Journal articles: 'Consensus functions'

1

Crown, Gary D., Melvin F. Janowitz, and Robert C. Powers. "Neutral consensus functions." Mathematical Social Sciences 25, no. 3 (May 1993): 231–50. http://dx.doi.org/10.1016/0165-4896(93)90029-i.

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2

Godfrey-Smith, Peter. "FUNCTIONS: CONSENSUS WITHOUT UNITY." Pacific Philosophical Quarterly 74, no. 3 (September 1993): 196–208. http://dx.doi.org/10.1111/j.1468-0114.1993.tb00358.x.

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3

Manita, Ghaith, Riadh Khanchel, and Mohamed Limam. "CONSENSUS FUNCTIONS FOR CLUSTER ENSEMBLES." Applied Artificial Intelligence 26, no. 6 (July 2012): 598–614. http://dx.doi.org/10.1080/08839514.2012.687668.

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4

Barthélemy, Jean-Pierre, F. R. McMorris, and R. C. Powers. "Dictatorial consensus functions on n-trees." Mathematical Social Sciences 25, no. 1 (December 1992): 59–64. http://dx.doi.org/10.1016/0165-4896(92)90025-z.

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5

Ben-Arieh, D., T. Easton, and B. Evans. "Minimum Cost Consensus With Quadratic Cost Functions." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 39, no. 1 (January 2009): 210–17. http://dx.doi.org/10.1109/tsmca.2008.2006373.

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6

CROSSON, BRUCE, and KATHLEEN Y. HAALAND. "Subcortical functions in cognition: Toward a consensus." Journal of the International Neuropsychological Society 9, no. 7 (November 2003): 1027–30. http://dx.doi.org/10.1017/s1355617703970068.

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Behavioral neurologists and neuropsychologists have debated the role of the thalamus and basal ganglia in cognition and behavior for more than a century (e.g., Bucy, 1942; Marie, 1906; Penfield & Roberts, 1959; Wernicke, 1874). However, over these 100-plus years, there is little consensus regarding whether or how these structures contribute to cognition. Fortunately, recent research findings are rapidly changing this state of affairs. It is now obvious we will not understand how the brain controls complex activities until we understand the contribution of these deep brain structures. In healthy and brain-damaged individuals, application of methodologies such as semantic priming, event related potentials, and functional neuroimaging to the question of subcortical functions is beginning to resolve this conundrum. This symposium demonstrates the utility of combining these different approaches. It features empirical work from six laboratories that have engaged in systematic inquiries regarding the role of the thalamus and basal ganglia in cognition. This body of work represents both new directions and convergence of recent findings in the quest to integrate our understanding of this complex issue.

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7

Monjardet, Bernard, and Vololonirina Raderanirina. "Lattices of choice functions and consensus problems." Social Choice and Welfare 23, no. 3 (December 2004): 349–82. http://dx.doi.org/10.1007/s00355-003-0251-9.

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8

Mirkin, Boris, and Fred S. Roberts. "Consensus functions and patterns in molecular sequences." Bulletin of Mathematical Biology 55, no. 4 (July 1993): 695–713. http://dx.doi.org/10.1007/bf02460669.

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9

MIRKIN, B., and F. ROBERTS. "Consensus functions and patterns in molecular sequences." Bulletin of Mathematical Biology 55, no. 4 (1993): 695–713. http://dx.doi.org/10.1016/s0092-8240(05)80185-5.

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10

Powers, R. C., and J. M. White. "Wilson's theorem for consensus functions on hierarchies." Discrete Applied Mathematics 156, no. 8 (April 2008): 1321–29. http://dx.doi.org/10.1016/j.dam.2007.05.034.

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11

Garcia-Martinez, C., F. R. McMorris, O. Ortega, and R. C. Powers. "Axioms for Consensus Functions on then-Cube." Journal of Applied Mathematics 2017 (2017): 1–5. http://dx.doi.org/10.1155/2017/8025616.

