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Strategic self-ignorance

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Abstract

We examine strategic self-ignorance—the use of ignorance as an excuse to over-indulge in pleasurable activities that may be harmful to one’s future self. Our model shows that guilt aversion provides a behavioral rationale for present-biased agents to avoid information about negative future impacts of such activities. We then confront our model with data from an experiment using prepared, restaurant-style meals—a good that is transparent in immediate pleasure (taste) but non-transparent in future harm (calories). Our results support the notion that strategic self-ignorance matters: nearly three of five subjects (58%) chose to ignore free information on calorie content, leading at-risk subjects to consume significantly more calories. We also find evidence consistent with our model on the determinants of strategic self-ignorance.

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Notes

  1. Wakker (1988) and Schlee (1990) show that the controversial “independence axiom” underlying expected utility theory is key to this implication; if the axiom is relaxed, an agent may strictly prefer to avoid costless information.

  2. In a dictator game, Dana et al. (2007) find that 74% of dictators choose the fairer distribution of money between themselves and a recipient when they are informed of the impact of their choice on the recipient. When dictators may choose to remain ignorant of that impact, however, only 47% of dictators both choose to be informed and choose the fairer option (also see the comment by Larson and Capra (2009)). Van der Weele (2012) finds that even when ignorance is associated with a small cost, more than 30% of subjects are strategically ignorant.

  3. There is a rich literature on dynamically inconsistent, or present-biased, preferences: see, e.g., Strotz (1955); Thaler (1980); Akerlof (1991); Ainslie (1992); Loewenstein and Prelec (1992); Laibson (1997); and O’Donoghue and Rabin (1999, 2003).

  4. This holds for a person who is unaware of his present-bias (is naïve). A person who is aware of his present-bias (is sophisticated) may commit himself to a decision path equivalent to that of a time-consistent person. For more on the implications of naïveté vs. sophistication, see O’Donoghue and Rabin (1999).

  5. Key to this finding, too, is that the person is naïve about his present-bias. Carillo and Mariotti (2000) show that if a person is sophisticated, he may use ignorance strategically as a commitment device, to mitigate his future selves’ preferences for immediate gratification.

  6. Guilt aversion has previously been studied in inter-personal conflicts: people experience a utility loss if they betray other people’s expectations, thereby letting them down (see, e.g., Charness and Dufwenberg 2006; Vanberg 2008; Reuben et al. 2009; and Ellingsen et al. 2010). To our knowledge, our analysis is the first to incorporate guilt in an intra-person conflict.

  7. Closely related, but less applicable to the setting of our model, is Hoy et al.’s demonstration (2014), building on Snow (2010) and Klibanoff et al. (2005), that information avoidance may be driven by ambiguity aversion rather than by anxiety over outcomes.

  8. Later studies that extend Bell’s approach include Gul (1991); Jia et al. (2001); and Delquié and Cillo (2006).

  9. Karlsson et al. (2009) develop a model that similarly combines all three approaches. In their model of what they label the “ostrich effect,” selectively ignoring bad news about the likely return on an asset reduces the impact on the agent’s utility from that news (a salience effect), slows down the rate at which he updates his loss aversion reference point (an optimal expectations effect), and affects the local curvature of her utility function (an information preference effect). They find that, consistent with their model’s predictions, Swedish and American investors monitor their portfolios less frequently when markets are flat or falling.

  10. An important difference between our model and the three other general approaches to modeling information avoidance is that the latter are driven by the notion that ignorance affects forward-looking, or “anticipatory” utility, i.e., anxiety or excitement about future outcomes. Because of this, agents need not be present-biased for information avoidance to occur, and present-bias may reduce agents’ proneness to avoid information. In contrast, in our model ignorance affects guilt, which as we noted above (referring to Baumeister et al. 2007) is inherently backward-looking, reminding a person of negative outcomes resulting from past transgressions. Because of this, we find that present-bias is needed for information avoidance (without present-bias, there would be no violations, and therefore no guilt to reduce), and that proneness to information avoidance unambiguously increases in present-bias.

