Publications by authors named "Atsuo Yamauchi"

We propose a network reciprocity model in which an agent probabilistically adopts learning or teaching strategies. In the learning adaptation mechanism, an agent may copy a neighbor's strategy through Fermi pairwise comparison. The teaching adaptation mechanism involves an agent imposing its strategy on a neighbor.

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Despite hundreds of studies on the Prisoner's Dilemma (PD) game, understanding about network reciprocity remains a unsolved puzzle. Thus, we performed a series of Full Factorial Design of Experiments (FFDOE) to evaluate what dominates emerging cooperation in the PD game on various networks. The results qualitatively reveal the influence of each factor and show that some combinations of factors have complicated interactions.

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The evolutionary Prisoner's Dilemma game in structured networks has been studied extensively to understand network reciprocity. However, in some cases results of these studies cannot be compared because not only the network structures but also the network parameters, rules for updating strategies, and update dynamics differ among them. In this study, we investigated the effect of experimental conditions by conducting a series of systematic factorial experiments.

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Using a cellular automaton traffic model based on the stochastic optimal velocity model with appropriate assumptions for both incoming and outgoing vehicle boundaries, the so-called bottleneck issue on a lane-closing section was investigated in terms of game theory. In the system, two classified driver agents coexist: C agents (cooperative strategy) always driving in the first lane and D agents (defective strategy) trying to drive in a lower-density lane whether the first or the second lane. In high-density flow, D agents' interruption into the first lane from the second just before the lane-closing section creates a heavier traffic jam, which reduces social efficiency.

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