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Apvalue of a sequenceπ=(x1,x2,…,xk)of elements of a finite metric space(X,d)is an elementxfor which∑i=1kdp(x,xi)is minimum. Thelp–function with domain the set of all finite sequences onXand defined bylp(π)={x: xis apvalue ofπ}is called thelp–function on(X,d). Thel1andl2functions are the well-studied median and mean functions, respectively. In this note, simple characterizations of thelp–functions on then-cube are given. In addition, the center function (using the minimax criterion) is characterized as well as new results proved for the median and antimedian functions.

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12

Monjardet, B. "Arrowian characterizations of latticial federation consensus functions." Mathematical Social Sciences 20, no. 1 (August 1990): 51–71. http://dx.doi.org/10.1016/0165-4896(90)90077-k.

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13

McMorris, F. R. "Axioms for consensus functions on undirected phylogenetic trees." Mathematical Biosciences 74, no. 1 (May 1985): 17–21. http://dx.doi.org/10.1016/0025-5564(85)90022-7.

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14

Wagner, Carl G. "Consensus for belief functions and related uncertainty measures." Theory and Decision 26, no. 3 (May 1989): 295–304. http://dx.doi.org/10.1007/bf00134110.

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15

Cortés, Jorge. "Distributed algorithms for reaching consensus on general functions." Automatica 44, no. 3 (March 2008): 726–37. http://dx.doi.org/10.1016/j.automatica.2007.07.022.

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16

Ostroumova, Olga D., Vladimir A. Parfenov, Tatiana M. Ostroumova, Nikolay N. Yakhno, Irina E. Chazova, Anna N. Bogolepova, Albert S. Galyavich, et al. "Expert consensus. Effect of antihypertensive therapy on cognitive functions." Systemic Hypertension 18, no. 1 (March 15, 2021): 5–12. http://dx.doi.org/10.26442/2075082x.2021.1.200575.

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Arterial hypertension (AH) is the major modifiable risk factor for cognitive impairment (CI), including dementia, CI in cerebrovascular and neurodegenerative diseases, including Alzheimers disease. By 2050, the number of people with dementia will approximately 3 times increase due to the aging population and limited opportunities for drug prevention and treatment of severe CI. In connection with the above, it seems necessary to create an expert consensus, which would summarize the evidence-based medicine data available to date on the effect of antihypertensive therapy (AHT) on cognitive function (CF). In the expert consensus, the data of prospective randomized clinical trials, observational and population studies, meta-analyzes on the effect of AHT on the risk of dementia and CI progression, including certain CF, were summarized and analyzed. The consensus considers the effect of antihypertensive drugs (AHD) on various cognitive domains. Literature data give evidence that AHT reduces the risk for dementia, including vascular dementia, reduces the risk of for dementia in Alzheimers disease, as well as reduces the risk and can prevent the progression of non-dementia CI. The effect of AHT on various CF has been little studied. Most meta-analyzes did not reveal the benefits of any class of AHD; however, one study demonstrated the advantage of angiotensin receptor blockers, while another study diuretics. The consensus emphasizes that, given the high incidence of AH in the general population, AHT may be one of the most effective ways to prevent CI or delay CI progression. The effect of different classes of AHD on CF requires further study. It is necessary to conduct a larger number of well-designed randomized clinical trials that would assess the state of executive functions in patients with AH.

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17

Liang, Quanyi, Zhikun She, Lei Wang, and Housheng Su. "General Lyapunov Functions for Consensus of Nonlinear Multiagent Systems." IEEE Transactions on Circuits and Systems II: Express Briefs 64, no. 10 (October 2017): 1232–36. http://dx.doi.org/10.1109/tcsii.2017.2647744.

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18

Thunberg, Johan, Xiaoming Hu, and Jorge Goncalves. "Local Lyapunov Functions for Consensus in Switching Nonlinear Systems." IEEE Transactions on Automatic Control 62, no. 12 (December 2017): 6466–72. http://dx.doi.org/10.1109/tac.2017.2652302.

