scholarly journals Optimal dynamic matching

2020 ◽  
Vol 15 (3) ◽  
pp. 1221-1278 ◽  
Author(s):  
Mariagiovanna Baccara ◽  
SangMok Lee ◽  
Leeat Yariv
Keyword(s):  
Welfare Gain ◽  
Trade Off ◽  
Dynamic Matching ◽  
Optimal Mechanism ◽  
Optimal Dynamic

We study a dynamic matching environment where individuals arrive sequentially. There is a trade‐off between waiting for a thicker market, allowing for higher‐quality matches, and minimizing agents' waiting costs. The optimal mechanism cumulates a stock of incongruent pairs up to a threshold and matches all others in an assortative fashion instantaneously. In discretionary settings, a similar protocol ensues in equilibrium, but expected queues are inefficiently long. We quantify the welfare gain from centralization, which can be substantial, even for low waiting costs. We also evaluate welfare improvements generated by alternative priority protocols.

scholarly journals A Simple and Approximately Optimal Mechanism for a Buyer with Complements

2020 ◽  
Author(s):  
Alon Eden ◽  
Michal Feldman ◽  
Ophir Friedler ◽  
Inbal Talgam-Cohen ◽  
S. Matthew Weinberg
Keyword(s):  
Real World ◽  
Recent Literature ◽  
Revenue Maximization ◽  
Trade Off ◽  
Optimal Mechanism

Recent literature on approximately optimal revenue maximization has shown that in settings where agent valuations for items are complement free, the better of selling the items separately and bundling them together guarantees a constant fraction of the optimal revenue. However, most real-world settings involve some degree of complementarity among items. The role that complementarity plays in the trade-off of simplicity versus optimality has been an obvious missing piece of the puzzle. In “A Simple and Approximately Optimal Mechanism for a Buyer with Complements,” the authors show that the same simple selling mechanism—the better of selling separately and as a grand bundle—guarantees a $\Theta(d)$ fraction of the optimal revenue, where $d$ is a measure of the degree of complementarity. One key modeling contribution is a tractable notion of “degree of complementarity” that admits meaningful results and insights—they demonstrate that previous definitions fall short in this regard.

Informational Herding, Optimal Experimentation, and Contrarianism

2021 ◽  
Author(s):  
Lones Smith ◽  
Peter Norman Sørensen ◽  
Jianrong Tian
Keyword(s):  
Social Learning ◽  
Bayesian Learning ◽  
Comparative Statics ◽  
Value Functions ◽  
Monotone Comparative Statics ◽  
Public Beliefs ◽  
Optimal Mechanism ◽  
Optimal Dynamic ◽  
Informational Externalities

Abstract In the standard herding model, privately informed individuals sequentially see prior actions and then act. An identical action herd eventually starts and public beliefs tend to “cascade sets” where social learning stops. What behaviour is socially efficient when actions ignore informational externalities? We characterize the outcome that maximizes the discounted sum of utilities. Our four key findings are: (a) Cascade sets shrink but do not vanish, and herding should occur but less readily as greater weight is attached to posterity. (b) An optimal mechanism rewards individuals mimicked by their successor. (c) Cascades cannot start after period one under a signal logconcavity condition. (d) Given this condition, efficient behaviour is contrarian, leaning against the myopically more popular actions in every period. We make two technical contributions: As value functions with learning are not smooth, we use monotone comparative statics under uncertainty to deduce optimal dynamic behaviour.We also adapt dynamic pivot mechanisms to Bayesian learning.

scholarly journals Optimal Dynamic Matching

2015 ◽  
Author(s):  
Mariagiovanna Baccara ◽  
SangMok Lee ◽  
Leeat Yariv
Keyword(s):  
Dynamic Matching ◽  
Optimal Dynamic

scholarly journals Trading-Off Static and Dynamic Regret in Online Least-Squares and Beyond

2020 ◽  
Vol 34 (04) ◽  
pp. 6712-6719
Author(s):  
Jianjun Yuan ◽  
Andrew Lamperski
Keyword(s):  
Least Squares ◽  
Path Length ◽  
Gradient Descent ◽  
Recursive Least Squares ◽  
Computationally Efficient ◽  
Step Size ◽  
Trade Off ◽  
Strongly Convex ◽  
Optimal Dynamic ◽  
Computationally Efficient Algorithms

