VLDB 2026 Research / reviewers in the wild / expert
Danny Cowser
dblp:429/6593
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 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.
| Artificial intelligence
1 paper |
Multi-agent systems · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Multi-agent systems
agent-based simulation |
1.0 | 1 | 2026 | Toward Simulating Networked Societies with Formal Institutions Using AI Agents · AAAI 2026 |
Computational social science and digital humanities
social simulation |
1.0 | 1 | 2026 | Toward Simulating Networked Societies with Formal Institutions Using AI Agents · AAAI 2026 |
Methods — techniques the papers use, named apart from their topics
simulation · 2.0AI agents · 1.0AI agent · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Toward Simulating Networked Societies with Formal Institutions Using AI AgentsabstractInstitutions are key to creating societies that are efficient, fair, and benevolent. Despite their importance, the complexities of human (networked) societies make it difficult to understand how formal institutions form and how they shape human communities. Artificial intelligence (AI) can potentially raise understanding in this regard. Thus, in this paper, we present a simulation model utilizing AI agents to simulate networked societies that contain formal institutions. We then observe the outputs of the resulting model under different societal conditions and formal institutions, and (where applicable) compare and contrast these outputs with political and economic theories. Our model outputs (a) address how inequality impacts societal prosperity, (b) illuminate how institutions can potentially impact poverty, and (c) give insights into the attributes of formal institutions that individuals are inclined to support. These and future simulation models can potentially inform how AI can support the design and development of institutions that facilitate healthier communities and nations. Michael Richards, Danny Cowser, Daniel Nielson, Jacob W. Crandall |
AAAI | 2 |