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Journal articles on the topic 'Imperfect Selection'

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Author: Grafiati
Published: 28 October 2022

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1

Mahoney, Neale, and E. Glen Weyl. "Imperfect Competition in Selection Markets." Review of Economics and Statistics 99, no. 4 (October 2017): 637–51. http://dx.doi.org/10.1162/rest_a_00661.

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2

Eguia, Jon X., Aniol Llorente-Saguer, Rebecca Morton, and Antonio Nicolò. "Equilibrium selection in sequential games with imperfect information." Games and Economic Behavior 109 (May 2018): 465–83. http://dx.doi.org/10.1016/j.geb.2018.01.008.

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3

Chen, Hsiao-Chi, and Yunshyong Chow. "Equilibrium Selection in Evolutionary Games with Imperfect Monitoring." Journal of Applied Probability 45, no. 02 (June 2008): 388–402. http://dx.doi.org/10.1017/s0021900200004307.

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In this paper we analyze players' long-run behavior in evolutionary coordination games with imperfect monitoring in a large population. Players can observe signals corresponding to other players' unseen actions and use the proposed simple or maximum likelihood estimation algorithm to extract information from the signals. In the simple learning process we find conditions for the risk-dominant and the non-risk-dominant equilibria to emerge alone in the long run. Furthermore, we find that the two equilibria can coexist in the long run. In contrast, the coexistence of the two equilibria is the only limit distribution under the maximum likelihood estimation learning algorithm. We also analyze the long-run equilibria of other 2x2 symmetric games under imperfect monitoring.
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4

Bisin, Alberto, and Thierry Verdier. "Agents with imperfect empathy may survive natural selection." Economics Letters 71, no. 2 (May 2001): 277–85. http://dx.doi.org/10.1016/s0165-1765(01)00376-7.

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5

Besancenot, Damien, João R. Faria, and Kim V. Huynh. "Congestion of Academic Journals Under Papers’ Imperfect Selection." B.E. Journal of Economic Analysis & Policy 14, no. 3 (July 1, 2014): 1145–67. http://dx.doi.org/10.1515/bejeap-2013-0093.

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Abstract This paper studies how the congestion of its editorial process affects an academic journal. In a publishing game played by researchers and editors, we assume that quality screening by editors depends on their ability to properly process the flow of submissions. When too many papers arrive, this ability declines and editors may reject good papers or accept papers with little contribution to scientific knowledge. In this game, a separating equilibrium always exists in which only good researchers submit their papers to the journal. Each paper is accepted and the quality of the journal reaches its highest level. However, when the researchers’ reward for each publication exceeds a given threshold, two hybrid equilibria are also feasible. In these equilibria, authors of low-quality papers submit their papers to the journal and, as the flow of papers exceeds the editors’ capacity of perfect assessment, the selection process becomes imperfect. This creates an opportunity for authors to submit poor quality papers, a behaviour which contributes to the congestion of the editorial process. The various strategies implemented by the editors to oppose congestion are then discussed.
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6

Chen, Hsiao-Chi, and Yunshyong Chow. "Equilibrium Selection in Evolutionary Games with Imperfect Monitoring." Journal of Applied Probability 45, no. 2 (June 2008): 388–402. http://dx.doi.org/10.1239/jap/1214950355.

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In this paper we analyze players' long-run behavior in evolutionary coordination games with imperfect monitoring in a large population. Players can observe signals corresponding to other players' unseen actions and use the proposed simple or maximum likelihood estimation algorithm to extract information from the signals. In the simple learning process we find conditions for the risk-dominant and the non-risk-dominant equilibria to emerge alone in the long run. Furthermore, we find that the two equilibria can coexist in the long run. In contrast, the coexistence of the two equilibria is the only limit distribution under the maximum likelihood estimation learning algorithm. We also analyze the long-run equilibria of other 2x2 symmetric games under imperfect monitoring.
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7

Pfennig, David W., and David W. Kikuchi. "Competition and the evolution of imperfect mimicry." Current Zoology 58, no. 4 (August 1, 2012): 608–19. http://dx.doi.org/10.1093/czoolo/58.4.608.

