Utilitarianism in minimal-group decision making is less common than equality-based morality, mostly harm-oriented, and rarely impartial

Negative emotionality downregulation affects moral choice but not moral judgement of harm: a pharmacological study

Previous neuroscientific research has expounded on the fundamental role played by emotion during moral decision-making. Negative emotionality has been observed to exert a general inhibitory effect towards harmful behaviors against others. Nevertheless, the downregulation of negative affects at different levels of moral processing (e.g. impersonal versus personal moral dilemmas) alongside its possible interactions with other factors (e.g. perspective taking) hasn't been directly assessed; both of which can assist in predicting future moral decision-making. In the present research, we empirically test (Study 1, N = 41) whether downregulating negative emotionality through pharmacological interventions using lorazepam (a GABA receptor agonist), modulate the permissibility of harm to others -i.e. if participants find it more morally permissible to harm others when harm is unavoidable (inevitable harm moral dilemmas), than when it may be avoided (evitable harm moral dilemmas). Furthermore, using another sample (Study 2, N = 31), we assess whether lorazepam's effect is modulated by different perspective-taking conditions during a moral dilemma task -e.g. "is it morally permissible for you to […]?" (1st person perspective), relative to "is it morally permissible for [x individual] to […]?" (3rd person perspective)-, where the outcome of the different scenarios is controlled. The results of both studies converge, revealing an emotion-dependent, rather than an outcome-dependent, pharmacological modulation. Lorazepam only influenced interpersonal moral judgments when not modulated by the evitable/inevitable condition. Furthermore, there was a significant interaction between perspective-taking and drug administration, as lorazepam exerted a larger effect in modulating moral choices rather than moral judgements.

Towards machine learning for moral choice analysis in health economics: A literature review and research agenda

BACKGROUND

Discrete choice models (DCMs) for moral choice analysis will likely lead to erroneous model outcomes and misguided policy recommendations, as only some characteristics of moral decision-making are considered. Machine learning (ML) is recently gaining interest in the field of discrete choice modelling. This paper explores the potential of combining DCMs and ML to study moral decision-making more accurately and better inform policy decisions in healthcare.

METHODS

An interdisciplinary literature search across four databases - PubMed, Scopus, Web of Science, and Arxiv - was conducted to gather papers. Based on the Preferred Reporting Items for Systematic and Meta-analyses (PRISMA) guideline, studies were screened for eligibility on inclusion criteria and extracted attributes from eligible papers. Of the 6285 articles, we included 277 studies.

RESULTS

DCMs have shortcomings in studying moral decision-making. Whilst the DCMs' mathematical elegance and behavioural appeal hold clear interpretations, the models do not account for the 'moral' cost and benefit in an individual's utility calculation. The literature showed that ML obtains higher predictive power, model flexibility, and ability to handle large and unstructured datasets. Combining the strengths of ML methods with DCMs has the potential for studying moral decision-making.

CONCLUSIONS

By providing a research agenda, this paper highlights that ML has clear potential to i) find and deepen the utility specification of DCMs, and ii) enrich the insights extracted from DCMs by considering the intrapersonal determinants of moral decision-making.