  11. In contrast, comparing (4) and (8) shows that x n < x ih: if the food is healthy, the person consumes less under ignorance than under full information.

  12. From (4),

    $$ \frac{d{x}^{*}}{d\phi }=\frac{f\hbox{'}\left({x}^{*}\right)}{e^{{\prime\prime}}\left({x}^{*}\right)-\phi f\hbox{'}\hbox{'}\left({x}^{*}\right)}<0. $$

  13. For comparison, Dumanovsky et al. (2009) report that the average calorie content of fast-food lunches is 823 calories.

  14. This is consistent with research by Burton et al. (2006) showing that people are generally unable to accurately determine the calorie content of prepared meals served away from home.

  15. The recruitment firm reported that subjects’ willingness to participate in the study was unaffected by the fact that they would be measured and weighed.

  16. This experimental set-up differs slightly from the set-up of our theoretical model: rather than deciding how much to consume of a single meal, subjects were asked to choose between two meals, knowing up front that one meal was high calorie and the other low calorie, but not knowing which meal was which. The only implication for our theoretical analysis is that the function e(x) mapping calories to enjoyment presumably differed across meals. That is, if we use A and B to denote the high- and the low-calorie meal, subjects who initially chose A faced the optimization problem modeled in Section 2 with enjoyment function e A (x), whereas subjects who initially chose B faced it with enjoyment function e B (x).

  17. The information provided was short—it simply stated which meal contained what number of calories. Subjects already knew that one meal contained 490 calories and the other 900 calories. We left the “no information” sheet blank due to the risk of any message on that sheet distorting the results (e.g., if subjects chose the no-information sheet out of curiosity).

  18. Evidence suggests that being observed, even by people other than the recipient, increases generosity in dictator games, while anonymity decreases it (see Hoffman et al. 1996; Bohnet and Frey 1999a, b; Andreoni and Petrie 2004; Soetevent 2005; Charness and Gneezy 2008; and Andreoni and Bernheim 2009). If being observed similarly pressures subjects to “do the right thing” even for behavior that does not directly impact others, then it may reduce their incentive to choose ignorance.

  19. Since subjects had no reason to anticipate this switching option, it does not affect the predictions from our theoretical analysis.

  20. Six subjects (all in the control group) lacked a recorded amount of consumption. We assigned these subjects 100% consumption of their consumed meal. In doing so, we assured that, if anything, the calorie consumption of our control group would be overestimated.

  21. Importantly, not everyone in this subgroup ended up actually consuming the high-calorie meal, since subjects were given the option of switching meals after learning the meals’ calorie content. As shown in Table 2, of the 56 high-calorie-meal “lovers” in the treatment group, 27 chose to find out the calorie content, and of those, 13 subsequently switched to the low-calorie meal; of the 36 high-calorie-meal “lovers” in the control group, 17 switched.

  22. As shown in rows 5–9 of Table 2, the more specific behavior of informed subjects is quite similar as well. Of the 27 treatment-group subjects who chose information and learned that their initial meal choice was high-calorie, 14 stuck with their meal choice but responded by eating on average less than the uninformed subjects did, while 13 responded by switching to the low-calorie meal. Similarly, of the 36 control-group subjects who received the same information, 19 stuck with their high-calorie meal choice but ate less, while 17 switched to the low-calorie meal.

  23. Importantly, our data do not support any broader claims about the importance that subjects attached to calories relative to other factors that might affect the healthiness of meals. In particular, some subjects—perhaps even the majority—may have viewed the high-calorie chicken meal as healthier than the low-calorie beef meal, for example because they perceived beef to contain more fat than chicken, or bulgur to be more nutritious than glass noodles. Given that we randomized the assignment of subjects to the treatment and control groups, and given that the only difference between these groups was whether or not subjects had the option to ignore calorie information, any differences between subjects unrelated to that option—including differences in their perceptions of meal healthiness for non-calorie-related reasons—should in expectation “wash out,” i.e., result in no systematic differences in observed behavior across the two groups. Conversely, any systematic differences in behavior that we did observe must have been driven by subjects’ perceptions about calorie content alone.

