The paper examines the legal regulation and governance of “generative artificial intelligence” (AI), “foundation AI,” “large language models” (LLMs), and the “general-purpose” AI models of the AI Act. Attention is drawn to two potential sorcerer’s apprentices, namely, in the spirit of J. W. Goethe’s poem, people who were unable to control a situation they created. Focus is on developers and producers of technologies, such as LLMs that bring about risks of discrimination and information hazards, malicious uses and environmental harms; furthermore, the analysis dwells on the normative attempt of European Union legislators to govern misuses and overuses of LLMs with the AI Act. Scholars, private companies, and organisations have stressed limits of such normative attempt. In addition to issues of competitiveness and legal certainty, bureaucratic burdens and standard development, the threat is the over-frequent revision of the law to tackle advancements of technology. The paper illustrates this threat since the inception of the AI Act and recommends some ways in which the law has not to be continuously amended to address the challenges of technological innovation.

We examine the ability of eye movement data to help understand the determinants of decision-making over risky prospects. We start with structural models of choice under risk, and use that structure to inform what we identify from the use of process data in addition to choice data. We find that information on eye movements does significantly affect the extent and nature of probability weighting behavior. Our structural model allows us to show the pathway of the effect, rather than simply identifying a reduced form effect. This insight should be of importance for the normative design of choice mechanisms for risky products. We also show that decision-response duration is no substitute for the richer information provided by eye-tracking.

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.

The personal experience of events such as financial crises and natural disasters can alter economic preferences. We administered a repeated cross-sectional preference survey during the early stages of the COVID-19 outbreak, collecting three bi-weekly samples from participants recruited through Amazon Mechanical Turk. The survey elicits economic preferences, self-reported fear of the pandemic, and beliefs about economic and health consequences. Preferences varied over time and across regions, and self-reported fear of the pandemic explains this variation. These findings suggest caution about the generalizability of some types of experimental work during times of heightened fear.

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.

There is scientific consensus that an earthquake of a magnitude of at least 7 will soon occur on the North Anatolian Fault, which runs south of İstanbul. This earthquake would render one-fifth of İstanbul’s buildings uninhabitable, which means that approximately 200,000 buildings would be expected to suffer moderate or severe damage. As a part of preparedness for the anticipated earthquake, people in İstanbul are invited to have their buildings risk tested. This article, pivoting on cultural anthropology and science and technology studies, investigates how earthquake-proneness of buildings in İstanbul is technically and legally examined and determined. It ethnographically analyzes the risk assessments and demonstrates that the risk is enacted differently through distinctive engineering practices and legal regulations in different networks. When the two different risk assessment processes are examined in İstanbul, a building that is categorized as risky due to its earthquake vulnerability could be regarded as sturdy in the other assessment.

In 1921, John Maynard Keynes and Frank Knight independently insisted on the importance of making a distinction between uncertainty and risk. Keynes referred to matters about which ‘there is no scientific basis on which to form any calculable probability whatever’. Knight claimed that ‘Uncertainty must be taken in a sense radically distinct from the familiar notion of Risk, from which it has never been properly separated’. Knightian uncertainty exists when people cannot assign probabilities to imaginable outcomes. People might know that a course of action might produce bad outcomes A, B, C, D and E, without knowing much or anything about the probability of each. Contrary to a standard view in economics, Knightian uncertainty is real, and it poses challenging and unresolved issues for decision theory and regulatory practice. It bears on many problems, potentially including those raised by artificial intelligence. It is tempting to seek to eliminate the worst-case scenario, and thus to adopt the maximin rule, which might seem to be the appropriate approach under Knightian uncertainty. But serious problems arise if eliminating the worst-case scenario would (1) impose high risks and costs, (2) eliminate large benefits or potential ‘miracles’ or (3) create uncertain risks.

In light of the growing threat of climate change and urgency of mitigation at the societal and individual level, an exponentially growing body of research has addressed how and what people think about climate change—ranging from basic judgments of truth and attitudes about risk to predictions of future outcomes. However, the field is also beset by a striking variety of items and scales used to measure climate change beliefs, with notable differences in content, untested structural assumptions, and unsatisfactory or unknown psychometric properties. In a series of four studies (total N = 2,678), scales for the assessment of climate change beliefs are developed that are comprehensive and balanced in content and psychometrically sound. The latent construct structure is tested, and evidence of high rank-order stability (1-year retest-reliability) and predictive validity (for policy preferences and actual behavior) provided.

