Migration as Submodular Optimization
Authors: Paul Gölz, Ariel D. Procaccia549-556
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To this end, we empirically evaluate the two approaches on all three models from Section 4. Our simulation code is written in Python; we use Gurobi for additive optimization and IGraph for computing maximum bipartite matchings. All code is open source and available for reproduction at https://github.com/pgoelz/migration, including the exact setup of our experiments as IPython notebooks. We provide additional simulation results in the full version (G olz and Procaccia 2018). For increased performance, we reuse estimations of expected employment. When a model is queried with a matching that puts the same agents of profession π or just the same agents in case of the coordination model in the same locality ℓas a previous matching, the previous estimation of expected employment is used. |
| Researcher Affiliation | Academia | Paul G olz, Ariel D. Procaccia Computer Science Department Carnegie Mellon University |
| Pseudocode | Yes | The greedy algorithm initializes S0 , N 0 N and t 1. Then, it proceeds through the following steps, wherein t denotes the number of the current iteration: Step 0. If N t 1 = , stop with St 1 as the greedy solution. Step 1. Select i(t) N t 1 for which z(St 1 {i(t)}) is maximal, with ties settled arbitrarily. Step 2a. If St 1 {i(t)} / F, set N t 1 N t 1 \ {i(t)} and return to step 0. Step 2b. If St 1 {i(t)} F, set ρt 1 ρi(t)(St 1), St St 1 {i(t)}, and N t N t 1 \ {i(t)}. Step 3. Set t t + 1 and continue from step 0. |
| Open Source Code | Yes | Our simulation code is written in Python; we use Gurobi for additive optimization and IGraph for computing maximum bipartite matchings. All code is open source and available for reproduction at https://github.com/pgoelz/migration, including the exact setup of our experiments as IPython notebooks. |
| Open Datasets | No | The paper discusses using 'historical data' by previous work (Bansak et al. 2018) and mentions other datasets like DIOC and German Socio Economic Panel as being difficult to obtain or insufficient for their needs. For their own simulations, they 'adopt a standard setting' and generate data based on models rather than using a publicly available dataset with concrete access information for their experiments. |
| Dataset Splits | No | The paper conducts simulations based on generated data and describes parameters like '1 000 simulations to estimate expected employment', but it does not specify training, validation, or test dataset splits as typically found in machine learning experiments with pre-existing datasets. |
| Hardware Specification | No | The paper states that the simulation code is written in Python and uses Gurobi and IGraph, but it does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run these simulations. |
| Software Dependencies | No | The paper mentions 'Python', 'Gurobi', and 'IGraph' as software components used for simulations. However, it does not specify version numbers for these software dependencies, which is crucial for reproducibility. |
| Experiment Setup | Yes | Due to the high number of parameters in all of our models, we adopt a standard setting that is applicable across all of them. Let N be a set of 100 agents, split equally between two professions, 1 and 2. While we vary the number of localities, we distribute 100 jobs 50 per profession randomly over the localities, ensuring that each locality has at least one job. Furthermore, we set the capacity of each locality to exactly its number of jobs. Finally, we average 1 000 simulations to estimate expected employment whenever our models are queried with a matching. |