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Reply to "The Limitations of Growth-Optimal Approaches to Decision Making Under Uncertainty"

Author

Listed:
  • Oliver Hulme
  • Arne Vanhoyweghen
  • Colm Connaughton
  • Ole Peters
  • Simon Steinkamp
  • Alexander Adamou
  • Dominik Baumann
  • Vincent Ginis
  • Bert Verbruggen
  • James Price
  • Benjamin Skjold

Abstract

In an article appearing concurrently with the present one, Matthew Ford and John Kay put forward their understanding of a decision theory which emerges in ergodicity economics. Their understanding leads them to believe that ergodicity economics evades the core problem of decisions under uncertainty and operates solely in a regime where there is no measurable uncertainty. If this were the case, then the authors’ critical stance would be justified and, as the authors point out, the decision theory would yield only trivial results, identical to a flavor of expected-utility theory. Here we clarify that the critique is based on a theoretical misunderstanding, and that uncertainty—quantified in any reasonable way—is large in the regime where the model operates. Our resolution explains the success of recent laboratory experiments, where ergodicity economics makes predictions different from expected-utility theory, contrary to the claim of equivalence by Ford and Kay. Also, a state of the world is identified where ergodicity economics outperforms expected-utility theory empirically.

Suggested Citation

  • Oliver Hulme & Arne Vanhoyweghen & Colm Connaughton & Ole Peters & Simon Steinkamp & Alexander Adamou & Dominik Baumann & Vincent Ginis & Bert Verbruggen & James Price & Benjamin Skjold, 2023. "Reply to "The Limitations of Growth-Optimal Approaches to Decision Making Under Uncertainty"," Econ Journal Watch, Econ Journal Watch, vol. 20(2), pages 335–348-3, September.
  • Handle: RePEc:ejw:journl:v:20:y:2023:i:2:p:335-348
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    More about this item

    Keywords

    Ergodicity economics; stochastic process; random variable; expected utility theory; experiments; random walks;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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