The literature on emotion and risk-taking is large and heterogeneous. Whereas some studies have found that positive emotions increase risk-taking and negative emotions increase risk aversion, others have found just the opposite. In this study, we investigated this question in the context of a risky decision-making task with embedded high-resolution sampling of participants’ subjective emotional valence. Across two large-scale experiments (N = 329 and 524), we consistently found evidence for a negative association between self-reported emotional valence and risk-taking behaviors. That is, more negative subjective affect was associated with increased risk-seeking, and more positive subjective affect was associated with increased risk aversion. This effect was evident both when we compared participants with different levels of mean emotional valence as well as when we considered within-participant emotional fluctuations over the course of the task. Prospect-theoretic computational modeling analyses suggested that both between- and within-participant effects were driven by an effect of emotional valence on the curvature of the subjective utility function (i.e., increased risk tolerance in more negative emotional states), as well as by an effect of within-person emotion fluctuations on loss aversion. We interpret findings in terms of a tendency for participants in negative emotional states to choose high-risk, high-reward options in an attempt to improve their emotional state.

We analyze the effect of green patents on G7 stock market returns. First, we build a small IS-LM model to identify the relevant channels, augmented with open-economy channels and the Green Tobin’s q (Faria et al., 2022). The model highlights that the intertemporal impacts of greening on stock returns are ambiguous. We then turn to an estimated global vector autoregressive model to more rigorously analyze the effect of monetary and green patenting shocks across the G7. Both shocks influence green patents through real and financial markets. As regards green patent shocks, results suggest that a tension exists over time between promoting pollution reduction and energy efficiency and the profitability of (green and brown) companies in the aggregate. We perform a variety of robustness exercises around our main results. Our results provide something of a challenge to the literature and call for more research effort to understand the various channels that might explain this dynamic—and in turn whether any particular policy recommendations follow.

Our study aims to contribute to the existing body of research on age-related changes in decision-making by investigating susceptibility to the attraction effect across adulthood. Prior studies have produced inconsistent conclusions regarding the decision-making abilities of older individuals, with some portraying them as easily manipulated and risk-averse, while others suggest the opposite. To address this issue, we conducted two experiments using a novel paradigm of the roulette task: (1) in an online environment with 357 participants and (2) in a laboratory setting with 173 participants. The results were consistent and demonstrated the robustness of the attraction effect. However, no age differences in susceptibility to the attraction effect as a common decision bias were found. As predicted, older adults were more likely to commit simple decision-making mistakes, especially in the preliminary trials, which could have serious financial or societal consequences. Additionally, older adults exhibited more risk-seeking behaviours. Furthermore, we observed that the dynamics of decision competence (as indicated by a decrease in the selection of erroneous decoy options and an increase in decision fluency) were similar for both younger and older adults, suggesting preservation of the ability to optimise decision-making while becoming familiar with new tasks. These findings provide insight into the cognitive functioning of older adults and indicate that decision-making abilities in late adulthood may be more complex than commonly assumed.

Although Lippmann's The Good Society was written to address the crisis in liberal democracies in the 1930s, we argue that it offers a novel and plausible institutionalist account of the productivity slowdown and the increase in the experience of insecurity that has characterised most liberal democracies over the last 20–40 years. Central to Lippmann's account is a Smithian-institutionalist model of growth where property rights have to evolve both to encourage continued levels of risk taking in the face of new uncertainties and also to offset new sources of unequal bargaining power that the very process of growth itself creates. When property rights fossilise and fail to evolve, as in the 1930s and plausibly also now, productivity growth slows down, insecurity rises, and illiberal political creeds prosper. To avoid this, Lippmann's analysis suggests that property rights have to change to re-energise risk taking and to offset the new sources of unequal bargaining power. For example, in current circumstances, new ‘positive’ property rights arising from the development of social insurance might encourage risk taking and new ‘negative’ property rights in personal data might help offset the new sources of unequal bargaining power that have emerged.

This article investigates the promise of Insurtech to expand the frontier of the insurance market to include vulnerable populations around the world. It contributes to current debates on the proliferation of new technologies for financial inclusion as embedded in contemporary forms of platform-based capitalism. We argue that Insurtech’s promises fall short of expectations because of the contradiction between the principles of platform scalability and insurance risk pooling. Such contradictions lead Insurtech platforms to unpool risk rather than provide innovative technologies to pool it. This ultimately limits the expansion of markets for inclusive insurance as Insurtech platforms struggle to scale up the market through their supposedly disruptive technology. Our analysis draws on a range of regulatory reports, complemented by in-depth interviews with Insurtech executive officers. The article offers insights into the contradictory principles currently driving the digital inclusive insurance market, as well as the limits to the ongoing global expansion of platform capitalism.

In previous research, several computational methods have been proposed to analyse the navigation, transportation safety and collision risks of maritime vessels. The objective of this study is to use Automatic Identification System (AIS) data to assess the collision risk between two vessels before an actual collision occurs. We introduce the concept of an angle interval in the model to enable real-time response to vessel collision risks. When predicting collision risks, we consider factors such as relative distance, relative velocity and phase between the vessels. Lastly, the collision risk is divided into different regions and represented by different colours. The green region represents a low-risk area, the yellow region serves as a cautionary zone and the red region indicates a high-alert zone. If a signal enters the red region, the vessel's control system will automatically intervene and initiate evasive manoeuvres. This reactive mechanism enhances the safety of vessel operations, ensuring the implementation of effective collision avoidance measures.