Fostering Cooperation in Structured Populations Through Local and Global Interference Strategies
Authors: The Anh Han, Simon Lynch, Long Tran-Thanh, Francisco C. Santos
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To this end, we test several interference paradigms resorting to simulations of agents facing a cooperative dilemma in a spatial arrangement. We systematically analyse and compare interference strategies rewarding local or global behavioural patterns. Our results show that taking into account the neighbourhood s local properties, such as its level of cooperativeness, can lead to a significant improvement regarding cost efficiency while guaranteeing high levels of cooperation. |
| Researcher Affiliation | Academia | 1School of Computing, Media and the Arts, Teesside University 2School of Electronics and Computer Science, University of Southampton 3INESC-ID and Instituto Superior Tecnico, Universidade de Lisboa |
| Pseudocode | No | The paper describes its methods in paragraph form in Section 3, without presenting any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements or links indicating that open-source code for the described methodology is provided. |
| Open Datasets | No | The paper describes simulating a population of agents initialized with random cooperators or defectors on a square lattice, but does not use or provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes simulation runs over '200 generations' and averaging '50 independent realisations', but does not specify train/validation/test dataset splits as it relies on simulations rather than external datasets. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for reproducibility (e.g., 'Python 3.8, PyTorch 1.9'). |
| Experiment Setup | Yes | We consider a population of agents on a square lattice of size Z = L L with periodic boundary conditions... The score for each agent is the sum of the payoffs in these encounters. At the start of the next generation, each agent s strategy is changed to that of its highest scored neighbour... all the simulations in this work (described in next section) converge quickly to such a state. For the sake of a clear and fair comparison, all simulations are run for 200 generations. Moreover, for each simulation, the results are averaged from additional 50 generations after that. Furthermore, to improve accuracy, for each set of parameter values, the final results are obtained from averaging 50 independent realisations... An investment in a cooperator consists of a cost θ > 0... Population composition based (POP): ...threshold, p C... Neighbourhood based (NEB): ...threshold, n C... Parameters: b = 1.8, L = 100... K = 0.3... |