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Journal articles on the topic 'Behaviour models'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Loehlin, John C. "Models of Behaviour and the Behaviour of Models." International Journal of Behavioral Development 12, no. 3 (September 1989): 403–6. http://dx.doi.org/10.1177/016502548901200308.

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A model proposed by Bateson suggests that the discrepancy between an adopted child's genotype for I.0. and the adoptive parents' I.Q. should contribute negatively to the child's intellectual development. In support, he cited evidence from an adoption study that the absolute difference between birth mothers' and adoptive mothers' I.Qs is negatively correlated with adopted child's I.Q. Further confirmation of the empirical relationship is reported; however, it is pointed out that such a correlation may artefactually result from a difference in parental means, and that the formal model presents other problems as well.

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2

Rank, Stefan. "Behaviour Coordination for Models of Affective Behaviour." KI - Künstliche Intelligenz 25, no. 3 (May 25, 2011): 265–68. http://dx.doi.org/10.1007/s13218-011-0111-1.

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3

Shenton, Andrew K., and Naomi V. Hay‐Gibson. "Information behaviour meta‐models." Library Review 61, no. 2 (February 24, 2012): 92–109. http://dx.doi.org/10.1108/00242531211220735.

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4

Ranta, Esa. "Models of Adaptive Behaviour." Ethology 107, no. 7 (July 24, 2001): 668–69. http://dx.doi.org/10.1046/j.1439-0310.2001.0686d.x.

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5

SIEGRIST, J. "Models of health behaviour." European Heart Journal 9, no. 6 (June 1988): 709–14. http://dx.doi.org/10.1093/oxfordjournals.eurheartj.a062573.

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6

Yang, Guo Wei, and Yang Yang. "Motion Models of Artificial Animals and Momentarily Continously Switching of the Models." Applied Mechanics and Materials 39 (November 2010): 295–305. http://dx.doi.org/10.4028/www.scientific.net/amm.39.295.

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Some local motion models of artificial animals and a method momentarily continuously to switch the models are given. A society behaviour system of artificial fishes based on the way and method is exploited, which can exhibit life behaviours and intelligence of animals. How momentarily continuously to switch the motion models of animated agents in the system is better settled. The system can illimitably generalizedly circularly run and has good man-computer interaction. Moreover the appearance, motion and behaviour of the animals in the system are lifelike and convinced. There are not bad visions such as ‘mutation’, ‘suddenly disappearring’, ‘jumpiness’of animated agents.

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7

Wilson, T. D. "Models in information behaviour research." Journal of Documentation 55, no. 3 (August 1999): 249–70. http://dx.doi.org/10.1108/eum0000000007145.

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8

O'neill, Philip. "Epidemic models featuring behaviour change." Advances in Applied Probability 27, no. 04 (December 1995): 960–79. http://dx.doi.org/10.1017/s0001867800047765.

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This paper considers a model for the spread of an epidemic in a closed population whose members are in either a high-risk or a low-risk activity group. Further, members of the high-risk group may change their behaviour by entering the low-risk group. Both stochastic and deterministic models are examined. A limiting model, appropriate when there is a large number of initially susceptible individuals, is used to provide a threshold analysis. The epidemic is compared to a single group epidemic, and to suitably parametrised two-group epidemics, using a coupling method. The total size distribution and effects of changing the behaviour change rate are considered.

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9

Feng, Jianfeng, and Guibin Li. "Behaviour of two-compartment models." Neurocomputing 38-40 (June 2001): 205–11. http://dx.doi.org/10.1016/s0925-2312(01)00510-0.

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10

Arcangelis, L. de. "Scaling Behaviour in Fracture Models." Physica Scripta T29 (January 1, 1989): 234–38. http://dx.doi.org/10.1088/0031-8949/1989/t29/045.

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11

Elek, Péter, and László Márkus. "Tail behaviour of -TARCH models." Statistics & Probability Letters 80, no. 23-24 (December 2010): 1758–63. http://dx.doi.org/10.1016/j.spl.2010.07.020.

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12

O'neill, Philip. "Epidemic models featuring behaviour change." Advances in Applied Probability 27, no. 4 (December 1995): 960–79. http://dx.doi.org/10.2307/1427931.

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This paper considers a model for the spread of an epidemic in a closed population whose members are in either a high-risk or a low-risk activity group. Further, members of the high-risk group may change their behaviour by entering the low-risk group. Both stochastic and deterministic models are examined. A limiting model, appropriate when there is a large number of initially susceptible individuals, is used to provide a threshold analysis. The epidemic is compared to a single group epidemic, and to suitably parametrised two-group epidemics, using a coupling method. The total size distribution and effects of changing the behaviour change rate are considered.

