In an era increasingly influenced by autonomous machines, it is only a matter of time before strategic individual decisions that impact collective goods will also be made virtually through the use of artificial delegates. Through a series of behavioral experiments that combine delegation to autonomous agents and different choice architectures, we pinpoint what may get lost in translation when humans delegate to algorithms. We focus on the collective-risk dilemma, a game where participants must decide whether or not to contribute to a public good, where the latter must reach a target in order for them to keep their personal endowments. To test the effect of delegation beyond its functionality as a commitment device, participants are asked to play the game a second time, with the same group, where they are given the chance to reprogram their agents. As our main result we find that, when the action space is constrained, people who delegate contribute more to the public good, even if they have experienced more failure and inequality than people who do not delegate. However, they are not more successful. Failing to reach the target, after getting close to it, can be attributed to precision errors in the agent’s algorithm that cannot be corrected amid the game. Thus, with the digitization and subsequent limitation of our interactions, artificial delegates appear to be a solution to help preserving public goods over many iterations of risky situations. But actual success can only be achieved if humans learn to adjust their agents’ algorithms.

Extracting from shared resources requires making choices to balance personal profit and sustainability. We present the results of a behavioural experiment wherein we manipulate the default extraction from a finite resource. Participants were exposed to two treatments -- pro-social or self-serving extraction defaults -- and a control without defaults. We examined the persistence of these nudges by removing the default after five rounds. Results reveal that a self-serving default increased the average extraction while present, whereas a pro-social default only decreased extraction for the first two rounds. Notably, the influence of defaults depended on individual inclinations, with cooperative individuals extracting more under a self-serving default, and selfish individuals less under a pro-social default. After the removal of the default, we observed no significant differences with the control treatment. Our research highlights the potential of defaults as cost-effective tools for promoting sustainability, while also advocating for a careful use to avoid adverse effects.

We experimentally study whether conciliation, a dispute resolution mechanism where a neutral third party makes non-binding suggestions of resolution to the parties, can improve bargaining efficiency. In conciliation, a neutral third party collaborates with the parties by suggesting resolutions to promote agreements. Unlike delegation or arbitration, conciliation fully preserves the autonomy of the parties. Unlike mediation, the conciliator cannot filter information. Whether conciliation can improve bargaining efficiency is an open question. In our laboratory experiment, two “litigants” bargain over the split of a loss in an unstructured protocol. In case of failure, a random split is implemented. In some conditions, a third party, the conciliator takes part in the bargaining by submitting non-binding suggestions to the litigants. We find that, on average, conciliation does not reduce the likelihood of failure, and does not affect the splits that are agreed upon by litigants. However, conciliation reduces bargaining delays: the time and the number of offers necessary to converge to an agreement are significantly reduced in the presence of a conciliator. In addition, conciliation has a heterogeneous effect: while its average effect is null, for bargaining pairs composed of selfish litigants, it results in more equal agreements. The social preferences of the conciliator matter too, both for the likelihood and nature of agreements.