Magnitude-sensitive preference formation`
Authors: Nisheeth Srivastava, Ed Vul, Paul R. Schrater
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show how this theory yields classical and anomalous supply-demand curves and predicts choices for a large panel of risky lotteries. Accurate replications of such phenomena without recourse to utility functions suggest that the theory proposed is both psychologically realistic and econometrically viable. and Finally, we ask: how well can our model fit the choice behavior of real humans making economic decisions? The simplest economic setup to perform such a test is in predicting choices between risky lotteries, since the human prediction is always treated as a stochastic choice preference that maps directly onto the output of our model. We use a basic expected utility calculation, where the desirability for lottery options is computed as in Equation 8. For a choice between a risky lottery x1 = {mh, ml} and a safe choice x2 = ms, with a win probability q and where mh > ms > ml, the value calculation for the risky option will take the form, ... Using Equation 12, where ψ is the c.d.f of a Pareto distribution, (θ = {2.9, 0.1, 1} fitted empirically), assuming that subjects distort perceived probabilities [18] via an inverse-S shaped weighting function4, and using an ϵ-random utility maximization decision rule5, we obtain choice predictions that match human performance (see Figure 4) on a large and comprehensive panel of risky choice experiments obtained from [19] to within statistical confidence6. |
| Researcher Affiliation | Academia | Nisheeth Srivastava Department of Psychology University of San Diego La Jolla, CA 92093 nisheeths@gmail.com Edward Vul Department of Psychology University of San Diego La Jolla, CA 92093 edwardvul@gmail.com Paul R Schrater Dept of Psychology University of Minnesota Minneapolis, MN, 55455 schrater@umn.edu |
| Pseudocode | No | The paper presents mathematical equations and conceptual diagrams, but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about making its source code publicly available or provide links to a code repository. |
| Open Datasets | No | The paper states it uses data 'obtained from [19]' but does not provide concrete access information (specific link, DOI, repository name) for a publicly available dataset itself. |
| Dataset Splits | No | The paper mentions using data from '35 different risky choice experiments obtained from [19]' but does not specify explicit training/validation/test dataset splits, percentages, or sample counts for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., GPU, CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper describes model parameters and functions used (e.g., Prelec's function, ϵ-random utility maximization) but does not list any specific software dependencies with version numbers required to reproduce the experiments. |
| Experiment Setup | Yes | Using Equation 12, where ψ is the c.d.f of a Pareto distribution, (θ = {2.9, 0.1, 1} fitted empirically), assuming that subjects distort perceived probabilities [18] via an inverse-S shaped weighting function4, and using an ϵ-random utility maximization decision rule5, we obtain choice predictions that match human performance (see Figure 4) on a large and comprehensive panel of risky choice experiments obtained from [19] to within statistical confidence6. and Footnote 4: We use Prelec s version of this function, with the slope parameter γ distributed N(0.65, 0.2) across our agent population. The quantitative values for γ are taken from (Zhang & Maloney, 2012). and Footnote 5: The value of ϵ is fitted to the data; we used ϵ = 0.25, the value that maximized our fit to the endpoints of the data. |