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13

Richards, Martin. "Animal models for reproductive behaviour." Journal of Reproductive and Infant Psychology 8, no. 4 (October 1990): 257. http://dx.doi.org/10.1080/02646839008403640.

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14

Haikala, Ilkka. "ARMA models of program behaviour." ACM SIGMETRICS Performance Evaluation Review 14, no. 1 (May 1986): 170–79. http://dx.doi.org/10.1145/317531.317550.

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15

Endres, Anthony L. "Geometrical models for poroelastic behaviour." Geophysical Journal International 128, no. 3 (March 1997): 522–32. http://dx.doi.org/10.1111/j.1365-246x.1997.tb05315.x.

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16

Toledo, Tomer. "Driving Behaviour: Models and Challenges." Transport Reviews 27, no. 1 (January 2007): 65–84. http://dx.doi.org/10.1080/01441640600823940.

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17

de Assis, Luciana Mafalda Elias, Raul Abreu de Assis, Moiseis Cecconello, and Ezio Venturino. "Models for alarm call behaviour." Theoretical Ecology 11, no. 1 (August 31, 2017): 1–18. http://dx.doi.org/10.1007/s12080-017-0345-0.

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18

Wagner, Udo, and Alfred Taudes. "Stochastic models of consumer behaviour." European Journal of Operational Research 29, no. 1 (April 1987): 1–23. http://dx.doi.org/10.1016/0377-2217(87)90189-5.

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19

Feda, Jaroslav. "Physical models of soil behaviour." Engineering Geology 72, no. 1-2 (March 2004): 121–29. http://dx.doi.org/10.1016/j.enggeo.2003.07.003.

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20

Yang, Guo Wei, and Wei Liu. "Some Motion Models of Artificial Animals and Momentarily Continously Switching of the Models." Key Engineering Materials 467-469 (February 2011): 1012–17. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.1012.

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Some local motion models of artificial animals and a method momentarily continuously to switch the models are given. A society behaviour system of artificial fishes based on the way and method is exploited, which can exhibit life behaviours and intelligence of animals. How momentarily continuously to switch the motion models of animated agents in the system is better settled. The system can illimitably generalizedly circularly run and has good man-computer interaction. Moreover the appearance, motion and behaviour of the animals in the system are lifelike and convinced. There are not bad visions such as ‘mutation’, ‘suddenly disappearring’, ‘jumpiness’of animated agents.

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21

Horvath, Blanka, Antoine Jacquier, and Chloé Lacombe. "Asymptotic behaviour of randomised fractional volatility models." Journal of Applied Probability 56, no. 2 (June 2019): 496–523. http://dx.doi.org/10.1017/jpr.2019.27.

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AbstractWe study the asymptotic behaviour of a class of small-noise diffusions driven by fractional Brownian motion, with random starting points. Different scalings allow for different asymptotic properties of the process (small-time and tail behaviours in particular). In order to do so, we extend some results on sample path large deviations for such diffusions. As an application, we show how these results characterise the small-time and tail estimates of the implied volatility for rough volatility models, recently proposed in mathematical finance.

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22

Penny, William. "Bayesian Models of Brain and Behaviour." ISRN Biomathematics 2012 (October 23, 2012): 1–19. http://dx.doi.org/10.5402/2012/785791.

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This paper presents a review of Bayesian models of brain and behaviour. We first review the basic principles of Bayesian inference. This is followed by descriptions of sampling and variational methods for approximate inference, and forward and backward recursions in time for inference in dynamical models. The review of behavioural models covers work in visual processing, sensory integration, sensorimotor integration, and collective decision making. The review of brain models covers a range of spatial scales from synapses to neurons and population codes, but with an emphasis on models of cortical hierarchies. We describe a simple hierarchical model which provides a mathematical framework relating constructs in Bayesian inference to those in neural computation. We close by reviewing recent theoretical developments in Bayesian inference for planning and control.

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23

Sewell, Geoffrey. "Quantum stochastic models with hydrodynamical behaviour." Reports on Mathematical Physics 59, no. 2 (April 2007): 243–55. http://dx.doi.org/10.1016/s0034-4877(07)80037-1.

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24

Stibel, JM. "Mental models and online consumer behaviour." Behaviour & Information Technology 24, no. 2 (March 2005): 147–50. http://dx.doi.org/10.1080/01449290512331321901.

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25

Migliorini, G., and F. Ritort. "Dynamical behaviour of low autocorrelation models." Journal of Physics A: Mathematical and General 27, no. 23 (December 7, 1994): 7669–86. http://dx.doi.org/10.1088/0305-4470/27/23/012.