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19

Beliakov, Gleb, Tomasa Calvo, and Simon James. "Consensus measures constructed from aggregation functions and fuzzy implications." Knowledge-Based Systems 55 (January 2014): 1–8. http://dx.doi.org/10.1016/j.knosys.2013.07.002.

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20

Barthélemy, J. P., F. R. McMorris, and R. C. Powers. "Stability conditions for consensus functions defined on n-trees." Mathematical and Computer Modelling 22, no. 1 (July 1995): 79–87. http://dx.doi.org/10.1016/0895-7177(95)00102-8.

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21

McMorris, F. R., and R. C. Powers. "Consensus functions on trees that satisfy an independence axiom." Discrete Applied Mathematics 47, no. 1 (November 1993): 47–55. http://dx.doi.org/10.1016/0166-218x(93)90151-d.

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22

Mou, Jinping, Dongbing Tong, Xianyi Shao, Huafeng Ge, and Yiling Lv. "Curvature- and Torsion-Based Consensus for the Markovian Switching Multiagent System with Communication of Noise Disturbance and Time Delay." Mathematical Problems in Engineering 2021 (March 24, 2021): 1–13. http://dx.doi.org/10.1155/2021/9028591.

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This paper investigates the consensus problem for the distributed multiagent system (MAS), where the trajectory of each agent is displayed by curvature and torsion, and the communication behaviors among agents are influenced by time delay and corrupted by noises. According to the Frenet–Serret formulas, a class of consensus protocols is designed for all agents, and a closed-loop system is obtained. Based on the Lyapunov method, several consensus criteria are derived, where the consensus criteria are characterized by curvature functions and torsion functions. Finally, one example shows the reliability of the proposed methods.

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23

McNally, F. J., and J. Rine. "A synthetic silencer mediates SIR-dependent functions in Saccharomyces cerevisiae." Molecular and Cellular Biology 11, no. 11 (November 1991): 5648–59. http://dx.doi.org/10.1128/mcb.11.11.5648-5659.1991.

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Copies of the mating-type genes are present at three loci on chromosome III of the yeast Saccharomyces cerevisiae. The genes at the MAT locus are transcribed, whereas the identical genes at the silent loci, HML and HMR, are not transcribed. Several genes, including the four SIR genes, and two sites, HMR-E and HMR-I, are required for repression of transcription at the HMR locus. Three elements have been implicated in the function of the HMR-E silencer: a binding site for the RAP1 protein, a binding site for the ABF1 protein, and an 11-bp consensus sequence common to nearly all autonomously replicating sequence (ARS) elements (putative origins of DNA replication). RAP1 and ABF1 binding sites of different sequence than those found at HMR-E were joined with an 11-bp ARS consensus sequence to form a synthetic silencer. The synthetic silencer was able to repress transcription of the HMRa1 gene, confirming that binding sites for RAP1 and ABF1 and the 11-bp ARS consensus sequence were the functional components of the silencer in vivo. Mutations in the ABF1 binding site or in the ARS consensus sequence of the synthetic silencer caused nearly complete derepression of transcription at HMR. The ARS consensus sequence mutation also eliminated the ARS activity of the synthetic silencer. These data suggested that replication initiation at the HMR-E silencer was required for establishment of the repressed state at the HMR locus.

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24

McNally, F. J., and J. Rine. "A synthetic silencer mediates SIR-dependent functions in Saccharomyces cerevisiae." Molecular and Cellular Biology 11, no. 11 (November 1991): 5648–59. http://dx.doi.org/10.1128/mcb.11.11.5648.

