Apology from the offender facilitates forgiveness and thus has the power to restore a broken relationship. Here we showed that apology from the offender not only reduces the victim's propensity to react aggressively but also alters the victim's implicit attitude and neural responses toward the offender. We adopted an interpersonal competitive game which consisted of two phases. In the first, "p...

Several studies have found behavioural differences between the interactions of opponents, early in a conflict. These differences consistently predict which individual will be the winner. Surprisingly, opponents appear not to use this information: they continue fighting and even proceed to highly damaging escalation. We attempted to resolve this paradox in a series of experiments on interactions...

This article considers multiagent algorithms that aim to find the best response in strategic interactions by learning about the game and their opponents from observations. In contrast to many state-of-the-art algorithms that assume repeated interaction with a fixed set of opponents (or even self-play), a learner in the real world is more likely to encounter the same strategic situation with cha...

We present a new multiagent learning algorithm, RVσ(t), that builds on an earlier version, ReDVaLeR . ReDVaLeR could guarantee (a) convergence to best response against stationary opponents and either (b) constant bounded regret against arbitrary opponents, or (c) convergence to Nash equilibrium policies in self-play. But it makes two strong assumptions: (1) that it can distinguish between self-...