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Metabolic State Alters Economic Decision Making under Risk in Humans – A critique

Metabolic State Alters Economic Decision Making under Risk in Humans – A critique. Symmonds , Emmanuel, Drew, Batterham & Dolan, 2010. 19 men with BMI of 22.6±1.7 Anthropometric measurements were taken Standardisation procedure Fasting until the next morning

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Metabolic State Alters Economic Decision Making under Risk in Humans – A critique

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  1. Metabolic State Alters Economic Decision Making under Risk in Humans – A critique Symmonds, Emmanuel, Drew, Batterham & Dolan, 2010

  2. 19 men with BMI of 22.6±1.7 • Anthropometric measurements were taken • Standardisation procedure • Fasting until the next morning • Blood samples were taken to measure glucose, acyl-ghrelin and leptin • Standardised meal • Multiple paired lottery choice task Overview: Methods

  3. Multiple Paired Lottery Choice Task Symmonds et al. (2010).

  4. Results: Risk-taking increases immediately and for up to an hour after a meal. Correlations with leptin & acyl-ghrelin. Conclusions: Risk preferences in humans is affected by metabolic state. Overview: Results & Conclusion

  5. This paper aims to provide a real world application of Prospect Theory. Prospect Theory focuses on gains and losses, rather than on final wealth position (Markowitz, 1952). Direct analogies from animals' foraging behaviour have been drawn from Prospect Theory's account of the relationship between risk-taking and reference points. Prospect theory

  6. Small sample size- only 19 participants' data included in most of the final analyses • All male sample- could affect risk taking (Wang et al. 2009) • Participants' nationalities are not known- levels of risk aversion affected by culture (Weber and Hsee, 1998) (Tse et al. 1988) Critique- Participants

  7. Volunteer sample • Greater sociability (Rosenthal, 1965, cited by Schultz, 1969) • Generalised risk-taking & sociality (Zuckerman & Kuhlman, 2000 ) • Body fat & BMI differences- sample differed significantly from national average • No cognitive bias checks in participant selection (Raghunathan & Pham, 1999) • Age range from 20-46 (Deakin et al, 2004) Critique- Participants

  8. Use of standardised meals for varied participants • Experience could be positive or negative • Could be similar to or different from normal diet • Different effects on people with different nutritional needs Critique- Methodology

  9. Allowed to drink water during fasting period- may have tried to make themselves feel full • Not in a controlled environment during fasting- trusting participants not to eat • Different eating rates among participants- finishing the meal at different times Critique- Methodology

  10. Fasting/ Satiated/ Post- satiated conditions occurred in the same order each time- possibility for practice/fatigue effects • Took place over the same time period every day- variation in mood during this period (Clark et al., 1989) • Gambles are hypothetical- participants' choices may have been different if they were actually risking money CRITIQUE- METHODOLOGY

  11. Significance level used for risk and body fat correlation unsatisfactory • Hunger may not directly affect risk. Hunger may affect mood, which then affects risk, for example • Sample size neglect • Confirmatory bias • This is where researchers may look for data to confirm their own beliefs/hypotheses. Critique- Results & conclusion

  12. Visual Analogue Scales good measure of hunger (Stubbs et al, 2000) Randomised lottery position to reduce habituation. Same lottery choices across all conditions. Performed an awareness check at debrief. Unlimited time to make lottery decision. Hunger levels underwent significant change. Positive methodology critiques

  13. Conclusion adds to field of research by showing findings not predicted by normative economic theory. Findings make interesting link under paradigm of economic prospect theory. Results have important implications to eating disorders The researchers had no declared competing interests. Positive conclusion critiques

  14. Conduct study again using mixed gender/ all female sample. (Dreber, Rand & Wernerfelt et al, 2011) Conduct using larger sample size. A study using recruited participants rather than volunteers is essential in removing extraneous variables. (Rosenthal, 1965, cited by Schultz, 1969; Zuckerman & Kuhlman) Directions for further research

  15. Directions for further research continued Environment for fasting element should be more controlled. Vary time of day in future conditions. Propose non-hypothetical gambling experiment.

  16. Clark, L. A., Watson, D., & Leeka, J. (1989) Diurnal Variation in thePositive Affects. Motivation and Emotion. 13 (3), 205-234 • Deakin, J., Aitken, M., Robbins, T., & Sahakian, B.J., (2004). Risk taking during decision-making in normal volunteers changes with age. Journal of the international neuropsychological society. 10 (4), 590-598 • Dreber A., Rand D.G., Wernerfelt, N., Garcia, J.R., Vilar, M.G., Lum, J.K., Zeckhauser, R.,(2011). Dopamine and risk choices in different domains: Findings among serious tournament bridge players. Journal of risk and Uncertainty. 43, (1), 19-38 • Human: Teen Brain Wired to Take Risks. Retrieved on 4 November, 2013 from http://news.discovery.com/human/teenager-brain-risky-behavior.htm • Kahneman, D., & Tversky, A. ( 1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-292. • Raghunathan, R., & Pham, M.T., (1999). All Negative Moods Are Not Equal: Motivational Influences of Anxiety and Sadness on Decision Making, Organizational Behaviour and Human Decision Processes. 79, (1) , 56–77 • Schultz, D. P. (1969). The Human Subject in Psychological Research. Psychological Bulletin, 72(3), 214-228. References

  17. Stubbs, R.J., Hughes, D.A., Johnstone, A.M., Rowley, E., Reid, C., Stratton, R., Delargy, H., King, N., & Blundell, J.E., (2000). The use of visual analogue scales to assess motivation to eat in human subjects: a review of their reliability and validity with an evaluation of new hand-held computerized systems for temporal tracking of appetite ratings. British Journal of Nutrition. 84 (4) p405-415. • Symmonds M, Emmanuel JJ, Drew ME, Batterham RL, Dolan RJ (2010) Metabolic State Alters Economic Decision Making under Risk in Humans. PLoS • ONE 5(6): e11090 • Tse, D. K., Lee, K-h., Vertinsky, I., & Wehrung, D. A. ( 1988). Does Culture Matter? A Cross-Cultural Study of Executives' Choice, Decisiveness , and Risk Adjustment in International Marketing. Journal of Marketing, 52(4), 81-95. • Wang, G-J., Volkow, N. D., Telang, F., Jayne, M., Ma, Y., Pradhan, K., Zhu, W., Wong, C. T., Thanos, P. K., Geliebter, A., Biegon, A., & Fowler, J. S. (2009). Evidence of gender differences in the ability to inhibit brain activation elicitng by food stimulation. Proceedings of the National Academy of Sciences of the United States of America. 106 (4), 1249-54. • Weber, E. U., & Hsee, C. ( 1998). Cross-cultural Differences in Risk Perception, but Cross-cultural Similarities in Attitudes Towards Perceived Risk. Management Science, 44, 1205-1217. • Zuckerman, M., & Kuhlman, D. M. ( 2000). Personality and Risk-Taking: Common Biosocial Factors. Journal of Personality, 68(6), 1000-1029. References

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