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Abstract Mimicry is widely used to exemplify natural selection’s power in promoting adaptation. Nonetheless, it has become increasingly clear that mimicry is frequently imprecise. Indeed, the phenotypic match is often poor between mimics and models in many Batesian mimicry complexes and among co-mimics in many Müllerian mimicry complexes. Here, we consider whether such imperfect mimicry represents an evolutionary compromise between predator-mediated selection favoring mimetic convergence on the one hand and competitively mediated selection favoring divergence on the other hand. Specifically, for mimicry to be effective, mimics and their models/co-mimics should occur together. Yet, co-occurring species that are phenotypically similar often compete for resources, successful reproduction, or both. As an adaptive response to minimize such costly interactions, interacting species may diverge phenotypically through an evolutionary process known as character displacement. Such divergence between mimics and their models/co-mimics may thereby result in imperfect mimicry. We review the various ways in which character displacement could promote imprecise mimicry, describe the conditions under which this process may be especially likely to produce imperfect mimicry, examine a possible case study, and discuss avenues for future research. Generally, character displacement may play an underappreciated role in fostering inexact mimicry.
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8

Choi, Michael. "Imperfect information transmission and adverse selection in asset markets." Journal of Economic Theory 176 (July 2018): 619–49. http://dx.doi.org/10.1016/j.jet.2018.04.007.

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9

Urban, Natasha A., Robert K. Swihart, Melissa C. Malloy, and John B. Dunning. "Improving selection of indicator species when detection is imperfect." Ecological Indicators 15, no. 1 (April 2012): 188–97. http://dx.doi.org/10.1016/j.ecolind.2011.09.031.

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10

Çanakoğlu, Ethem, and Süleyman Özekici. "Portfolio selection with imperfect information: A hidden Markov model." Applied Stochastic Models in Business and Industry 27, no. 2 (March 2011): 95–114. http://dx.doi.org/10.1002/asmb.885.

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11

Taghiyar, M. Jafar, Sami Muhaidat, Jie Liang, and Mehrdad Dianati. "Relay Selection with Imperfect CSI in Bidirectional Cooperative Networks." IEEE Communications Letters 16, no. 1 (January 2012): 57–59. http://dx.doi.org/10.1109/lcomm.2011.111011.111774.

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12

Britton, L. C., T. A. R. Clark, and D. F. Ball. "Executive Search and Selection: Imperfect Theory or Intractable Industry?" Service Industries Journal 12, no. 2 (April 1992): 238–50. http://dx.doi.org/10.1080/02642069200000025.

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13

Witt, Ulrich. "Firms' market behavior under imperfect information and economic natural selection." Journal of Economic Behavior & Organization 7, no. 3 (September 1986): 265–90. http://dx.doi.org/10.1016/0167-2681(86)90032-6.

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14

Chassagnon, Arnold, and Bertrand Villeneuve. "Optimal risk-sharing under adverse selection and imperfect risk perception." Canadian Journal of Economics/Revue canadienne d'conomique 38, no. 3 (August 2005): 955–78. http://dx.doi.org/10.1111/j.0008-4085.2005.00311.x.

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15

Bennett, David P. "Large Magellanic Cloud Microlensing Optical Depth with Imperfect Event Selection." Astrophysical Journal 633, no. 2 (November 10, 2005): 906–13. http://dx.doi.org/10.1086/432830.

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16

Inwood, Kris, and Hamish Maxwell-Stewart. "Selection Bias and Social Science History." Social Science History 44, no. 3 (2020): 411–16. http://dx.doi.org/10.1017/ssh.2020.18.

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AbstractHistorians and social scientists routinely, and inevitably, rely on sources that are unrepresentative of the past. The articles in this special issue of the journal illustrate the widespread prevalence of selection bias in historical sources, and the ways in which historians negotiate this challenge to reach useful conclusions from valuable, if imperfect sources.
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17

Pan, Ya-Han, and Sonia Aïssa. "Performance of selective space-time coding and selection diversity under perfect and imperfect CSI." Wireless Communications and Mobile Computing 9, no. 2 (February 2009): 209–16. http://dx.doi.org/10.1002/wcm.601.

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18

Fewster, Paul F. "A `beam-selection' high-resolution X-ray diffractometer." Journal of Applied Crystallography 37, no. 4 (July 17, 2004): 565–74. http://dx.doi.org/10.1107/s0021889804011094.

