexical analysis to the evaluation of the semantic expression generated by the syntactical analyzer.

L’objectif général de cette recherche a consisté à apprécier le degré de concordance entre un système de classification imposé (le DSM III) et un système de classification naturel ou intuitif. Pour ce faire, on a mis en correspondance des observations en langage naturel (diagnostic en clair) fournies par 158 psychiatres différents sur 999 malades psychiatriques avec les diagnostics établis selon les critères du DSM III. Dans cet échantillon, 823 malades ont relevé d'un traitement antidépresseur; ils ont été répartis en 7 groupes de dépression (majeure simple isolée, mélancolique, bipolaire, atypique, atypique avec symptômes obsessionnels, avec alcoolisme, avec symptômes obsessionnels et alcoolisme) et un groupe répondant au diagnostic de schizophrénie. Les observations en langage naturel ont été traitées de façon entièrement automatique par le programme SPAD. Ont été analysées successivement la fréquence de certains mots clés, les formes graphiques (lexicales) caractéristiques et les phrases modales prononcées par les psychiatres à propos de chaque groupe de malades. Ces données ont été synthétisées dans l'analyse factorielle lexicale qui fournit une représentation simultanée des formes graphiques et des groupes diagnostiques. Les résultats font apparaître une concordance satisfaisante entre les diagnostics du DSM III et les observations en langage naturel à l'exception cependant de la dépression majeure et de la mélancolie qui se trouvent quasiment confondues dans les descriptions. Ces résultats ont été discutés en termes d'interactions entre systèmes diagnostiques imposés et systèmes diagnostiques spontanés.