The Sar1 Gtpase Coordinates Biosynthetic Cargo Selection with Endoplasmic Reticulum Export Site Assembly

Cargo selection and export from the endoplasmic reticulum is mediated by the COPII coat machinery that includes the small GTPase Sar1 and the Sec23/24 and Sec13/31 complexes. We have analyzed the sequential events regulated by purified Sar1 and COPII coat complexes during synchronized export of cargo from the ER in vitro. We find that activation of Sar1 alone, in the absence of other cytosolic components, leads to the formation of ER-derived tubular domains that resemble ER transitional elements that initiate cargo selection. These Sar1-generated tubular domains were shown to be transient, functional intermediates in ER to Golgi transport in vitro. By following cargo export in live cells, we show that ER export in vivo is also characterized by the formation of dynamic tubular structures. Our results demonstrate an unanticipated and novel role for Sar1 in linking cargo selection with ER morphogenesis through the generation of transitional tubular ER export sites

Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems

We study a model of user decision-making in the context of recommender systems via numerical simulation. Our model provides an explanation for the findings of Nguyen, et. al (2014), where, in environments where recommender systems are typically deployed, users consume increasingly similar items over time even without recommendation. We find that recommendation alleviates these natural filter-bubble effects, but that it also leads to an increase in homogeneity across users, resulting in a trade-off between homogenizing across-user consumption and diversifying within-user consumption. Finally, we discuss how our model highlights the importance of collecting data on user beliefs and their evolution over time both to design better recommendations and to further understand their impact

The Social Network Mixtape: Essays on the Economics of the Digital World

This dissertation studies economic issues in the digital economy with a specific focus on the economic aspects of how firms acquire and use consumer data. Chapter 1 empirically studies the drivers of digital attention in the space of social media applications. In order to do so I conduct an experiment where I comprehensively monitor how participants spend their time on digital services and use parental control software to shut off access to either their Instagram or YouTube. I characterize how participants substitute their time during and after the restrictions. I provide an interpretation of the substitution during the restriction period that allows me to conclude that relevant market definitions may be broader than those currently considered by regulatory authorities, but that the substantial diversion towards non-digital activities indicates significant market power from the perspective of consumers for Instagram and YouTube. I then use the results on substitution after the restriction period to motivate a discrete choice model of time usage with inertia and, using the estimates from this model, conduct merger assessments between social media applications. I find that the inertia channel is important for justifying blocking mergers, which I use to argue that currently debated policies aimed at curbing digital addiction are important not only just in their own right but also from an antitrust perspective and, in particular, as a potential policy tool for promoting competition in these markets. More broadly, my paper highlights the utility of product unavailability experiments for demand and merger analysis of digital goods. I thank Maayan Malter for working together with me on collecting the data for this paper. Chapter 2 then studies the next step in consumer data collection process – the extent to which a firm can collect a consumer’s data depends on privacy preferences and the set of available privacy tools. This chapter studies the impact of the General Data Protection Regulation on the ability of a data-intensive intermediary to collect and use consumer data. We find that the opt-in requirement of GDPR resulted in 12.5% drop in the intermediary-observed consumers, but the remaining consumers are trackable for a longer period of time. These findings are consistent with privacy-conscious consumers substituting away from less efficient privacy protection (e.g, cookie deletion) to explicit opt out—a process that would make opt-in consumers more predictable. Consistent with this hypothesis, the average value of the remaining consumers to advertisers has increased, offsetting some of the losses from consumer opt-outs. This chapter is jointly authored with Yeon-Koo Che and Tobias Salz. Chapter 3 and Chapter 4 make up the third portion of the dissertation that studies one of the most prominent uses of consumer data in the digital economy – recommendation systems. This chapter is a combination of several papers studying the economic impact of these systems. The first paper is a joint paper with Duarte Gonçalves which studies a model of strategic interaction between producers and a monopolist platform that employs a recommendation system. We characterize the consumer welfare implications of the platform’s entry into the production market. The platform’s entry induces the platform to bias recommendations to steer consumers towards its own goods, which leads to equilibrium investment adjustments by the producers and lower consumer welfare. Further, we find that a policy separating recommendation and production is not always welfare improving. Our results highlight the ability of integrated recommender systems to foreclose competition on online platforms. The second paper turns towards understanding how such systems impact consumer choices and is joint with Duarte Gonçalves and Shan Sikdar. In this paper we study a model of user decision-making in the context of recommender systems via numerical simulation. Our model provides an explanation for the findings of Nguyen et. al (2014), where, in environments where recommender systems are typically deployed, users consume increasingly similar items over time even without recommendation. We find that recommendation alleviates these natural filter-bubble effects, but that it also leads to an increase in homogeneity across users, resulting in a trade-off between homogenizing across-user consumption and diversifying within-user consumption. Finally, we discuss how our model highlights the importance of collecting data on user beliefs and their evolution over time both to design better recommendations and to further understand their impact