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A new diffractometer that can be described as a high-intensity low-background high-resolution diffractometer for analysing perfect, nearly perfect and highly imperfect materials on a routine basis is presented. The instrumentation is very simple and uncomplicated, yet the way in which it works is less obvious. The sample requires minimal sample alignment, the resolution can be adjusted to optimize the experiment and the wavelength dispersion can be controlled. This diffractometer can produce near perfect profiles from bent and imperfect samples. The illuminated area can easily be varied from greater than 3 mm down to 50 µm diameter, offering great opportunities in microdiffraction with high resolution. The instrument appears similar to a double-crystal diffractometer in reverse,i.e.the sample and collimating crystal of a conventional double-crystal diffractometer are reversed; however, the concept is quite different.
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19

Guowei, Lei, Liu Yuanan, and Xiao Xuefang. "Effect of imperfect CSI on STBC‐MISO system via antenna selection." IET Signal Processing 10, no. 2 (April 2016): 115–24. http://dx.doi.org/10.1049/iet-spr.2015.0127.

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20

Dewangan, Vikas Kumar, and Neelesh B. Mehta. "Capture-Induced, Fast, Distributed, Splitting Based Selection with Imperfect Power Control." IEEE Transactions on Communications 62, no. 1 (January 2014): 74–84. http://dx.doi.org/10.1109/tcomm.2013.112913.130263.

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21

Ma, Qian, and Cihan Tepedelenlioglu. "Antenna selection for space-time coded systems with imperfect channel estimation." IEEE Transactions on Wireless Communications 6, no. 2 (February 2007): 710–19. http://dx.doi.org/10.1109/twc.2007.05392.

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22

Cheng, Chien-Chun, Serdar Sezginer, Hikmet Sari, and Yu T. Su. "Robust MIMO Detection Under Imperfect CSI Based on Bayesian Model Selection." IEEE Wireless Communications Letters 2, no. 4 (August 2013): 375–78. http://dx.doi.org/10.1109/wcl.2013.042313.130148.

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23

Altenberg, Lee, and Marcus W. Feldman. "Selection, Generalized Transmission and the Evolution of Modifier Genes. I. The Reduction Principle." Genetics 117, no. 3 (November 1, 1987): 559–72. http://dx.doi.org/10.1093/genetics/117.3.559.

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ABSTRACT Modifier gene models are used to explore the evolution of features of organisms, such as the genetic system, that are not directly involved in the determination of fitness. Recent work has shown that a general "reduction principle" holds in models of selectively neutral modifiers of recombination, mutation, and migration. Here we present a framework for models of modifier genes that shows these reduction results to be part of a more general theory, for which recombination and mutation are special cases.—The deterministic forces that affect the genetic composition of a population can be partitioned into two categories: selection and transmission. Selection includes differential viabilities, fertilities, and mating success. Imperfect transmission occurs as a result of such phenomena as recombination, mutation and migration, meiosis, gene conversion, and meiotic drive. Selectively neutral modifier genes affect transmission, and a neutral modifier gene can evolve only by generating association with selected genes whose transmission it affects.–We show that, in randomly mating populations at equilibrium, imperfect transmission of selected genes allows a variance in their marginal fitnesses to be maintained. This variance in the marginal fitnesses of selected genes is what drives the evolution of neutral modifier genes. Populations with a variance in marginal fitnesses at equilibrium are always subject to invasion by modifier genes that bring about perfect transmission of the selected genes. It is also found, within certain constraints, that for modifier genes producing what we call "linear variation" in the transmission processes, a new modifier allele can invade a population at equilibrium if it reduces the level of imperfect transmission acting on the selected genes, and will be expelled if it increases the level of imperfect transmission. Moreover, the strength of the induced selection on the modifier gene is shown to range up to the order of the departure of the genetic system from perfect transmission.
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24

Salhab, Anas M., and Salam A. Zummo. "Multiuser Cognitive Networks with Nth-Best User Selection and Imperfect Channel Estimation." Arabian Journal for Science and Engineering 39, no. 12 (November 15, 2014): 8979–87. http://dx.doi.org/10.1007/s13369-014-1458-z.

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25

Martimort, David, Aggey Semenov, and Lars Stole. "Optimal stationary contract with two-sided imperfect enforcement and persistent adverse selection." Economics Letters 159 (October 2017): 18–22. http://dx.doi.org/10.1016/j.econlet.2017.07.007.

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26

Deng, Sen, Bo Jing, and Hongliang Zhou. "Heuristic particle swarm optimization approach for test point selection with imperfect test." Journal of Intelligent Manufacturing 28, no. 1 (August 24, 2014): 37–50. http://dx.doi.org/10.1007/s10845-014-0960-1.

