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Partition Dependence in Decision Analysis, Resource Allocation, and Consumer Choice

In: Experimental Business Research

Author

Listed:
  • Craig R. Fox

    (UCLA Anderson School)

  • David Bardolet

    (UCLA Anderson School)

  • Daniel Lieb

    (Duke University)

Abstract

In this chapter we explore a wide range of judgment and decision tasks in which people are called on to allocate a scarce resource (e.g., money, choices, belief) over a fixed set of possibilities (e.g., investment opportunities, consumption options, events). We observe that in these situations people tend to invoke maximum entropy heuristics in which they are biased toward even allocation. Moreover, we argue that before applying these heuristics, decision makers subjectively partition the set of options into groups over which they apply even allocation. As a result, allocations vary systematically with the particular partition that people happen to invoke, a phenomenon called partition dependence. We review evidence for maximum entropy heuristics and partition dependence in the following domains: (1) decision analysis in which degree of belief and importance weights must be distributed among possible events and attributes, respectively; (2) managerial decision making in which money and other organizational resources are allocated among risky projects, divisions, and organizational stakeholders; and (3) consumer choice in which individuals make selections among various consumption goods and consumption time periods.

Suggested Citation

  • Craig R. Fox & David Bardolet & Daniel Lieb, 2005. "Partition Dependence in Decision Analysis, Resource Allocation, and Consumer Choice," Springer Books, in: Rami Zwick & Amnon Rapoport (ed.), Experimental Business Research, chapter 0, pages 229-251, Springer.
  • Handle: RePEc:spr:sprchp:978-0-387-24244-6_10
    DOI: 10.1007/0-387-24244-9_10
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    Citations

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    Cited by:

    1. Eric Johnson & Suzanne Shu & Benedict Dellaert & Craig Fox & Daniel Goldstein & Gerald Häubl & Richard Larrick & John Payne & Ellen Peters & David Schkade & Brian Wansink & Elke Weber, 2012. "Beyond nudges: Tools of a choice architecture," Marketing Letters, Springer, vol. 23(2), pages 487-504, June.
    2. Simon Kloker & Tim Straub & Christof Weinhardt, 2019. "Moderators for Partition Dependence in Prediction Markets," Group Decision and Negotiation, Springer, vol. 28(4), pages 723-756, August.
    3. Feng, Zhiyu & Liu, Yukun & Wang, Zhen & Savani, Krishna, 2020. "Let’s choose one of each: Using the partition dependence effect to increase diversity in organizations," Organizational Behavior and Human Decision Processes, Elsevier, vol. 158(C), pages 11-26.
    4. A Morton & B Fasolo, 2009. "Behavioural decision theory for multi-criteria decision analysis: a guided tour," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(2), pages 268-275, February.
    5. Sarah K. Jacobi & Benjamin F. Hobbs, 2007. "Quantifying and Mitigating the Splitting Bias and Other Value Tree-Induced Weighting Biases," Decision Analysis, INFORMS, vol. 4(4), pages 194-210, December.
    6. Emily Ho & David V. Budescu & Valentina Bosetti & Detlef P. Vuuren & Klaus Keller, 2019. "Not all carbon dioxide emission scenarios are equally likely: a subjective expert assessment," Climatic Change, Springer, vol. 155(4), pages 545-561, August.
    7. Steve Alpern & Alec Morton & Katerina Papadaki, 2011. "Patrolling Games," Operations Research, INFORMS, vol. 59(5), pages 1246-1257, October.
    8. Craig R. Fox & Robert T. Clemen, 2005. "Subjective Probability Assessment in Decision Analysis: Partition Dependence and Bias Toward the Ignorance Prior," Management Science, INFORMS, vol. 51(9), pages 1417-1432, September.
    9. Gilberto Montibeller & Detlof von Winterfeldt, 2015. "Cognitive and Motivational Biases in Decision and Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 35(7), pages 1230-1251, July.
    10. Alpern, Steven & Morton, Alec & Papadaki, Katerina, 2011. "Patrolling games," LSE Research Online Documents on Economics 32210, London School of Economics and Political Science, LSE Library.
    11. Chenmu Xing & Katherine Williams & Jamie Hom & Meghana Kandlur & Praise Owoyemi & Joanna Paul & Ray Alexander & Elizabeth Shackney & Hilary Barth, 2020. "Partition dependence in financial aid distribution to income categories," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-13, April.

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