The Behavioral Economics of Artificial Intelligence: Lessons from Experiments with Computer Players

March, Christoph

doi:10.2139/ssrn.3485475

Cited by 5 publications

(3 citation statements)

References 84 publications

(117 reference statements)

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“…In [8] it is pointed out that more experimental research is needed to really understand how human strategic decision-making changes when interacting with autonomous agents. Following on this [9], compiles a review of more than 90 experimental studies that have made use of computerized players. Its main conclusions validate that indeed, human behavior changes when some of the other players are artificial, and furthermore, the behavior deviates to become more rational (or in other words, selfish), where humans are observed to actually try to exploit the artificial players.…”

Section: Related Workmentioning

confidence: 99%

“…Even though many different works have advocated for the introduction of beneficial AI to promote human prosociality [5][6][7], others have pointed out that humans may be keen to exploit this benevolent AI behavior in their own favor [8][9][10][11]. Thus, before flooding society with AI applications with the promise that they could solve some of the most pressing societal issues, it is worth asking: What behavioral responses can be expected in the presence of AI partners?…”

Section: Introductionmentioning

confidence: 99%

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The art of compensation: How hybrid teams solve collective-risk dilemmas

Terrucha,

Fernández Domingos,

C. Santos

et al. 2024

PLoS ONE

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It is widely known how the human ability to cooperate has influenced the thriving of our species. However, as we move towards a hybrid human-machine future, it is still unclear how the introduction of artificial agents in our social interactions affect this cooperative capacity. In a one-shot collective risk dilemma, where enough members of a group must cooperate in order to avoid a collective disaster, we study the evolutionary dynamics of cooperation in a hybrid population. In our model, we consider a hybrid population composed of both adaptive and fixed behavior agents. The latter serve as proxies for the machine-like behavior of artificially intelligent agents who implement stochastic strategies previously learned offline. We observe that the adaptive individuals adjust their behavior in function of the presence of artificial agents in their groups to compensate their cooperative (or lack of thereof) efforts. We also find that risk plays a determinant role when assessing whether or not we should form hybrid teams to tackle a collective risk dilemma. When the risk of collective disaster is high, cooperation in the adaptive population falls dramatically in the presence of cooperative artificial agents. A story of compensation, rather than cooperation, where adaptive agents have to secure group success when the artificial agents are not cooperative enough, but will rather not cooperate if the others do so. On the contrary, when risk of collective disaster is low, success is highly improved while cooperation levels within the adaptive population remain the same. Artificial agents can improve the collective success of hybrid teams. However, their application requires a true risk assessment of the situation in order to actually benefit the adaptive population (i.e. the humans) in the long-term.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The art of compensation: How hybrid teams solve collective-risk dilemmas

Terrucha,

Fernández Domingos,

C. Santos

et al. 2024

PLoS ONE

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show abstract

“…On the other hand, an analysis of additional results shows that the effect of autonomy is not so clear and using these technologies in social or moral context could have clear detrimental effects. For example, recent investigations suggest that people tend to act more selfishly when they are in interaction against a computer player [95] and are more prompt for cheating [96]. Manistersky et al [97] reported that, in a resource allocation game, participants who played the game through selfdesigned autonomous agents, designed autonomous systems to improve their own performance and less for cooperation, contrasting with the results found by [91].…”

Section: The Ugly: Autonomous Systems and Moral Decision-makingmentioning

confidence: 99%

Change in Human (Moral) Decision-Making and Performance with Intelligent Machines: The Good, the Bad, and the Ugly in Human-Autonomous Systems Interactions

Prével¹,

Salatino²,

Bue³

2023

Preprint

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Autonomous systems and intelligent machines are involved in almost all areas of human activity and they are now more and more present in our everyday life. The reason for this extensive use certainly resides in all the benefits these machines offer to the users. In experimental settings, numerous studies have demonstrated the positive effects that the introduction of autonomous systems have on human decision-making and performance. However, studies have shown in addition that the introduction of these systems can have important negative effects as well. Considering that autonomous systems are now introduced in sensitive domains like the military or medicine, we need more than ever a comprehensive understanding of the effects they cause on human performance and decision-making, and particularly in tasks and contexts with a social or moral dimension. The aim of this narrative review is threefold. First, we will provide an overview of the main effects on a human agent’s decision-making and performance produced by the introduction of autonomous systems. Second, we will review the conditions identified as underlying factors of these effects, and see how current models of human – autonomous systems interaction integrate those conditions. Third, we will conclude this review by highlighting new directions for future investigations.

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Promises and trust in human–robot interaction

Cominelli

Feri

Garofalo

et al. 2021

Sci Rep

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Understanding human trust in machine partners has become imperative due to the widespread use of intelligent machines in a variety of applications and contexts. The aim of this paper is to investigate whether human-beings trust a social robot—i.e. a human-like robot that embodies emotional states, empathy, and non-verbal communication—differently than other types of agents. To do so, we adapt the well-known economic trust-game proposed by Charness and Dufwenberg (2006) to assess whether receiving a promise from a robot increases human-trust in it. We find that receiving a promise from the robot increases the trust of the human in it, but only for individuals who perceive the robot very similar to a human-being. Importantly, we observe a similar pattern in choices when we replace the humanoid counterpart with a real human but not when it is replaced by a computer-box. Additionally, we investigate participants’ psychophysiological reaction in terms of cardiovascular and electrodermal activity. Our results highlight an increased psychophysiological arousal when the game is played with the social robot compared to the computer-box. Taken all together, these results strongly support the development of technologies enhancing the humanity of robots.

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The Behavioral Economics of Artificial Intelligence: Lessons from Experiments with Computer Players

Cited by 5 publications

References 84 publications

The art of compensation: How hybrid teams solve collective-risk dilemmas

The art of compensation: How hybrid teams solve collective-risk dilemmas

Change in Human (Moral) Decision-Making and Performance with Intelligent Machines: The Good, the Bad, and the Ugly in Human-Autonomous Systems Interactions

Promises and trust in human–robot interaction

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