arXiv subjects

Mihnea C. Moldoveanu

Publications and source records attributed to Mihnea C. Moldoveanu.

6 recordsLinked to original sources

arXiv2026

Epistemic Networks, Collective Misperception, and the Manipulation of Social Knowledge

We investigate the structure of interactive beliefs in networks: the epistemic state in which agents hold, revise, and act on their models of the epistemic states of other agents. What a group believes depends on what each member agent takes the others to believe, and on what each takes the others to believe about still others. We posit that the proper unit of social-epistemic analysis is not the individual belief but the tensor of mutual attribution, the array that records what every agent takes every agent to believe. We separate three layers of this object: what is privately held, what is publicly expressed, and what is to be believed by others. Social belief evolves by contraction of the tensor against a signed matrix of epistemic influence. Collective misperception decomposes exactly into a component that observation dissolves and a component that observation cannot touch. Social conformity amplifies pluralistic ignorance without generating it. The stable forms of collective epistemic consensus, polarization, entrenched misperception, and instability are spectral regimes of a single operator. Higher-order belief reduces to walks in the epistemic network, under a stated assumption of cognitive consistency whose boundary we mark precisely.

cs.SI↗

arXiv2026

Interactionalism: Re-Designing Higher Learning for the Large Language Agent Era

We introduce Interactionalism as a new set of guiding principles and heuristics for the design and architecture of learning now available due to Generative AI (GenAI) platforms. Specifically, we articulate interactional intelligence as a net new skill set that is increasingly important when core cognitive tasks are automatable and augmentable by GenAI functions. We break down these skills into core sets of meta-cognitive and meta-emotional components and show how working with Large Language Model (LLM)-based agents can be proactively used to help develop learners. Interactionalism is not advanced as a theory of learning; but as a blueprint for the practice of learning - in coordination with GenAI.

cs.HC↗

arXiv2026

Trust and Its Betrayal under Three Representational Strategies

Trust is a propositional attitude of a distinctive kind: to trust is to rely on another under conditions where reliance could be disappointed, and the disappointment of trust---betrayal---differs qualitatively from the disappointment of a prediction. We treat trust as a \emph{subjunctive} epistemic state: $A$ trusts $B$'s competence when $A$ believes that \emph{were $P$ true, $B$ would know it}, and $B$'s integrity when $A$ believes that \emph{were $B$ to know $P$, he would disclose it to $A$}. We develop three representations of this state---as lexicographic \emph{assumption} as \emph{ordinal closeness} in a Lewis--Stalnaker sphere system , and as \emph{strong belief} in a conditional probability system and for each we ask whether the Brandenburger--Keisler impossibility on common belief survives when the assumption of rationality is replaced by an assumption of trustworthiness. The three representations agree that every \emph{finite} depth of common trust is realizable while the \emph{completed} common-trust fixed point is the locus of difficulty, but they differ sharply in \emph{how} the difficulty manifests, and---our organizing finding---in how each survives a concrete betrayal. W show that the same betrayal refutes an agent's \emph{level ordering} under the lexicographic representation, contaminates her \emph{closeness ordering} in proportion to the betrayer's deliberateness under the ordinal representation, and merely \emph{shifts her operative conditioning hypothesis} while leaving her belief structure coherent under the strong-belief representation.

cs.AI↗

arXiv2026

Adversarial Social Epistemology for Assemblies of Humans and Large Language Models

We outline an adversarial social epistemology (ASE) for densely interactive communicative landscapes in which public assertions are scaffolded by chains of testimony, inference, institutional certification, and tacit trust. In such landscapes, agents have incentives and affordances to distort, color, omit, fabricate, or strategically under-specify information for private, reputational, rhetorical, or material gains. We argue that these phenomena are not adequately captured by familiar descriptions of epistemic bubbles, echo chambers, or misinformation diffusion. What requires explanation is how communicative agents exploit the commitments and entitlements that normally make scaffolded assertions trustworthy. We provide language that delivers the requisite analysis, outline mechanisms that subvert trust in scaffolded public communications, and outline machinery for auditing and redressing trust breaches arising from subverting the auditability of inferential chains, drawing on epistemic networks, enriched with an inferentialist semantics for interpreting assertions.

cs.AI↗

arXiv2021

Knowledge, Trust, Security and Covertness In Massively Distributed Social Platforms: An Epistemic Networks Approach

Social networks seeded, crystallized and structured by large-scale social platforms like Facebook, LinkedIn, Slack, Twitter, WhatsApp, Instagram and WeChat exhibit complicated epistemic structures and dynamics that arise from the nature of interactive knowledge that users/participants possess, share and shape. What users know about what other users know, about what other users know they know, and so forth, plays an important role in channeling user behavior on the platform as well as the structuring and dynamics of the social network the platform engenders. We use a novel approach to characterize knowledge states and flows in social networks, based on epistemic networks, or epinets, to study the relationship between the structure of platforms and the structure of social networks that form on them.

cs.SI↗

arXiv2021

Epistemic Networks

We show how important phenomena in social networks like coordination, trust and the communication of unsubstantiated information (gossip) can be modelled and understood using epistemic networks or epinets: directed graphs comprising networked agents and the key facts, statements or other kinds of propositional beliefs relevant to their actions. We use epinets to sharpen the explanatory and reach of social network analysis to situations problematic to both network-structural approaches and epistemic game theory.

cs.SI↗