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Research into Design for a Connected World pp 321–332Cite as

An Experimental Study of Human Decisions in Sequential Information Acquisition in Design: Impact of Cost and Task Complexity

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 134))

Abstract

An important type of process-level decisions in design is information acquisition decisions which includes deciding whether to acquire information about a concept, which concepts to test, whether to run simulations or conduct experiments, etc. To improve design processes, it is important to understand how individuals make these decisions under different problem and process settings. Therefore, the objective of this paper is to understand which strategies individuals follow during sequential information acquisition, and how various factors such as cost and task complexity impact their strategies. Towards this objective, a behavioral experiment involving the function optimization task is conducted using student subjects, and Bayesian inference is performed to estimate the closeness of the subjects’ decisions to predictions from different decision models.

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References

  1. Loch, C.H., Terwiesch, C., Thomke, S.: Parallel and sequential testing of design alternatives. Manage. Sci. 47(5), 663–678 (2001)

    Article  Google Scholar 

  2. Gero, J.S.: Design prototypes: a knowledge representation schema for design. AI Mag. 11(4), 26 (1990)

    Google Scholar 

  3. Hazelrigg, G.A.: A framework for decision-based engineering design. J. Mech. Des. 120(4), 653–658 (1998)

    Article  Google Scholar 

  4. Papalambros, P.Y.: Principles of Optimal Design: modeling and Computation. Cambridge University Press (2000)

    Google Scholar 

  5. Deb, K.: Optimization for Engineering Design: algorithms and Examples. PHI Learning Pvt. Ltd. (2012)

    Google Scholar 

  6. Moore, R.A., Romero, D.A., Paredis, C.J.: Value-based global optimization. J. Mech. Des. 136(4) (2014)

    Article  Google Scholar 

  7. Wang, G.G., Shan, S.: Review of metamodeling techniques in support of engineering design optimization. J. Mech. Des. 129(4), 370–380 (2007)

    Article  Google Scholar 

  8. Xiong, Y., Chen, W., Tsui, K.-L.: A new variable-fidelity optimization framework based on model fusion and objective-oriented sequential sampling. J. Mech. Des. 130(11), 111401 (2008)

    Article  Google Scholar 

  9. Borji, A., Itti, L.: Bayesian optimization explains human active search. In: Advances in Neural Information Processing Systems (2013)

    Google Scholar 

  10. Griffiths, T., Lucas, C., Williams, J., Kalish, M.: Modeling human function learning with Gaussian processes. In: Advances in Neural Information Processing Systems (2009)

    Google Scholar 

  11. Smithers, T., Troxell, W.: Design is intelligent behaviour, but what’s the formalism? AI EDAM 4(3), 89–98 (1990)

    Google Scholar 

  12. McComb, C., Cagan, J., Kotovsky, K.: Utilizing Markov chains to understand operation sequencing in design tasks. In: Design Computing and Cognition (2016)

    Google Scholar 

  13. Flager, F., Gerber, D.J., Kallman, B.: Measuring the impact of scale and coupling on solution quality for building design problems. Des. Stud. 35(2), 180–199 (2014)

    Article  Google Scholar 

  14. Hirschi, N., Frey, D.: Cognition and complexity: an experiment on the effect of coupling in parameter design. Res. Eng. Des. 13(3), 123–131 (2002)

    Article  Google Scholar 

  15. Grogan, P.T., de Weck, O.L.: Collaboration and complexity: an experiment on the effect of multi-actor coupled design. Res. Eng. Des. 27(3), 221–235 (2016)

    Article  Google Scholar 

  16. Sha, Z., Kannan, K.N., Panchal, J.H.: Behavioral experimentation and game theory in engineering systems design. J. Mech. Des. 137(5), 051405 (2015)

    Article  Google Scholar 

  17. Simon, H.A., Newell, A.: Human problem solving: the state of the theory in 1970. Am. Psychol. 26(2), 145 (1971)

    Article  Google Scholar 

  18. Chen, D.L., Schonger, M., Wickens, C.: oTree—an open-source platform for laboratory, online, and field experiments. J. Behav. Exp. Finan. 9, 88 (2016)

    Google Scholar 

  19. Bishop, C.M.: The Gaussian distribution. In: Pattern Recognition and Machine Learning, pp. 67–127. Springer (2006)

    Google Scholar 

  20. Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black-box functions. J. Glob. Optimization 13(4), 455–492 (1998)

    Google Scholar 

  21. Browne, G.J., Pitts, M.G.: Stopping rule use during information search in design problems. Org. Behav. Hum. Decis. Process. 95(2), 208–224 (2004)

    Article  Google Scholar 

  22. Montgomery, H.: Decision rules and the search for a dominance structure: towards a process model of decision making. Adv. Psychol. 14, 343–369 (1983)

    Article  Google Scholar 

  23. Chaudhari, A.M.: Crowdsourcing for engineering design: theoretical and experimental studies. ProQuest Dissertations & Theses Global, Purdue University (2017)

    Google Scholar 

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Authors and Affiliations

  1. School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA

    Ashish M. Chaudhari & Jitesh H. Panchal

Authors
  1. Ashish M. Chaudhari
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  2. Jitesh H. Panchal
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Correspondence to Ashish M. Chaudhari .

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Editors and Affiliations

  1. Centre for Product Design and Manufacturing, Indian Institute of Science Bangalore, Bangalore, India

    Amaresh Chakrabarti

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Chaudhari, A.M., Panchal, J.H. (2019). An Experimental Study of Human Decisions in Sequential Information Acquisition in Design: Impact of Cost and Task Complexity. In: Chakrabarti, A. (eds) Research into Design for a Connected World. Smart Innovation, Systems and Technologies, vol 134. Springer, Singapore. https://doi.org/10.1007/978-981-13-5974-3_28

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  • DOI: https://doi.org/10.1007/978-981-13-5974-3_28

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-5973-6

  • Online ISBN: 978-981-13-5974-3

  • eBook Packages: EngineeringEngineering (R0)

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