The Power of Local Manipulation Strategies in Assignment Mechanisms
Authors: Timo Mennle, Michael Weiss, Basil Philipp, Sven Seuken
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | First, we present results from a behavioral experiment (conducted on Amazon Mechanical Turk) which demonstrate that human manipulation strategies can largely be explained by local manipulation strategies. ... We conducted a behavioral experiment on Mechanical Turk to identify the way in which humans approach the manipulation problem. ... We answer these questions via large-scale simulations. |
| Researcher Affiliation | Academia | Timo Mennle and Michael Weiss and Basil Philipp and Sven Seuken Department of Informatics University of Zurich {mennle,seuken}@ifi.uzh.ch {michael.weiss2,basil.philipp}@uzh.ch |
| Pseudocode | No | The paper describes the mechanisms and strategies verbally but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access (e.g., a link or explicit statement) to the source code for the methodology it describes. |
| Open Datasets | No | The paper describes using human subjects from Amazon Mechanical Turk for behavioral experiments and generated data for simulations ("sampled 10,000 preference profiles for each treatment and then used Gibbs sampling to sample 1,000 utility functions for agent 1"), but it does not provide access information (link, DOI, formal citation) for a publicly available dataset used for training/evaluation. |
| Dataset Splits | No | The paper describes the experimental setup for human subjects and the simulation setup for generating data, but it does not specify explicit training/validation/test dataset splits (e.g., percentages, sample counts, or cross-validation details) for its data. |
| Hardware Specification | No | The paper mentions that simulations "already took a full day on a powerful compute cluster" but does not provide specific hardware details such as GPU/CPU models, memory, or specific cloud instance types. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, specific libraries or solvers) that would be needed to replicate the experiment or simulations. |
| Experiment Setup | Yes | For our experiment we recruited 489 human subjects from Amazon Mechanical Turk [Mason and Suri, 2012]... To create a general model, we include the following variations: a treatment consists of a mechanism f {PS, NBM, ABM}, a number of objects m {3, 4, 5}, a capacity q {1, 2, 3} (and fixing the number of agents n = q m, such that supply exactly satisfies demand), a correlation α 0, 1. Sample preference profiles were obtained by drawing utility profiles with normally distributed utility values and then correlated them by α. We sampled 10,000 preference profiles for each treatment and then used Gibbs sampling to sample 1,000 utility functions for agent 1. |