The secrets of cooperation

Bob Holmes

Knowablemagazine.org

Originally published 29 MAR 23

Here are two excerpts:

Human cooperation takes some explaining — after all, people who act cooperatively should be vulnerable to exploitation by others. Yet in societies around the world, people cooperate to their mutual benefit. Scientists are making headway in understanding the conditions that foster cooperation, research that seems essential as an interconnected world grapples with climate change, partisan politics and more — problems that can be addressed only through large-scale cooperation.

Behavioral scientists’ formal definition of cooperation involves paying a personal cost (for example, contributing to charity) to gain a collective benefit (a social safety net). But freeloaders enjoy the same benefit without paying the cost, so all else being equal, freeloading should be an individual’s best choice — and, therefore, we should all be freeloaders eventually.

Many millennia of evolution acting on both our genes and our cultural practices have equipped people with ways of getting past that obstacle, says Muthukrishna, who coauthored a look at the evolution of cooperation in the 2021 Annual Review of Psychology. This cultural-genetic coevolution stacked the deck in human society so that cooperation became the smart move rather than a sucker’s choice. Over thousands of years, that has allowed us to live in villages, towns and cities; work together to build farms, railroads and other communal projects; and develop educational systems and governments.

Evolution has enabled all this by shaping us to value the unwritten rules of society, to feel outrage when someone else breaks those rules and, crucially, to care what others think about us.

“Over the long haul, human psychology has been modified so that we’re able to feel emotions that make us identify with the goals of social groups,” says Rob Boyd, an evolutionary anthropologist at the Institute for Human Origins at Arizona State University.

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Reputation is more powerful than financial incentives in encouraging cooperation

Almost a decade ago, Yoeli and his colleagues trawled through the published literature to see what worked and what didn’t at encouraging prosocial behavior. Financial incentives such as contribution-matching or cash, or rewards for participating, such as offering T-shirts for blood donors, sometimes worked and sometimes didn’t, they found. In contrast, reputational rewards — making individuals’ cooperative behavior public — consistently boosted participation. The result has held up in the years since. “If anything, the results are stronger,” says Yoeli.

Financial rewards will work if you pay people enough, Yoeli notes — but the cost of such incentives could be prohibitive. One study of 782 German residents, for example, surveyed whether paying people to receive a Covid vaccine would increase vaccine uptake. It did, but researchers found that boosting vaccination rates significantly would have required a payment of at least 3,250 euros — a dauntingly steep price.

And payoffs can actually diminish the reputational rewards people could otherwise gain for cooperative behavior, because others may be unsure whether the person was acting out of altruism or just doing it for the money. “Financial rewards kind of muddy the water about people’s motivations,” says Yoeli. “That undermines any reputational benefit from doing the deed.”

The info is here.

Observation moderates the moral licensing effect: A meta-analytic test of interpersonal and intrapsychic mechanisms.

Rotella, A., Jung, J., Chinn, C., 

& Barclay, P. (2023, March 28).

PsyArXiv.com

https://doi.org/10.31234/osf.io/tmhe9

Abstract

Moral licensing occurs when someone who initially behaved morally subsequently acts less morally. We apply reputation-based theories to predict when and why moral licensing would occur. Specifically, our pre-registered predictions were that (1) participants observed during the licensing manipulation would have larger licensing effects, and (2) unambiguous dependent variables would have smaller licensing effects. In a pre-registered multi-level meta-analysis of 111 experiments (N = 19,335), we found a larger licensing effect when participants were observed (Hedge’s g = 0.61) compared to unobserved (Hedge’s g = 0.14). Ambiguity did not moderate the effect. The overall moral licensing effect was small (Hedge’s g = 0.18). We replicated these analyses using robust Bayesian meta-analysis and found strong support for the moral licensing effect only when participants are observed. These results suggest that the moral licensing effect is predominantly an interpersonal effect based on reputation, rather than an intrapsychic effect based on self-image.

