Learn More
This paper considers the design of agent strategies for deciding whether to help other members of a group with whom an agent is engaged in a collaborative activity. Three characteristics of collaborative planning must be addressed by these decision-making strategies: agents may have only partial information about their partners' plans for sub-tasks of the(More)
This paper presents a machine-learning approach to modeling human behavior in one-shot games. It provides a framework for representing and reasoning about the social factors that affect people's play. The model predicts how a human player is likely to react to different actions of another player, and these predictions are used to determine the best possible(More)
Computer agents are increasingly deployed in settings in which they make decisions with people, such as electronic commerce, collaborative interfaces, and cognitive assistants. However, the scientific evaluation of computational strategies for human-computer decision-making is a costly process, involving time, effort and personnel. This paper investigates(More)
This paper proposes a novel technique for allocating information gathering actions in settings where agents need to choose among several alternatives, each of which provides a stochastic outcome to the agent. Samples of these outcomes are available to agents prior to making decisions and obtaining further samples is associated with a cost. The paper(More)
Online labor markets such as Amazon Mechanical Turk (MTurk) offer an unprecedented opportunity to run economic game experiments quickly and inexpensively. Using Mturk, we recruited 756 subjects and examined their behavior in four canonical economic games, with two payoff conditions each: a stakes condition, in which subjects' earnings were based on the(More)
The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters. Abstract. Computer systems increasingly carry out tasks in mixed networks, that is in group settings in which they interact both with other computer systems and with people. Participants in these heterogeneous human-computer groups(More)
Multi-agent systems that use game-theoretic analysis for decision making traditionally take a normative approach, in which agents' decisions are derived rationally from the game description. This approach is insufficient to capture the decision making processes of real-life agents. Such agents may be partially irrational, they may use models other than the(More)
This paper addresses the problem of automated advice provision in scenarios that involve repeated interactions between people and computer agents. This problem arises in many applications such as route selection systems, office assistants and climate control systems. To succeed in such settings agents must reason about how their advice influences people’s(More)