An evolutionary explanation for ineffective altruism

Burum, B., Nowak, M.A. & Hoffman, M. 

Nat Hum Behav (2020). 

https://doi.org/10.1038/s41562-020-00950-4

Abstract

We donate billions to charities each year, yet much of our giving is ineffective. Why are we motivated to give but not to give effectively? Building on evolutionary game theory, we argue that donors evolved (genetically or culturally) to be insensitive to efficacy because people tend not to reward efficacy, as social rewards tend to depend on well-defined and highly observable behaviours. We present five experiments testing key predictions of this account that are difficult to reconcile with alternative accounts based on cognitive or emotional limitations. Namely, we show that donors are more sensitive to efficacy when helping themselves or their families. Moreover, social rewarders don’t condition on efficacy or other difficult-to-observe behaviours, such as the amount donated.

From the Conclusion

This paper has argued that altruism in a behavioural sense is an act that benefits another person, while it is altruistically motivated when the ultimate goal of such act is the welfare of that other. In evolutionary sense, altruism means the sacrifice of fitness for the benefit of other organisms. 

According to the evolutionary theories of altruism, behaviour which promotes the reproductive success of the receiver at the cost of the altruist is favoured by natural selection, because it is either beneficial for the altruist in the long run, or for his genes, or for the group he belongs to. Thus, in line with Trivers, it can be argued that “models that attempt to explain altruistic behaviour in terms of natural selection are models designed to take the altruism out of altruism” (Trivers 1971: 35).

Strategic Regulation of Empathy.

Weisz, E., & Cikara, M. (2020, October 9).

https://doi.org/10.31234/osf.io/2kr46

Abstract

Empathy is an integral part of socio-emotional well-being, yet recent research has highlighted some of its downsides. Here we examine literature that establishes when, how much, and what aspects of empathy promote specific outcomes. After reviewing a theoretical framework which characterizes empathy as a suite of separable components, we examine evidence showing how dissociations of these components affect important socio-emotional outcomes and describe emerging evidence suggesting that these components can be independently and deliberately modulated. Finally, we advocate for a new approach to a multi-component view of empathy which accounts for the interrelations among components. This perspective advances scientific conceptualization of empathy and offers suggestions for tailoring empathy to help people realize their social, emotional, and occupational goals.

From the Conclusion

The goal of this review has been to evaluate the burgeoning literature on how components of empathy—in isolation or in concert—differentially affect key outcomes including prosocial behavior, relationship quality, occupational burnout, and negotiation. As such, an important takeaway from this review is that components of empathy can be leveraged to facilitate attainment of important goals. A second takeaway is that in order to effectively intervene on empathy in service of promoting specific outcomes, it is important to understand how these components track together (or not) in people’s everyday experiences. Relatedly, the field of empathy would benefit from thoroughly characterizing the structural and temporal relationships among these components to better understand how they work together (or in isolation) to drive key outcomes. 

Thus it seems that the time is right for the field of empathy research to enter anew wave, which explicitly examines the spontaneous separation or co-occurrence of dissociable empathy-related components, especially in behavioral—both laboratory and field—experiments. Several social neuroscience studies have indicated that this is an important aspect of empathy-related inquiry; as such, it is a promising next step for empathy-related research in more naturalistic contexts. The next wave of empathy research is in position to make incredibly important discoveries about when and for whom specific empathic components reliably predict behavioral outcomes, and to understand how empathy can be regulated to help people realize critical social, emotional and occupational goals.

Artificial Intelligence and Legal Disruption: A New Model for Analysis

Hin-Yan Liu,  et .al (2020) 

Law, Innovation and Technology, 

12:2, 205-258

DOI: 10.1080/17579961.2020.1815402

Abstract

Artificial intelligence (AI) is increasingly expected to disrupt the ordinary functioning of society. From how we fight wars or govern society, to how we work and play, and from how we create to how we teach and learn, there is almost no field of human activity which is believed to be entirely immune from the impact of this emerging technology. This poses a multifaceted problem when it comes to designing and understanding regulatory responses to AI. This article aims to: (i) defend the need for a novel conceptual model for understanding the systemic legal disruption caused by new technologies such as AI; (ii) to situate this model in relation to preceding debates about the interaction of regulation with new technologies (particularly the ‘cyberlaw’ and ‘robolaw’ debates); and (iii) to set out a detailed model for understanding the legal disruption precipitated by AI, examining both pathways stemming from new affordances that can give rise to a regulatory ‘disruptive moment’, as well as the Legal Development, Displacement or Destruction that can ensue. The article proposes that this model of legal disruption can be broadly generalisable to understanding the legal effects and challenges of other emerging technologies.

