Individual Choice Behavior: A Theoretical Analysis

By R. Luce. 1979

Subfields: Economics & Operations Research

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Citations in the corpus

Direct Preference Optimization: Your Language Model is Secretly a Reward Model (2023, methodology): “6 uses the Bradley-Terry model, we can similarly derive expressions under the more general Plackett-Luce models [30, 21], shown in Appendix A.3.”
Deep Reinforcement Learning from Human Preferences (2017, methodology): “This follows the Bradley-Terry model (Bradley and Terry, 1952) for estimating score functions from pairwise preferences, and is the specialization of the Luce-Shephard choice rule (Luce, 2005; Shepard, 1957) to preferences over trajectory segments.”
Generalized Results for the Existence and Consistency of the MLE in the Bradley-Terry-Luce Model (2021, methodology): “Among them, the renown Bradley-Terry-Luce (BTL) model (Bradley & Terry, 1952; Luce, 2012) has experienced a surge in popularity and use, due to its high interpretability and expressive power.”
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables (2016, methodology): “To motivate the construction of Concrete random variables, we review a method for sampling from discrete distributions called the Gumbel-Max trick (Luce, 1959; Yellott, 1977; Papandreou & Yuille, 2011; Hazan & Jaakkola, 2012; Maddison et al., 2014).”
Generalization Through the Recurrent Interaction of Episodic Memories (2012, methodology): “Choice probabilities were then determined by applying the Luce choice rule (equivalent to the softmax function; Luce, 1959) to the activity values of the relevant units in the response layer (e.g., B and D units in the transitivity task): P 1 ey1/ ey1/ ey2/ , where P(1) is the probability of…”
Learning Deep Generative Models of Graphs (2018, neutral): “In particular, in the learning to rank literature there is an extensive body of work on learning distributions over permutations, for example the Mallows model (Mallows, 1957) and the Plackett-Luce model (Plackett, 1975; Luce, 1959), which may be used here.”
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations (2019, methodology): “This form of loss function follows from the classic Bradley-Terry and Luce-Shephard models of preferences (Bradley & Terry, 1952; Luce, 2012) and has been shown to be effective for training neural networks from preferences (Christiano et al., 2017; Ibarz et al., 2018).”
Addressing Score Bias and Differential Item Functioning Due to Individual Differences in Response Style (2009, methodology): “Nominal Response Model The NRM (Bock, 1972, 1997) is a well-known IRT model rooted in the classical models of choice behavior presented by Bradley and Terry (1952) and Luce (1959).”
Safe models for risky decisions (2017, methodology): “This assumption is formalized by the softmax choice rule, also known as the ratio-of-strength choice rule (Luce, 1959):”
Accounting for Individual Differences in Bradley-Terry Models by Means of Recursive Partitioning (2011, methodology): “In a measurement-theoretic approach, Luce (1959) showed that the model can be derived from a simple axiom for the choice probabilities.”
Random Choice as Behavioral Optimization (2014, background): “The Luce rule (Luce (1959)) is a well-known behavioral optimization model that retains the simplicity of a deterministic theory.”
Algorithms for multi-armed bandit problems (2014, methodology): “Boltzmann Exploration (Softmax) Softmax methods are based on Luce’s axiom of choice (1959) and pick each arm with a probability that is proportional to its average reward.”
Adoption of renewable heating systems: An empirical test of the diffusion of innovation theory (2017, background): “All households, however, are assumed to choose according to a random utility approach, which is consistent with the maintained assumption of rational choice behaviour [48], [49].”
Costly enforcement of property rights and the Coase theorem (2008, unknown): “6Clark and Riis [9] axiomatize the case f(e) = e, and show in an n player setting that the assumption of ' entering multiplicatively in the CSF is logically equivalent to Luces [29] Choice Axiom.”
Proxemic behaviors as predictors of aggression towards Black (but not White) males in an immersive virtual environment (2009, methodology): “Luce’s Choice Model (Luce, 1959) was used to derive scale values for each zone across the 72 participants.”
Cupid’s Invisible Hand: Social Surplus and Identification in Matching Models (2015, unknown): “In his famous discussion of Luce (1959), Debreu (1960) showed that given IIA, introducing irrelevant attributes would change choice probabilities.”
Identifying and Quantifying Multisensory Integration: A Tutorial Review (2014, methodology): “FLMP utilizes a formula similar to Luce’s choice rule (Luce 1959) to describe how auditory and visual cues are combined.”