This paper examines crowdfunding of a risky, constant-returns project. The crowdfunder chooses a threshold and interest rate. These jointly determine profit and welfare outcomes by influencing investors’ decisions to acquire information and to bid and by aggregating the information their bids reveal into a project funding decision. A higher threshold funds fewer projects but, fixing investor strategies, raises the ratio of good to bad quality among those that get funded by a factor we call the “crowdblessing.” At the same time, raising the threshold, or the interest rate, reduces incentives to acquire and use private information. Optimal design trades off crowdblessing against investment scale. Surprisingly, profit-maximizers sometimes induce excessive information acquisition to limit investor rent. In our comparative statics, information acquisition falls with the cost of information and with prior optimism but precision has an ambiguous impact. Costly information reduces welfare but may raise profits by facilitating rent-extraction.
Auctioning access rights to private providers through competitive tendering has become a central instrument for governments to regulate markets and public services. While efficient in allocating licenses, such auctions may generate monopolistic market structures with adverse effects on consumer welfare. We experimentally evaluate two policy interventions to mitigate these outcomes: introducing post-tender competition and imposing minimum production obligations on winning firms. Our results show that performance obligations substantially improve welfare by raising output and consumer surplus, albeit at the cost of reduced auction revenue. Extending the design with additional treatments, we demonstrate that genuine competition yields even stronger welfare gains. These findings highlight a trade-off between government revenue and consumer welfare in auction design. We conclude that while obligations are an effective tool to counter monopoly distortions, sustaining competition remains essential to maximise total surplus at the market level.
Computing the (pre-)nucleolus of a coalitional game with transferable utility (TU game) is generally NP-hard. Therefore, no universal polynomial-time algorithm exists. However, polynomial-time algorithms exist for specific game classes. The most general approach in the literature is based on the ellipsoid method and Maschler’s approximation scheme. In contrast, we propose polynomial-time solvability by using a Fenchel-Moreau conjugation-based approach from convex analysis that applies the method of Meinhardt (2013) to compute a pre-kernel element. This method not only allows the computation of the pre-nucleolus for game classes with a singleton pre-kernel with a runtime complexity of O(n3). It also works for game classes with a set-valued pre-kernel whenever the pre-nucleolus is a vertex or a separated point within the pre-kernel set. Finally, we focus on the replication of the pre-nucleolus in the sense of Meinhardt (2024) for a class of related games to demonstrate the versatility of the Fenchel-Moreau conjugation-based approach while referring to group algebra.
