We measured brain activity using a functional magnetic resonance imaging (fMRI) paradigm and conducted a whole-brain analysis while healthy adult Democrats and Republicans made non-hypothetical food choices. While the food purchase decisions were not significantly different, we found that brain activation during decision-making differs according to the participant’s party affiliation. Models of partisanship based on left insula, ventromedial prefrontal cortex, precuneus, superior frontal gyrus, or premotor/supplementary motor area activations achieve better than expected accuracy. Understanding the differential function of neural systems that lead to indistinguishable choices may provide leverage in explaining the broader mechanisms of partisanship.

Bilinguals use languages strategically and make decisions differently depending on the language context. Here, we explored whether verbal feedback modulates language use and risk-taking in bilinguals engaged in a coin-drawing game that incentivises lying. In the game, participants announced bets in Chinese or English, and feedback on the outcome of the current bet was given in the same language. They selected Chinese over English after receiving positive feedback in Chinese, and no language difference was found when feedback was provided in English. They also tended to take more risks after receiving positive than negative feedback. Furthermore, participants were more likely to switch from one language to the other following negative feedback as compared to positive feedback, and when telling the truth, they were faster after negative than positive feedback. Thus, the language in which bilinguals receive feedback constrains language use, which may have implications for understanding interactions in multilingual communities.

Risk aversion—the preference for certainty over potential gains or losses—is reduced when using a foreign language. We investigated 2 mechanisms for this foreign language effect using incentivized gambles with verbal probability expressions: (1) that people perceive prospects of winning as larger when a decision is made in their foreign language; (2) that people experience reduced negative affect toward risk in a foreign language. In Experiment 1, N = 229 proficient Polish–English bilinguals, using ungridded slider, assigned numerical values to 29 verbal probability expressions in both languages. We found small bidirectional differences in 13 of them, leading us to reject the first mechanism. In Experiment 2, N = 281 participants gambled in incentivized neutral expected value lotteries using a sample of the verbal probability expressions from Experiment 1. Participants gambled in either their native or foreign language, where participants could either accept around 50% of gambles (debiased to risk-neutral) or more than 50% (biased to risk-seeking). Surprisingly, we observed no significant risk aversion in either language condition, with participants’ gambling behavior close to 50% in both cases. The finding that participants showed no risk aversion in native language condition meant we could not test whether foreign language reduces risk aversion. However, this result did show that using a foreign language does not promote excessive risk-taking. Our findings suggest that using verbal probability expressions does not bias participants’ responses, and may inherently reduce risk aversion.

We develop a new design for the experimental beauty-contest game (BCG) that is suitable for children in school age and test it with 114 schoolchildren aged 9–11 years as well as with adults. In addition, we collect a measure for cognitive skills to link these abilities with successful performance in the game. Results demonstrate that children can successfully understand and play a BCG. Choices start at a slightly higher level than those of adults but learning over time and depth of reasoning are largely comparable with the results of studies run with adults. Cognitive skills, measured as fluid IQ, are predictive only of whether children choose weakly dominated strategies but are neither associated with lower choices in the first round nor with successful performance in the BCG. In the implementation of our new design of the BCG with adults we find results largely in line with behavior in the classical BCG. Our new design for the experimental BCG allows to study the development of strategic interaction skills starting already in school age.

In today’s world, smart algorithms—artificial intelligence (AI) and other intelligent systems—are pivotal for promoting the development agenda. They offer novel support for decision-making across policy planning domains, such as analysing poverty alleviation funds and predicting mortality rates. To comprehensively assess their efficacy and implications in policy formulation, this paper conducts a systematic review of 207 publications. The analysis underscores their integration within and across stages of the policy planning cycle: problem diagnosis and goal articulation; resource and constraint identification; design of alternative solutions; outcome projection; and evaluation. However, disparities exist in smart algorithm applications across stages, economic development levels, and Sustainable Development Goals (SDGs). While these algorithms predominantly focus on resource identification (29%) and contribute significantly to designing alternatives—such as long-term national energy policies—and projecting outcomes, including predicting multi-scenario land-use ecological security strategies, their application in evaluation remains limited (10%). Additionally, low-income nations have yet to fully harness AI’s potential, while upper-middle-income countries effectively leverage it. Notably, smart algorithm applications for SDGs also exhibit unevenness, with more emphasis on SDG 11 than on SDG 5 and SDG 17. Our study identifies literature gaps. Firstly, despite theoretical shifts, a disparity persists between physical and socioeconomic/environmental planning applications. Secondly, there is limited attention to policy-making in development initiatives, which is critical for improving lives. Future research should prioritise developing adaptive planning systems using emerging powerful algorithms to address uncertainty and complex environments. Ensuring algorithmic transparency, human-centered approaches, and responsible AI are crucial for AI accountability, trust, and credibility.

Effect modification occurs when a covariate alters the relative effectiveness of treatment compared to control. It is widely understood that, when effect modification is present, treatment recommendations may vary by population and by subgroups within the population. Population-adjustment methods are increasingly used to adjust for differences in effect modifiers between study populations and to produce population-adjusted estimates in a relevant target population for decision-making. It is also widely understood that marginal and conditional estimands for non-collapsible effect measures, such as odds ratios or hazard ratios, do not in general coincide even without effect modification. However, the consequences of both non-collapsibility and effect modification together are little-discussed in the literature.

