Computational models can offer mechanistic insight into cognition and therefore have the potential to transform our understanding of psychiatric disorders and their treatment. For translational efforts to be successful, it is imperative that computational measures capture individual characteristics reliably. Here we examine the reliability of reinforcement learning and economic models derived from two commonly used tasks. Healthy individuals (N = 50) completed a restless four-armed bandit and a calibrated gambling task twice, two weeks apart. Reward and punishment learning rates from the reinforcement learning model showed good reliability and reward and punishment sensitivity from the same model had fair reliability; while risk aversion and loss aversion parameters from a prospect theory model exhibited good and excellent reliability, respectively. Both models were further able to predict future behaviour above chance within individuals. This prediction was better when based on participants\' own model parameters than other participants\' parameter estimates. These results suggest that reinforcement learning, and particularly prospect theory parameters, as derived from a restless four-armed bandit and a calibrated gambling task, can be measured reliably to assess learning and decision-making mechanisms. Overall, these findings indicate the translational potential of clinically-relevant computational parameters for precision psychiatry.

In patients with mood disorders, negative affective biases - systematically prioritising and interpreting information negatively - are common. A translational cognitive task testing this bias has shown that depressed patients have a reduced preference for a high reward under ambiguous decision-making conditions. The precise mechanisms underscoring this bias are, however, not yet understood. We therefore developed a set of measures to probe the underlying source of the behavioural bias by testing its relationship to a participant\'s reward sensitivity, value sensitivity and reward learning rate. One-hundred-forty-eight participants completed three online behavioural tasks: the original ambiguous-cue decision-making task probing negative affective bias, a probabilistic reward learning task probing reward sensitivity and reward learning rate, and a gambling task probing value sensitivity. We modelled the learning task through a dynamic signal detection theory model and the gambling task through an expectation-maximisation prospect theory model. Reward sensitivity from the probabilistic reward task (β = 0.131, p = 0.024) and setting noise from the probabilistic reward task (β = -0.187, p = 0.028) both predicted the affective bias score in a logistic regression. Increased negative affective bias, at least on this specific task, may therefore be driven in part by a combination of reduced sensitivity to rewards and more variable responses.

Most of life\'s decisions involve risk and uncertainty regarding whether reward or loss will follow. Decision makers often face uncertainty not only about the likelihood of outcomes (what are the chances that I will get a raise if I ask my supervisor? What are the chances that my supervisor will be upset with me for asking?) but also the magnitude of outcomes (if I do get a raise, how large will it be? If my supervisor gets upset, how bad will the consequences be for me?). Only a few studies have investigated economic decision making with ambiguous likelihoods, and even fewer have investigated ambiguous outcome magnitudes. In the present report, we investigated the effects of ambiguous outcome magnitude, risk, and gains/losses in an economic decision-making task with low stakes (Study 1; $3.60-$5.70; N = 367) and high stakes (Study 2; $6-$48; N = 210) using a within-subjects design. We conducted computational modeling to determine individuals\' preferences/aversions for ambiguous outcome magnitudes, risk, and gains/losses. We additionally investigated the association between trait anxiety and trait depression and decision-making parameters. Our results show that increasing stakes increased ambiguous gain aversion and unambiguous risk aversion but increased ambiguous sure loss preference; participants also became more averse to ambiguous sure gains relative to unambiguous risky gains. There were no significant effects of trait anxiety or trait depression on economic decision making. Our results suggest that as stakes increase, people tend to avoid uncertainty in the gain domain (especially ambiguous gains) but prefer ambiguous vs unambiguous sure losses.

While Prospect Theory helps to explain decision-making under risk, studies often base frames on hypothetical events and fail to acknowledge that many individuals lack the ability and motivation to engage in complex thinking. We use an original survey of US adults (N = 2813) to test Prospect Theory in the context of the May 2023 debt ceiling negotiations in the US Congress and assess whether objective numeracy moderates framing effects. We hypothesize and find evidence to suggest that most respondents are risk-averse to potential gains and risk-accepting to potential losses; however, high numerates are more risk-averse and risk-accepting to gains and losses, respectively, than low numerates. We also find that need for cognition interacts with numeracy to moderate framing effects for prospective losses, such that higher need for cognition attenuates risk-acceptance among low numerates and exacerbates risk-acceptance among high numerates. Our results are robust to a range of other covariates and in models accounting for the interaction between political knowledge and need for cognition, indicating joint moderating effects from two knowledge domains similarly conditioned by the desire to engage in effortful thinking. Our findings demonstrate that those who can understand and use objective information may remain subjectively persuaded by certain policy frames.

Using a large, non-student sample, we assess and differentiate between borrowers\' Risk Aversion and Ambiguity Aversion levels and their willingness to pay to resolve a mortgage default settlement negotiation. Ambiguity Aversion is found to be negatively associated with willingness to pay for borrowers with high financial literacy in both the gain and loss domains, whereas personality traits matter more for borrowers with low financial literacy. This finding is important to policymakers in that they should adopt differential resolution strategies for defaulting borrowers based on these intervening variables.

