Loss aversion, one more victim to replication crisis? (no)

I saw somewhere a link to working paper by Gal & Rucker entitled The Loss of Loss Aversion: Will It Loom Larger Than Its Gain?, with the comment that loss aversion is one more psychological phenomenon not replicating. I was surprised by this claim because the mechanisms behind loss aversion, or something like it, are very much related to affect psychology. The context of replicability implies that loss aversion was never a thing, it was just a statistical fluke resulting from questionable research practices like social priming and Bem’s psi findings. But that’s not what the linked manuscript says. It’s a review that never actually questions whether such a phenomenon exists at all – rather it’s discussing the scope and an alternative conceptualization of the phenomenon. I’m not that familiar with this literature to assess whether the review really is impartial or whether it cherrypicks its findings (as it’s written quite obviously with a particular conclusion in mind, self-citing a lot), but clearly it should not be cited as evidence that loss aversion as a phenomenon is a result of QRPs. I’m a bit annoyed that the title and even the abstract plays like a clickbait and makes it easy to link the normal theoretical discussion about the limits of a phenomenon to the replicability issue.

In addition to enabling the misreading of this manuscript’s position in the literature, I was slightly miffed that it’s at least partly based on a fundamental misunderstanding of how the mind works. The authors describe a strong and a weak form of loss aversion in order to compare them to evidence, both of them in terms of it being a general, universal principle that can be applied to any human behavior: the strong form as absolute (that “one should not observe cases where gains have a propensity to be weighted more than losses of similar magnitude”, p.9), and the weak form as relative (“on average, one expects the data would largely reveal a greater impact of losses than of gains”, p.10). It may be that this is a feature in decision making research in general rather than a view held only by the authors, but from the point of view of affect psychology, it makes little sense. It’s a strawman, because I don’t think there is anything in psychology that can be considered a universal law like this, at the level of observable outcomes. Human mind does not work on “principles” or “laws” like this, because it is an immensely complex system of reacting, predicting, and self-correcting processes. There is no single process reaching through the whole of human mind, always (or even mostly, on average) producing the same results regardless of circumstances, because that would not be adaptive for the complex physical and social environment our mental machinery. And even if we focus on a very high level, it’s a dubious notion to begin with that all decision making would be governed by a single process translating all kinds of decisions into simple losses and gains.

I admit that I think some things as “principles” of human mind, and negativity bias (related but not identical to loss aversion) sounds like a good candidate, but it does not mean that at the level of observable outcomes, regardless of circumstances, we should see (absolutely or on average) a particular pattern of behavior. Rather, it means that some parts of the system tend to process information in certain ways, and in specific circumstances – where we can somehow control that specifically these processes are the ones influencing the outcomes the most – we can indeed see patterns in behavior.

That said, the alternative conceptualizations – such as propensity towards inaction or status quo – are interesting, and worth considering (assuming the review is not horribly biased) for anyone working with loss aversion. It is very likely true that an intuitively appealing conceptualization tends to be overgeneralized and that scientists easily persist even in face of evidence to the contrary.

 


Gal, D., & Rucker, D. (2017). The Loss of Loss Aversion: Will It Loom Larger Than Its Gain? (SSRN Scholarly Paper No. ID 3049660). Rochester, NY: Social Science Research Network. Retrieved on 12 Jul 2018 from https://papers.ssrn.com/abstract=3049660

TIL depression as an unfortunate result of emotional recalibration

(And re: previous post – no, I’m not horribly depressed, nor am I working full-time again. I’m doing things I enjoy in order to get better, and emotion theory happens to be one of them.)

Reading Tooby & Cosmides (2005), Conceptual Foundations of Evolutionary Psychology, as a part of refamiliarizing myself with basics of evopsych. It is a very good description of a lot of basic ideas behind evopsych, having mostly familiar stuff and surprisingly little stuff I disagree with, but the new part I had not run into before was the idea of recalibrational emotion programs.

