Contributions in Merlin

Interference across competing firms in RCTs can be informative about market structure. An experiment that subsidizes a random subset of traders who buy cocoa from farmers in Sierra Leone illustrates this idea. Interpreting treatment-control differences in prices and quantities purchased from farmers through a model of Cournot competition reveals differentiation between traders is low. Combining this result with quasi-experimental variation in world prices shows that the number of traders competing is 50 percent higher than the number operating in a village. Own-price and cross-price supply elasticities are high. Farmers face a competitive market in this first stage of the value chain.

Inspired by an industry initiative to address the celebrated market for lemons (poor-quality used cars), we investigate how incentives for a permissioned blockchain-based system in the automobile ecosystem can be designed to ensure high-quality data storage and use by different stakeholders. The peer-to-peer distributed ledger platform connects organizations and car owners with disparate interests and hidden intentions. While previous literature has chiefly examined incentives for permissionless platforms, we leverage studies about crowdsensing applications to stimulate research on incentives in permissioned blockchains. This paper uses the action design research approach to create an incentive system featuring a rating mechanism influenced by data correction measures. Furthermore, we propose relying on certain institutions capable of assessing data generated within the system. This combined approach of a decentralized data correction and an institutionalized data assessment is distinct from similar incentive systems suggested by literature. By using an agent-based model with strategy evolution, we evaluate the proposed incentive system. Our findings indicate that a rating-based revenue distribution leads to markedly higher data quality in the system. Additionally, the incentive system reveals hidden information of the agents and alleviates agency problems, contributing to an understanding of incentive design in inter-organizational blockchain-based data platforms. Furthermore, we explore incentive design in permissioned blockchains and discuss its latest implications.

Internet-of-Things (IoT), Artificial Intelligence (AI), and Blockchains (BCs) are essential techniques that are heavily researched and investigated today. This work here specifies, implements, and evaluates an IoT architecture with integrated BC and AI functionality to manage access control based on facial detection and recognition by incorporating the most recent state-of-the-art techniques. The system developed uses IoT devices for video surveillance, AI for face recognition, and BCs for immutable permanent storage to provide excellent properties in terms of image quality, end-to-end delay, and energy efficiency.

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Humans form impressions toward individuals of their own social groups (ingroup members) and of different social groups (outgroup members). Outgroup-focused theories predict that intergroup impressions are mainly shaped by experiences with outgroup individuals, while ingroup-focused theories predict that ingroup experiences play a dominant role. Here we test predictions from these two psychological theories by estimating how intergroup impressions are dynamically shaped when people learn from both ingroup and outgroup experiences. While undergoing fMRI, male participants had identical experiences with different ingroup or outgroup members and rated their social closeness and impressions toward the ingroup and the outgroup. Behavioral results showed an initial ingroup bias in impression ratings which was significantly reduced over the course of learning, with larger effects in individuals with stronger ingroup identification. Computational learning models revealed that these changes in intergroup impressions were predicted by the weight given to ingroup prediction errors. Neurally, the individual weight for ingroup prediction errors was related to the coupling between the left inferior parietal lobule and the left anterior insula, which, in turn, predicted learning-related changes in intergroup impressions. Our findings provide computational and neural evidence for ingroup-focused theories, highlighting the importance of ingroup experiences in shaping social impressions in intergroup settings.

How are social entrepreneurs different from commercial entrepreneurs? This study sheds light on this issue by applying the perspective of entrepreneurial cognition and by arguing that social entrepreneurs are even more susceptible to cognitive biases than commercial entrepreneurs. The empirical study of 205 Swiss entrepreneurs could confirm that social entrepreneurs tend to be more overconfident and prone to escalation of commitment than commercial entrepreneurs, while the study found no differences for illusion of control. The findings indicate that cognitive biases are an important puzzle piece to understand the differences between social and commercial entrepreneurs.

Previous studies indicate a substantial time-variation in the co-movement of commodity futures markets and economic fundamentals. This paper examines the connectedness and directional spillovers for both the agricultural commodity futures markets and the corresponding sentiment indices. We first construct dynamic time-varying connectedness measures both for the agricultural commodity returns and sentiments. Then, we use panel data regressions and time-varying Granger causality tests to evaluate whether the spillovers between these returns and sentiments are influenced by the economic and financial uncertainties, including the global COVID-19 pandemic. In particular, we document that the COVID-19 induced uncertainty influences agricultural commodity returns and sentiments significantly around the first cycle of the pandemic in 2020. Last but not least, economic policy and financial market uncertainty are also found to be significant determinants of the connectedness between agricultural commodity returns and sentiment spillovers.

Non-communicable diseases are the cause for 71% of global deaths. Cases of non-communicable diseases are rising. To fight non-communicable diseases, changing behavior that is causing the disease like tabaco use, physical inactivity, and unhealthy nutrition, is essential. One big problem is that adherence to interventions is bad at only around 50% in developed countries. This bad adherence leads to suboptimal benefit and increases health care costs by a factor of 3-4 times in direct costs. In this literature review our goal was to find how behavior change can be supported with digital health for people with non-communicable diseases. To do so we searched Scopus for relevant literature using a predefined search query. We systematically reduced the literature and ended up reviewing 65 papers. By reviewing meta-analyses prior to our analysis, we found many strategies used to support behavior change using digital health for people with non-communicable diseases. This was the basis for our coding schema to support our analysis. We synthesized design principles from the reviewed literature for the different strategies. Further we categorize the strategies by assigning them to elements of the Fogg Behavior Model and to a phase of the plan-do-study-act cycle. This step allowed us to see the focus and potential gaps of recent literature. We conclude that many areas are covered well but find some gaps, most importantly in the area or prompts. Further, a shortcoming of literature found were missing long-term studies. We also present what practitioners and developers can do to implement a digital agent.