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26

Csanyi, G., and J. Kertesz. "Scaling behaviour in discrete traffic models." Journal of Physics A: Mathematical and General 28, no. 16 (August 21, 1995): L427—L432. http://dx.doi.org/10.1088/0305-4470/28/16/002.

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27

Csányi, G., and J. Kertész. "Scaling behaviour in discrete traffic models." Journal of Physics A: Mathematical and General 29, no. 2 (January 21, 1996): 471. http://dx.doi.org/10.1088/0305-4470/29/2/024.

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28

Maillard, G., and T. S. Mountford. "Ergodic behaviour of “signed voter models”." Annales de l'Institut Henri Poincaré, Probabilités et Statistiques 49, no. 1 (February 2013): 13–35. http://dx.doi.org/10.1214/12-aihp511.

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29

Kantor, Y., and Y. Shapir. "Crossover behaviour of truncated Eden models." Journal of Physics A: Mathematical and General 18, no. 10 (July 11, 1985): L567—L570. http://dx.doi.org/10.1088/0305-4470/18/10/002.

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30

Grübel, Rudolf. "Local behaviour of simple stochastic models." Stochastic Processes and their Applications 26 (1987): 232. http://dx.doi.org/10.1016/0304-4149(87)90168-2.

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31

Martin, C. B. "Animal models to study fetal behaviour." European Journal of Obstetrics & Gynecology and Reproductive Biology 21, no. 5-6 (May 1986): 279–82. http://dx.doi.org/10.1016/0028-2243(86)90005-5.

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32

Ghali, Moheb A. "Models of production and inventory behaviour." Economic Modelling 10, no. 1 (January 1993): 57–63. http://dx.doi.org/10.1016/0264-9993(93)90012-5.

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33

Jurkiewicz, J. "Chaotic behaviour in one-matrix models." Physics Letters B 261, no. 3 (May 1991): 260–68. http://dx.doi.org/10.1016/0370-2693(91)90325-k.

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34

Bortolussi, Luca. "Hybrid behaviour of Markov population models." Information and Computation 247 (April 2016): 37–86. http://dx.doi.org/10.1016/j.ic.2015.12.001.

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35

Matthews, J. R. "Computer models for fuel element behaviour." Bulletin of Materials Science 8, no. 2 (May 1986): 93–106. http://dx.doi.org/10.1007/bf02744175.

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36

Macdonald, Iain L., and David Raubenheimer. "Hidden Markov Models and Animal Behaviour." Biometrical Journal 37, no. 6 (1995): 701–12. http://dx.doi.org/10.1002/bimj.4710370606.

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37

Hart, Paul J. B., and Robert Arlinghaus. "Representing human behaviour in ecosystem models." Fish and Fisheries 22, no. 2 (February 25, 2021): 241–42. http://dx.doi.org/10.1111/faf.12544.

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38

Alrajeh, D., J. Kramer, A. Russo, and S. Uchitel. "Deriving non-Zeno behaviour models from goal models using ILP." Formal Aspects of Computing 22, no. 3 (October 6, 2009): 217–41. http://dx.doi.org/10.1007/s00165-009-0128-5.

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39

Loehlin, John C. "Additive Models may Mislead: A Commentary on Loehlin 1989 Models of Behaviour and the Behaviour of Models: A Reply to Bateson." International Journal of Behavioral Development 12, no. 3 (September 1989): 413–14. http://dx.doi.org/10.1177/016502548901200310.

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40

Lovekin, C. C., and J. A. Guzik. "Behaviour of Pulsations in Hydrodynamic Models of Massive Stars." Proceedings of the International Astronomical Union 9, S307 (June 2014): 176–81. http://dx.doi.org/10.1017/s174392131400667x.

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AbstractWe have calculated the pulsations of massive stars using a nonlinear hydrodynamic code including time-dependent convection. The basic structure models are based on a standard grid published by Meynet et al. (1994). Using the basic structure, we calculated envelope models, which include the outer few percent of the star. These models go down to depths of at least 2 million K. These models, which range from 40 to 85 solar masses, show a range of pulsation behaviours. We find models with very long period pulsations (> 100 d), resulting in high amplitude changes in the surface properties. We also find a few models that show outburst-like behaviour. The details of this behaviour are discussed, including calculations of the resulting wind mass-loss rates.

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41

Rahmawati, Rizqi, Saiful Ridlo, and Sri Sukaesih. "Analysis of the Effectiveness of Learning Models against Students’ Innovative Behaviour." Journal of Biology Education 7, no. 2 (September 7, 2018): 127–36. http://dx.doi.org/10.15294/jbe.v7i2.24266.

