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Emerging crowdsourcing and crowdfunding mechanisms enable citizens to decide which research projects should be funded. By transferring control from professional scientists, these mechanisms reflect a broader shift towards more open “Mode 2” knowledge production that allows non-academic stakeholders to shape the direction of science. Although this may lead to a greater emphasis on the social impact of research, there is no systematic evidence on how crowd evaluators weigh social impact relative to other criteria such as scientific merit or team qualifications. There are also concerns that the personal financial costs associated with crowdfunding prevent certain socio-economic groups from participating, reducing the representativeness of opinions. Similarly, it is not clear what role citizens’ personal interest in particular topics plays in shaping their evaluations. We provide empirical evidence using data from over 2,300 crowd evaluators who assessed four research proposals in different fields and could express their support using a crowdsourcing mechanism (i.e., recommendation) and a crowdfunding mechanism (i.e., donation of own money). We confirm that crowd evaluators give significant weight to perceived social impact, although this weight is not larger than that of scientific merit. Compared to crowdsourcing, crowdfunding gives greater voice to citizens with higher income and education. Personal interest in a topic tends to be associated with greater project support, which may partly reflect an inflated assessment of social impact. Despite these general patterns, we also observe differences across projects – illustrating context-specificity and variability that make open Mode 2 processes difficult to predict and control.

with permission of Elsevier

with permission of Elsevier

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We provide a canonical and tractable model of a trade platform enabling buyers and sellers to transact. The platform charges a percentage fee on third-party product sales and decides whether to be "hybrid", like Amazon, by selling its own product. It thereby controls the number of differentiated products (variety) it hosts and their prices. Using the mixed market demand system, we capture interactions between monopolistically competitive sellers and a sizeable platform product. Using long-run aggregative games with free entry, we endogenize seller participation through an aggregate variable manipulated by the platform's fee. We show that a higher quality (or lower cost) of the platform's product increases its market share and the seller fee, and lowers consumer surplus. Banning hybrid mode benefits consumers. The hybrid platform might favor its product and debase third-party products if the own product advantage is sufficiently high. We also provide some tax policy implications.

© 2024 The Author(s). The RAND Journal of Economics published by Wiley Periodicals LLC on behalf of The RAND Corporation.

In this paper, we explore how to uncover an adverse issue that may occur in organizations with the capability to evade detection. To that end, we formalize the problem of designing efficient auditing and remedial strategies as a dynamic mechanism design model. In this setup, a principal seeks to uncover and remedy an issue that occurs to an agent at a random point in time and that harms the principal if not addressed promptly. Only the agent observes the issue’s occurrence, but the principal may uncover it by auditing the agent at a cost. The agent, however, can exert effort to reduce the audit’s effectiveness in discovering the issue. We first establish that this setup reduces to the optimal stochastic control of a piecewise deterministic Markov process. The analysis of this process reveals that the principal should implement a dynamic cyclic auditing and remedial cost-sharing mechanism, which we characterize in closed form. Importantly, we find that the principal should randomly audit the agent unless the agent’s evasion capacity is not very effective, and the agent cannot afford to self-correct the issue. In this latter case, the principal should follow predetermined audit schedules.

© 2024, INFORMS