Nudging cooperation among agents in an experimental social network 

Jensen, G.G., Busch, M.B., Piovesan, M., Haerter J. O. Nudging cooperation among agents in an experimental social network. Appl Netw Sci 8, 62 (2023).  

We investigate the development of cooperative behavior in networks over time. In our controlled laboratory experiment, subjects can cooperate by sending costly messages that contain valuable information for the receiver or other subjects in the network. Any message sent can increase the chance that subjects find the information they are looking for and consequently their profit. We find that cooperation emerges spontaneously and remains stable over time. In an additional treatment, we provide a non-binding suggestion about who to contact at the beginning of the experiment. We find that subjects partially follow our recommendation, and this increases their own and others’ profit. Despite the removal of suggestions, subjects build long-lasting relationships with the suggested contacts.   

[Publication] 

Statistical inference in social networks:

how sampling bias and uncertainty shape decisions  

(co-authored with Andreas Bjerre-Nielsen)

We investigate how individuals form beliefs about population behavior using statistical inference based on observations of their social relations. Sampling bias due to the friendship paradox and uncertainty due to small samples lead to misperceptions about others' connectedness and behavior. In a game where actions are strategic complements, we characterize the equilibrium and analyze equilibrium behavior. We show how population behavior depends on the extent of the two biases and agents' level of sophistication to account for them. We demonstrate the role of uncertainty for estimation precision and illustrate when sampling uncertainty plays a critical role compared to sampling bias. We outline how our framework is useful for further analysis of behavior by agents embedded in networks. 

 [Working Paper] 

HOPR as the transport layer of privacy-aware, GDPR-compliant IoT health systems   

(co-authored with Lucas Benedicic, Sebastian Bürgel, Robert Kiel and Rich McDowell)

HOPR is a modern, permissionless, GDPR-compliant network for transport of private and confidential data. Implemented as a decentralized mixnet, HOPR provides network metadata protection and communication privacy, as well as compliance with data protection regulatory frameworks like GDPR. This work validates HOPR’s compliance by presenting relevant GDPR mandates (e.g., articles and recitals) and evaluating them against HOPR features. As the practical case study for this validation, one of the most strictly regulated environments has been chosen: health data and their applications in an IoT setting. HOPR can provide a readily available, autonomous transport layer for private health data while fulfilling GDPR regulatory requirements, i.e., GDPR’s stipulations for handling sensitive data. As such, HOPR positions itself at the intersection of technology, compliance, and healthcare, where new developments are literally lifesaving.  

 [Working Paper] 

Statistical Inference with Sample Selection in Games  

(co-authored with Andreas Bjerre-Nielsen)

We investigate sample selection as a source of misperceptions in games with complete information. We analyze equilibrium behavior in binary action games where the decision to take the action depends on the estimated share of others taking it. With sample selection, agents observe an unrepresentative sample of others’ behavior. We show and disentangle how sample selection, sample size, and heterogeneity in agents' inference procedures affects equilibrium behavior. We outline how our analysis of equilibrium behavior can be useful to analyze the welfare implications of sample selection.

[Working Paper]