Recursive least-squares algorithms often use forgetting factors as a heuristic to adapt to non-stationary data streams. The first contribution of this paper rigorously characterizes the effect of forgetting factors for a class of online Newton algorithms. For exp-concave and strongly convex objectives, the algorithms achieve the dynamic regret of max{O(log T),O(√TV)}, where V is a bound on the path length of the comparison sequence. In particular, we show how classic recursive least-squares with a forgetting factor achieves this dynamic regret bound. By varying V, we obtain a trade-off between static and dynamic regret. In order to obtain more computationally efficient algorithms, our second contribution is a novel gradient descent step size rule for strongly convex functions. Our gradient descent rule recovers the order optimal dynamic regret bounds described above. For smooth problems, we can also obtain static regret of O(T1-β) and dynamic regret of O(Tβ V*), where β ∈ (0,1) and V* is the path length of the sequence of minimizers. By varying β, we obtain a trade-off between static and dynamic regret.

On the Futility of Dynamics in Robust Mechanism Design

2021 ◽  
Author(s):  
Santiago R. Balseiro ◽  
Anthony Kim ◽  
Daniel Russo
Keyword(s):  
Private Information ◽  
Broad Class ◽  
Dynamic Mechanism ◽  
Minimax Regret ◽  
Worst Case ◽  
Optimal Mechanism ◽  
Optimal Dynamic ◽  
Aggregate Utility ◽  
Static Mechanism ◽  
Robust Mechanism Design

We consider a principal who repeatedly interacts with a strategic agent holding private information. In each round, the agent observes an idiosyncratic shock drawn independently and identically from a distribution known to the agent but not to the principal. The utilities of the principal and the agent are determined by the values of the shock and outcomes that are chosen by the principal based on reports made by the agent. When the principal commits to a dynamic mechanism, the agent best-responds to maximize his aggregate utility over the whole time horizon. The principal’s goal is to design a dynamic mechanism to minimize his worst-case regret, that is, the largest difference possible between the aggregate utility he could obtain if he knew the agent’s distribution and the actual aggregate utility he obtains. We identify a broad class of games in which the principal’s optimal mechanism is static without any meaningful dynamics. The optimal dynamic mechanism, if it exists, simply repeats an optimal mechanism for a single-round problem in each round. The minimax regret is the number of rounds times the minimax regret in the single-round problem. The class of games includes repeated selling of identical copies of a single good or multiple goods, repeated principal-agent relationships with hidden information, and repeated allocation of a resource without money. Outside this class of games, we construct examples in which a dynamic mechanism provably outperforms any static mechanism.

A Flow-Preserving Algorithm for the Time-Cost Trade-Off Problem

1982 ◽  
Vol 14 (2) ◽  
pp. 109-113 ◽  
Author(s):  
Suleyman Tufekci
Keyword(s):  
Time Cost ◽  
Trade Off

Dual Goals for Speed and Accuracy on the Same Performance Task

2012 ◽  
Vol 11 (3) ◽  
pp. 118-126 ◽  
Author(s):  
Olive Emil Wetter ◽  
Jürgen Wegge ◽  
Klaus Jonas ◽  
Klaus-Helmut Schmidt
Keyword(s):  
Memory Scanning ◽  
Performance Task ◽  
Performance Tasks ◽  
Trade Off ◽  
Test Experiment ◽  
Trade Offs ◽  
New Finding ◽  
Sternberg Paradigm ◽  
Speed Accuracy ◽  
Speed And Accuracy

In most work contexts, several performance goals coexist, and conflicts between them and trade-offs can occur. Our paper is the first to contrast a dual goal for speed and accuracy with a single goal for speed on the same task. The Sternberg paradigm (Experiment 1, n = 57) and the d2 test (Experiment 2, n = 19) were used as performance tasks. Speed measures and errors revealed in both experiments that dual as well as single goals increase performance by enhancing memory scanning. However, the single speed goal triggered a speed-accuracy trade-off, favoring speed over accuracy, whereas this was not the case with the dual goal. In difficult trials, dual goals slowed down scanning processes again so that errors could be prevented. This new finding is particularly relevant for security domains, where both aspects have to be managed simultaneously.

Sign in / Sign up

Export Citation Format

Share Document