Information Processing and Constraint Satisfaction in Wason’s Selection Task

In Wason’s Selection Task, subjects: (i) process information from the instructions and build a mental representation of the problem, then: (ii) select a course of action to solve the problem,under the constraints imposed by the instructions. We analyze both aspects as part of a constraint satisfaction problem without assuming Wason’s ‘logical’ solution to be the correct one. We show that outcome of step (i) may induce mutually inconsistent constraints, causing subjects to select at step (ii) solutions that violate some of them. Our analysis explains why inconsistent constraints are less likely disrupt non-abstract (or “thematic”) versions of the tasks, but unlike Bayesians does not posit different mechanisms in abstract and thematic variants. We then assess the logicality of the task, and conclude on cognitive tasks as coordination problem

A "Game of Like" : Online Social Network Sharing As Strategic Interaction

We argue that behavioral science models of online content-sharing overlook the role of strategic interactions between users. Borrowing from accuracy-nudges studies decision-theoretic models, we propose a basic game model and explore special cases with idealized parameter settings to identify refinements necessary to capture real-world online social network behavior. Anticipating those refinements, we sketch a strategic analysis of content amplification and draw a connection between Keynes's beauty contest analogy and recent social-epistemological work on echo chambers. We conclude on the model's prospects from analytical and empirical perspectives.Comment: In Proceedings TARK 2023, arXiv:2307.0400

Taking Problem-Solving Seriously

Instructions in Wason’s Selection Task underdetermine empirical subjects’ representation of the underlying problem, and its admissible solutions. We model the Selection Task as an (ambiguous) interrogative learning problem, and reasoning to solutions as: (a) selection of a representation of the problem; and: (b) strategic planning from that representation. We argue that recovering Wason’s ‘normative’ selection is possible only if both stages are constrained further than they are by Wason’s formulation. We conclude comparing our model with other explanatory models, w.r.t. to empirical adequacy, and modeling of bounded rationality

The interrogative model of inquiry and inquiry learning

Hakkarainen and Sintonen (2002) praise the descriptive adequacy of Hintikka's Interrogative Model of Inquiry (imi) to describe children's practices in an inquiry-based learning context. They further propose to use the imi as a starting point for developing new pedagogical methods and designing new didactic tools. We assess this proposal in the light of the formal results that in the imi characterize interrogative learning strategies. We nd that these results actually reveal a deep methodological issue for inquiry-based learning, namely that educators cannot guarantee that learners will successfully acquire a content, without limiting learner's autonomy, and that a trade-off between success and autonomy is unavoidable. As a by-product of our argument, we obtain a logical characterization of serendipity

Semantic Games for Algorithmic Players

We describe a class of semantic extensive entailment game (eeg) with algorithmic players, related to game-theoretic semantics (gts), and generalized to classical first-order semantic entailment. Players have preferences for parsimonious spending of computational resources, and compute partial strategies, under qualitative uncertainty about future histories. We prove the existence of local preferences for moves, and strategic fixpoints, that allow to map eeg game-tree to the building rules and closure rules of Smullyan's semantic tableaux (st). We also exhibit a strategy profile that solves the fixpoint selection problem, and can be mapped to systematic constructions of semantic trees, yielding a completeness result by translation. We conclude on possible generalizations of our games

The Best of All PossibleWorlds: Where Interrogative Games Meet Research Agendas

Erik J. Olsson and David Westlund have recently argued that the standard belief revision representation of an epistemic state is defective. In order to adequately model an epistemic state one needs, in addition to a belief set (or corpus, or theory, i.e. a set closed under deduction) K and (say) an entrenchment relation E, a research agenda A, i.e. a set of questions satisfying certain corpus-relative preconditions (hence called K-questions) the agent would like to have answers to. Informally, the preconditions guarantee that the set of potential answers represent a partition of possible expansions of K, hence are equivalent to well-behaved sets of alternative hypotheses

The Fall of Reichenbach

Reichenbach’s constraint is the methodological imperative formulated by Reichenbach in the following passage: “If we want to construct a philosophy of science, we have to distinguish carefully between two kinds of context in which scientific theories may be considered. The context of discovery is to be separated from the context of justification; the former belongs to the psychology of scientific discovery, the latter alone is to be the object of the logic of science.” (Reichenbach, 1938, p. 36.) Reichenbach’s constraint is usually understood as barring epistemological models from attempting rational reconstructions of discovery processes. This paper shows that Reichenbach’s constraint also bars epistemological models from capturing inquiry processes as genuine learning processes

A Computational Model of Wason's Selection Task

We apply an algorithmic learning model of inquiry to model reasoning carried by experimental subjects in Wason's _Selection Task_ that represents reasoning in the task as computation of a decision tree that supervenes on (partial) semantic representations. We argue that the resulting model improves on previous probabilistic (Bayesian) and pragmatic (Relevance theory) models of the task. In particular, it suggests that subjects' selection could in fact be guided by sophisticated patterns of argumentative reasoning