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Survival versus Profit Maximization in a Dynamic Stochastic Experiment

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  • Ryan Oprea

Abstract

Subjects in a laboratory experiment withdraw earnings from a cash reserve evolving according to an arithmetic Brownian motion in near‐continuous time. Aggressive withdrawal policies expose subjects to risk of bankruptcy, but the policy that maximizes expected earnings need not maximize the odds of survival. When profit maximization is consistent with high rates of survival (HS parameters), subjects adjust decisively towards the optimum. When survival and profit maximization are sharply at odds (LS parameters), subjects persistently (and sub‐optimally) hoard excess cash in an evident effort to improve survival rates. The design ensures that this hoarding is not due to standard risk aversion. Analysis of period‐to‐period adjustments in strategies suggests instead that hoarding is due to a widespread bias towards survival in the subject population. Robustness treatments varying feedback, parameters, and framing fail to eliminate the bias.

Suggested Citation

  • Ryan Oprea, 2014. "Survival versus Profit Maximization in a Dynamic Stochastic Experiment," Econometrica, Econometric Society, vol. 82, pages 2225-2255, November.
    • Handle: RePEc:wly:emetrp:v:82:y:2014:i::p:2225-2255
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    Cited by:

    1. Bigoni, Maria & Camera, Gabriele & Casari, Marco, 2020. "Money is more than memory," Journal of Monetary Economics, Elsevier, vol. 110(C), pages 99-115.
    2. Matthew Embrey & Christian Seel & J. Philipp Reiss, 2020. "Gambling in Risk-Taking Contests: Experimental Evidence," Working Paper Series 1620, Department of Economics, University of Sussex Business School.
    3. repec:sus:susewp:0623 is not listed on IDEAS
    4. Moshe Levy, 2022. "An evolutionary explanation of the Allais paradox," Journal of Evolutionary Economics, Springer, vol. 32(5), pages 1545-1574, November.
    5. Kendall, Chad & Oprea, Ryan, 2018. "Are biased beliefs fit to survive? An experimental test of the market selection hypothesis," Journal of Economic Theory, Elsevier, vol. 176(C), pages 342-371.
    6. Philipp Mundt & Simone Alfarano & Mishael Milaković, 2022. "Survival and the Ergodicity of Corporate Profitability," Management Science, INFORMS, vol. 68(5), pages 3726-3734, May.
    7. Stanton Hudja & Daniel Woods, 2024. "Exploration versus exploitation: A laboratory test of the single‐agent exponential bandit model," Economic Inquiry, Western Economic Association International, vol. 62(1), pages 267-286, January.
    8. Adams, Nathan R. & Waddell, Glen R., 2018. "Performance and risk taking under threat of elimination," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 41-54.
    9. Pedro M. Gardete, 2016. "Competing Under Asymmetric Information: The Case of Dynamic Random Access Memory Manufacturing," Management Science, INFORMS, vol. 62(11), pages 3291-3309, November.
    10. Michele Fioretti & Alexander Vostroknutov & Giorgio Coricelli, 2022. "Dynamic Regret Avoidance," American Economic Journal: Microeconomics, American Economic Association, vol. 14(1), pages 70-93, February.
    11. Hermann, Daniel & Sauthoff, Saramena & Mußhoff, Oliver, 2017. "Ex-ante evaluation of policy measures to enhance carbon sequestration in agricultural soils," Ecological Economics, Elsevier, vol. 140(C), pages 241-250.
    12. Michele Fioretti & Alexander Vostroknutov & Giorgio Coricelli, 2022. "Dynamic Regret Avoidance," SciencePo Working papers hal-03562318, HAL.

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