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Copies of the mating-type genes are present at three loci on chromosome III of the yeast Saccharomyces cerevisiae. The genes at the MAT locus are transcribed, whereas the identical genes at the silent loci, HML and HMR, are not transcribed. Several genes, including the four SIR genes, and two sites, HMR-E and HMR-I, are required for repression of transcription at the HMR locus. Three elements have been implicated in the function of the HMR-E silencer: a binding site for the RAP1 protein, a binding site for the ABF1 protein, and an 11-bp consensus sequence common to nearly all autonomously replicating sequence (ARS) elements (putative origins of DNA replication). RAP1 and ABF1 binding sites of different sequence than those found at HMR-E were joined with an 11-bp ARS consensus sequence to form a synthetic silencer. The synthetic silencer was able to repress transcription of the HMRa1 gene, confirming that binding sites for RAP1 and ABF1 and the 11-bp ARS consensus sequence were the functional components of the silencer in vivo. Mutations in the ABF1 binding site or in the ARS consensus sequence of the synthetic silencer caused nearly complete derepression of transcription at HMR. The ARS consensus sequence mutation also eliminated the ARS activity of the synthetic silencer. These data suggested that replication initiation at the HMR-E silencer was required for establishment of the repressed state at the HMR locus.

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25

Edy, Jill A., and Patrick C. Meirick. "Consensus without focus." Agenda Setting Journal 3, no. 2 (September 25, 2019): 108–22. http://dx.doi.org/10.1075/asj.18026.mei.

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Abstract Generating public consensus on issue priorities is one of the most important functions of news agenda setting. However, the nature of that consensus is not well understood. Agenda setting might build a public consensus focused on a limited set of priorities, but it also has potential to build a consensus that broadens the public’s issue agenda by generating shared concerns about problems beyond the bounds of personal experience. Evidence shows that from 1968–2010, broadcast news’ agenda-setting effect tended to broaden the public agenda rather than focus it. This tendency of news agenda setting to broaden the public agenda is not affected by the news agenda’s breadth or by which issues dominate the news, although issue-level agenda setting effects may, under some circumstances, focus the public agenda. If broadcast news does not focus the public agenda, it is unlikely a focused agenda will be generated by a more fragmented media ecology.

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26

周, 宇. "Improved Proof of Authority Consensus Based on Verifiable Random Functions." Computer Science and Application 11, no. 02 (2021): 383–93. http://dx.doi.org/10.12677/csa.2021.112038.

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27

Boesch, R. Paul, Karthik Balakrishnan, Sari Acra, Dan T. Benscoter, Shelagh A. Cofer, Joseph M. Collaco, John P. Dahl, et al. "Structure and Functions of Pediatric Aerodigestive Programs: A Consensus Statement." Pediatrics 141, no. 3 (February 7, 2018): e20171701. http://dx.doi.org/10.1542/peds.2017-1701.

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28

Olshevsky, Alex, and John N. Tsitsiklis. "On the Nonexistence of Quadratic Lyapunov Functions for Consensus Algorithms." IEEE Transactions on Automatic Control 53, no. 11 (December 2008): 2642–45. http://dx.doi.org/10.1109/tac.2008.2007878.

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29

Yoon, Myunggon. "Minimal-order transfer functions of dynamical multi-agent consensus systems." IET Control Theory & Applications 10, no. 7 (April 25, 2016): 838–42. http://dx.doi.org/10.1049/iet-cta.2015.0905.

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30

McMorris, F. R., and Robert C. Powers. "Some axiomatic limitations for consensus and supertree functions on hierarchies." Journal of Theoretical Biology 404 (September 2016): 342–47. http://dx.doi.org/10.1016/j.jtbi.2016.06.016.

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31

Moreno-García, Carlos Francisco, and Francesc Serratosa. "Correspondence consensus of two sets of correspondences through optimisation functions." Pattern Analysis and Applications 20, no. 1 (May 28, 2015): 201–13. http://dx.doi.org/10.1007/s10044-015-0486-y.

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32

Day, William H. E., F. R. McMorris, and David B. Meronk. "Axioms for consensus functions based on lower bounds in posets." Mathematical Social Sciences 12, no. 2 (October 1986): 185–90. http://dx.doi.org/10.1016/0165-4896(86)90036-3.

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33

Jonason, Peter K. "Four Functions for Four Relationships: Consensus Definitions of University Students." Archives of Sexual Behavior 42, no. 8 (November 2013): 1407–14. http://dx.doi.org/10.1007/s10508-013-0189-7.

