Repeated Multimarket Contact with Private Monitoring: A Belief-Free Approach

Authors: Atsushi Iwasaki, Tadashi Sekiguchi, Shun Yamamoto, Makoto Yokoo2038-2045

AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical This paper studies repeated games where two players play multiple duopolistic games simultaneously (multimarket contact). A key assumption is that each player receives a noisy and private signal about the other s actions (private monitoring or observation errors). There has been no game-theoretic support that multimarket contact facilitates collusion or not, in the sense that more collusive equilibria in terms of permarket profits exist than those under a benchmark case of one market. An equilibrium candidate under the benchmark case is belief-free strategies. We are the first to construct a non-trivial class of strategies that exhibits the effect of multimarket contact from the perspectives of simplicity and mild punishment.
Researcher Affiliation Academia University of Electro-Communications, Kyoto University, Kyushu University, RIKEN AIP a2c.iwasaki@gmail.com, sekiguchi@kier.kyoto-u.ac.jp, syamamoto@agent.inf.kyushu.ac.jp, yokoo@inf.kyushu.ac.jp
Pseudocode Yes Figure 1 illustrates EV, which is a variant of the well-known tit-for-tat strategy. A player first cooperates and keeps cooperation as long as she observes a signal suggesting cooperation. Once she observes a signal suggesting defection, she defects with a given probability and cooperates with the remaining probability. Similarly, when she defects, she keeps defection as long as she observes a good signal, she returns to cooperation with another given probability and defects with the remaining probability.
Open Source Code No The paper is theoretical and focuses on constructing strategies and proving their properties. There is no mention of releasing source code for the proposed methods.
Open Datasets No The paper is theoretical and does not use or reference any datasets for training or evaluation.
Dataset Splits No The paper is theoretical and does not involve empirical experiments with data splits for training, validation, or testing.
Hardware Specification No As a theoretical paper, it does not describe or require any specific hardware for experiments.
Software Dependencies No The paper focuses on theoretical game-theoretic analysis and does not specify any software dependencies or versions.
Experiment Setup No This paper is theoretical, developing strategies and proving conditions, and therefore does not include details of an experimental setup or hyperparameters.