DOI: 10.5061/dryad.0rxwdbs1z
title: Data from: Delegation to artificial agents fosters prosocial behaviors in the collective risk dilemma

description: Home assistant chat-bots, self-driving cars, drones or automated
 negotiation systems are some of the several examples of autonomous
 (artificial) agents that have pervaded our society. These agents enable
 the automation of multiple tasks, saving time and (human) effort. However,
 their presence in social settings raises the need for a better
 understanding of their effect on social interactions and how they may be
 used to enhance cooperation towards the public good, instead of hindering
 it. To this end, we present an experimental study of human delegation to
 autonomous agents and hybrid human-agent interactions centered on a
 non-linear public goods dilemma with uncertain returns in which
 participants face a collective risk. Our aim is to understand
 experimentally whether the presence of autonomous agents has a positive or
 negative impact on social behaviour, equality and cooperation in such a
 dilemma. Our results show that cooperation and group success increases
 when participants delegate their actions to an artificial agent that plays
 on their behalf. Yet, this positive effect is less pronounced when humans
 interact in hybrid human-agent groups, where we mostly observe that humans
 in successful hybrid groups make higher contributions earlier in the game.
 Also, we show that participants wrongly believe that artificial agents
 will contribute less to the collective effort. In general, our results
 suggest that delegation to autonomous agents has the potential to work as
 commitment devices, which prevent both the temptation to deviate to an
 alternate (less collectively good) course of action, as well as limiting
 responses based on betrayal aversion.
description: The data was collected through behavioral economic experiments
 performed in a laboratory. Participants remain anonymous throughout the
 experiment and interact with other through a tablet computer. The
 experiment was divided into a control and 3 treatments. The
 <strong>control (treatment 1 - humans)</strong> uses a similar
 setting to Milinski et al. 2008, where participants play a collective risk
 dilemma (CRD) with 90% of risk and 10 rounds. Participants interact in
 groups of 6 subjects to which they are randomly assigned before the
 experiment starts. Jointly they are required to contribute to a public
 account from their endowment (40 EMUs) to achieve a total of 120 EMUs by
 the end of the experiment. If they achieve or surpass this value,
 participants keep in their account whatever was not contributed, otherwise
 their final payoff will be 0 EMUs with 90% of probability.
 <strong>Treatment 2 (delegate)</strong> evaluates how
 the outcome of the dilemma changes is participants have to select one out
 of 5 possible agents which will act on behalf the participant during the
 experiment. Participants are shown what is the full algorithm which
 describes the behavior of the agent.
 <strong>Treatment 3 (customize)</strong> is similar
 to the delegate treatment, except that in this case participants can
 customize the behavior of their agent by selecting 5 parameters.
 <strong>Treatment 4 (nudge)</strong> reproduces the
 setting of the control treatment with only human players, but in this case
 participants interact in hybrid groups formed by 3 human participants and
 3 artificial agents. The artificial agents are sampled from the successful
 groups of the customize treatment. Nevertheless, participants are only
 told that the agents have been programmed by humans to act on their behalf
 during the experiment.
description: Please refer to the file "README.md" for information on
 the meaning of each feature (column) in the dataset.