Skip to main content

Modeling Human Decisions in Performance and Dependability Models

  • Conference paper
  • First Online:
Computer Performance Engineering (EPEW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9951))

Included in the following conference series:

  • 587 Accesses

Abstract

Many systems are driven partially by human operators who decide about basic operations that influence system behavior. Therefore the performance and dependability depend on the technical system and the human operator. Performance and dependability models usually include a detailed model of the technical infrastructure but the human decision maker is only roughly modeled by simple probabilities or delays. However, in psychology much more sophisticated models of human decision making exist. For tasks with two choices usually diffusion models are applied. These models include information about the process of human decision making based on perception or memory retrieval and take into account the time pressure under which decisions have to be made. In this paper we combine these diffusion models with Markov models for performance and dependability analysis. By using a discretization approach for the diffusion model the combined model is a Markov chain which can be analyzed with standard means. The approach allows one to integrate detailed models of human two-way decisions in performance and dependability models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ajmone-Marsan, M., Balbo, G., Conte, G., Donatelli, S., Franceschinis, G.: Modelling with Generalized Stochastic Petri Nets. Wiley, New York (1995)

    MATH  Google Scholar 

  2. Buchholz, P., Dayar, T.: Block SOR for Kronecker structured representations. Linear Algebra Appl. 386, 83–109 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  3. Cerrato, M., Lo, C.C., Skindilias, K.: Adaptive continuous time Markov chain approximation model to general jump-diffusions. Working Paper 2011–16, University of Clasgow, Business School of Economics (2011)

    Google Scholar 

  4. Curram, S.: Representing intelligent decision making in discrete event simulation: a stochastic neural network approach. Ph.D. thesis, University of Warwick (1997)

    Google Scholar 

  5. Dhillon, B.S.: Human Reliability, Error, and Human Factors in Power Generation. Springer, Heidelberg (2014)

    Book  Google Scholar 

  6. Diederich, A.: Simple matrix methods for analyzing diffusion models of choice probability, choice response time, and simple response time. J. Math. Psychol. 47, 304–322 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  7. Diederich, A.: A multi-stage attention-switching model account for payoff effects on perceptual decision taks with manipulated prcoessing order. Decision 3(2), 81–114 (2016)

    Article  Google Scholar 

  8. Flitman, A.M., Hurrion, R.D.: Linking discrete-event simulation models with expert systems. J. Oper. Res. Soc. 38(8), 723–733 (1987)

    Article  Google Scholar 

  9. Hillston, J.: A compositional approach for performance modelling. Ph.D. thesis, University of Edinburgh, Department of Computer Science (1994)

    Google Scholar 

  10. Lee, S., Son, Y.: Integrated human decision making model under belief-desire-intention framework for crowd simulation. In: Proceedings of the Winter Simulation Conference, pp. 886–894 (2008)

    Google Scholar 

  11. Lee, S., Son, Y., Jin, J.: An integrated human decision making model for evacuation scenarios under a BDI framework. ACM Trans. Model. Comput. Simul. 20(4), 23:1–23:24 (2010)

    Article  Google Scholar 

  12. Lyu, J., Gunasekaran, A.: An intelligent simulation model to evaluate scheduling strategies in a steel company. Int. J. Syst. Sci. 28(6), 611–616 (1997)

    Article  MATH  Google Scholar 

  13. Norling, E., Sonenberg, L., Rönnquist, R.: Enhancing multi-agent based simulation with human-like decision making strategies. In: Moss, S., Davidsson, P. (eds.) MABS 2000. LNCS (LNAI), vol. 1979, pp. 214–228. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  14. Plateau, B., Fourneau, J.M.: A methodology for solving Markov models of parallel systems. J. Parallel Distrib. Comput. 12, 370–387 (1991)

    Article  Google Scholar 

  15. Ratcliff, R.: A theory of memory retrieval. Psychol. Rev. 85, 59–108 (1978)

    Article  Google Scholar 

  16. Ratcliff, R., Smith, P.L., Brown, S.D., McKoon, G.: Diffusion decision model: current issues and history. Trends Cogn. Sci. 20(4), 260–281 (2016)

    Article  Google Scholar 

  17. Robinson, S.: Modeling human interaction in organizational systems. In: Fishwick, P. (ed.) Handbook of Dynamic System Modeling. CRC Press, Boca Raton (2007)

    Google Scholar 

  18. Robinson, S., Edwards, J.S., Yongfa, W.: An expert systems approach to simulating the human decision maker. In: Proceedings of the Winter Simulation Conference (1998)

    Google Scholar 

  19. Shen, W., Maturana, F., Norrie, D.H.: MetaMorph II: an agent-based architecture for distributed intelligent design and manufacturing. J. Intell. Manuf. 11(3), 237–251 (2000)

    Article  MATH  Google Scholar 

  20. Sridharan, V., Mohanavadivu, P.: Reliability and availability analysis for two non-identical unit parallel systems with common cause failures and human errors. Microelectron. Reliab. 37(5), 747–752 (1997)

    Article  Google Scholar 

  21. Stewart, W.J.: Introduction to the Numerical Solution of Markov chains. Princeton University Press, Princeton (1994)

    MATH  Google Scholar 

  22. Stewart, W.J.: Probability, Markov Chains, Queues, and Simulation. Princeton University Press, Princeton (2009)

    MATH  Google Scholar 

  23. Thümmler, A., Buchholz, P., Telek, M.: A novel approach for phase-type fitting with the EM algorithm. IEEE Trans. Dependable Secur. Comput. 3(3), 245–258 (2006)

    Article  Google Scholar 

  24. Tsetsos, K., Usher, M., McCelland, J.L.: Testing multi-alternative decision models with non-stationary evidence. Front. Neurosci. 5, 63 (2011)

    Article  Google Scholar 

  25. Tuerlinckx, F., Maris, E., Ratcliff, R., Boeck, P.D.: A comparison of four methods for simulating the diffusion process. Behav. Res. Methods Instrum. Comput. 33(4), 443–456 (2001)

    Article  Google Scholar 

  26. van Dam, K.H.: Capturing socio-technical systems with agent-based modelling. Ph.D. thesis, Technology, Policy and Management, TU Delft (2009)

    Google Scholar 

  27. Williams, T.: Simulating the man-in-the-loop. OR Insight 4(9), 17–21 (1996)

    Article  Google Scholar 

  28. Zhao, X., Venkateswaran, J., Son, Y.-J.: Modeling human operator decision-making in manufacturing systems using BDI agent paradigm. In: IIE Annual Conference and Exposition (2005)

    Google Scholar 

  29. Zülch, G.: Modelling and simulation of human decision-making in manufacturing systems. In: Proceedings of the Winter Simulation Conference, pp. 947–953 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jan Kriege .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Buchholz, P., Felko, I., Kriege, J., Rinkenauer, G. (2016). Modeling Human Decisions in Performance and Dependability Models. In: Fiems, D., Paolieri, M., Platis, A. (eds) Computer Performance Engineering. EPEW 2016. Lecture Notes in Computer Science(), vol 9951. Springer, Cham. https://doi.org/10.1007/978-3-319-46433-6_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46433-6_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46432-9

  • Online ISBN: 978-3-319-46433-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics