Most political science studies are, at root, about how people make decisions—how voters choose whether and for whom to vote, how prejudice influences political choices, and the effects of emotions and morals on political choice. However, what people are thinking during these decisions remains obscure; currently utilized methods leave us with a “black box” of decision making. Eye tracking offers a deeper insight into these processes by capturing respondents’ attention, salience, emotion, and understanding. But how applicable is this method to political science questions, and how does one go about using it? Here, we explain what eye tracking allows researchers to measure, how these measures are relevant to political science questions, and how political scientists without expertise in the method can nonetheless use it effectively. In particular, we clarify how researchers can understand the choices made in preset software in order to arrive at correct inferences from their data and discuss new developments in eye tracking methodology, including webcam eye tracking. We additionally provide templates for preregistering eye tracking studies in political science, as well as starter code for processing and analyzing eye tracking data.

Eliciting the level of risk aversion of experimental subjects is of crucial concern to experimenters. In the literature there are a variety of methods used for such elicitation; the concern of the experiment reported in this paper is to compare them. The methods we investigate are the following: Holt–Laury price lists; pairwise choices, the Becker–DeGroot–Marschak method; allocation questions. Clearly their relative efficiency in measuring risk aversion depends upon the numbers of questions asked; but the method itself may well influence the estimated risk-aversion. While it is impossible to determine a ‘best’ method (as the truth is unknown) we can look at the differences between the different methods. We carried out an experiment in four parts, corresponding to the four different methods, with 96 subjects. In analysing the data our methodology involves fitting preference functionals; we use four, Expected Utility and Rank-Dependent Expected Utility, each combined with either a CRRA or a CARA utility function. Our results show that the inferred level of risk aversion is more sensitive to the elicitation method than to the assumed-true preference functional. Experimenters should worry most about context.

We carry out two experiments to test a model of herd behaviour based on the work of Banerjee (Quarterly Journal of Economics, CVII, 797-817,1992). He shows that herding occurs as a result of people observing the actions of others and using this information in their own decision rule. In our experiments herding does not occur as frequently as Banerjee predicts. Contrary to his results, the subjects’ behaviour appears to depend on the probabilities of receiving a signal and of this signal being correct. Furthermore, Banerjee finds that the pattern of decision making over a number of rounds of the game is volatile whereas we find that decision making is volatile within rounds.

The house money effect predicts that individuals show increased risk-seeking behavior in the presence of prior windfall gains. Although the effect’s existence is widely accepted, experimental studies that compare individuals’ risk-taking behavior using house money to individuals’ risk-taking behavior using their own money produce contradictory results. This experimental field study analyzes the gambling behavior of 917 casino customers who face real losses. We find that customers who received free play at the entrance showed not higher but significantly lower levels of risk-taking behavior during their casino visit, expressed through lower average wagers. This study thus provides field evidence against the house money effect. Moreover, as a result of lower levels of risk seeking, endowed customers yield better economic results in the form of smaller own-money losses when leaving the casino.

We develop a new experimental paradigm to study how emotions affect decision-making. We use it to investigate the impact of short-term fluctuations in incidental happiness on economic decisions. Experimental subjects watch an NFL football game in a sports bar. At various commercial breaks, we measure subjects’ happiness and observe their decisions regarding charitable giving, willingness to pay for a consumer good, risk taking, and trust. We find that events in the game impact the incidental happiness of our subjects, and these changes lead to predictable changes in choices. We provide a simple model that rationalizes how subjects’ behavior varies with incidental happiness and provides insight into how mood can be tractably included in economics models. Our experimental paradigm can be leveraged by other researchers interested in exploring the impact of emotions on behavior.

Despite public health efforts, uptake of preventive health technologies remains low in many settings. In this paper, we develop a formal model of prevention and test it through a laboratory experiment. In the model, rational agents decide whether to take up health technologies that reduce, but do not eliminate the risk of adverse health events. As long as agents are sufficiently risk averse and priors are diffuse, we show that initial uptake of effective technologies will be limited. Over time, the model predicts that take-up will decline as users with negative experiences revise their effectiveness priors towards zero. In our laboratory experiments, we find initial uptake rates between 65 and 80% for effective technologies with substantial declines over time, consistent with the model’s predictions. We also find evidence of decision-making not consistent with our model: subjects respond most strongly to the most recent health outcomes, and react to negative health outcomes by increasing their willingness to invest in prevention, even when health risks without prevention are known by all subjects. Our findings suggest that high uptake of preventive technologies should only be expected if the risk of adverse health outcomes without prevention is high, or if preventive technologies are so effective that the risk of adverse outcomes is negligible with prevention.

Pain is a highly salient and attention-demanding experience that motivates people to act. We investigated the effect of pain on decision making by delivering acute thermal pain to participants’ forearm while they made risky and intertemporal choices involving money. Participants (n = 107) were more risk seeking under pain than in a no-pain control condition when decisions involved gains but not when they involved equivalent losses. Pain also resulted in greater preference for immediate (smaller) over future (larger) monetary rewards. We interpret these results as a motivation to offset the aversive, pain-induced state, where monetary rewards become more appealing under pain than under no pain and when delivered sooner rather than later. Our findings add to the long-standing debate regarding the role of intuition and reflection in decision making.

In this comment, I examine the results of two studies (Shafir, 1993 and Chandrashekar et al., 2021) that relied on the same stimuli to examine the effect of framing selection tasks in terms of choosing versus rejecting, and discuss how, despite the failure of the later study to replicate the results of the earlier one, analyzing the similarities and differences between the two advances our understanding of the processes underlying decisions in general, and decision in such tasks in particular.

How does power affect threat perception? Drawing on advances in psychological research on power, I find that the sense of state power inflates the perception of threats. The sense of power activates intuitive thinking in the decision-making process, including a reliance on gut feelings and cognitive shortcuts like heuristics and prior beliefs. In turn, as psychological IR research shows, these mechanisms tend to inflate threat perception. The powerful assess threats from the gut rather than the head. Experimental evidence from the US and China, a reanalysis of a survey of Russian elites, and a large-scale text analysis of Cold War US foreign policy elites lend support to this expectation. The findings help to psychologically reconcile enduring theoretical puzzles—from “underbalancing” to “overextension”—and generate entirely new ones, like the possibility that decision makers of rising, not declining, states feel more fear. Together, the paper offers a “first image reversed” challenge to bottom-up accounts of psychological IR. Decision-maker psychology is also a dependent variable shaped by the balance of power, with important implications for a world returning to great power competition.

This article reviews recent advances in the psychometric and econometric modeling of eye-movements during decision making. Eye movements offer a unique window on unobserved perceptual, cognitive, and evaluative processes of people who are engaged in decision making tasks. They provide new insights into these processes, which are not easily available otherwise, allow for explanations of fundamental search and choice phenomena, and enable predictions of future decisions. We propose a theoretical framework of the search and choice tasks that people commonly engage in and of the underlying cognitive processes involved in those tasks. We discuss how these processes drive specific eye-movement patterns. Our framework emphasizes the central role of task and strategy switching for complex goal attainment. We place the extant literature within that framework, highlight recent advances in modeling eye-movement behaviors during search and choice, discuss limitations, challenges, and open problems. An agenda for further psychometric modeling of eye movements during decision making concludes the review.

Decision making usually involves uncertainty and risk. Understanding which parts of the human brain are activated during decisions under risk and which neural processes underly (risky) investment decisions are important goals in neuroeconomics. Here, we analyze functional magnetic resonance imaging (fMRI) data on 17 subjects who were exposed to an investment decision task from Mohr, Biele, Krugel, Li, and Heekeren (in NeuroImage 49, 2556–2563, 2010b). We obtain a time series of three-dimensional images of the blood-oxygen-level dependent (BOLD) fMRI signals. We apply a panel version of the dynamic semiparametric factor model (DSFM) presented in Park, Mammen, Wolfgang, and Borak (in Journal of the American Statistical Association 104(485), 284–298, 2009) and identify task-related activations in space and dynamics in time. With the panel DSFM (PDSFM) we can capture the dynamic behavior of the specific brain regions common for all subjects and represent the high-dimensional time-series data in easily interpretable low-dimensional dynamic factors without large loss of variability. Further, we classify the risk attitudes of all subjects based on the estimated low-dimensional time series. Our classification analysis successfully confirms the estimated risk attitudes derived directly from subjects’ decision behavior.

Decision making can be a complex process requiring the integration of several attributes of choice options. Understanding the neural processes underlying (uncertain) investment decisions is an important topic in neuroeconomics. We analyzed functional magnetic resonance imaging (fMRI) data from an investment decision study for stimulus-related effects. We propose a new technique for identifying activated brain regions: cluster, estimation, activation, and decision method. Our analysis is focused on clusters of voxels rather than voxel units. Thus, we achieve a higher signal-to-noise ratio within the unit tested and a smaller number of hypothesis tests compared with the often used General Linear Model (GLM). We propose to first conduct the brain parcellation by applying spatially constrained spectral clustering. The information within each cluster can then be extracted by the flexible dynamic semiparametric factor model (DSFM) dimension reduction technique and finally be tested for differences in activation between conditions. This sequence of Cluster, Estimation, Activation, and Decision admits a model-free analysis of the local fMRI signal. Applying a GLM on the DSFM-based time series resulted in a significant correlation between the risk of choice options and changes in fMRI signal in the anterior insula and dorsomedial prefrontal cortex. Additionally, individual differences in decision-related reactions within the DSFM time series predicted individual differences in risk attitudes as modeled with the framework of the mean-variance model.

Health technology assessment (HTA) agencies assess evidence to support decision making about which technologies to provide and pay for in the health system. HTA impact is understood as the influence that HTA report findings can have in the health system, including impacts on reimbursement decisions, changes to health outcomes, or broader system or societal impacts. The International Network of Agencies for Health Technology Assessment (INAHTA) is a global network of publicly funded HTA agencies. INAHTA’s mission, in part, is to advance the impact of HTA to support reimbursement decisions and the optimal use of health system resources. Each year, INAHTA awards the David Hailey Award for Best Impact Story to the member agency that shares the best story, as voted by fellow members, about HTA impact. The impact story sharing program in INAHTA contributes to a deeper understanding of what works well (or not so well) in achieving HTA impact. This paper provides six impact stories from agencies that were finalists for the 2021 and 2022 David Hailey Impact Award for Best Impact Story: the Institut national d’excellence en santé et en services sociaux, the Malaysian Health Technology Assessment Section, Ontario Health, the Center for Drug Evaluation, the National Institute for Health and Care Excellence, and Health Technology Wales. These stories demonstrate that HTA agencies can, in differing ways, effectively support governments in their efforts to place evidence at the centre of decision making.