In this article, we set out the definitions of conditional and marginal estimands, illustrate their properties when effect modification is present, and discuss the implications for decision-making. In particular, we show that effect modification can result in conflicting treatment rankings between conditional and marginal estimates. This is because conditional and marginal estimands correspond to different decision questions that are no longer aligned when effect modification is present. For time-to-event outcomes, the presence of covariates implies that marginal hazard ratios are time-varying, and effect modification can cause marginal hazard curves to cross. We conclude with practical recommendations for decision-making in the presence of effect modification, based on pragmatic comparisons of both conditional and marginal estimates in the decision target population. Currently, multilevel network meta-regression is the only population-adjustment method capable of producing both conditional and marginal estimates, in any decision target population.

This paper derives career lessons for economists by examining the unusual career of Brian J. Loasby in terms of Loasby’s own key ideas. Loasby’s early career ran into difficulties after he chose to become a postgraduate for lifestyle rather than career reasons, with his insufficiently ambitious reference standards resulting in unduly narrow search for both options and new connections. His eventual success came because of a path-dependent process whereby he developed an organizing framework that was conducive to finding problems and making intellectual connections. It was a process in which making personal connections also had a key role, though some intellectual connections that he earlier failed to make resulted from trusting other people. In the market for contributions to knowledge, his way of branding and positioning his work in relation to management decision-making and the growth of knowledge gave it a wider appeal than would have been the case if he had aligned himself with a particular school of thought or ‘heterodox economics’ in general.

Navigational safety is one of the important focuses of Maritime Education and Training (MET), and the quality of MET is the key to cultivating competent officers at sea. This study aims to understand better the effects of a rapid training method on ship handling and navigation in restricted waters, as well as decision-making skills under stressful situations. Tests were carried out in a simulator-based maritime training environment to explore the decision-making skills of maritime students in stressful situations under different training levels and methods. This study compares routine maritime training and task-aimed rapid training in improving manoeuvring and navigational and decision-making skills, and examines the training outcomes. The data used in this study is based on comparing the task performance and stress levels of the two groups of students using simulator-based training results from a designed scenario. The results analyse the training outcomes of decision-making skills and maritime operation performance by applying a specific decision-making model. In addition, the impact of students' stress levels was examined, both subjectively and objectively. The paper concludes with a set of recommendations for the design of future MET. The research helps enhance decision-making skills in maritime training programmes and understanding how learning in simulator-based maritime training environments can be improved.

Online experiments allow researchers to collect datasets at times not typical of laboratory studies. We recruit 2336 participants from Amazon Mechanical Turk to examine if participant characteristics and behaviors differ depending on whether the experiment is conducted during the day versus night, and on weekdays versus weekends. Participants make incentivized decisions involving prosociality, punishment, and discounting, and complete a demographic and personality survey. We find no time or day differences in behavior, but do find that participants at nights and on weekends are less experienced with online studies; on weekends are less reflective; and at night are less conscientious and more neurotic. These results are largely robust to finer-grained measures of time and day. We also find that those who participated earlier in the course of the study are more experienced, reflective, and agreeable, but less charitable than later participants.

This article presents a joint modeling framework of ordinal responses and response times (RTs) for the measurement of latent traits. We integrate cognitive theories of decision-making and confidence judgments with psychometric theories to model individual-level measurement processes. The model development starts with the sequential sampling framework which assumes that when an item is presented, a respondent accumulates noisy evidence over time to respond to the item. Several cognitive and psychometric theories are reviewed and integrated, leading us to three psychometric process models with different representations of the cognitive processes underlying the measurement. We provide simulation studies that examine parameter recovery and show the relationships between latent variables and data distributions. We further test the proposed models with empirical data measuring three traits related to motivation. The results show that all three models provide reasonably good descriptions of observed response proportions and RT distributions. Also, different traits favor different process models, which implies that psychological measurement processes may have heterogeneous structures across traits. Our process of model building and examination illustrates how cognitive theories can be incorporated into psychometric model development to shed light on the measurement process, which has had little attention in traditional psychometric models.

An observer is to make inference statements about a quantity p, called a propensity and bounded between 0 and 1, based on the observation that p does or does not exceed a constant c. The propensity p may have an interpretation as a proportion, as a long-run relative frequency, or as a personal probability held by some subject. Applications in medicine, engineering, political science, and, most especially, human decision making are indicated. Bayes solutions for the observer are obtained based on prior distributions in the mixture of beta distribution family; these are then specialized to power-function prior distributions. Inference about log p and log odds is considered. Multiple-action problems are considered in which the focus of inference shifts to the process generating the propensities p, both in the case of a process parameter π known to the subject and unknown. Empirical Bayes techniques are developed for observer inference about c when π is known to the subject. A Bayes rule, a minimax rule and a beta-minimax rule are constructed for the subject when he is uncertain about π.