  24. Our analytical framework predicts how these parameters affect calorie consumption levels as well. Unfortunately, because the predictions vary with the subjects’ information, testing them requires separate regressions for x n, x iu, and x ih. The resulting sample sizes turn out to be too small to estimate effects with reasonable precision. We therefore do not report the results.

  25. BMI is calculated by dividing a person’s body mass (weight in kg) by the square of his or her height (in meters). A person with a BMI between 18.5 and 25 is considered normal, a BMI between 25 and 30 overweight, and a BMI above 30 obese.

  26. Of the 93 subjects in the treatment group, we dropped 14 from the regression analysis due to missing values for one or more of the explanatory variables.

  27. To check for robustness, we estimated a number of alternative specifications. Using agreement level 5 or 6 rather than 4 as the cutoff for coding the Health concern dummy reduces the magnitude and significance of its estimated effect, without changing the sign or materially affecting the remaining estimates. A dummy for whether subjects perceived themselves to be overweight has the same negative effect as actual BMI, but is statistically insignificant. A continuous estimate of income, using the midpoints of the income intervals that subjects were asked about, has the same negative and statistically highly significant effect as the dummy for above-average income. Similarly, a continuous estimate of education, using reasonable guesses at the years required to attain subjects’ reported degrees, has the same positive and statistically highly significant effect as the dummy for college education.

  28. The negative effect of health knowledge appears to be driven by correct answers to two questions in particular: one pertaining to the recommended daily calorie intake of middle-aged women and one pertaining to dietary guidelines for trans fats.

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Acknowledgments

We thank David Granlund for helpful comments and suggestions, and seminar participants at the FOI, University of Copenhagen, and participants at the AAEA Annual Meeting 2012. Financial support is gratefully acknowledged from the Swedish Council for Working Life and Social Research, the Swedish Retail and Wholesale Development Council, and University of Wyoming’s College of Agriculture and Natural Resources’ Global Perspectives Funds. The collection of data from human subjects for this study has been approved by the Ethics Committee at Lund University. We thank Carin Blom for excellent research assistance.

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Authors and Affiliations

  1. HUI Research AB, 103 29, Stockholm, Sweden

    Linda Thunström

  2. Department of Economics and Finance, University of Wyoming, 1000 E. University Ave., Laramie, WY, 82071, USA

    Linda Thunström, Jason F. Shogren & Klaas van’t Veld

  3. Department of Economics, Lund University, Box 117, 221 00, Lund, Sweden

    Jonas Nordström

  4. Department of Food and Resource Economics, University of Copenhagen, Rolighedsvej 25, 1958, Frederiksberg C, Denmark

    Jonas Nordström

  5. Department of Agricultural and Applied Economics, University of Wyoming, 1000 E. University Ave., Laramie, WY, 82071, USA

    Mariah Ehmke

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Correspondence to Linda Thunström.

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Appendix

Appendix

Figure 3 shows a non-parametric scatterplot smooth, using locally weighted regression, of our data on self-ignorance against our proxy for present-bias (overlapping data points were vertically jittered to give a better sense of their distribution). The figure indicates that, except for one outlier with β < 0.9 and four outliers with β < 1.05, self-ignorance is negatively related to β.

Fig. 3
figure 3

Data on self-ignorance against β, with non-parametric smooth

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Thunström, L., Nordström, J., Shogren, J.F. et al. Strategic self-ignorance. J Risk Uncertain 52, 117–136 (2016). https://doi.org/10.1007/s11166-016-9236-9

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