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34

McMorris, F. R., Henry Martyn Mulder, Beth Novick, and Robert C. Powers. "An ABC-Problem for location and consensus functions on graphs." Discrete Applied Mathematics 207 (July 2016): 15–28. http://dx.doi.org/10.1016/j.dam.2015.12.008.

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35

Rurainski, Alexander, Andreas Hildebrandt, and Hans-Peter Lenhof. "A consensus line search algorithm for molecular potential energy functions." Journal of Computational Chemistry 30, no. 9 (July 15, 2009): 1499–509. http://dx.doi.org/10.1002/jcc.21175.

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36

Saker, Halima, Rainer Machné, Jörg Fallmann, Douglas B. Murray, Ahmad M. Shahin, and Peter F. Stadler. "Weighted Consensus Segmentations." Computation 9, no. 2 (February 5, 2021): 17. http://dx.doi.org/10.3390/computation9020017.

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The problem of segmenting linearly ordered data is frequently encountered in time-series analysis, computational biology, and natural language processing. Segmentations obtained independently from replicate data sets or from the same data with different methods or parameter settings pose the problem of computing an aggregate or consensus segmentation. This Segmentation Aggregation problem amounts to finding a segmentation that minimizes the sum of distances to the input segmentations. It is again a segmentation problem and can be solved by dynamic programming. The aim of this contribution is (1) to gain a better mathematical understanding of the Segmentation Aggregation problem and its solutions and (2) to demonstrate that consensus segmentations have useful applications. Extending previously known results we show that for a large class of distance functions only breakpoints present in at least one input segmentation appear in the consensus segmentation. Furthermore, we derive a bound on the size of consensus segments. As show-case applications, we investigate a yeast transcriptome and show that consensus segments provide a robust means of identifying transcriptomic units. This approach is particularly suited for dense transcriptomes with polycistronic transcripts, operons, or a lack of separation between transcripts. As a second application, we demonstrate that consensus segmentations can be used to robustly identify growth regimes from sets of replicate growth curves.

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37

Dussault, Antoine C. "Two Notions of Ecological Function." Philosophy of Science 89, no. 1 (January 2022): 171–79. http://dx.doi.org/10.1017/psa.2021.20.

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AbstractThis paper discusses Millstein’s (2020) criticism of the consensus view formed against selected-effects ecological functions. I argue that Millstein’s defense of coevolution-based selected-effects ecological functions applies to a notion of function as an activity, whereas proponents of the consensus view are concerned with a notion of ecological function as the contribution of an organism, population, species, or abiotic item to the maintenance of its community and/or the functioning of its ecosystem. Millstein’s arguments hence do not invalidate the consensus view but draw attention to a notion of function that has been neglected in philosophical discussions and that deserves more attention.

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38

Tsui, Ming-sum. "Functions of social work supervision in Hong Kong." International Social Work 48, no. 4 (July 2005): 485–93. http://dx.doi.org/10.1177/0020872805053471.

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English The functions of social work supervision in Hong Kong are characterized by three distinct features: consensus between the supervisor and the supervisee, discussion of personal matters and team building. These distinct features reflect characteristics of the Chinese cultural context. Supervision is not only organizational and professional, but also personal and cultural. French Trois aspects caractérisent les fonctions du travail social à Hong Kong: le consensus entre le superviseur et le supervisé, le partage de préoccupations personnelles et la construction d'un esprit d'équipe. Ces caractéristiques témoignent du contexte culturel chinois. La supervision ne se préoccupe pas seulement des dimensions organisationnelles et professionnelles mais également des dimensions personnelles et culturelles. Spanish La función de la supervisión en el trabajo social en Hong Kong tiene tres características: el consenso entre el supervisor y el supervisado, la atención a los asuntos personales, y la formación de equipo. Estas características reflejan el contexto cultural chino. La supervisión no es sólo organizacional y profesional sino también personal y cultural.

