Cognitive Bias and Belief Revision

In this paper we formalise three types of cognitive bias within the framework of belief revision: confirmation bias, framing bias, and anchoring bias. We interpret them generally, as restrictions on the process of iterated revision, and we apply them to three well-known belief revision methods: conditioning, lexicographic revision, and minimal revision. We investigate the reliability of biased belief revision methods in truth tracking. We also run computer simulations to assess the performance of biased belief revision in random scenarios.Comment: In Proceedings TARK 2023, arXiv:2307.0400

The cost of consistency: information economy in Paraconsistent Belief Revision

By Belief Revision it is understood a system that logically explains the rational process of changing beliefs by taking into account a new piece of information. The most influential approach in this field of study, the AGM system, proposed by Alchourrón, Gärdenfors, and Makinson, postulates rationality criteria for different types of belief change. In this paper I shall assess the relationship between those criteria and argue for an opposition between the principles of Information Economy and Consistency. Furthermore, I shall argue that Paraconsistent Belief Revision manages to minimise this friction in the best possible way

Tracking probabilistic truths: a logic for statistical learning

We propose a new model for forming and revising beliefs about unknown probabilities. To go beyond what is known with certainty and represent the agent’s beliefs about probability, we consider a plausibility map, associating to each possible distribution a plausibility ranking. Beliefs are defined as in Belief Revision Theory, in terms of truth in the most plausible worlds (or more generally, truth in all the worlds that are plausible enough). We consider two forms of conditioning or belief update, corresponding to the acquisition of two types of information: (1) learning observable evidence obtained by repeated sampling from the unknown distribution; and (2) learning higher-order information about the distribution. The first changes only the plausibility map (via a ‘plausibilistic’ version of Bayes’ Rule), but leaves the given set of possible distributions essentially unchanged; the second rules out some distributions, thus shrinking the set of possibilities, without changing their plausibility ordering.. We look at stability of beliefs under either of these types of learning, defining two related notions (safe belief and statistical knowledge), as well as a measure of the verisimilitude of a given plausibility model. We prove a number of convergence results, showing how our agent’s beliefs track the true probability after repeated sampling, and how she eventually gains in a sense (statistical) knowledge of that true probability. Finally, we sketch the contours of a dynamic doxastic logic for statistical learning.publishedVersio

Paraconsistent Belief Revision Based on a Formal Consistency Operator (PhD Thesis)

"Paraconsistent Belief Revision Based on a Formal Consistency Operator" delves into Belief Revision—a significant area of research in Formal Philosophy that uses logic to model the ways in which human and artificial agents modify their beliefs in response to new information and examines how these changes can be considered rational. Originally authored as a PhD thesis (previously published in Portuguese), this work provides a novel epistemic interpretation of Paraconsistency through Paraconsistent Belief Revision systems. It explores the concept of paraconsistency from the standpoint of epistemic attitudes of acceptance and rejection. This work challenges the traditional notion that accepting a new belief requires retracting its negation from the current epistemic state. The author contends that such reflexive retraction goes against the principle of informational economy, which is a crucial aspect of rationality in the context of belief change. Consequently, the phenomenon of paraconsistency is further examined from this fresh perspective of belief change, shedding light on the complexities of the Logics of Formal Inconsistency (LFIs). These LFIs provide the foundational logic, offering a comprehensive framework for understanding and implementing paraconsistent principles within belief revision systems. This thesis was supervised by Marcelo Esteban Coniglio and co-supervised by Márcio Moretto Ribeiro, as part of Rafael Rodrigues Testa's doctoral studies at the University of Campinas (Unicamp), Brazil

Revisão de Crenças Paraconsistente baseada em um operador formal de consistência

A Revisão de Crenças estuda como agentes racionais mudam suas crenças ao receberem novas informações. O sistema AGM, trabalho mais influente desta área apresentado por Alchourrón, Gärdenfos e Makinson, postula critérios de racionalidade para os diferentes tipos de mudança de crenças e oferece construções explícitas para tais - a equivalência entre os postulados e operações é chamado de teroema da representação. Trabalhos recentes mostram como o paradigma AGM pode ser compatível com diferentes lógicas não-clássicas, o que é chamado de AGM-compatibilidade - este é o caso da família de lógicas paraconsistentes que analisamos, as Lógicas da Inconsistência Formal (LFIs, da sigla em inglês). A despeito da AGM-compatibilidade, ao se partir de uma nova lógica sua racionalidade subjacente deve ser entendida e sua linguagem deve ser efetivamente usada. Propomos assim novas construções que de fato capturam a intuição presente na LFIs - é o que chamamos de sistema AGMo. Com isso, possibilitamos a estas lógicas uma nova interpretação, na esteira da epistemologia formal. Em uma abordagem alternativa, ao se partir da AGM-compatibilidade os resultados AGM podem ser diretamente aplicados às LFIs - o que chamamos de sistema AGMp. Em ambas abordagens, provamos os respectivos teoremas da representação sempre que necessário

The learning power of belief revision

Belief revision theory aims to describe how one should change one's beliefs when they are contradicted by newly input information. The guiding principle of belief revision theory is to change one's prior beliefs as little as possible in order to maintain consistency with the new information. Learning theory focuses, instead, on learning power: the ability to arrive at true beliefs in a wide range of possible environments. The goal of this paper is to bridge the two approaches by providing a learning theoretic analysis of the learning power of belief revision methods proposed by Spohn, Boutilier, Darwiche and Pearl, and others. The results indicate that learning power depends sharply on details of the methods. Hence, learning power can provide a well-motivated constraint on the design and implementation of concrete belief revision methods.