Dongjin Hwang

dblp:410/2486 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
0009-0004-6863-0495ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Theory of computation · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Algorithmic game theory and mechanism design · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational finance and economics · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design
consumer search
0.912025
Competitive Information Disclosure with Heterogeneous Consumer Search · EC 2025
Algorithmic game theory and mechanism design › mechanism design
information design
0.912025
Competitive Information Disclosure with Heterogeneous Consumer Search · EC 2025

Methods — techniques the papers use, named apart from their topics

equilibrium analysis · 1.7
YearPublicationVenuePosition
2025 Competitive Information Disclosure with Heterogeneous Consumer Search
abstract
We study a model of competitive information design in an oligopoly search market with heterogeneous consumer search costs. A unique class of equilibria—upper-censorship equilibria—emerges under intense competition. In equilibrium, firms balance competitive pressure with local monopoly power granted by search frictions. Notably, firms disclose only partial information even as the number of firms approaches infinity. The maximal informativeness of equilibrium decreases under first-order shifts in the search cost distribution, but varies non- monotonically under mean-preserving spreads. Instead, informativeness increases in the evenness of the search cost distribution, measured by minc H(c)/c. Finally, the model nests two canonical benchmarks as limiting cases: it converges to full disclosure as search frictions vanish and to no disclosure when search costs become homogeneous.
Dongjin Hwang, Ilwoo Hwang
EC1