Statement of Relevance

When and why will people behave morally?Everyday, people make decisions to act in ways that are more or less moral –holding a door open for others, donating to charity, or assistant a colleague. Yet, it is not well understood how people’s prior actions influence their subsequent behaviors. In this study, we investigated how observation influences the moral licensing effect, which is when someone who was initially moral subsequently behaves less morally, as if they had“license” to act badly.  In a review of existing literature, we found a larger moral licensing effect when people were seen to act morally compared to when they were unobserved, which suggests that once someone establishes a moral reputation to others, they can behave slightly less moral and maintain a moral reputation. This finding advances our understanding of the moral licensing mechanism and how reputation and observation impact moral actions.

The research is here.

Filling in the Gaps: False Memories and Partisan Bias

Armaly, M.T. & Enders, A.

Political Psychology, Vol. 0, No. 0, 2022

doi: 10.1111/pops.12841

Abstract

While cognitive psychologists have learned a great deal about people's propensity for constructing and acting on false memories, the connection between false memories and politics remains understudied. If partisan bias guides the adoption of beliefs and colors one's interpretation of new events and information, so too might it prove powerful enough to fabricate memories of political circumstances. Across two studies, we first distinguish false memories from false beliefs and expressive responses; false political memories appear to be genuine and subject to partisan bias. We also examine the political and psychological correlates of false memories. Nearly a third of respondents reported remembering a fabricated or factually altered political event, with many going so far as to convey the circumstances under which they “heard about” the event. False-memory recall is correlated with the strength of partisan attachments, interest in politics, and participation, as well as narcissism, conspiratorial thinking, and cognitive ability.

Conclusion

While cognitive psychologists have learned a great deal about people’s propensity for constructing and acting on false memories, the role of false memories in political attitudes has received scant attention. In this study, we built on previous work by investigating the partisan foundations and political and psychological correlates of false memories. We found that nearly a third of respondents reported remembering a fabricated or factually altered political event. These false memories are not mere beliefs or expressive responses; indeed, most respondents conveyed where they “heard about” at least one event in question, with some providing vivid details of their circumstances. We also found that false memory is associated with the strength of one’s partisan attachments, conspiracism, and interest in politics, among other factors.

Altogether, false memories seem to behave like a form of partisan bias: The more in touch one is with politics, especially the political parties, the more susceptible they are to false- memory construction. While we cannot ascribe causality, uncovering this (likely) mechanism has several implications. First, the more polarized we become, the more likely individuals may be to con-struct false memories about in-  and outgroups. In turn, the falser memories one constructs about the greatness of one’s ingroup and the evil doings of the outgroup, the higher the temperature of polarization rises. Second, false- memory construction may be one mechanism by which mis-information takes hold psychologically. By exposing people to information they are motivated to believe, skilled traffickers of misinformation may be able to not only convince one to be-lieve something but convince them that something which never transpired actually did so. The conviction that accompanies memory— people’s natural tendency to believe their memories are trustworthy— makes false memories a particularly pernicious route by which to manipulate those subject to this bias. Indeed, this is precisely the concern presented by “deepfakes”— images and videos that have been expertly altered or fabricated for the purpose of exploiting targeted viewers. Finally, and relatedly, politicians may be able to induce false memories, strategically molding a past reality to suit their political will.

The research is here.

A dark side of hope: Understanding why investors cling onto losing stocks

Luo, S. X., et al. (2022).

Journal of Behavioral Decision Making.

https://doi.org/10.1002/bdm.2304

Abstract

Investors are often inclined to keep losing stocks too long, despite this being irrational. This phenomenon is part of the disposition effect (“people ride losers too long, and sell winners too soon”). The current research examines the role of hope as a potential explanation of why people ride losers too long. Three correlational studies (1A, 1B, and 2) find that people's trait hope is positively associated with their inclination to keep losing stocks, regardless of their risk-seeking tendency (Study 2). Further, three experimental studies (3, 4, and 5) reveal that people are inclined to hold on to losing (vs. not-losing) stocks because of their hope to break even and not because of their hope to gain. Studies 4 and 5 provide process evidence confirming the role of hope and indicate potential interventions to decrease people's tendency to keep losing stocks by reducing the hope. The findings contribute to the limited empirical literature that has investigated how emotions influence the disposition effect by providing empirical evidence for the role of hope. Moreover, the findings add to the literature of hope by revealing its role in financial decision-making and show a “dark side” of this positive emotion.