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27

Zhang, Yangyang, Jianhua Ge, Erchin Serpedin, Redha M. Radaydeh, and Yifan Hu. "On cooperative NOMA relay selection under Nakagami‐mfading and imperfect channel estimation." Transactions on Emerging Telecommunications Technologies 29, no. 12 (November 19, 2018): e3535. http://dx.doi.org/10.1002/ett.3535.

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28

Rezaei, Jafar, and Mansoor Davoodi. "A deterministic, multi-item inventory model with supplier selection and imperfect quality." Applied Mathematical Modelling 32, no. 10 (October 2008): 2106–16. http://dx.doi.org/10.1016/j.apm.2007.07.009.

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29

Taghiyar, M. J., J. Liang, and S. Muhaidat. "Max–min relay selection in bidirectional cooperative networks with imperfect channel estimation." IET Communications 6, no. 15 (October 16, 2012): 2497–502. http://dx.doi.org/10.1049/iet-com.2011.0572.

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30

Seyfi, Mehdi, Sami Muhaidat, and Jie Liang. "Amplify-and-Forward Selection Cooperation over Rayleigh Fading Channels with Imperfect CSI." IEEE Transactions on Wireless Communications 11, no. 1 (January 2012): 199–209. http://dx.doi.org/10.1109/twc.2011.120611.11006.

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31

Crawford, James, and Youngwook Ko. "Cooperative OFDM-IM Relay Networks With Partial Relay Selection Under Imperfect CSI." IEEE Transactions on Vehicular Technology 67, no. 10 (October 2018): 9362–69. http://dx.doi.org/10.1109/tvt.2018.2853672.

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32

Ma, Bei, Hailin Zhang, and Zhaowei Zhang. "Joint power allocation and mode selection for D2D communications with imperfect CSI." China Communications 12, no. 7 (July 2015): 73–81. http://dx.doi.org/10.1109/cc.2015.7188526.

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33

Tukmanov, Anvar, Said Boussakta, Zhiguo Ding, and Abbas Jamalipour. "Outage Performance Analysis of Imperfect-CSI-Based Selection Cooperation in Random Networks." IEEE Transactions on Communications 62, no. 8 (August 2014): 2747–57. http://dx.doi.org/10.1109/tcomm.2014.2322093.

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34

Liang, Xuesong, Yongpeng Wu, Derrick Wing Kwan Ng, Shi Jin, Yingbiao Yao, and Tao Hong. "Outage Probability of Cooperative NOMA Networks Under Imperfect CSI With User Selection." IEEE Access 8 (2020): 117921–31. http://dx.doi.org/10.1109/access.2020.2995875.

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35

Hongyu Cui, Rongqing Zhang, Lingyang Song, and Bingli Jiao. "Capacity Analysis of Bidirectional AF Relay Selection with Imperfect Channel State Information." IEEE Wireless Communications Letters 2, no. 3 (June 2013): 255–58. http://dx.doi.org/10.1109/wcl.2013.020513.120933.

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36

Clemenz, Gerhard. "Adverse selection and imperfect monitoring in a labour market: Some game-theoretic remarks." Empirica 14, no. 2 (1987): 213–26. http://dx.doi.org/10.1007/bf00924956.

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37

Yu, XiangBin, TingTing Zhou, Yun Rui, Xin Yin, and Ming Chen. "Cross-layer design for cooperative MIMO systems with relay selection and imperfect CSI." Science China Information Sciences 56, no. 2 (February 2013): 1–10. http://dx.doi.org/10.1007/s11432-012-4774-5.

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38

Zhiguo Ding and K. K. Leung. "Impact of Imperfect Channel State Information on Bi-Directional Communications With Relay Selection." IEEE Transactions on Signal Processing 59, no. 11 (November 2011): 5657–62. http://dx.doi.org/10.1109/tsp.2011.2163403.

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39

Riecke, Hermann. "Imperfect wave-number selection by ramps in a model for Taylor vortex flow." Physical Review A 37, no. 2 (January 1, 1988): 636–38. http://dx.doi.org/10.1103/physreva.37.636.

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40

Qihui Wu, Zongsheng Zhang, and Jinlong Wang. "Outage Analysis of Cognitive Relay Networks with Relay Selection under Imperfect CSI Environment." IEEE Communications Letters 17, no. 7 (July 2013): 1297–300. http://dx.doi.org/10.1109/lcomm.2013.052413.130424.