The core idea is that unlike many other emotions*, emotions such as guilt, grief, shame, gratitude, and depression, have not evolved for producing any immediate behavior change. Instead, drawing from the computational approach to psychology, the idea is that behavior generally is dependent on a lot of (nonconscious) regulatory variables that track the relatively stable circumstances of one’s life. This way the brain does not have to calculate things like the estimate of social support, evaluation of a particular person’s likelihood of reciprocating kindness (or aggression), or present health and energy of own body, on the fly when already in a situation. But these variables need to be updated constantly, and sometimes the act of updating a variable itself should cause changes in other evaluations, in default modes of behavior in related situations, and so on. The authors use guilt as an example (p. 59):

Imagine a mechanism that evolved to allocate food according to Hamilton’s rule, situated, for example, in a hunter-gatherer woman. The mechanism in the woman has been using the best information available to her to weight the relative values of
the meat to herself and her sister, perhaps reassuring her that it is safe to be away from her sister for a short time. The sudden discovery that her sister, since she was last contacted, has been starving and has become sick functions as an information-dense situation allowing the recalibration of the algorithms that weighted the relative values of the meat to self and sister. The sister’s sickness functions as a cue that the previous allocation weighting was in error and that the variables need to be reweighted—including all of the weightings embedded in habitual action sequences. Guilt functions as an emotion mode specialized for recalibration of regulatory variables that control trade-offs in welfare between self and others […]  Previous courses of action are brought to mind (“I could have helped then; why didn’t I think to?”), with the effect of resetting choice points in decision rules.

The authors briefly mention depression as well: “Former actions that seemed pleasurable in the past, but which ultimately turned out to lead to bad outcomes, are reexperienced in imagination with a new affective coloration, so that in the future entirely different weightings are called up during choices.”

I have always considered the functional explanations of depression (or sadness) suspect, because they have typically only briefly mentioned “deattachment” or something similar as the function, and that has sounded… not right. Why would depression have such a horrible feeling if it was simply aimed at deattaching or realigning goals or something? An adaptation that seems to make you passive, drive you to ruin your relationships, and ultimately kill yourself does not seem very adaptive**.

The idea of recalibration makes this so much more understandable! It is not that depression is an adaptation in itself. Instead, (I now hypothesize,) it is the result of the recalibration program accidentally recalibrating all the core motivations at the same time to zero. Normally, the recalibration operates on one core motivation at the time – it gets set to zero, but other motivations are still running, so the behavior is directed more adaptively. But in a case where you only happen to have a couple of core motivations, and they all get set to zero due to some recalibration (that is not maybe based on the most objective of evaluations if the preceding states are already biased), you end up in the state where you have no core motivations left: depression. The program signals that you should not be doing these things, do the other things, but there are no other things left to do.


 

*) The authors strongly subscribe to a discrete emotion view, but their arguments can be read as somewhat inconsistent with the basic emotion theories.

**) Yes, yes, suicide can be adaptive from the gene’s point of view. But depression does not seem a reliable result of situations where suicide would actually be adaptive.

Reference

Tooby, J., & Cosmides, L. (2005). Conceptual Foundations of Evolutionary Psychology. In D. M. Buss (Ed.), The handbook of evolutionary psychology (pp. 5–67). Hoboken, NJ: Wiley.

no title

This is the hardest post I’ve written.

Competition is often touted as the most efficient way to get best results. Typically its proponents do not mention why it is efficient*.

Sometimes it motivates people to do their best. But when the motivation originates from fear of losing rather than intrinsic motivation for winning, it burns up psychological/emotional resources that are not reflected by anything immediately observable or measurable.

Sometimes it gets its efficiency from externalizing costs to competitors. When not all win, but all used resources (psychological/emotional, but also work time, opportunities, etc.) to try to win, the organizer of the competition only pays the winners, and the losers bear their costs themselves.

Academia uses both of these.

Science would be better if scientists did the best they could do because they wanted to, and did not take the safest course because they were afraid (see replication crisis).

Academia would be better if it did not make academics burn up resources that should be used for something else than competing meaninglessly. In many – maybe most – cases, the funders burn up more resources from the whole population of competitors than they give out to the winners. And despite all this waste, we have no evidence that the function of distributing the resources (funding, positions) to scientists is better than random.

I would be better if my self-worth was not so integrally tied to being a researcher, that when the soul-crushing competition for funding takes away my resources – the resources that I use at least partly on the expense of my family, because they appreciate that science is important to me – from actually doing research, I feel like I’m not doing enough.

This post was not neutral, nor well sourced. I’m bitter, and depressed, and burnt out, and now starting a sick leave because of that. I have a strong passion for science, but the competition in academia is actively keeping me from doing science.


*) When it is – it is entirely dependent on how the competition is arranged whether the efficiency is actually directed at what is really wanted. You get what you measure, etc.