General Discussion

Investors are reluctant to sell their losing stocks, which is part of the well-known disposition effect (Shefrin & Statman, 1985). Why would investors do so, especially when it is a suboptimal financial decision? In a series of studies, we found consistent support for the idea that the emotion of hope explains at least partly why people hold on to their losing stocks. Studies 1A and 1B revealed that an increase in people's trait hope (measured by two trait hope scales) increases their inclination to keep losing stocks. Study 2 further confirmed that the trait hope is positively associated with the inclination to keep losing stocks, controlling for the influence of the risk-taking tendency of real-world investors. In Study 3, we developed a simple and effective experimental design to examine whether losing influences hope and people's tendency to keep stocks in the same way. In addition, it differentiated between what people hope for: to break even versus to gain. The results indicate that when one's stocks are losing, compared with when they are not, people experience a stronger hope to break even and an inclination to keep, but not a stronger hope to gain. In addition, Study 3 found that losing (vs. not losing) leads to a stronger inclination to keep stocks.

Moreover, the hope to break even (but not the hope to gain) mediated the effect of losing on the inclination to keep. Study 4 found that reducing people's hope to break even decreases their inclination to keep their losing stocks to the same level as when their stocks did not decrease in price. Study 5 found that people tend to have a lower hope to break even when holding stocks on behalf of others (vs. for themselves) and thus tend to be less likely to keep the losing stocks. Studies 4 and 5 provided process evidence that reducing hope attenuates the inclination to keep, suggesting two possible interventions focusing on the possibility or the desire feature of hope. In a series of studies, we found that people cling to losing stocks because they hope to break even, and reducing this hope decreases their inclination to keep the losing stocks.

The research is here.

A Prosociality Paradox: How Miscalibrated Social Cognition Creates a Misplaced Barrier to Prosocial Action

Epley, N., Kumar, A., Dungan, J., &

Echelbarger, M. (2023).

Current Directions in Psychological Science,

32(1), 33–41. 

https://doi.org/10.1177/09637214221128016

Abstract

Behaving prosocially can increase well-being among both those performing a prosocial act and those receiving it, and yet people may experience some reluctance to engage in direct prosocial actions. We review emerging evidence suggesting that miscalibrated social cognition may create a psychological barrier that keeps people from behaving as prosocially as would be optimal for both their own and others’ well-being. Across a variety of interpersonal behaviors, those performing prosocial actions tend to underestimate how positively their recipients will respond. These miscalibrated expectations stem partly from a divergence in perspectives, such that prosocial actors attend relatively more to the competence of their actions, whereas recipients attend relatively more to the warmth conveyed. Failing to fully appreciate the positive impact of prosociality on others may keep people from behaving more prosocially in their daily lives, to the detriment of both their own and others’ well-being.

Undervaluing Prosociality

It may not be accidental that William James (1896/1920) named “the craving to be appreciated” as “the deepest principle in human nature” only after receiving a gift of appreciation that he described as “the first time anyone ever treated me so kindly.” “I now perceive one immense omission in my [Principles of Psychology],” he wrote regarding the importance of appreciation. “I left it out altogether . . . because I had never had it gratified till now” (p. 33).