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Practicum become an appropriate method to give provisions in empirical experience especially for college student of Biology Education. Practicum was believed could show university students’ innovative behavior. The concrete form from using practicum method is learning model application that include learning syntax from the beginning to the end. Therefore, a research was did to analyze relation between practicum method towards innovative behaviour and the effectivity of it. Based on the result of regression analysis, all models give significance value p < 0.05 so could be known that every learning model give significance influence toward the formation of university students’ innovative behavior. Based on correlation coefficient analysis could be known that PJBL learning model give contribution value of 86.2% followed by guided inquiry of 85.4%, experiment model of 76.3% and observation model with contribution value of 64.7% toward the formation of university students’ innovative behavior. Based on the results of variance similarity analysis could be known that PJBL model is the most effictive model to form innovative behavior with difference value of 17.80 compared with other learning models. The result of research showing that all of the learning models when used on practicum relating the formation of innovative behaviour. Project based learning model gave contribution most effective towards innovative behaviour of the college students in Biology Education study program.

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42

Marsden-Smedley, JB, and WR Catchpole. "Fire Behaviour Modelling in Tasmanian Buttongrass Moorlands .II. Fire Behaviour." International Journal of Wildland Fire 5, no. 4 (1995): 215. http://dx.doi.org/10.1071/wf9950215.

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An experimental burning program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. A range of previously developed prediction models were examined, but none provided adequate fire behaviour predictions. Empirical models were then developed to predict rate of fire spread and flame height in flat terrain, using the variables site age, dead fuel moisture content and surface wind speed. The models should provide good predictions for low to moderate intensity fires and adequate predictions for high intensity wildfires.

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43

Żach, Piotr. "HYPERELASTIC POLYNOMIAL MODELS IN PLASTICS BEHAVIOUR ANALYSES." Journal of KONES. Powertrain and Transport 20, no. 1 (January 25, 2013): 381–86. http://dx.doi.org/10.5604/12314005.1136223.

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44

Rook, A. J., and P. D. Penning. "Stochastic models of grazing behaviour in sheep." Applied Animal Behaviour Science 32, no. 2-3 (November 1991): 167–77. http://dx.doi.org/10.1016/s0168-1591(05)80040-1.

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45

JOHNSTON, STUART T. "MATHEMATICAL MODELS FOR QUANTIFYING COLLECTIVE CELL BEHAVIOUR." Bulletin of the Australian Mathematical Society 97, no. 2 (February 20, 2018): 349–52. http://dx.doi.org/10.1017/s0004972717001149.

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46

Damour, Thibault, and Marc Henneaux. "Oscillatory behaviour in homogeneous string cosmology models." Physics Letters B 488, no. 2 (August 2000): 108–16. http://dx.doi.org/10.1016/s0370-2693(00)00838-8.

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47

Papadimitriou, Eleonora, George Yannis, and John Golias. "A critical assessment of pedestrian behaviour models." Transportation Research Part F: Traffic Psychology and Behaviour 12, no. 3 (May 2009): 242–55. http://dx.doi.org/10.1016/j.trf.2008.12.004.

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48

Kozáková, Mária, and Kristína Krúpová. "Dynamic Models of the Creative Industries’ Behaviour." SHS Web of Conferences 83 (2020): 01036. http://dx.doi.org/10.1051/shsconf/20208301036.

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The aim of the paper is to analyse the models describing the development of the Creative Industries in Slovakia. Creative industries are described as the industrial components of the economy in which creativity is an input and content or intellectual property is the output. The creative industries have therefore appeared to be newly represented as a significant and rapidly growing set of industries; an important sector, in other words, for policy consideration. Based on the following findings, we can conclude that the second model is precisely predicting the relationship between the growth in the creative industries and in the aggregate economy in Slovakia. With improved cultural statistics, also a more developed and theoretically better founded analysis would be possible. We therefore see our article primarily as a much-needed step towards developing statistical tools in empirical cultural policy on a consistent basis.

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49

Abraham, C., P. Sheeran, and M. Henderson. "Extending social cognition models of health behaviour." Health Education Research 26, no. 4 (March 31, 2011): 624–37. http://dx.doi.org/10.1093/her/cyr018.

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50

Comerton-Forde, Carole, Michael A. O'Brien, and P. Joakim Westerholm. "An Empirical Analysis of Strategic Behaviour Models." Australian Journal of Management 32, no. 2 (December 2007): 181–203. http://dx.doi.org/10.1177/031289620703200202.

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