Failures of Contingent Thinking
- URL: http://arxiv.org/abs/2007.07703v3
- Date: Mon, 3 Jul 2023 12:15:09 GMT
- Title: Failures of Contingent Thinking
- Authors: Evan Piermont and Peio Zuazo-Garin
- Abstract summary: We show that a wide range of behavior observed in experimental settings manifest as failures to perceive implications.
We show that an agent's account of implication identifies a subjective state-space that underlies her behavior.
- Score: 2.055949720959582
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we provide a theoretical framework to analyze an agent who
misinterprets or misperceives the true decision problem she faces. We show that
a wide range of behavior observed in experimental settings manifest as failures
to perceive implications, in other words, to properly account for the logical
relationships between various payoff relevant contingencies. We present a
behavioral definition of perceived implication, thereby providing an
elicitation technique, and show that an agent's account of implication
identifies a subjective state-space that underlies her behavior. By analyzing
this state-space, we characterize distinct benchmarks of logical sophistication
that drive empirical phenomena. We disentangle static and dynamic rationality.
Thus, our framework delivers both a methodology for assessing an agent's level
of contingent thinking and a strategy for identifying her beliefs in the
absence full rationality.
Related papers
- Disentangling Deception and Hallucination Failures in LLMs [7.906722750233381]
We propose an internal, mechanism-oriented perspective that separates Knowledge Existence from Behavior Expression.<n> hallucination and deception correspond to two qualitatively different failure modes that may appear similar at the output level but differ in their underlying mechanisms.<n>We analyze these failure modes through representation separability, sparse interpretability, and inference-time activation steering.
arXiv Detail & Related papers (2026-02-16T07:36:49Z) - Epistemic Traps: Rational Misalignment Driven by Model Misspecification [36.837352790122544]
We show that safety is a discrete phase determined by the agent's priors rather than a continuous function of reward magnitude.<n>This establishes Subjective Model Engineering as a necessary condition for robust alignment.
arXiv Detail & Related papers (2026-01-27T09:21:36Z) - Analyzing Reasoning Consistency in Large Multimodal Models under Cross-Modal Conflicts [74.47786985522762]
We identify a critical failure mode termed textual inertia, where models tend to blindly adhere to the erroneous text while neglecting conflicting visual evidence.<n>We propose the LogicGraph Perturbation Protocol that structurally injects perturbations into the reasoning chains of diverse LMMs.<n>Results reveal that models successfully self-correct in less than 10% of cases and predominantly succumb to blind textual error propagation.
arXiv Detail & Related papers (2026-01-07T16:39:34Z) - Exploring Syntropic Frameworks in AI Alignment: A Philosophical Investigation [0.0]
I argue that AI alignment should be reconceived as architecting syntropic, reasons-responsive agents through process-based, multi-agent, developmental mechanisms.<n>I articulate the specification trap'' argument demonstrating why content-based value specification appears structurally unstable.<n>I propose syntropy as an information-theoretic framework for understanding multi-agent alignment dynamics.
arXiv Detail & Related papers (2025-11-19T23:31:29Z) - DeceptionBench: A Comprehensive Benchmark for AI Deception Behaviors in Real-world Scenarios [57.327907850766785]
characterization of deception across realistic real-world scenarios remains underexplored.<n>We establish DeceptionBench, the first benchmark that systematically evaluates how deceptive tendencies manifest across different domains.<n>On the intrinsic dimension, we explore whether models exhibit self-interested egoistic tendencies or sycophantic behaviors that prioritize user appeasement.<n>We incorporate sustained multi-turn interaction loops to construct a more realistic simulation of real-world feedback dynamics.
arXiv Detail & Related papers (2025-10-17T10:14:26Z) - LLMs as Strategic Agents: Beliefs, Best Response Behavior, and Emergent Heuristics [0.0]
Large Language Models (LLMs) are increasingly applied to domains that require reasoning about other agents' behavior.<n>We show that current frontier models exhibit belief-coherent best-response behavior at targeted reasoning memorization.<n>Under increasing complexity, explicit recursion gives way to internally generated rules of choice that are stable, model-specific, and distinct from known human biases.
arXiv Detail & Related papers (2025-10-12T21:40:29Z) - A Comment On "The Illusion of Thinking": Reframing the Reasoning Cliff as an Agentic Gap [0.39073867995073247]
We argue that the observed failure is not evidence of a fundamental cognitive boundary, but rather a predictable outcome of system-level constraints.<n>A model, initially declaring a puzzle impossible when confined to text-only generation, now employs agentic tools to not only solve it but also master variations of complexity far beyond the reasoning cliff it previously failed to surmount.
arXiv Detail & Related papers (2025-06-23T17:14:21Z) - When Counterfactual Reasoning Fails: Chaos and Real-World Complexity [1.9223856107206057]
We investigate the limitations of counterfactual reasoning within the framework of Structural Causal Models.