From Concluding Thoughts

As artificial intelligence is often claimed to be an exponential technology, and law progresses incrementally in a linear fashion, there is bound to be a point at which the exponential take off crosses the straight line if these assumptions hold. Everything to the left of this intersection, where AI is below the line, is where hype about the technology does not quite live up to expectations and is generally disappointing in terms of functioning and capability. To the right of this intersection, however, the previously dull technology takes on a surprising and startling tone as it rapidly outpaces both predictions about its capacities and collective abilities to contextualise, accommodate or situate it. It is widely claimed that we are now nearing this intersection. If these claims hold up, the law is one of the institutions that stands to be shocked by the rapid progression and incorporation of AI into society. If this is right, then it is important to start projecting forward in an attempt to minimise the gap between exponential technologies and linear expectations. The legal disruption framework we have presented does exactly this. Furthermore, even if these claims turn out to be misguided, thinking though such transformations sheds different light upon the legal enterprise which hopes to illuminate the entire law.

A pdf of the article is here.

The Value of Not Knowing: Partisan Cue-Taking and Belief Updating of the Uninformed, the Ambiguous, and the Misinformed

Jianing Li & Michael W Wagner

Journal of Communication, Volume 70,

Issue 5, October 2020, Pages 646–669.

https://doi.org/10.1093/joc/jqaa022

Abstract

The problem of a misinformed citizenry is often used to motivate research on misinformation and its corrections. However, researchers know little about how differences in informedness affect how well corrective information helps individuals develop knowledge about current events. We introduce a Differential Informedness Model that distinguishes between three types of individuals, that is, the uninformed, the ambiguous, and the misinformed, and establish their differences with two experiments incorporating multiple partisan cues and issues. Contrary to the common impression, the U.S. public is largely uninformed rather than misinformed of a wide range of factual claims verified by journalists. Importantly, we find that the success of belief updating after exposure to corrective information (via a fact-checking article) is dependent on the presence, the certainty, and the accuracy of one’s prior belief. Uninformed individuals are more likely to update their beliefs than misinformed individuals after exposure to corrective information. Interestingly, the ambiguous individuals, regardless of whether their uncertain guesses were correct, do not differ from uninformed individuals with respect to belief updating.

From the Discussion Section

First, and contrary to the impression that many citizens are misinformed, the majority of our respondents are uninformed of a wide range of claims important enough to be verified by journalists. Only a small group of respondents hold confident, inaccurate beliefs.  This builds on the work of Pasek et al. (2015) by distinguishing between the uninformed, who admit that they “don’t know,” the ambiguous, who take a guess with varying degrees of accuracy, and the misinformed, who hold steadfast false beliefs. In the current environment where concerns over misinformation often lead to heightened attention to belief accuracy, our findings highlight the necessity to bridge between work on political ignorance and misperception and the benefit of leveraging belief accuracy, belief presence and belief certainty to better assess public informedness.

(emphasis added)

A pdf of the research is here.

The epidemiology of moral bioenhancement

R. B. Gibson

Medicine, Health Care and Philosophy 

https://doi.org/10.1007/s11019-020-09980-1

Abstract 

In their 2008 paper, Persson and Savulescu suggest that for moral bioenhancement (MBE) to be effective at eliminating the danger of ‘ultimate harm’ the intervention would need to be compulsory. This is because those most in need of MBE would be least likely to undergo the intervention voluntarily. By drawing on concepts and theories from epidemiology, this paper will suggest that MBE may not need to be universal and compulsory to be effective at significantly improving the collective moral standing of a human populace and reducing the threat of ultimate harm. It will identify similarities between the mechanisms that allow biological contagions (such as a virus) and behaviours (such as those concerned with ethical and unethical actions) to develop, spread, and be reinforced within a population. It will then go onto suggest that, just as with the epidemiological principle of herd immunity, if enough people underwent MBE to reach a minimum threshold then the incidence and spread of immoral behaviours could be significantly reduced, even in those who have not received MBE.

