Self-progressive choice models
Consider a population of heterogenous agents whose choice behaviors are partially comparable according to given primitive orderings. The set of choice functions admissible in the population specifies a choice model. A choice model is self-progressive if each aggregate choice behavior consistent with the model is uniquely representable as a probability distribution over admissible choice functions that are comparable. We establish an equivalence between self-progressive choice models and well-known algebraic structures called lattices. This equivalence provides for a precise recipe to restrict or extend any choice model for unique orderly representation. To prove out, we characterize the minimal self-progressive extension of rational choice functions, explaining why agents might exhibit choice overload. We provide necessary and sufficient conditions for the identification of a (unique) primitive ordering that renders our choice overload representation to a choice model.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Generating Correct Answers for Progressive Matrices Intelligence Tests
Raven's Progressive Matrices are multiple-choice intelligence tests, where one tries to complete the missing location in a $3\times 3$ grid of abstract images. Previous attempts to address this test have focused solely o…
Multiple-choiceInclusion-of-Thoughts: Mitigating Preference Instability via Purifying the Decision Space
Multiple-choice questions (MCQs) are widely used to evaluate large language models (LLMs). However, LLMs remain vulnerable to the presence of plausible distractors. This often diverts attention toward irrelevant choices,…
One Self-Configurable Model to Solve Many Abstract Visual Reasoning Problems
Abstract Visual Reasoning (AVR) comprises a wide selection of various problems similar to those used in human IQ tests. Recent years have brought dynamic progress in solving particular AVR tasks, however, in the contempo…
Odd One OutTransfer LearningVisual ReasoningReverse Multi-Choice Dialogue Commonsense Inference with Graph-of-Thought
With the proliferation of dialogic data across the Internet, the Dialogue Commonsense Multi-choice Question Answering (DC-MCQ) task has emerged as a response to the challenge of comprehending user queries and intentions.…
Question AnsweringLearning Abstract Visual Reasoning via Task Decomposition: A Case Study in Raven Progressive Matrices
Learning to perform abstract reasoning often requires decomposing the task in question into intermediate subgoals that are not specified upfront, but need to be autonomously devised by the learner. In Raven Progressive M…
Visual Reasoning