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39

Tanphaichitr, Nongnuj, Kym F. Faull, Arman Yaghoubian, and Hongbin Xu. "Lipid Rafts and Sulfogalactosylglycerolipid (SGG) in Sperm Functions: Consensus and Controversy." Trends in Glycoscience and Glycotechnology 19, no. 106 (2007): 67–83. http://dx.doi.org/10.4052/tigg.19.67.

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40

HAYASHI, Naoki, Yuichi KAJIYAMA, and Shigemasa TAKAI. "Consensus-Based Quantized Algorithm for Convex Optimization with Smooth Cost Functions." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E103.A, no. 2 (February 1, 2020): 435–42. http://dx.doi.org/10.1587/transfun.2019map0008.

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41

Hilton, Denis J., Richard H. Smith, and Mark D. Alicke. "Knowledge-based information acquisition: Norms and the functions of consensus information." Journal of Personality and Social Psychology 55, no. 4 (1988): 530–40. http://dx.doi.org/10.1037/0022-3514.55.4.530.

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42

McMorris, F. R., and R. C. Powers. "Consensus functions on tree quasi-orders that satisfy an independence condition." Mathematical Social Sciences 48, no. 2 (September 2004): 183–92. http://dx.doi.org/10.1016/j.mathsocsci.2004.01.003.

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43

Wu, Zhibin, and Jiuping Xu. "Consensus reaching models of linguistic preference relations based on distance functions." Soft Computing 16, no. 4 (August 21, 2011): 577–89. http://dx.doi.org/10.1007/s00500-011-0756-6.

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44

Sevillano, Xavier, Francesc Alías, and Joan Claudi Socoró. "Positional and confidence voting-based consensus functions for fuzzy cluster ensembles." Fuzzy Sets and Systems 193 (April 2012): 1–32. http://dx.doi.org/10.1016/j.fss.2011.09.007.

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45

Yuan, Julie S., Philaretos C. Kousis, Sara Suliman, Ioana Visan, and Cynthia J. Guidos. "Functions of Notch Signaling in the Immune System: Consensus and Controversies." Annual Review of Immunology 28, no. 1 (March 2010): 343–65. http://dx.doi.org/10.1146/annurev.immunol.021908.132719.

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46

Naga Madhavilatha, K., and G. Rama Mohan Babu. "Systematic approach for enrichment of docking outcome using consensus scoring functions." Journal of Physics: Conference Series 1228 (May 2019): 012019. http://dx.doi.org/10.1088/1742-6596/1228/1/012019.

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47

HAYASHI, N., T. USHIO, F. HARADA, and A. OHNO. "Consensus Problem of Multi-Agent Systems with Non-linear Performance Functions." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E90-A, no. 10 (October 1, 2007): 2261–64. http://dx.doi.org/10.1093/ietfec/e90-a.10.2261.

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48

Ariizumi, K., H. Takahashi, M. Nakamura, and H. Ariga. "Negative transcriptional regulatory element that functions in embryonal carcinoma cells." Molecular and Cellular Biology 9, no. 9 (September 1989): 4032–37. http://dx.doi.org/10.1128/mcb.9.9.4032-4037.1989.

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We have cloned the polyomavirus mutant fPyF9, which persists in an episomal state in F9 embryonal carcinoma cells (K. Ariizumi and H. Ariga, Mol. Cell. Biol. 6:3920-3927, 1986). fPyF9 carries three copies of exogenous sequences, the prototype of which is a 21-base-pair repeat (box DNA), in the region of the enhancer B domain of wild-type polyomavirus DNA. The consensus sequence, GCATTCCATTGTT, is 13 base pairs long. The box DNA inserted into fPyF9 appeared to come from a cellular sequence and was present in many kinds of DNAs, including F9 chromosomal DNA. The biological function of box DNA was analyzed by chloramphenicol acetyltransferase expression assays, using chimeric plasmids containing box DNA conjugated with simian virus 40 promoter elements. The results showed that box DNA repressed the activities both of the simian virus 40 promoter and enhancer only in transfected undifferentiated F9 cells and not in differentiated LTK- cells. Box DNA functioned independently of orientation and position with respect to the promoter in an enhancerlike manner, although the effect of box DNA was opposite that of the enhancer. The XhoI linker insertion into the consensus sequences of box DNA abolished the repression activity, and the protein(s) recognizing the consensus sequences was identified only in F9 cells, not in L cells. These analyses suggest that box DNA may be a negative regulatory element that functions in undifferentiated cells.

