76 research outputs found
Incentive-Aware Models of Financial Networks
Financial networks help firms manage risk but also enable financial shocks to
spread. Despite their importance, existing models of financial networks have
several limitations. Prior works often consider a static network with a simple
structure (e.g., a ring) or a model that assumes conditional independence
between edges. We propose a new model where the network emerges from
interactions between heterogeneous utility-maximizing firms. Edges correspond
to contract agreements between pairs of firms, with the contract size being the
edge weight. We show that, almost always, there is a unique "stable network."
All edge weights in this stable network depend on all firms' beliefs.
Furthermore, firms can find the stable network via iterative pairwise
negotiations. When beliefs change, the stable network changes. We show that
under realistic settings, a regulator cannot pin down the changed beliefs that
caused the network changes. Also, each firm can use its view of the network to
inform its beliefs. For instance, it can detect outlier firms whose beliefs
deviate from their peers. But it cannot identify the deviant belief: increased
risk-seeking is indistinguishable from increased expected profits. Seemingly
minor news may settle the dilemma, triggering significant changes in the
network
Decompositions of Triangle-Dense Graphs
High triangle density -- the graph property stating that a constant fraction
of two-hop paths belong to a triangle -- is a common signature of social
networks. This paper studies triangle-dense graphs from a structural
perspective. We prove constructively that significant portions of a
triangle-dense graph are contained in a disjoint union of dense, radius 2
subgraphs. This result quantifies the extent to which triangle-dense graphs
resemble unions of cliques. We also show that our algorithm recovers planted
clusterings in approximation-stable k-median instances.Comment: 20 pages. Version 1->2: Minor edits. 2->3: Strengthened {\S}3.5,
removed appendi
Storia: Summarizing Social Media Content based on Narrative Theory using Crowdsourcing
People from all over the world use social media to share thoughts and
opinions about events, and understanding what people say through these channels
has been of increasing interest to researchers, journalists, and marketers
alike. However, while automatically generated summaries enable people to
consume large amounts of data efficiently, they do not provide the context
needed for a viewer to fully understand an event. Narrative structure can
provide templates for the order and manner in which this data is presented to
create stories that are oriented around narrative elements rather than
summaries made up of facts. In this paper, we use narrative theory as a
framework for identifying the links between social media content. To do this,
we designed crowdsourcing tasks to generate summaries of events based on
commonly used narrative templates. In a controlled study, for certain types of
events, people were more emotionally engaged with stories created with
narrative structure and were also more likely to recommend them to others
compared to summaries created without narrative structure
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