Search for: All records

We model the oligopoly competition in a dockless bike-sharing (DLB) market as a bilevel game. Each DLB operator is first committed to an action tied to a specific objective, such as maximizing profit. Then, the operators play a lower-level game to achieve their individual goals and finally reach a subgame perfect Nash equilibrium by making tactic decisions (e.g., pricing and fleet sizing). We define a Nash equilibrium under either weak or strong preference to characterize the likely outcomes of the bilevel game and formulate the demand-supply equilibrium of a DLB market that accounts for key operational features and mode choice. Using the oligopoly game model calibrated with empirical data, we show that if an operator seeks to maximize its market share with a budget constraint, all other operators must either respond in kind or be driven out of the market. When all operators compete for market dominance, even a slight efficiency edge gained by one operator can significantly shift the outcome, which signals high volatility. Moreover, even if all operators agree to focus on making money rather than ruinously seeking dominance, profitability still plunges quickly with the number of operators. Taken together, the results explain why an unregulated DLB market is often oversupplied and prone to collapse under competition. We also show that this market failure may be prevented by a fleet cap regulation, which sets an upper limit on each operator’s fleet size. Funding: This research was supported by the U.S. National Science Foundation’s Civil Infrastructure System (CIS) Program under the award CMMI no. 2225087. K. Zhang received financial support from the Swiss National Science Foundation [Grant 219232]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0846 . 

Abstract Motivation Minimizers are efficient methods to sample k-mers from genomic sequences that unconditionally preserve sufficiently long matches between sequences. Well-established methods to construct efficient minimizers focus on sampling fewer k-mers on a random sequence and use universal hitting sets (sets of k-mers that appear frequently enough) to upper bound the sketch size. In contrast, the problem of sequence-specific minimizers, which is to construct efficient minimizers to sample fewer k-mers on a specific sequence such as the reference genome, is less studied. Currently, the theoretical understanding of this problem is lacking, and existing methods do not specialize well to sketch specific sequences. Results We propose the concept of polar sets, complementary to the existing idea of universal hitting sets. Polar sets are k-mer sets that are spread out enough on the reference, and provably specialize well to specific sequences. Link energy measures how well spread out a polar set is, and with it, the sketch size can be bounded from above and below in a theoretically sound way. This allows for direct optimization of sketch size. We propose efficient heuristics to construct polar sets, and via experiments on the human reference genome, show their practical superiority in designing efficient sequence-specific minimizers. Availability and implementation A reference implementation and code for analyses under an open-source license are at https://github.com/kingsford-group/polarset. Supplementary information Supplementary data are available at Bioinformatics online.