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41

Waller, John, and Erik I. Svensson. "The measurement of selection when detection is imperfect: How good are naïve methods?" Methods in Ecology and Evolution 7, no. 5 (December 12, 2015): 538–48. http://dx.doi.org/10.1111/2041-210x.12498.

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42

Ho-Van, Khuong. "Outage analysis in cooperative cognitive networks with opportunistic relay selection under imperfect channel information." AEU - International Journal of Electronics and Communications 69, no. 11 (November 2015): 1700–1708. http://dx.doi.org/10.1016/j.aeue.2015.08.004.

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43

Kajin, Maja, and Carlos Eduardo Viveiros Grelle. "Microhabitat selection when detection is imperfect: the case of an endemic Atlantic forest mammal." Ecological Research 27, no. 6 (September 6, 2012): 1005–13. http://dx.doi.org/10.1007/s11284-012-0977-x.

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44

Sun, Hui, and Mort Naraghi-Pour. "Hop-by-hop Relay Selection strategy for multi-hop relay networks with imperfect CSI." IET Communications 11, no. 9 (June 22, 2017): 1387–95. http://dx.doi.org/10.1049/iet-com.2016.1454.

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45

van Barneveld, Erik M., Leida M. Lamers, René C. J. A. van Vliet, and Wynand P. M. M. van de Ven. "Risk sharing as a supplement to imperfect capitation: a tradeoff between selection and efficiency." Journal of Health Economics 20, no. 2 (March 2001): 147–68. http://dx.doi.org/10.1016/s0167-6296(00)00077-1.

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46

ZHANG, K., and Z. NIU. "Adaptive Receive Antenna Selection for Orthogonal Space-Time Block Codes with Imperfect Channel Estimation." IEICE Transactions on Communications E90-B, no. 12 (December 1, 2007): 3695–98. http://dx.doi.org/10.1093/ietcom/e90-b.12.3695.

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47

Lee, Donghun. "Performance Analysis of Dual Selection With Maximal Ratio Combining Over Nonidentical Imperfect Channel Estimation." IEEE Transactions on Vehicular Technology 67, no. 3 (March 2018): 2819–23. http://dx.doi.org/10.1109/tvt.2017.2774365.

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48

El-Mahassni, Edwin. "Data fusion and sensor selection from imperfect sources with regards to the operating environment." International Journal of Intelligent Computing and Cybernetics 6, no. 4 (November 25, 2013): 358–69. http://dx.doi.org/10.1108/ijicc-09-2011-0040.

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49

Johnes, Geraint, and John Ruggiero. "Revenue efficiency in higher education institutions under imperfect competition." Public Policy and Administration 32, no. 4 (June 7, 2016): 282–95. http://dx.doi.org/10.1177/0952076716652935.

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A number of studies have considered the evaluation of efficiency in higher education institutions. In this paper, we focus on the issue of revenue efficiency, in particular ascertaining the extent to which, given output prices, producers choose the revenue maximising vector of outputs. We then relax the price taking assumption to consider the case in which the market for some outputs is characterised by monopolistic competition. We evaluate efficiencies for English institutions of higher education for the academic year 2012–13 and find considerable variation across institutions in revenue efficiency. The relaxation of the price-taking assumption leads to relatively small changes, in either direction, to the estimated revenue efficiency scores. A number of issues surrounding the modelling process are raised and discussed, including the determination of the demand function for each type of output and the selection of inputs and outputs to be used in the model.
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50

DANIELSON, MATS. "HANDLING IMPERFECT USER STATEMENTS IN REAL-LIFE DECISION ANALYSIS." International Journal of Information Technology & Decision Making 03, no. 03 (September 2004): 513–34. http://dx.doi.org/10.1142/s0219622004001173.

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Using established techniques from operations research (linear programming) and well-known measures of probability and value, we present a computational representation and evaluation of imperfect, imprecise user statements in decision analysis. The presentation starts with the structure of a decision problem and a model of the situation is discussed. The courses of action are represented by consequence sets in the decision structure. Statements could have an interval form to reflect the translation of the imperfect input data. Statistical decision theory is utilized to discriminate between alternatives. Dealing with imprecise statements means encountering decision situations where different alternatives are to prefer in different parts of the consistent solution space to the constraints. Consequently, selection rules based on traditional admissibility are not enough to indicate preferred choices. New admissibility concepts and a procedure for analyzing such situations are discussed. An algorithm for optimizing bilinear functions that does not even require LP is presented.
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