Social Norms of Cooperation With Costly Reputation Building
Authors: Fernando Santos, Jorge Pacheco, Francisco Santos
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that only two norms can sustain cooperation under costly reputation building... To answer these questions, we develop a model based on evolutionary game theory (EGT) (Sigmund 2010) in which agents play with each other the donation game described in Fig. 1 and revise their behaviors through social learning. Employing the framework just described, we now investigate the three main research questions. We find that the capacity of agents to anticipate the reporting intentions of their opponents is sufficient to allow cooperation to emerge in a context of costly reputation building. This, however, happens only under specific social norms. As Fig. 2 conveys, there are social norms that efficiently allow cooperation to be sustained. |
| Researcher Affiliation | Academia | Fernando P. Santos,1,4 Jorge M. Pacheco,2,3,4 Francisco C. Santos1,4 1 INESC-ID and Instituto Superior T ecnico, Universidade de Lisboa, IST-Taguspark, 2744-016 Porto Salvo, Portugal 2 Centro de Biologia Molecular e Ambiental, Universidade do Minho, 4710-057 Braga, Portugal 3 Departamento de Matem atica e Aplicac oes, Universidade do Minho, 4710-057 Braga, Portugal 4 ATP-group, P-2744-016 Porto Salvo, Portugal |
| Pseudocode | No | The paper describes the model and dynamics using mathematical equations and textual explanations but does not include any pseudocode or explicitly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper describes a model based on evolutionary game theory with a finite population of agents, indicating a simulation-based approach rather than the use of an external, publicly available dataset. |
| Dataset Splits | No | The paper describes a simulation-based model and does not use or specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper describes a simulation-based model and does not provide any specific details about the hardware used for running the simulations or experiments. |
| Software Dependencies | No | The paper describes a theoretical model and its simulations but does not list any specific software dependencies or version numbers. |
| Experiment Setup | Yes | The paper provides specific parameter values used in the simulations, for example, 'Z = 50, b = 5, c = 1, c R = 0.1, χ = ϵ = α = τ = 0.01 (when not explicitly varied)' in the caption of Figure 2, and 'Z = 50, b = 5, c = 1, χ = α = ϵ = 0.01' in the caption of Figure 3. |