Demo Data from EDBT 2026 / reviewers in the wild / expert
Tyrone Strangway
dblp:172/1194
· DBLP profile ↗
4ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 4 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 3
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
3 papers |
Algorithmic game theory and mechanism design · 89% Algorithms and data structures · 11% | |
| Artificial intelligence
1 paper |
Knowledge representation and reasoning · 100% |
Topics — the 7 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning
description logic |
0.8 | 1 | 2024 | Primarily about primaries · Artif. Intell. 2024 |
Algorithmic game theory and mechanism design
social choice |
0.7 | 2 | 2019 | Primarily about Primaries · AAAI 2019 Big City vs. the Great Outdoors: Voter Distribution and How It Affects Gerrymandering · IJCAI 2018 |
Algorithmic game theory and mechanism design › social choice › computational social choice
voting rules |
0.7 | 2 | 2019 | Primarily about Primaries · AAAI 2019 Convergence and Quality of Iterative Voting Under Non-Scoring Rules · IJCAI 2017 |
Algorithmic game theory and mechanism design › social choice
gerrymandering |
0.3 | 1 | 2018 | Big City vs. the Great Outdoors: Voter Distribution and How It Affects Gerrymandering · IJCAI 2018 |
Algorithmic game theory and mechanism design › social choice
computational social choice |
0.3 | 1 | 2017 | Convergence and Quality of Iterative Voting Under Non-Scoring Rules · IJCAI 2017 |
Algorithmic game theory and mechanism design › game dynamics
equilibrium convergence |
0.3 | 1 | 2017 | Convergence and Quality of Iterative Voting Under Non-Scoring Rules · IJCAI 2017 |
Algorithmic game theory and mechanism design › social choice › strategic voting
iterative voting |
0.3 | 1 | 2017 | Convergence and Quality of Iterative Voting Under Non-Scoring Rules · IJCAI 2017 |
Methods — techniques the papers use, named apart from their topics
algorithmic gerrymandering · 0.3game-theoretic equilibrium analysis · 0.3empirical analysis · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Primarily about primaries
Allan Borodin, Omer Lev, Nisarg Shah 0001, Tyrone Strangway |
Artif. Intell. | 4 |
| 2019 | Primarily about PrimariesabstractMuch of the social choice literature examines direct voting systems, in which voters submit their ranked preferences over candidates and a voting rule picks a winner. Real-world elections and decision-making processes are often more complex and involve multiple stages. For instance, one popular voting system filters candidates through primaries: first, voters affiliated with each political party vote over candidates of their own party and the voting rule picks a candidate from each party, which then compete in a general election.We present a model to analyze such multi-stage elections, and conduct the first quantitative comparison (to the best of our knowledge) of the direct and primary voting systems with two political parties in terms of the quality of the elected candidate. Our main result is that every voting rule is guaranteed to perform almost as well (i.e., within a constant factor) under the primary system as under the direct system. Surprisingly, the converse does not hold: we show settings in which there exist voting rules that perform significantly better under the primary system than under the direct system. Allan Borodin, Omer Lev, Nisarg Shah 0001, Tyrone Strangway |
AAAI | 4 |
| 2018 | Big City vs. the Great Outdoors: Voter Distribution and How It Affects GerrymanderingabstractGerrymandering is the process by which parties manipulate boundaries of electoral districts in order to maximize the number of districts they can win. Demographic trends show an increasingly strong correlation between residence and party affiliation; some party’s supporters congregate in cities, while others stay in more rural areas. We investigate both theoretically and empirically the effect of this trend on a party's ability to gerrymander in a two-party model ("urban party" and "rural party"). Along the way, we propose a definition of the gerrymandering power of a party, and an algorithmic approach for near-optimal gerrymandering in large instances. Our results suggest that beyond a fairly small concentration of urban party's voters, the gerrymandering power of a party depends almost entirely on the level of concentration, and not on the party's share of the population. As partisan separation grows, the gerrymandering power of both parties converge so that each party can gerrymander to get only slightly more than what its voting share warrants, bringing about, ultimately, a more representative outcome. Moreover, there seems to be an asymmetry between the gerrymandering power of the parties, with the rural party being more capable of gerrymandering. Allan Borodin, Omer Lev, Nisarg Shah 0001, Tyrone Strangway |
IJCAI | 4 |
| 2017 | Convergence and Quality of Iterative Voting Under Non-Scoring RulesabstractIterative voting is a social choice mechanism that assumes all voters are strategic, and allows voters to change their stated preferences as the vote progresses until an equilibrium is reached (at which point no player wishes to change their vote). Previous research established that this process converges to an equilibrium for the plurality and veto voting methods and for no other scoring rule. We consider iterative voting for non-scoring rules, examining the major ones, and show that none of them converge when assuming (as most research has so far) that voters pursue a best response strategy. We investigate other potential voter strategies, with a more heuristic flavor (since for most of these voting rules, calculating the best response is NP-hard); we show that they also do not converge. We then conduct an empirical analysis of the iterative voting winners for these non-scoring rules, and compare the winner quality of various strategies. Aaron Koolyk, Tyrone Strangway, Omer Lev, Jeffrey S. Rosenschein |
IJCAI | 2 |