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Does suffering dominate enjoyment in the animal kingdom? An update to welfare biology

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Abstract

Ng (Biol Philos 10(3):255–285, 1995. https://doi.org/10.1007/bf00852469) models the evolutionary dynamics underlying the existence of suffering and enjoyment and concludes that there is likely to be more suffering than enjoyment in nature. In this paper, we find an error in Ng’s model that, when fixed, negates the original conclusion. Instead, the model offers only ambiguity as to whether suffering or enjoyment predominates in nature. We illustrate the dynamics around suffering and enjoyment with the most plausible parameters. In our illustration, we find surprising results: the rate of failure to reproduce can improve or worsen average welfare depending on other characteristics of a species. Our illustration suggests that for organisms with more intense conscious experiences, the balance of enjoyment and suffering may lean more toward suffering. We offer some suggestions for empirical study of wild animal welfare. We conclude by noting that recent writings on wild animal welfare should be revised based on this correction to have a somewhat less pessimistic view of nature.

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Notes

  1. It may be the case, of course, that the concavity of the square of a function changes over its domain, so while this condition is sufficient, it is not necessary.

  2. Evidence of concave utility functions in economics traditionally draws on research into risk aversion, which shows that averaging a concave (Bernoulli) utility function across possible outcomes gives rise to risk-averse behavior (Pratt 1964). Behavioral economics modifies the traditional picture of risk aversion to include the possibility of loss aversion, where both the utility of gains and negative utility of losses are concave (Tversky and Kahneman 1992).

    While Tversky and Kahneman’s finding supports the concavity of suffering and enjoyment, it may seem to undermine the claim of symmetry because people tend to weigh losses more than gains. On further inspection, this result is entirely consistent with symmetric evolutionary cost functions based on the simple fact that apparent losses in the wild more often pose a risk of failure to survive than successes. Weber et al. (2004) note that prospect theory closely represents the energy budget rule for animals’ risk-related behavior (Caraco 1980; Stephens 1981), because apparent losses generally pose a greater risk of starvation than equally-sized apparent gains. This implies that even if the evolutionary costs of suffering and enjoyment are identical, organisms should be more averse to losses than to gains. Hence available evidence is consistent with symmetrical functions for the evolutionary cost of suffering and enjoyment.

  3. In the original paper (Ng 1995), n was defined as the number of failing organisms per successful organism, but this leads to a problem in the interpretation of a budget of the form CE + nCS = M. As n increases, two different attributes of the species increase: the failure rate and the total number of organisms whose costs are included by the budget. When n = 5, for example, the constant budget must be divided over six organisms. This is a problem, because we are interested in the optimization for each individual organism. We can think of there being a probability p of an organism succeeding and p − 1 of an organism failing, so that pCE + (p − 1)CS = M. This simplifies to CE + ((p − 1)/p)CS = M, so we can think of n as being equal to (p − 1)/p, or the ratio of the rate of failure to the rate of success, with no loss of generality.

    An alternative way of dealing with this problem would be to include a further parameter, say b, for the number of successful organisms constrained by the budget, as Dawrst (2009) does. This would give us bCE + bnCS = M. This leads to the same conclusions, as the equation simplifies to CE + nCS = M/b. Note that M/b is exactly what we have defined M as above: the evolutionary suffering and enjoyment budget for an individual organism. Hence we can safely define n as the ratio between failure and success, and M as the individual budget constraint.

  4. The optimization problem here is to maximize the total extent of affective emotions per organism, that is, E(CE) + S(CS), subject to the posited constraint, CE+nCS=M. Ng (1995) argues that genetic selection maximizes the per-organism difference between enjoyment in the case of success and negative enjoyment in the case of failure, which is equivalent to the sum of enjoyment per reproductive success and suffering per failure.

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Acknowledgements

This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. 1656518.

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Correspondence to Zach Groff.

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Appendix: Derivation of Eq. (5)

Appendix: Derivation of Eq. (5)

We derive Eq. (5) starting with the following maximization problem:

$$\begin{aligned} & \max_{{\left\{ {C_{E} , C_{S} } \right\}}} \ln (\alpha C_{E} + 1) + \ln (\upalpha{\text{C}}_{\text{S}} + 1) \\ & \quad \quad s.t.\quad M = \frac{1}{n + 1}C_{E} + \frac{n}{n + 1} C_{S} \\ \end{aligned}$$

This yields the Lagrangian:

$$L = \ln (\alpha C_{E} + 1) + \ln (\upalpha{\text{C}}_{\text{S}} + 1) + \mu (M(n + 1) - C_{E} - nC_{S} )$$

Leading to first-order conditions:

$$0 = \frac{\partial L}{{\partial C_{E} }} = \frac{\alpha }{{\alpha C_{E} + 1}} - \mu$$
$$0 = \frac{\partial L}{{\partial C_{S} }} = \frac{\alpha }{{\alpha C_{S} + 1}} - n\mu$$

Combining and rearranging terms gives:

$$C_{E} = nC_{S} + \frac{n - 1}{\alpha }$$

Plugging this into the budget leads to the following:

$$M(n + 1) = n C_{S} + \frac{n - 1}{\alpha } + nC_{S} = 2nC_{S} + \frac{n - 1}{\alpha }$$
$$C_{S} = \frac{M}{2n}(n + 1) - \frac{n - 1}{2n\alpha }$$

Combining this with the equation for \(C_{E}\) in terms of \(C_{S}\), we get:

$$C_{E} = \frac{M}{2}(n + 1) + \frac{n - 1}{2\alpha }$$

Now we can calculate the balance of suffering and enjoyment by taking suffering minus enjoyment per individual:

$$\begin{aligned} \frac{1}{n + 1}(nS(C_{S} ) - E(C_{E} )) & \quad = \frac{1}{n + 1}\left( {n\ln \left[ {\frac{M\alpha }{2n}(n + 1) - \frac{n - 1}{2n} + 1} \right] - \ln \left[ {\frac{M\alpha }{2}(n + 1) + \frac{n - 1}{2} + 1} \right]} \right) \\ & \quad = \frac{1}{n + 1}\left( {n\ln \left[ {\frac{1}{n}\left( {\frac{M\alpha }{2}(n + 1) + \frac{n + 1}{2}} \right)} \right] - \ln \left[ {\frac{M\alpha }{2}(n + 1) + \frac{n + 1}{2}} \right]} \right) \\ & \quad= \frac{1}{n + 1}\left( {\ln \left[ {\frac{1}{{n^{n} }}\left( {\frac{(M\alpha + 1)(n + 1)}{2}} \right)^{n - 1} } \right]} \right) \\ &\quad = \left( {\frac{1}{n + 1}} \right)\ln \left[ {\frac{{(\upalpha{\text{M}} + 1)^{{{\text{n}} - 1}} (n + 1)^{n - 1} }}{{2^{n - 1} n^{n} }}} \right] \\ \end{aligned}$$

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Groff, Z., Ng, YK. Does suffering dominate enjoyment in the animal kingdom? An update to welfare biology. Biol Philos 34, 40 (2019). https://doi.org/10.1007/s10539-019-9692-0

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