A Probabilistic Modelling Approach for Rational Belief in Meta-Epistemic Contexts

This work is part of the larger project INTEGRITY. Integrity develops a conceptual frame integrating beliefs with individual (and consensual group) decision making and action based on belief awareness. Comments and criticisms are most welcome via email. The text introduces the conceptual (internalism, externalism), quantitative (probabilism) and logical perspectives (logics for reasoning about probabilities by Fagin, Halpern, Megiddo and MEL by Banerjee, Dubois) for the framework

A Probabilistic Modelling Approach for Rational Belief in Meta-Epistemic Contexts

This work is part of the larger project INTEGRITY. Integrity develops a conceptual frame integrating beliefs with individual (and consensual group) decision making and action based on belief awareness. Comments and criticisms are most welcome via email. Starting with a thorough discussion of the conceptual embedding in existing schools of thought and liter- ature we develop a framework that aims to be empirically adequate yet scalable to epistemic states where an agent might testify to uncertainly believe a propositional formula based on the acceptance that a propositional formula is possible, called accepted truth. The familiarity of human agents with probability assignments make probabilism particularly appealing as quantitative modelling framework for defeasible reasoning that aspires empirical adequacy for gradual belief expressed as credence functions. We employ the inner measure induced by the probability measure, going back to Halmos, interpreted as estimate for uncertainty. Doing so omits generally requiring direct probability assignments testi�ed as strength of belief and uncertainty by a human agent. We provide a logical setting of the two concepts uncertain belief and accepted truth, completely relying on the the formal frameworks of 'Reasoning about Probabilities' developed by Fagin, Halpern and Megiddo and the 'Metaepistemic logic MEL' developed by Banerjee and Dubois. The purport of Probabilistic Uncertainty is a framework allowing with a single quantitative concept (an inner measure induced by a probability measure) expressing two epistemological concepts: possibilities as belief simpliciter called accepted truth, and the agents' credence called uncertain belief for a criterion of evaluation, called rationality. The propositions accepted to be possible form the meta-epistemic context(s) in which the agent can reason and testify uncertain belief or suspend judgement

A Probabilistic Modelling Approach for Rational Belief in Meta-Epistemic Contexts

This work is part of the larger project INTEGRITY. Integrity develops a conceptual frame integrating beliefs with individual (and consensual group) decision making and action based on belief awareness. Comments and criticisms are most welcome via email. Starting with a thorough discussion of the conceptual embedding in existing schools of thought and liter- ature we develop a framework that aims to be empirically adequate yet scalable to epistemic states where an agent might testify to uncertainly believe a propositional formula based on the acceptance that a propositional formula is possible, called accepted truth. The familiarity of human agents with probability assignments make probabilism particularly appealing as quantitative modelling framework for defeasible reasoning that aspires empirical adequacy for gradual belief expressed as credence functions. We employ the inner measure induced by the probability measure, going back to Halmos, interpreted as estimate for uncertainty. Doing so omits generally requiring direct probability assignments testi�ed as strength of belief and uncertainty by a human agent. We provide a logical setting of the two concepts uncertain belief and accepted truth, completely relying on the the formal frameworks of 'Reasoning about Probabilities' developed by Fagin, Halpern and Megiddo and the 'Metaepistemic logic MEL' developed by Banerjee and Dubois. The purport of Probabilistic Uncertainty is a framework allowing with a single quantitative concept (an inner measure induced by a probability measure) expressing two epistemological concepts: possibilities as belief simpliciter called accepted truth, and the agents' credence called uncertain belief for a criterion of evaluation, called rationality. The propositions accepted to be possible form the meta-epistemic context(s) in which the agent can reason and testify uncertain belief or suspend judgement

A Probabilistic Modelling Approach for Rational Belief in Meta-Epistemic Contexts

