scholarly journals What Causes Herding: Information Cascade or Search Cost?

2010 ◽  
Author(s):  
William T. LIn ◽  
Shih-Chuan Tsai ◽  
David S. Sun
Keyword(s):  
Search Cost ◽  
Information Cascade

scholarly journals Nonsequential Search Equilibrium with Search Cost Heterogeneity

2010 ◽  
Author(s):  
José Luis Moraga-González ◽  
Zsolt Sandor ◽  
Matthijs R. Wildenbeest
Keyword(s):  
Search Cost ◽  
Search Equilibrium

scholarly journals Information cascade on networks

2016 ◽  
Vol 450 ◽  
pp. 570-584 ◽  
Author(s):  
Masato Hisakado ◽  
Shintaro Mori
Keyword(s):  
Information Cascade

scholarly journals Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
José Carlos Ortiz-Bayliss ◽  
Ivan Amaya ◽  
Santiago Enrique Conant-Pablos ◽  
Hugo Terashima-Marín
Keyword(s):  
Constraint Satisfaction ◽  
Constraint Satisfaction Problems ◽  
Search Cost ◽  
High Quality ◽  
Simple Method ◽  
Variable Ordering ◽  
It Performance ◽  
The Cost ◽  
The Impact ◽  
Variable Ordering Heuristics

When solving constraint satisfaction problems (CSPs), it is a common practice to rely on heuristics to decide which variable should be instantiated at each stage of the search. But, this ordering influences the search cost. Even so, and to the best of our knowledge, no earlier work has dealt with how first variable orderings affect the overall cost. In this paper, we explore the cost of finding high-quality orderings of variables within constraint satisfaction problems. We also study differences among the orderings produced by some commonly used heuristics and the way bad first decisions affect the search cost. One of the most important findings of this work confirms the paramount importance of first decisions. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. Another one is the evidence that many of the existing variable ordering heuristics fail to appropriately select the first variable to instantiate. We propose a simple method to improve early decisions of heuristics. By using it, performance of heuristics increases.

Consumer Search and Filtering on Online Retail Platforms

2020 ◽  
Vol 57 (5) ◽  
pp. 900-916
Author(s):  
Baojun Jiang ◽  
Tianxin Zou
Keyword(s):  
Consumer Search ◽  
Third Party ◽  
Demand Elasticity ◽  
Search Cost ◽  
Online Retail ◽  
The Third ◽  
Retail Prices ◽  
The Third Party

This article examines how the consumer’s search cost and filtering on a retail platform affect the platform, the third-party sellers, and the consumers. The authors show that, given the platform’s percentage referral fee, a lower search cost can either increase or decrease the platform’s profit. By contrast, if the platform optimally adjusts its referral fee, a lower search cost will increase the platform’s profit. As the consumer’s search cost decreases, if the platform’s demand elasticity increases significantly, the platform should reduce its fee, potentially resulting in an all-win outcome for the platform, the sellers, and the consumers; otherwise, a lower search cost will increase the platform’s optimal fee percentage, potentially leading to higher equilibrium retail prices. Furthermore, the availability of filtering on the platform will in expectation induce consumers to search fewer products but buy products with higher match values, and filtering can either increase or decrease equilibrium retail prices. When filtering reveals only a small amount of the products’ match-value variations, it will benefit the platform, the sellers, and the consumers. This article shows that the effects of filtering and those of a decrease in search cost are qualitatively different.

scholarly journals Search cost for a nearly optimal path in a binary tree

2009 ◽  
Vol 19 (4) ◽  
pp. 1273-1291 ◽  
Author(s):  
Robin Pemantle
Keyword(s):  
Binary Tree ◽  
Optimal Path ◽  
Search Cost

Analysis of Robin Hood and Other Hashing Algorithms Under the Random Probing Model, With and Without Deletions

2018 ◽  
Vol 28 (4) ◽  
pp. 600-617
Author(s):  
P. V. POBLETE ◽  
A. VIOLA
Keyword(s):  
Search Algorithm ◽  
Recurrence Equation ◽  
Constant Time ◽  
Collision Resolution ◽  
Load Factor ◽  
Hash Tables ◽  
Search Cost ◽  
Robin Hood ◽  
Small Constant ◽  
Load Factors

Thirty years ago, the Robin Hood collision resolution strategy was introduced for open addressing hash tables, and a recurrence equation was found for the distribution of its search cost. Although this recurrence could not be solved analytically, it allowed for numerical computations that, remarkably, suggested that the variance of the search cost approached a value of 1.883 when the table was full. Furthermore, by using a non-standard mean-centred search algorithm, this would imply that searches could be performed in expected constant time even in a full table.In spite of the time elapsed since these observations were made, no progress has been made in proving them. In this paper we introduce a technique to work around the intractability of the recurrence equation by solving instead an associated differential equation. While this does not provide an exact solution, it is sufficiently powerful to prove a bound of π2/3 for the variance, and thus obtain a proof that the variance of Robin Hood is bounded by a small constant for load factors arbitrarily close to 1. As a corollary, this proves that the mean-centred search algorithm runs in expected constant time.We also use this technique to study the performance of Robin Hood hash tables under a long sequence of insertions and deletions, where deletions are implemented by marking elements as deleted. We prove that, in this case, the variance is bounded by 1/(1−α), where α is the load factor.To model the behaviour of these hash tables, we use a unified approach that we apply also to study the First-Come-First-Served and Last-Come-First-Served collision resolution disciplines, both with and without deletions.

Sign in / Sign up

Export Citation Format

Share Document