The paper examines whether an institution has a differing impact on cooperation if it is introduced by a representative of the affected subjects rather than exogenously imposed. The experimental design controls for selection effects arising from the endogenous policy choice. The treatment varies whether the decision-maker is elected or randomly appointed. There is evidence of a large democracy premium in the sense that endogenously chosen institutions lead to more cooperation than identical exogenous institutions, but only if the group leader is democratically chosen. Especially the subjects who initially did not prefer the policy are more likely to cooperate if it was brought about by an elected representative. There is no democracy premium for randomly appointed group leaders.

How do people attribute responsibility when an outcome is not caused by an individual but results from a decision chain involving several people? We study this question in an experiment, in which five voters sequentially decide on how to distribute money between them and five recipients. The recipients can reward or punish each voter, which we use as measures of responsibility attribution. In the aggregate, we find that responsibility is attributed mostly according to the voters’ choices and the pivotality of the decision, but not for being the initial voter. On the individual level, we find substantial heterogeneity with three overall patterns: Little to no responsibility attribution, pivotality-driven, and focus on choices. These patterns are similar when praising voters for good outcomes and blaming voters for bad outcomes.

This policy-oriented article explores the sustainability dimension of digitalisation and artificial intelligence (AI). While AI can contribute to halting climate change via targeted applications in specific domains, AI technology in general could also have detrimental effects for climate policy goals. Moreover, digitalisation and AI can have an indirect effect on climate policy via their impact on political processes. It will be argued that, if certain conditions are fulfilled, AI-facilitated digital tools could help with setting up frameworks for bottom-up citizen participation that could generate the legitimacy and popular buy-in required for speedy transformations needed to reach net zero such as radically revamping the energy infrastructure among other crucial elements of the green transition. This could help with ameliorating a potential dilemma of voice versus speed regarding the green transition. The article will further address the nexus between digital applications such as AI and climate justice. Finally, the article will consider whether innovative governance methods could instil new dynamism into the multi-level global climate regime, such as by facilitating interlinkages and integration between different levels. Before implementing innovative governance arrangements, it is crucial to assess whether they do not exacerbate old or even generate new inequalities of access and participation.

Collective decision-making by a swarm of robots is of paramount importance. In particular, the problem of collective perception wherein a swarm of robots aims to achieve consensus on the prevalent feature in the environment. Recently, this problem has been formulated as a discrete collective estimation scenario to estimate their proportion rather than deciding about the prevalent one. Nevertheless, the performance of the existing strategies to resolve this scenario is either poor or depends on higher communication bandwidth. In this work, we propose a novel decision-making strategy based on maximum likelihood estimate sharing (MLES) to resolve the discrete collective estimation scenario. Experimentally, we compare the tradeoff speed versus accuracy of MLES with state-of-the-art methods in the literature, such as direct comparison (DC) and distributed Bayesian belief sharing (DBBS). Interestingly, MLES achieves an accurate consensus nearly 20% faster than DBBS, its communication bandwidth requirement is the same as DC but six times less than DBBS, and its computational complexity is $O(1)$. Furthermore, we investigate how noisy sensors affect the effectiveness of the strategies under consideration, with MLES showing better sustainability.

Democracy relies on citizens who are politically knowledgeable and engaged. However, when a voter gains political knowledge regarding important issues, through television, town halls, or social media, she also learns that there are many other politically knowledgeable voters, highlighting the importance of social knowledge in political participation. Will a voter with concrete—as opposed to hypothetical—knowledge about other voters’ political knowledge have an increased incentive to participate? Or instead, will concrete social knowledge about other voters actually inhibit participation? In this article, we develop a novel experimental design that focuses on whether concrete knowledge about other voters’ political knowledge influences political participation. Our main result shows that concrete social knowledge decreases individual voters’ willingness to vote, and thereby reduces the probability democracy chooses the majority preferred alternative, i.e. the quality of democratic choice.

Behavioral economics posits a number of cognitive biases and limitations, which raises questions as to whether revealed willingness to pay equals true willingness to pay. If so, benefit-cost analysis, with a number of methodological advantages, would need to be replaced. Prior analyses of the issue by Sunstein, Sugden, and Bernheim and Rangel fail to offer guidance that would avoid substituting centralized judgments for decentralized information on benefits and costs. Alternatives including using post-implementation valuations, libertarian paternalism, and direct democracy on policy issues also have conceptual or practical limitations. A tentative suggestion is democratic delegation, somewhat appealing because it is already applied to cope with bounded rationality and non-efficiency values. Viewing benefit-cost analysis as a market analogue, and restricting the domain of behavioral economics to uninformed consumers, may be useful guides. The most important guidance may be to require very strong evidence of substantial choice failure before abandoning benefit-cost analysis.

How large should a monetary policy committee be? Which voting rule should a monetary policy committee adopt? This paper builds on Condorcet's jury theorem to analyse the relationships between committee size and voting rules in a model where policy discussions are subject to a time constraint. It suggests that in large committees majority voting is likely to enhance policy outcomes. Under unanimity (consensus) it is preferable to limit the size of the committee. Finally, supermajority voting rules are social contrivances that contribute to policy performance in a more uncertain environment, when initial policy proposals are less likely to be correct, or when payoffs are asymmetric.