Conclusion 

The phenomenon of herd immunity is one that is critical in the field of vaccine epidemiology and public health. Once it takes effect, even those individuals who are unable to undergo vaccination are still able to benefit from a functional immunity from a biological agent. As such, a compulsory and universal programme of vaccination is not always necessary to achieve a sufficient protection rate against a contagious biological agent. It is this same line of reasoning which this paper has sought to employ, envisioning MBE as a form of vaccination against those types of behaviour that would lead to the realisation of UH (Ultimate Harm). Consequentially, this allows for the possibility of sufficient protection against the undesirable behaviours that would lead to UH without a need for a universal and compulsory enhancement programme.

A pdf can be found here.

Blind loyalty? When group loyalty makes us see evil or engage in it

J. A. Hildreth, F. Gino, & M. Bazerman

Organizational Behavior and 

Human Decision Processes

Volume 132, January 2016, 16-36

Abstract

Loyalty often drives corruption. Corporate scandals, political machinations, and sports cheating highlight how loyalty’s pernicious nature manifests in collusion, conspiracy, cronyism, nepotism, and other forms of cheating. Yet loyalty is also touted as an ethical principle that guides behavior. Drawing on moral psychology and behavioral ethics research, we developed hypotheses about when group loyalty fosters ethical behavior and when it fosters corruption. Across nine studies, we found that individuals primed with loyalty cheated less than those not primed (Study 1A and 1B). Members more loyal to their fraternities (Study 2A) and students more loyal to their study groups (Study 2B) also cheated less than their less loyal counterparts due to greater ethical salience when they pledged their loyalty (Studies 3A and 3B). Importantly, competition moderated these effects: when competition was high, members more loyal to their fraternities (Study 4) or individuals primed with loyalty (Studies 5A and 5B) cheated more.

Highlights

• We define loyalty as the principle of partiality toward an object (e.g. group).

• Across nine studies we found that loyalty reduced rather than increased cheating when group goals were unclear.

• Pledging loyalty increased the salience of ethics which led to less cheating.

• Competition moderated these effects: when competition was high the loyal cheated more.

• The findings are consistent with loyalty’s role as an ethical principle.

A pdf is here.

The psychologist rethinking human emotion

David Shariatmadari

The Guardian

Originally posted 25 Sept 20

Here is an excerpt:

Barrett’s point is that if you understand that “fear” is a cultural concept, a way of overlaying meaning on to high arousal and high unpleasantness, then it’s possible to experience it differently. “You know, when you have high arousal before a test, and your brain makes sense of it as test anxiety, that’s a really different feeling than when your brain makes sense of it as energised determination,” she says. “So my daughter, for example, was testing for her black belt in karate. Her sensei was a 10th degree black belt, so this guy is like a big, powerful, scary guy. She’s having really high arousal, but he doesn’t say to her, ‘Calm down’; he says, ‘Get your butterflies flying in formation.’” That changed her experience. Her brain could have made anxiety, but it didn’t, it made determination.”

In the lectures Barrett gives to explain this model, she talks of the brain as a prisoner in a dark, silent box: the skull. The only information it gets about the outside world comes via changes in light (sight), air pressure (sound) exposure to chemicals (taste and smell), and so on. It doesn’t know the causes of these changes, and so it has to guess at them in order to decide what to do next.

How does it do that? It compares those changes to similar changes in the past, and makes predictions about the current causes based on experience. Imagine you are walking through a forest. A dappled pattern of light forms a wavy black shape in front of you. You’ve seen many thousands of images of snakes in the past, you know that snakes live in the forest. Your brain has already set in train an array of predictions.

The point is that this prediction-making is consciousness, which you can think of as a constant rolling process of guesses about the world being either confirmed or proved wrong by fresh sensory inputs. In the case of the dappled light, as you step forward you get information that confirms a competing prediction that it’s just a stick: the prediction of a snake was ultimately disproved, but not before it grew so strong that neurons in your visual cortex fired as though one was actually there, meaning that for a split second you “saw” it. So we are all creating our world from moment to moment. If you didn’t, your brain wouldn’t be able make the changes necessary for your survival quickly enough. If the prediction “snake” wasn’t already in train, then the shot of adrenaline you might need in order to jump out of its way would come too late.

The info is here.

Do antidepressants work?