We find that realistic assumptions, such as low degrees of model uncertainty or chaotic dynamics, can result in counterintuitive outcomes.
This work urges caution when applying counterfactual reasoning in settings characterized by chaos and uncertainty.
arXiv Detail & Related papers (2025-03-31T08:14:51Z) - Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Models [76.6028674686018]
We introduce thought-tracing, an inference-time reasoning algorithm to trace the mental states of agents.
Our algorithm is modeled after the Bayesian theory-of-mind framework.
We evaluate thought-tracing on diverse theory-of-mind benchmarks, demonstrating significant performance improvements.
arXiv Detail & Related papers (2025-02-17T15:08:50Z) - Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach [61.04606493712002]
Susceptibility to misinformation describes the degree of belief in unverifiable claims that is not observable.
Existing susceptibility studies heavily rely on self-reported beliefs.
We propose a computational approach to model users' latent susceptibility levels.
arXiv Detail & Related papers (2023-11-16T07:22:56Z) - A Semantic Approach to Decidability in Epistemic Planning (Extended
Version) [72.77805489645604]
We use a novel semantic approach to achieve decidability.
Specifically, we augment the logic of knowledge S5$_n$ and with an interaction axiom called (knowledge) commutativity.
We prove that our framework admits a finitary non-fixpoint characterization of common knowledge, which is of independent interest.
arXiv Detail & Related papers (2023-07-28T11:26:26Z) - Towards Trustworthy Explanation: On Causal Rationalization [9.48539398357156]
We propose a new model of rationalization based on two causal desiderata, non-spuriousness and efficiency.
The superior performance of the proposed causal rationalization is demonstrated on real-world review and medical datasets.
arXiv Detail & Related papers (2023-06-25T03:34:06Z) - Understanding the Unforeseen via the Intentional Stance [0.0]
We present an architecture and system for understanding novel behaviors of an observed agent.
The two main features of our approach are the adoption of Dennett's intentional stance and analogical reasoning.
arXiv Detail & Related papers (2022-11-01T14:14:14Z) - Logical Satisfiability of Counterfactuals for Faithful Explanations in
NLI [60.142926537264714]
We introduce the methodology of Faithfulness-through-Counterfactuals.
It generates a counterfactual hypothesis based on the logical predicates expressed in the explanation.
It then evaluates if the model's prediction on the counterfactual is consistent with that expressed logic.
arXiv Detail & Related papers (2022-05-25T03:40:59Z) - Empirical Estimates on Hand Manipulation are Recoverable: A Step Towards
Individualized and Explainable Robotic Support in Everyday Activities [80.37857025201036]
Key challenge for robotic systems is to figure out the behavior of another agent.
Processing correct inferences is especially challenging when (confounding) factors are not controlled experimentally.
We propose equipping robots with the necessary tools to conduct observational studies on people.
arXiv Detail & Related papers (2022-01-27T22:15:56Z) - Active Inference in Robotics and Artificial Agents: Survey and
Challenges [51.29077770446286]
We review the state-of-the-art theory and implementations of active inference for state-estimation, control, planning and learning.
We showcase relevant experiments that illustrate its potential in terms of adaptation, generalization and robustness.
arXiv Detail & Related papers (2021-12-03T12:10:26Z) - Nested Counterfactual Identification from Arbitrary Surrogate
Experiments [95.48089725859298]
We study the identification of nested counterfactuals from an arbitrary combination of observations and experiments.
Specifically, we prove the counterfactual unnesting theorem (CUT), which allows one to map arbitrary nested counterfactuals to unnested ones.
arXiv Detail & Related papers (2021-07-07T12:51:04Z) - Unscrambling the omelette of causation and inference: The framework of
causal-inferential theories [0.0]
We introduce the notion of a causal-inferential theory using a process-theoretic formalism.
Recasting the notions of operational and realist theories in this mold clarifies what a realist account of an experiment offers beyond an operational account.
We argue that if one can identify axioms for a realist causal-inferential theory such that the notions of causation and inference can differ from their conventional (classical) interpretations, then one has the means of defining an intrinsically quantum notion of realism.
arXiv Detail & Related papers (2020-09-07T17:58:22Z) - Cognitive Argumentation and the Suppression Task [1.027974860479791]
This paper addresses the challenge of modeling human reasoning, within a new framework called Cognitive Argumentation.
The framework relies on cognitive principles, based on empirical and theoretical work in Cognitive Science, to adapt a general and abstract framework of computational argumentation from AI.
We argue that Cognitive Argumentation provides a coherent and cognitively adequate model for human conditional reasoning.
arXiv Detail & Related papers (2020-02-24T10:30:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.