This work is part of the larger project INTEGRITY. Integrity develops a conceptual frame integrating beliefs with individual (and consensual group) decision making and action based on belief awareness. Comments and criticisms are most welcome via email. The text introduces the conceptual (internalism, externalism), quantitative (probabilism) and logical perspectives (logics for reasoning about probabilities by Fagin, Halpern, Megiddo and MEL by Banerjee, Dubois) for the framework

Neural representation in active inference: using generative models to interact with -- and understand -- the lived world

This paper considers neural representation through the lens of active inference, a normative framework for understanding brain function. It delves into how living organisms employ generative models to minimize the discrepancy between predictions and observations (as scored with variational free energy). The ensuing analysis suggests that the brain learns generative models to navigate the world adaptively, not (or not solely) to understand it. Different living organisms may possess an array of generative models, spanning from those that support action-perception cycles to those that underwrite planning and imagination; namely, from "explicit" models that entail variables for predicting concurrent sensations, like objects, faces, or people - to "action-oriented models" that predict action outcomes. It then elucidates how generative models and belief dynamics might link to neural representation and the implications of different types of generative models for understanding an agent's cognitive capabilities in relation to its ecological niche. The paper concludes with open questions regarding the evolution of generative models and the development of advanced cognitive abilities - and the gradual transition from "pragmatic" to "detached" neural representations. The analysis on offer foregrounds the diverse roles that generative models play in cognitive processes and the evolution of neural representation

Semantic Responsibility

In this paper I attempt to develop a notion of responsibility (semantic responsibility) that is to the notion of belief what epistemic responsibility is to the notion of justification. 'Being semantically responsible' is shown to involve the fulfilment of cognitive duties which allow the agent to engage in the kind of reason-laden discourses which render her beliefs appropriately sensitive to correction. The concept of semantic responsibility suggests that the notion of belief found in contemporary philosophical debates about content implicitly encompasses radically different classes of beliefs. In what follows I make those different types explicit, and sketch some implications for naturalisation projects in semantics and for accounts of the (putative) non-conceptual content of perceptual experiences

Emotional eloquence : the argument from pathos in deliberation

The argument from pathos is one of the three normative modes of persuasion in deliberation. The argument from pathos in deliberation serves six functions. It serves as a perceptual capacity; it is a constituent element of deliberative judgment: it communicates importance: it is a powerful motivator: it serves several aesthetic functions, and it is expressive. An examination of the cognitive structure of the emotions reveals the epistemic potential of the emotions. The success conditions necessary for an emotion to grasp its object yields three epistemic results. The apprehension of particular object of an emotion confers salience: the formal object names a quality that conceptually relates the emotion to a normative principle, and the propositional object provides the connection to semantic matters. The semantic properties of emotional language help structure and determine the sophistication of one’s emotional responses

The Blaming Function of Entity Criminal Liability

Application of the doctrine of entity criminal liability, which had only a thin tort-like rationale at inception, now sometimes instantiates a social practice of blaming institutions. Examining that social practice can ameliorate persistent controversy over entity liability\u27s place in the criminal law. An organization\u27s role in its agent\u27s bad act is often evaluated with a moral slant characteristic of judgments of criminality and with inquiry into whether the institution qua institution contributed to the agent\u27s wrong. Legal process, by lending clarity and authority, enhances the communicative impact, in the form of reputational effects, of blaming an institution for a wrong. Reputational effects can flow through to individuals in ways that reduce probability of future wrongdoing by altering individual preferences and forcing reevaluation and reform of institutional arrangements. Blame and utility are closely connected here: the impulse to blame organizations and the beneficial effects of doing so both appear to depend on the degree of institutional influence on the agent. These insights imply that the doctrine should be tailored, unlike present law, to more fully exploit criminal law\u27s expressive capital by selecting cases according to entity blameworthiness. Barriers to describing the phenomenon of organizational influence and culture prevent discovery of a first-best rule of institutional responsibility. A second-best step would be to enhance the existing doctrine\u27s examination of agent mens rea, to impose fault only if the agent acted primarily with the intent to benefit the firm