James does not seem to be unique in failing to recognize the positive impact that appreciation can have on recipients. In one experiment (Kumar & Epley, 2018, Experiment 1), MBA students thought of a person they felt grateful to, but to whom they had not yet expressed their appreciation. The students, whom we refer to as expressers, wrote a gratitude letter to this person and then reported how they expected the recipient would feel upon receiving it: how surprised the recipient would be to receive the letter, how surprised the recipient would be about the content, how negative or positive the recipient would feel, and how awkward the recipient would feel. Expressers willing to do so then provided recipients’ email addresses so the recipients could be contacted to report how they actually felt receiving their letter. Although expressers recognized that the recipients would feel positive, they did not recognize just how positive the recipients would feel: Expressers underestimated how surprised the recipients would be to receive the letter, how surprised the recipients would be by its content, and how positive the recipients would feel, whereas they overestimated how awkward the recipients would feel. Table 1 shows the robustness of these results across an additional published experiment and 17 subsequent replications (see Fig. 1 for overall results; full details are available at OSF: osf.io/7wndj/). Expressing gratitude has a reliably more positive impact on recipients than expressers expect.

Conclusion

How much people genuinely care about others has been debated for centuries. In summarizing the purely selfish viewpoint endorsed by another author, Thomas Jefferson (1854/2011) wrote, “I gather from his other works that he adopts the principle of Hobbes, that justice is founded in contract solely, and does not result from the construction of man.” Jefferson felt differently: “I believe, on the contrary, that it is instinct, and innate, that the moral sense is as much a part of our constitution as that of feeling, seeing, or hearing . . . that every human mind feels pleasure in doing good to another” (p. 39).

Such debates will never be settled by simply observing human behavior because prosociality is not simply produced by automatic “instinct” or “innate” disposition, but rather can be produced by complicated social cognition (Miller, 1999). Jefferson’s belief that people feel “pleasure in doing good to another” is now well supported by empirical evidence. However, the evidence we reviewed here suggests that people may avoid experiencing this pleasure not because they do not want to be good to others, but because they underestimate just how positively others will react to the good being done to them.

The info is here.

Responsible Agency and the Importance of Moral Audience

Jefferson, A., Sifferd, K. 

Ethic Theory Moral Prac (2023).

https://doi.org/10.1007/s10677-023-10385-1

Abstract

Ecological accounts of responsible agency claim that moral feedback is essential to the reasons-responsiveness of agents. In this paper, we discuss McGeer’s scaffolded reasons-responsiveness account in the light of two concerns. The first is that some agents may be less attuned to feedback from their social environment but are nevertheless morally responsible agents – for example, autistic people. The second is that moral audiences can actually work to undermine reasons-responsiveness if they espouse the wrong values. We argue that McGeer’s account can be modified to handle both problems. Once we understand the specific roles that moral feedback plays for recognizing and acting on moral reasons, we can see that autistics frequently do rely on such feedback, although it often needs to be more explicit. Furthermore, although McGeer is correct to highlight the importance of moral feedback, audience sensitivity is not all that matters to reasons-responsiveness; it needs to be tempered by a consistent application of moral rules. Agents also need to make sure that they choose their moral audiences carefully, paying special attention to receiving feedback from audiences which may be adversely affected by their actions.

Conclusions

In this paper we raised two challenges to McGeer’s scaffolded reasons-responsiveness account: agents who are less attuned to social feedback such as autistics, and corrupting moral audiences. We found that, once we parsed the two roles that feedback from a moral audience play, autistics provide reasons to revise the scaffolded reasons-responsiveness account. We argued that autistic persons, like neurotypicals, wish to justify their behaviour to a moral audience and rely on their moral audience for feedback. However, autistic persons may need more explicit feedback when it comes to effects their behaviour has on others. They also compensate for difficulties they have in receiving information from the moral audience by justifying action through appeal to moral rules. This shows that McGeer’s view of moral agency needs to include observance of moral rules as a way of reducing reliance on audience feedback. We suspect that McGeer would approve of this proposal, as she mentions that an instance of blame can lead to vocal protest by the target, and a possible renegotiation of norms and rules for what constitutes acceptable behaviour (2019). Consideration of corrupting audiences highlights a different problem from that of resisting blame and renegotiating norms. It draws attention to cases where individual agents must try to go beyond what is accepted in their moral environment, a significant challenge for social beings who rely strongly on moral audiences in developing and calibrating their moral reasons-responsiveness. Resistance to a moral audience requires the capacity to evaluate the action differently; often this will be with reference to a moral rule or principle.