Jacob Stegenga

aeon.co

Originally published 5 Mar 19

Here is an excerpt:

To see this, consider an analogy. Imagine we are testing a drug for weight loss. For every 100 subjects in the drug group, three subjects lose one kilogramme and 97 subjects gain five kilos. For every 100 subjects in the placebo group, two lose four kilos and 98 subjects do not gain or lose any weight. How effective is the drug for weight loss? The odds ratio of weight loss is 1.5, and yet this number tells us nothing about how much weight people on average gain or lose – indeed, the number entirely conceals the real effects of the drug. Though this is an extreme analogy, it shows how cautious we must be when interpreting this celebrated meta-analysis. Unfortunately, however, in response to this work, many leading psychiatrists celebrated, and news headlines misleadingly claimed ‘The drugs do work.’ On the winding route from the hard work of these researchers to the news reports where you were most likely to hear about that study, a simple number became a lie.

When analysed properly, the best evidence indicates that antidepressants are not clinically beneficial. The meta-analyses worth considering, such as the one above, involve attempts to gather evidence from all trials on antidepressants, including those that remain unpublished. Of course it is impossible to know that a meta-analysis includes all unpublished evidence, because publication bias is characterised by deception, either inadvertent or wilful. Nevertheless, these meta-analyses are serious attempts to address publication bias by finding as much data as possible. What, then, do they show?

In meta-analyses that include as much of the evidence as possible, the severity of depression among subjects who receive antidepressants goes down by approximately two points compared with subjects who receive a placebo. Two points. Remember, a depression score can go down by double that amount simply if a subject stops fidgeting. This result, found by both champions and critics of antidepressants, has been replicated year after year for more than a decade (see, for example, the meta-analyses led by Irving Kirsch in 2008, by J C Fournier in 2010, and by Janus Christian Jakobsen in 2017). The phenomena of blind-breaking, the placebo effect and unresolved publication bias could easily account for this trivial two-point reduction in severity scores.

The info is here.

Using Machine Learning to Generate Novel Hypotheses: Increasing Optimism About COVID-19 Makes People Less Willing to Justify Unethical Behaviors

Sheetal A, Feng Z, Savani K. 

Psychological Science. 2020;31(10):

1222-1235. 

doi:10.1177/0956797620959594

Abstract

How can we nudge people to not engage in unethical behaviors, such as hoarding and violating social-distancing guidelines, during the COVID-19 pandemic? Because past research on antecedents of unethical behavior has not provided a clear answer, we turned to machine learning to generate novel hypotheses. We trained a deep-learning model to predict whether or not World Values Survey respondents perceived unethical behaviors as justifiable, on the basis of their responses to 708 other items. The model identified optimism about the future of humanity as one of the top predictors of unethicality. A preregistered correlational study (N = 218 U.S. residents) conceptually replicated this finding. A preregistered experiment (N = 294 U.S. residents) provided causal support: Participants who read a scenario conveying optimism about the COVID-19 pandemic were less willing to justify hoarding and violating social-distancing guidelines than participants who read a scenario conveying pessimism. The findings suggest that optimism can help reduce unethicality, and they document the utility of machine-learning methods for generating novel hypotheses.

Here is how the research article begins:

Unethical behaviors can have substantial consequences in times of crisis. For example, in the midst of the COVID-19 pandemic, many people hoarded face masks and hand sanitizers; this hoarding deprived those who needed protective supplies most (e.g., medical workers and the elderly) and, therefore, put them at risk. Despite escalating deaths, more than 50,000 people were caught violating quarantine orders in Italy, putting themselves and others at risk. Governments covered up the scale of the pandemic in that country, thereby allowing the infection to spread in an uncontrolled manner. Thus, understanding antecedents of unethical behavior and identifying nudges to reduce unethical behaviors are particularly important in times of crisis.

Here is part of the Discussion

We formulated a novel hypothesis—that optimism reduces unethicality—on the basis of the deep-learning model’s finding that whether people think that the future of humanity is bleak or bright is a strong predictor of unethicality. This variable was not flagged as a top predictor either by the correlational analysis or by the lasso regression. Consistent with this idea, the results of a correlational study showed that people higher on dispositional optimism were less willing to engage in unethical behaviors. A following experiment found that increasing participants’ optimism about the COVID-19 epidemic reduced the extent to which they justified unethical behaviors related to the epidemic. The behavioral studies were conducted with U.S. American participants; thus, the cultural generalizability of the present findings is unclear. Future research needs to test whether optimism reduces unethical behavior in other cultural contexts.