scholarly journals An instance space analysis of combat simulations to understand the impact of force and information advantage on survival ratios

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 95
Author(s):  
Luiz Henrique dos Santos Fernandes ◽  
Ana Carolina Lorena ◽  
Kate Smith-Miles

Various criteria and algorithms can be used for clustering, leading to very distinct outcomes and potential biases towards datasets with certain structures. More generally, the selection of the most effective algorithm to be applied for a given dataset, based on its characteristics, is a problem that has been largely studied in the field of meta-learning. Recent advances in the form of a new methodology known as Instance Space Analysis provide an opportunity to extend such meta-analyses to gain greater visual insights of the relationship between datasets’ characteristics and the performance of different algorithms. The aim of this study is to perform an Instance Space Analysis for the first time for clustering problems and algorithms. As a result, we are able to analyze the impact of the choice of the test instances employed, and the strengths and weaknesses of some popular clustering algorithms, for datasets with different structures.


2005 ◽  
Vol 42 (4) ◽  
pp. 383-392 ◽  
Author(s):  
C. S. Indulkar

In this paper, the state space analysis of a ladder network representing a transmission line is used to determine the transient voltage and current distribution along the line following the impact of a step function of voltage at the input terminal.


Author(s):  
Arnaud De Coster ◽  
Nysret Musliu ◽  
Andrea Schaerf ◽  
Johannes Schoisswohl ◽  
Kate Smith-Miles

2016 ◽  
Vol 92 (4) ◽  
pp. 115-143 ◽  
Author(s):  
Sabrina S. Chi ◽  
Devin M. Shanthikumar

ABSTRACT We examine the impact of distance on internet search, and the effect of the “local bias” in search on the stock market response around earnings announcements. We find significant local bias in search behavior. Motivated by theories explaining local bias, local information advantage, and familiarity bias, we predict and find that firms with higher local bias in search experience higher bid-ask spreads, lower trading volumes, and lower earnings response coefficients at the time of earnings announcements, consistent with non-local investors relying more than locals on public information announcements. Consistent with local information advantage, we find that in the week prior to the announcement, firms with higher local bias have higher bid-ask spreads, higher trading volumes, and returns that are more predictive of the coming earnings surprise. Consistent with familiarity bias, firms with higher local bias in search experience stronger post-earnings announcement drift. We use unique predictions, propensity score matching, and two-stage least squares to identify the effects of local bias separately from the effects of overall visibility. Overall, we show there is significant local bias in search, and that this local bias has a significant impact on the market response around earnings announcements.


2016 ◽  
Vol 42 (2) ◽  
pp. 95-117
Author(s):  
Tae-Nyun Kim ◽  
Bumseok Chun

Purpose – The purpose of this paper is to propose several potential determinants of the distance between acquirer and target in M & A deals and examine the negative impact of the acquirer-target distance on announcement returns of acquiring firms. Design/methodology/approach – By employing two-stage regression model, the authors control for the potential endogeneity of acquirer-target distance. The authors use excess distance instead of raw distance between acquirer and target to look at the impact of acquirer-target distance on announcement returns. Findings – The authors find that acquirer-target distance in M & A tends to be longer when major hub airports are located closer to acquiring and target firms, target firm is located in a region with higher level of unemployment rate and median household income, and target firm is smaller and has more cash holdings. When controlling for the potential determinants of acquirer-target distance, including the level of targets information asymmetry, the authors still find that the excess distance between acquirer and target has a negative impact on announcement returns of acquiring firms. Originality/value – This study provides three main contributions to the literature. First, the authors find that acquirer-target distance in M & A is not exogenous and determined by several firm characteristics and regional economic factors. Second, the authors show that the acquirer-target distance has a negative impact on announcement returns even when controlling for the potential determinants. Third, by including information asymmetry measures as potential determinants of acquirer-target distance, the authors show that information advantage of local bidders may not be the most critical factor for their higher returns compared to the bidders from remote areas.


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