Many heads are more utilitarian than one

Keshmirian, A., Deroy, O, & Bahrami, B.

Cognition

Volume 220, March 2022, 104965

Abstract

Moral judgments have a very prominent social nature, and in everyday life, they are continually shaped by discussions with others. Psychological investigations of these judgments, however, have rarely addressed the impact of social interactions. To examine the role of social interaction on moral judgments within small groups, we had groups of 4 to 5 participants judge moral dilemmas first individually and privately, then collectively and interactively, and finally individually a second time. We employed both real-life and sacrificial moral dilemmas in which the character's action or inaction violated a moral principle to benefit the greatest number of people. Participants decided if these utilitarian decisions were morally acceptable or not. In Experiment 1, we found that collective judgments in face-to-face interactions were more utilitarian than the statistical aggregate of their members compared to both first and second individual judgments. This observation supported the hypothesis that deliberation and consensus within a group transiently reduce the emotional burden of norm violation. In Experiment 2, we tested this hypothesis more directly: measuring participants' state anxiety in addition to their moral judgments before, during, and after online interactions, we found again that collectives were more utilitarian than those of individuals and that state anxiety level was reduced during and after social interaction. The utilitarian boost in collective moral judgments is probably due to the reduction of stress in the social setting.

Highlights

• Collective consensual judgments made via group interactions were more utilitarian than individual judgments.

• Group discussion did not change the individual judgments indicating a normative conformity effect.

• Individuals consented to a group judgment that they did not necessarily buy into personally.

• Collectives were less stressed than individuals after responding to moral dilemmas.

• Interactions reduced aversive emotions (e.g., stressed) associated with violation of moral norms.

From the Discussion

Our analysis revealed that groups, in comparison to individuals, are more utilitarian in their moral judgments. Thus, our findings are inconsistent with Virtue-Signaling (VS), which proposed the opposite

effect. Crucially, the collective utilitarian boost was short-lived: it was only seen at the collective level and not when participants rated the same questions individually again. Previous research shows that moral change at the individual level, as the result of social deliberation, is rather long-lived and not transient (e.g., see Ueshima et al., 2021). Thus, this collective utilitarian boost could not have resulted from deliberation and reasoning or due to conscious application of utilitarian principles with authentic reasons to maximize the total good. If this was the case, the effect would have persisted in the second individual judgment as well. That was not what we observed. Consequently, our findings are inconsistent with the Social Deliberation (SD) hypotheses.

A pdf of the research is here.

Why Is It So Hard to Be Rational?

Joshua Rothman

The New Yorker

Originally published 16 Aug 21

Here is an excerpt:

Knowing about what you know is Rationality 101. The advanced coursework has to do with changes in your knowledge. Most of us stay informed straightforwardly—by taking in new information. Rationalists do the same, but self-consciously, with an eye to deliberately redrawing their mental maps. The challenge is that news about distant territories drifts in from many sources; fresh facts and opinions aren’t uniformly significant. In recent decades, rationalists confronting this problem have rallied behind the work of Thomas Bayes, an eighteenth-century mathematician and minister. So-called Bayesian reasoning—a particular thinking technique, with its own distinctive jargon—has become de rigueur.

There are many ways to explain Bayesian reasoning—doctors learn it one way and statisticians another—but the basic idea is simple. When new information comes in, you don’t want it to replace old information wholesale. Instead, you want it to modify what you already know to an appropriate degree. The degree of modification depends both on your confidence in your preexisting knowledge and on the value of the new data. Bayesian reasoners begin with what they call the “prior” probability of something being true, and then find out if they need to adjust it.

Consider the example of a patient who has tested positive for breast cancer—a textbook case used by Pinker and many other rationalists. The stipulated facts are simple. The prevalence of breast cancer in the population of women—the “base rate”—is one per cent. When breast cancer is present, the test detects it ninety per cent of the time. The test also has a false-positive rate of nine per cent: that is, nine per cent of the time it delivers a positive result when it shouldn’t. Now, suppose that a woman tests positive. What are the chances that she has cancer?