For both neurotypical and autistic individuals, consistent application of moral rules or principles can reinforce and bring back to mind important moral commitments when we are led astray by our own desires or specific (im)moral audiences. But moral audiences still play a crucial role to developing and maintaining reasons-responsiveness. First, they are essential to the development and maintenance of all agents’ moral sensitivity. Second, they can provide an important moral corrective where people may have moral blindspots, especially when they provide insights into ways in which a person has fallen short morally by not taking on board reasons that are not obvious to them. Often, these can be reasons which pertain to the respectful treatment of others who are in some important way different from that person.

The info is here.

In sum: Be responsible and accountable in your actions, as your moral audience is always watching. Doing the right thing matters not just for your reputation, but for the greater good. #ResponsibleAgency #MoralAudience

ChatGPT in the Clinic? Medical AI Needs Ethicists

Emma Bedor Hiland

The Hastings Center

Originally published by 10 MAR 23

Concerns about the role of artificial intelligence in our lives, particularly if it will help us or harm us, improve our health and well-being or work to our detriment, are far from new. Whether 2001: A Space Odyssey’s HAL colored our earliest perceptions of AI, or the much more recent M3GAN, these questions are not unique to the contemporary era, as even the ancient Greeks wondered what it would be like to live alongside machines.

Unlike ancient times, today AI’s presence in health and medicine is not only accepted, it is also normative. Some of us rely upon FitBits or phone apps to track our daily steps and prompt us when to move or walk more throughout our day. Others utilize chatbots available via apps or online platforms that claim to improve user mental health, offering meditation or cognitive behavioral therapy. Medical professionals are also open to working with AI, particularly when it improves patient outcomes. Now the availability of sophisticated chatbots powered by programs such as OpenAI’s ChatGPT have brought us closer to the possibility of AI becoming a primary source in providing medical diagnoses and treatment plans.

Excitement about ChatGPT was the subject of much media attention in late 2022 and early 2023. Many in the health and medical fields were also eager to assess the AI’s abilities and applicability to their work. One study found ChatGPT adept at providing accurate diagnoses and triage recommendations. Others in medicine were quick to jump on its ability to complete administrative paperwork on their behalf. Other research found that ChatGPT reached, or came close to reaching, the passing threshold for United States Medical Licensing Exam.

Yet the public at large is not as excited about an AI-dominated medical future. A study from the Pew Research Center found that most Americans are “uncomfortable” with the prospect of AI-provided medical care. The data also showed widespread agreement that AI will negatively affect patient-provider relationships, and that the public is concerned health care providers will adopt AI technologies too quickly, before they fully understanding the risks of doing so.

The info is here.

In sum: As AI is increasingly used in healthcare, this article argues that there is a need for ethical considerations and expertise to ensure that these systems are designed and used in a responsible and beneficial manner. Ethicists can play a vital role in evaluating and addressing the ethical implications of medical AI, particularly in areas such as bias, transparency, and privacy.

Produced and counterfactual effort contribute to responsibility attributions in collaborative tasks

Xiang, Y., Landy, J., et al. (2023, March 8). 

PsyArXiv

https://doi.org/10.31234/osf.io/jc3hk

Abstract

How do people judge responsibility in collaborative tasks? Past work has proposed a number of metrics that people may use to attribute blame and credit to others, such as effort, competence, and force. Some theories consider only the produced effort or force (individuals are more responsible if they produce more effort or force), whereas others consider counterfactuals (individuals are more responsible if some alternative behavior on their or their collaborator's part could have altered the outcome). Across four experiments (N = 717), we found that participants’ judgments are best described by a model that considers both produced and counterfactual effort. This finding generalized to an independent validation data set (N = 99). Our results thus support a dual-factor theory of responsibility attribution in collaborative tasks.

