We provide the first large-scale data collection of real-world approval-based committee elections. These elections have been conducted on the Polkadot blockchain as part of their Nominated Proof-of-Stake mechanism and contain around one thousand candidates and tens of thousands of (weighted) voters each. We conduct an in-depth study of application-relevant questions, including a quantitative and qualitative analysis of the outcomes returned by different voting rules. Besides considering proportionality measures that are standard in the multiwinner voting literature, we pay particular attention to less-studied measures of overrepresentation, as these are closely related to the security of the Polkadot network. We also analyze how different design decisions such as the committee size affect the examined measures.

We consider a real-world problem faced in some blockchain ecosystems that select their active validators-the actors that maintain the blockchain-from a larger set of candidates through an electionbased mechanism. Specifically, we focus on Polkadot, a protocol that aggregates preference lists from another set of actors, nominators, that contain a limited number of trusted validators and thereby influence the election's outcome. This process is financially incentivized but often overwhelms human decision makers due to the problem's complexity and the multitude of available alternatives. This paper presents a decision support system (DSS) to help the nominators choose the validators in an environment with frequently changing data. The system structures the relevant multiple attribute problem and incorporates a dedicated active learning algorithm. Its goal is to find a sufficiently small set of pairwise elicitation questions to infer nominators' preferences. We test the proposed solution in an experiment with 115 real nominators from the Polkadot ecosystem. The empirical results confirm that our approach outperforms the unaided process in terms of required interaction time, imposed cognitive effort, and offered efficacy. The developed DSS can be easily extended to other blockchain ecosystems.& COPY; 2023 Elsevier Ltd. All rights reserved.

This paper investigates the impact of markets on moral reasoning. Whereas the current literature focuses on morally relevant decisions that arise in markets, little is known about whether the exposition to markets shapes subsequent moral reasoning. To close this gap, we run a large-scale online experiment with 3 conditions: In Baseline, participants make a choice in a moral dilemma. In the other two conditions, participants are exposed to either a Non-market or Market environment, before facing the identical choice in the moral dilemma. We hypothesize that being exposed to Market induces cost-benefit considerations, which translate into modified reasoning in the subsequent moral dilemma. Compared to the baseline distribution, we indeed find a substantial effect in Market. However, similar choices can be observed in Non-market. We discuss potential explanations for these results, and suggest avenues for future research.

Combining a lab-in-the-field experiment with field data, we study the effect of social preferences on performance in a modified teamwork setting, where the production of a public good serves as basis for incentivized individual performance, but is not a goal in itself. Examples of such modified team settings are knowledge sharing, peer coaching, and cooperative learning—all highly relevant topics for organizations today. As opposed to a standard public good setting, we find that conditional cooperators and their team partners are not more successful in producing the target output. In contrast, selfish individuals tend to perform better individually, without generating negative externalities for their team partners, as measured by the incentivized individual performance.

There is a long ongoing debate on whether interaction in a market influences moral decisions of individuals. While some studies show that individuals tend to decide less morally when being exposed to a market environment, other studies argue that the experience of mar- ket interaction promotes moral behavior. We add to this discussion by distinguishing between two moral concepts: consequentialism and deontology. According to consequentialism, actions are evaluated only by their consequences. Contrary to that, deontology focuses solely on the morality of the action itself. We design an online experiment in order to investigate the e ect of market interaction on moral deci- sion making in a subsequent moral dilemma. Taking into account how markets make cost benefit considerations salient, we hypothesize that individuals are more likely to focus on consequences if they interacted in a market before.

This article illustrates the implementation of websockets in oTree (Chen et al. 2016) to allow for real-time interactions. While oTree generally allows to overcome the need for participants to be at the same location to interact with each other, a real-time module in the sense that the user interface responds within milliseconds to actions from other participants is currently not available. We address this gap and further develop oTree by making real-time interactions between a large number of players with immediate updates possible. As a first application, we run a continuous double auction market on Amazon Mechanical Turk to validate its functionality. This ready-to-use software is of special interest for the research of large (online) markets and for teaching purposes. We provide the code open-source on GitHub, thereby encouraging users to develop it further and applying the websocket technology to other real-time settings.