When actual doctors answer this question, Pinker reports, many say that the woman has a ninety-per-cent chance of having it. In fact, she has about a nine-per-cent chance. The doctors have the answer wrong because they are putting too much weight on the new information (the test results) and not enough on what they knew before the results came in—the fact that breast cancer is a fairly infrequent occurrence. To see this intuitively, it helps to shuffle the order of your facts, so that the new information doesn’t have pride of place. Start by imagining that we’ve tested a group of a thousand women: ten will have breast cancer, and nine will receive positive test results. Of the nine hundred and ninety women who are cancer-free, eighty-nine will receive false positives. Now you can allow yourself to focus on the one woman who has tested positive. To calculate her chances of getting a true positive, we divide the number of positive tests that actually indicate cancer (nine) by the total number of positive tests (ninety-eight). That gives us about nine per cent.

The info is here.

A normative account of self-deception, overconfidence, and paranoia

Rossi-Goldthorpe, R., Leong, et al.

(2021, April 12).

https://doi.org/10.31234/osf.io/9fkb5

Abstract

Self-deception, paranoia, and overconfidence involve misbeliefs about self, others, and world. They are often considered mistaken. Here we explore whether they might be adaptive, and further, whether they might be explicable in normative Bayesian terms. We administered a difficult perceptual judgment task with and without social influence (suggestions from a cooperating or competing partner). Crucially, the social influence was uninformative. We found that participants heeded the suggestions most under the most uncertain conditions and that they did so with high confidence, particularly if they were more paranoid. Model fitting to participant behavior revealed that their prior beliefs changed depending on whether the partner was a collaborator or competitor, however, those beliefs did not differ as a function of paranoia. Instead, paranoia, self-deception, and overconfidence were associated with participants’ perceived instability of their own performance. These data are consistent with the idea that self-deception, paranoia, and overconfidence flourish under uncertainty, and have their roots in low self-esteem, rather than excessive social concern. The normative model suggests that spurious beliefs can have value – self-deception is irrational yet can facilitate optimal behavior. This occurs even at the expense of monetary rewards, perhaps explaining why self-deception and paranoia contribute to costly decisions which can spark financial crashes and costly wars.

The research is here.

How peer influence shapes value computation in moral decision-making

Yu, H., Siegel, J., Clithero, J., & Crockett, M. 

(2021, January 16).

https://doi.org/10.31234/osf.io/hg79v

Abstract

Moral behavior is susceptible to peer influence. How does information from peers influence moral preferences? We used drift-diffusion modeling to show that peer influence changes the value of moral behavior by prioritizing the choice attributes that align with peers’ goals. Study 1 (N = 100; preregistered) showed that participants accurately inferred the goals of prosocial and antisocial peers when observing their moral decisions. In Study 2 (N = 68), participants made moral decisions before and after observing the decisions of a prosocial or antisocial peer. Peer observation caused participants’ own preferences to resemble those of their peers. This peer influence effect on value computation manifested as an increased weight on choice attributes promoting the peers’ goals that occurred independently from peer influence on initial choice bias. Participants’ self-reported awareness of influence tracked more closely with computational measures of prosocial than antisocial influence. Our findings have implications for bolstering and blocking the effects of prosocial and antisocial influence on moral behavior.

Probabilistic Biases Meet the Bayesian Brain.

Chater N, et al.

Current Directions in Psychological Science. 

2020;29(5):506-512. 

doi:10.1177/0963721420954801

Abstract

In Bayesian cognitive science, the mind is seen as a spectacular probabilistic-inference machine. But judgment and decision-making (JDM) researchers have spent half a century uncovering how dramatically and systematically people depart from rational norms. In this article, we outline recent research that opens up the possibility of an unexpected reconciliation. The key hypothesis is that the brain neither represents nor calculates with probabilities but approximates probabilistic calculations by drawing samples from memory or mental simulation. Sampling models diverge from perfect probabilistic calculations in ways that capture many classic JDM findings, which offers the hope of an integrated explanation of classic heuristics and biases, including availability, representativeness, and anchoring and adjustment.