Competing Bandits: The Perils of Exploration Under Competition

Most online platforms strive to learn from interactions with users, and many engage in exploration: making potentially suboptimal choices for the sake of acquiring new information. We study the interplay between exploration and competition: how such platforms balance the exploration for learning and the competition for users. Here users play three distinct roles: they are customers that generate revenue, they are sources of data for learning, and they are self-interested agents which choose among the competing platforms. We consider a stylized duopoly model in which two firms face the same multi-armed bandit problem. Users arrive one by one and choose between the two firms, so that each firm makes progress on its bandit problem only if it is chosen. Through a mix of theoretical results and numerical simulations, we study whether and to what extent competition incentivizes the adoption of better bandit algorithms, and whether it leads to welfare increases for users. We find that stark competition induces firms to commit to a "greedy" bandit algorithm that leads to low welfare. However, weakening competition by providing firms with some "free" users incentivizes better exploration strategies and increases welfare. We investigate two channels for weakening the competition: relaxing the rationality of users and giving one firm a first-mover advantage. Our findings are closely related to the "competition vs. innovation" relationship, and elucidate the first-mover advantage in the digital economy.Comment: merged and extended version of arXiv:1702.08533 and arXiv:1902.0559

Trafficking defects and loss of ligand binding are the underlying causes of all reported DDR2 missense mutations found in SMED-SL patients

Spondylo-meta-epiphyseal dysplasia (SMED) with short limbs and abnormal calcifications (SMED-SL) is a rare, autosomal recessive human growth disorder, characterized by disproportionate short stature, short limbs, short broad fingers, abnormal metaphyses and epiphyses, platyspondyly and premature calcifications. Recently, three missense mutations and one splice-site mutation in the DDR2 gene were identified as causative genetic defects for SMED-SL, but the underlying cellular and biochemical mechanisms were not explored. Here we report a novel DDR2 missense mutation, c.337G>A (p.E113K), that causes SMED-SL in two siblings in the United Arab Emirates. Another DDR2 missense mutation, c.2254C>T (p.R752C), matching one of the previously reported SMED-SL mutations, was found in a second affected family. DDR2 is a plasma membrane receptor tyrosine kinase that functions as a collagen receptor. We expressed DDR2 constructs with the identified point mutations in human cell lines and evaluated their localization and functional properties. We found that all SMED-SL missense mutants were defective in collagen-induced receptor activation and that the three previously reported mutants (p.T713I, p.I726R and p.R752C) were retained in the endoplasmic reticulum. The novel mutant (p.E113K), in contrast, trafficked normally, like wild-type DDR2, but failed to bind collagen. This finding is in agreement with our recent structural data identifying Glu113 as an important amino acid in the DDR2 ligand-binding site. Our data thus demonstrate that SMED-SL can result from at least two different loss-of-function mechanisms: namely defects in DDR2 targeting to the plasma membrane or the loss of its ligand-binding activity

Sar1 assembly regulates membrane constriction and ER export

While dynamin pinches vesicles from the plasma membrane, the Sar1 GTPase specializes in cinching ER membrane tubules

Event-B Patterns for Specifying Fault-Tolerance in Multi-Agent Interaction

Interaction in a multi-agent system is susceptible to failure. A rigorous development of a multi-agent system must include the treatment of fault-tolerance of agent interactions for the agents to be able to continue to function independently. Patterns can be used to capture fault-tolerance techniques. A set of modelling patterns is presented that specify fault-tolerance in Event-B specifications of multi-agent interactions. The purpose of these patterns is to capture common modelling structures for distributed agent interaction in a form that is re-usable on other related developments. The patterns have been applied to a case study of the contract net interaction protocol

Regulation of Sar1 NH2 terminus by GTP binding and hydrolysis promotes membrane deformation to control COPII vesicle fission

The mechanisms by which the coat complex II (COPII) coat mediates membrane deformation and vesicle fission are unknown. Sar1 is a structural component of the membrane-binding inner layer of COPII (Bi, X., R.A. Corpina, and J. Goldberg. 2002. Nature. 419:271–277). Using model liposomes we found that Sar1 uses GTP-regulated exposure of its NH2-terminal tail, an amphipathic peptide domain, to bind, deform, constrict, and destabilize membranes. Although Sar1 activation leads to constriction of endoplasmic reticulum (ER) membranes, progression to effective vesicle fission requires a functional Sar1 NH2 terminus and guanosine triphosphate (GTP) hydrolysis. Inhibition of Sar1 GTP hydrolysis, which stabilizes Sar1 membrane binding, resulted in the formation of coated COPII vesicles that fail to detach from the ER. Thus Sar1-mediated GTP binding and hydrolysis regulates the NH2-terminal tail to perturb membrane packing, promote membrane deformation, and control vesicle fission