General discussion

Responsibility for the outcomes of collaborations is often distributed unevenly. For example, the lead author on a project may get the bulk of the credit for a scientific discovery, the head of a company may  shoulder the blame for a failed product, and the lazier of two friends may get the greater share of blame  for failing to lift a couch.  However, past work has provided conflicting accounts of the computations that drive responsibility attributions in collaborative tasks.  Here, we compared each of these accounts against human responsibility attributions in a simple collaborative task where two agents attempted to lift a box together.  We contrasted seven models that predict responsibility judgments based on metrics proposed in past work, comprising three production-style models (Force, Strength, Effort), three counterfactual-style models (Focal-agent-only, Non-focal-agent-only, Both-agent), and one Ensemble model that combines the best-fitting production- and counterfactual-style models.  Experiment 1a and Experiment 1b showed that theEffort model and the Both-agent counterfactual model capture the data best among the production-style models and the counterfactual-style models, respectively.  However, neither provided a fully adequate fit on their own.  We then showed that predictions derived from the average of these two models (i.e., the Ensemble model) outperform all other models, suggesting that responsibility judgments are likely a combination of production-style reasoning and counterfactual reasoning.  Further evidence came from analyses performed on individual participants, which revealed that he Ensemble model explained more participants’ data than any other model.  These findings were subsequently supported by Experiment 2a and Experiment 2b, which replicated the results when additional force information was shown to the participants, and by Experiment 3, which validated the model predictions with a broader range of stimuli.

The research is here.

Summary: Effort exerted by each member & counterfactual thinking play a crucial role in attributing responsibility for success or failure in collaborative tasks. This study suggests that higher effort leads to more responsibility for success, while lower effort leads to more responsibility for failure.

A Psychologist Explains How AI and Algorithms Are Changing Our Lives

Danny Lewis

The Wall Street Journal

Originally posted 21 MAR 23

In an age of ChatGPT, computer algorithms and artificial intelligence are increasingly embedded in our lives, choosing the content we’re shown online, suggesting the music we hear and answering our questions.

These algorithms may be changing our world and behavior in ways we don’t fully understand, says psychologist and behavioral scientist Gerd Gigerenzer, the director of the Harding Center for Risk Literacy at the University of Potsdam in Germany. Previously director of the Center for Adaptive Behavior and Cognition at the Max Planck Institute for Human Development, he has conducted research over decades that has helped shape understanding of how people make choices when faced with uncertainty. 

In his latest book, “How to Stay Smart in a Smart World,” Dr. Gigerenzer looks at how algorithms are shaping our future—and why it is important to remember they aren’t human. He spoke with the Journal for The Future of Everything podcast.

The term algorithm is thrown around so much these days. What are we talking about when we talk about algorithms?

It is a huge thing, and therefore it is important to distinguish what we are talking about. One of the insights in my research at the Max Planck Institute is that if you have a situation that is stable and well defined, then complex algorithms such as deep neural networks are certainly better than human performance. Examples are [the games] chess and Go, which are stable. But if you have a problem that is not stable—for instance, you want to predict a virus, like a coronavirus—then keep your hands off complex algorithms. [Dealing with] the uncertainty—that is more how the human mind works, to identify the one or two important cues and ignore the rest. In that type of ill-defined problem, complex algorithms don’t work well. I call this the “stable world principle,” and it helps you as a first clue about what AI can do. It also tells you that, in order to get the most out of AI, we have to make the world more predictable.

So after all these decades of computer science, are algorithms really just still calculators at the end of the day, running more and more complex equations?

What else would they be? A deep neural network has many, many layers, but they are still calculating machines. They can do much more than ever before with the help of video technology. They can paint, they can construct text. But that doesn’t mean that they understand text in the sense humans do.

The article is here.