Introduction

Human probabilistic reasoning gets bad press. Decades of brilliant experiments, most notably by Daniel Kahneman and Amos Tversky (e.g., Kahneman, 2011; Kahneman, Slovic, & Tversky, 1982), have shown a plethora of ways in which people get into a terrible muddle when wondering how probable things are. Every psychologist has learned about anchoring, conservatism, the representativeness heuristic, and many other ways that people reveal their probabilistic incompetence. Creating probability theory in the first place was incredibly challenging, exercising great mathematical minds over several centuries (Hacking, 1990). Probabilistic reasoning is hard, and perhaps it should not be surprising that people often do it badly. This view is the starting point for the whole field of judgment and decision-making (JDM) and its cousin, behavioral economics.

Oddly, though, human probabilistic reasoning equally often gets good press. Indeed, many psychologists, neuroscientists, and artificial-intelligence researchers believe that probabilistic reasoning is, in fact, the secret of human intelligence.

The article is here.

How Do Expectations Shape Perception?

Floris P. de Lange, Micha Heilbron, & Peter Kok
Trends in Cognitive Sciences
Available online 29 June 2018

Abstract

Perception and perceptual decision-making are strongly facilitated by prior knowledge about the probabilistic structure of the world. While the computational benefits of using prior expectation in perception are clear, there are myriad ways in which this computation can be realized. We review here recent advances in our understanding of the neural sources and targets of expectations in perception. Furthermore, we discuss Bayesian theories of perception that prescribe how an agent should integrate prior knowledge and sensory information, and investigate how current and future empirical data can inform and constrain computational frameworks that implement such probabilistic integration in perception.

Highlights

• Expectations play a strong role in determining the way we perceive the world.

• Prior expectations can originate from multiple sources of information, and correspondingly have different neural sources, depending on where in the brain the relevant prior knowledge is stored.

• Recent findings from both human neuroimaging and animal electrophysiology have revealed that prior expectations can modulate sensory processing at both early and late stages, and both before and after stimulus onset. The response modulation can take the form of either dampening the sensory representation or enhancing it via a process of sharpening.

• Theoretical computational frameworks of neural sensory processing aim to explain how the probabilistic integration of prior expectations and sensory inputs results in perception.

The research is here.

Heuristics and Public Policy: Decision Making Under Bounded Rationality

Sanjit Dhami, Ali al-Nowaihi, and Cass Sunstein
SSRN.com
Posted June 20, 2018

Abstract

How do human beings make decisions when, as the evidence indicates, the assumptions of the Bayesian rationality approach in economics do not hold? Do human beings optimize, or can they? Several decades of research have shown that people possess a toolkit of heuristics to make decisions under certainty, risk, subjective uncertainty, and true uncertainty (or Knightian uncertainty). We outline recent advances in knowledge about the use of heuristics and departures from Bayesian rationality, with particular emphasis on growing formalization of those departures, which add necessary precision. We also explore the relationship between bounded rationality and libertarian paternalism, or nudges, and show that some recent objections, founded on psychological work on the usefulness of certain heuristics, are based on serious misunderstandings.

The article can be downloaded here.

Making better decisions in groups

Dan Bang, Chris D. Frith
Published 16 August 2017.
DOI: 10.1098/rsos.170193

Abstract

We review the literature to identify common problems of decision-making in individuals and groups. We are guided by a Bayesian framework to explain the interplay between past experience and new evidence, and the problem of exploring the space of hypotheses about all the possible states that the world could be in and all the possible actions that one could take. There are strong biases, hidden from awareness, that enter into these psychological processes. While biases increase the efficiency of information processing, they often do not lead to the most appropriate action. We highlight the advantages of group decision-making in overcoming biases and searching the hypothesis space for good models of the world and good solutions to problems. Diversity of group members can facilitate these achievements, but diverse groups also face their own problems. We discuss means of managing these pitfalls and make some recommendations on how to make better group decisions.

The article is here.