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49

Ariizumi, K., H. Takahashi, M. Nakamura, and H. Ariga. "Negative transcriptional regulatory element that functions in embryonal carcinoma cells." Molecular and Cellular Biology 9, no. 9 (September 1989): 4032–37. http://dx.doi.org/10.1128/mcb.9.9.4032.

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We have cloned the polyomavirus mutant fPyF9, which persists in an episomal state in F9 embryonal carcinoma cells (K. Ariizumi and H. Ariga, Mol. Cell. Biol. 6:3920-3927, 1986). fPyF9 carries three copies of exogenous sequences, the prototype of which is a 21-base-pair repeat (box DNA), in the region of the enhancer B domain of wild-type polyomavirus DNA. The consensus sequence, GCATTCCATTGTT, is 13 base pairs long. The box DNA inserted into fPyF9 appeared to come from a cellular sequence and was present in many kinds of DNAs, including F9 chromosomal DNA. The biological function of box DNA was analyzed by chloramphenicol acetyltransferase expression assays, using chimeric plasmids containing box DNA conjugated with simian virus 40 promoter elements. The results showed that box DNA repressed the activities both of the simian virus 40 promoter and enhancer only in transfected undifferentiated F9 cells and not in differentiated LTK- cells. Box DNA functioned independently of orientation and position with respect to the promoter in an enhancerlike manner, although the effect of box DNA was opposite that of the enhancer. The XhoI linker insertion into the consensus sequences of box DNA abolished the repression activity, and the protein(s) recognizing the consensus sequences was identified only in F9 cells, not in L cells. These analyses suggest that box DNA may be a negative regulatory element that functions in undifferentiated cells.

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50

FEDRIZZI, MARIO, MICHELE FEDRIZZI, and R. A. MARQUES PEREIRA. "CONSENSUS MODELLING IN GROUP DECISION MAKING: DYNAMICAL APPROACH BASED ON FUZZY PREFERENCES." New Mathematics and Natural Computation 03, no. 02 (July 2007): 219–37. http://dx.doi.org/10.1142/s1793005707000744.

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The. notion of consensus plays an important role in group decision making, particularly when the collective preference structure is generated by a dynamical aggregation process of the single individual preference structures. In this dynamical process of aggregation each single decision maker gradually transforms his/her preference structure by combining it, through iterative weighted averaging, with the preference structures of the remaining decision makers. In this way, the collective decision emerges dynamically as a result of the consensual interaction among the various decision makers in the group. From the point of view of applied mathematics, the models of consensual dynamics stand in the context of multi-agent complex systems, with interactive and nonlinear dynamics. The consensual interaction among the various agents (decision makers) acts on their state variables (the preferences) in order to optimize an appropriate measure of consensus, which can be of type 'hard' (unanimous agreement within the group of decision makers) or 'soft' (partial agreement within the group of decision makers). In this paper, we study the modelling of consensus reaching when the individual testimonies are assumed to be expressed as fuzzy preference relations. Here consensus is meant as the degree to which most of the experts agree on the preferences associated to the most relevant alternatives. First of all we derive a degree of dissensus based on linguistic quantifiers and then we introduce a form of network dynamics in which the quantifiers are represented by scaling functions. Finally, assuming that the decision makers can express their preferences in a more flexible way, i.e. by using triangular fuzzy numbers, we describe the iterative process of opinion transformation towards consensus via the gradient dynamics of a cost function expressed as a linear combination of a dissensus cost function and an inertial cost function.

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Journal articles on the topic 'Consensus functions'

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