Negotiating with other minds: the role of recursive theory of mind in negotiation with incomplete information

Weerd, Harmen de; Verbrugge, Rineke; Verheij, Bart

doi:10.1007/s10458-015-9317-1

Cited by 49 publications

(60 citation statements)

References 62 publications

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“…But do people really use it? De Weerd and colleagues [48] showed that, by letting people play unknowingly against a second-order theory of mind agent in a repeated negotiation game, people are enticed to apply second-order theory of mind, which became much more prevalent than when the participants played against zero-order or first-order agents.…”

Section: Considering the Opponent's Perspectivementioning

confidence: 99%

What Drives People's Choices in Turn-Taking Games, if not Game-Theoretic Rationality?

Ghosh

Heifetz

Verbrugge

et al. 2017

Electron. Proc. Theor. Comput. Sci.

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In an earlier experiment, participants played a perfect information game against a computer, which was programmed to deviate often from its backward induction strategy right at the beginning of the game. Participants knew that in each game, the computer was nevertheless optimizing against some belief about the participant's future strategy. In the aggregate, it appeared that participants applied forward induction. However, cardinal effects seemed to play a role as well: a number of participants might have been trying to maximize expected utility.In order to find out how people really reason in such a game, we designed centipede-like turntaking games with new payoff structures in order to make such cardinal effects less likely. We ran a new experiment with 50 participants, based on marble drop visualizations of these revised payoff structures. After participants played 48 test games, we asked a number of questions to gauge the participants' reasoning about their own and the opponent's strategy at all decision nodes of a sample game. We also checked how the verbalized strategies fit to the actual choices they made at all their decision points in the 48 test games.Even though in the aggregate, participants in the new experiment still tend to slightly favor the forward induction choice at their first decision node, their verbalized strategies most often depend on their own attitudes towards risk and those they assign to the computer opponent, sometimes in addition to considerations about cooperativeness and competitiveness.

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Section: Considering the Opponent's Perspectivementioning

confidence: 99%

What Drives People's Choices in Turn-Taking Games, if not Game-Theoretic Rationality?

Ghosh

Heifetz

Verbrugge

et al. 2017

Electron. Proc. Theor. Comput. Sci.

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“…In strategic games, participants are typically found to rely on low orders of theory of mind, and to be slow to adjust their level of theory of mind reasoning to more sophisticated opponents [11,3,32,10]. Earlier empirical research suggests that the use of first-order and secondorder theory of mind in games can be facilitated by creating a believable story or insightful visual representation around an abstract problem [5], by creating a clear competition or negotiation setting [10,31], or by providing stepwise training from games that require zero-order ToM to second-order ToM games, as we did in [25].…”

Section: General Discussion and Conclusionmentioning

confidence: 99%

“…Many social skills vitally depend on the ability of the person to reason about others as goal-oriented agents with their own beliefs, goals, and intentions, an important part of social cognition. This ability to reason explicitly about unobservable mental content of others, known as theory of mind [20], has been associated with pro-social behavior [13], social competences [15], and negotiation skills [31], as well as in producing and interpreting prosody [4] and nonverbal communication through body language and gestures [18,19]. But while adults show impressive theory of mind abilities in some experiments that rely on communication, people are typically slow to take advantage of their theory of mind ability in strategic settings [11,3,32,10].…”

Section: Introductionmentioning

confidence: 99%

Training the use of theory of mind using artificial agents

Veltman

Weerd

Verbrugge

2018

J Multimodal User Interfaces

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When engaging in social interaction, people rely on their ability to reason about unobservable mental content of others, which includes goals, intentions, and beliefs. This so-called theory of mind ability allows them to more easily understand, predict, and influence the behavior of others. People even use their theory of mind to reason about the theory of mind of others, which allows them to understand sentences like 'Alice believes that Bob does not know about the surprise party'. But while the use of higher orders of theory of mind is apparent in many social interactions, empirical evidence so far suggests that people do not use this ability spontaneously when playing strategic games, even when doing so would be highly beneficial. In this paper, we attempt to encourage participants to engage in higher-order theory of mind reasoning by letting them play a game against computational agents. Since previous research suggests that competitive games may encourage the use of theory of mind, we investigate a particular competitive game, the Mod game, which can be seen as a much larger variant of the well-known rockpaper-scissors game. By using a combination of computational agents and Bayesian model selection, we simultaneously determine to what extent people make use of higher-order theory of mind reasoning, as well as to what extent computational agents can encourage the use of higher-order theory of mind in their human opponents.Our results show that participants who play the Mod game against computational theory of mind agents adjust their level of theory of mind reasoning to that of their computer opponent. Earlier experiments with other strategic games show that participants only engage in low orders of theory of mind reasoning. Surprisingly, we find that participants who knowingly play against second-and third-order theory of mind agents apply up to fourth-order theory of mind themselves, and achieve higher scores as a result.

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“…In addition to experiments with humans, some research pitted artificial recursive reasoning agents against each other to see what reasoning depths are most useful. For example, de Weerd et al (2017de Weerd et al ( , 2013 test their specific agents in domains such as repeated rock-paper-scissors and sequential negotiation, and find that reasoning levels deeper than 2 do not provide significant benefits in their setting.…”

Section: Recursive Reasoningmentioning

confidence: 99%

Autonomous agents modelling other agents: A comprehensive survey and open problems

Albrecht

Stone

2018

Artificial Intelligence

292

208

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Much research in artificial intelligence is concerned with the development of autonomous agents that can interact effectively with other agents. An important aspect of such agents is the ability to reason about the behaviours of other agents, by constructing models which make predictions about various properties of interest (such as actions, goals, beliefs) of the modelled agents. A variety of modelling approaches now exist which vary widely in their methodology and underlying assumptions, catering to the needs of the different sub-communities within which they were developed and reflecting the different practical uses for which they are intended. The purpose of the present article is to provide a comprehensive survey of the salient modelling methods which can be found in the literature. The article concludes with a discussion of open problems which may form the basis for fruitful future research.

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Negotiating with other minds: the role of recursive theory of mind in negotiation with incomplete information

Cited by 49 publications

References 62 publications

What Drives People's Choices in Turn-Taking Games, if not Game-Theoretic Rationality?

What Drives People's Choices in Turn-Taking Games, if not Game-Theoretic Rationality?

Training the use of theory of mind using artificial agents

Autonomous agents modelling other agents: A comprehensive survey and open problems

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