It remains a pressing concern to understand how neural computations relate to risky decisions. However, most observations of brain-behavior relationships in the risk-taking domain lack a rigorous computational basis or fail to emulate of the dynamic, sequential nature of real-life risky decision-making. Recent advances emphasize the role of neural prediction error (PE) signals. We modeled, according to prospect theory, the choices of n = 43 human participants (33 females, 10 males) performing an EEG version of the hot Columbia Card Task, featuring rounds of sequential decisions between stopping (safe option) and continuing with increasing odds of a high loss (risky option). Single-trial regression EEG analyses yielded a subjective value signal at centroparietal (300-700 ms) and frontocentral (>800 ms) electrodes and in the delta band, as well as PE signals tied to the feedback-related negativity, P3a, and P3b, and in the theta band. Higher risk preference (total number of risky choices) was linked to attenuated subjective value signals but increased PE signals. Higher P3-like activity associated with the most positive PE in each round predicted stopping in the present round but not risk-taking in the subsequent round. Our findings indicate that decreased representation of decision values and increased sensitivity to winning despite low odds (positive PE) facilitate risky choices at the subject level. Strong neural responses when gains are least expected (the most positive PE on each round) adaptively contribute to safer choices at the trial-by-trial level but do not affect risky choice at the round-by-round level.

Low-carbon buildings (LCBs) are still in the early stages of development in China. The promotion and implementation of associated policies are not yet fully matured. Meanwhile, their status as public goods exacerbates the uncertainty and complexity regarding anticipated gains and potential losses. Few studies have explored the impact of perception parameters on the decision-making processes of LCBs\' stakeholders. Thus, combined with prospect theory, this paper establishes a tripartite game model composed of governments, developers, and consumers to explore their interactions and influences in different stages. Real-life scenarios are further utilized to validate the effectiveness of the model in predicting the behaviors under respective preferences. The results show that the increase in subsidy and penalty intensity instead diminishes the enthusiasm for LCBs. More specifically, the existing subsidy policies offer limited incentives to consumers. With the addition of the perception parameter, there exist differences in the sensitivity of consumers and developers towards risk levels and potential losses. The findings also highlight the importance of consumers in the LCBs market. Future policies should encourage developers and consumers to jointly promote the LCBs implementation.

Under the dual-carbon background, coal power enterprises are required to actively fulfill their social responsibility in order to achieve energy saving and emission reduction as soon as possible. Considering the uncertainty of the external environment and the potential conflict of interest of the key stakeholders in the fulfillment of corporate social responsibility, coal power enterprises are not always positive in fulfilling their social responsibility. This paper combines prospect theory and mental account theory with evolutionary game to construct an evolutionary game model involving coal power enterprises, government regulators and the public to study the social responsibility behavior of coal power enterprises. The results of the study show that: (1) The social responsibility behavior of coal power enterprises under the dual-carbon background is a typical cost-driven behavior, and coal power enterprises are more sensitive to costs compared to benefits. (2) The formulation of regulatory policies by government regulators largely depends on the decision inertia of coal power enterprises, and the formulation of regulatory policies by government regulators will also affect the decision inertia of coal power enterprises. (3) The public\'s strategic choices do not entirely depend on the strategic choices of coal and power enterprises and government regulators, and are more closely related to the setting of the reference point. (4) In addition to the realistic factors, the subjective factors of decision makers are also important factors affecting the fulfillment of social responsibility of coal and power enterprises. Based on the results of the study, this paper proposes countermeasures to enhance the internal driving force of coal power enterprises to fulfill their social responsibility behaviors from the aspects of establishing a communication mechanism, improving the reward and punishment system, and strengthening risk management.

Householders are increasingly responsible for managing residual flood risk at property level. Yet, consumers are observed to adopt irrational behaviors under scenarios of risk, often making suboptimal decisions. Therefore, the question is raised, if householders are required to manage flood risk at household level, how can this be made fair and efficient? Policy instruments often incorporate \"fairness\" by subsidizing the costs of mitigation options, assuming a linear relationship between available finances and the uptake of risk mitigation measures. To integrate behavior into the assessment of policy instruments, this article develops a method to compare the uptake of flood mitigation between agents following models of Expected Monetary Value (EMV) and Prospect Theory (PT). An agent-based model is created, offering the option to apply either EMV or PT frameworks. EMV represents a rational gamble, determining whether an agent \"should\" invest in insurance or property-level protection. On the other hand, the PT option incorporates behavioral aspects to examine how agents are likely to respond to investment decisions in flood risk environments. The models are applied to Flood Re, a policy aimed at ensuring affordable insurance access for all individuals in flood risk areas. The results demonstrate that PT models exhibit greater similarity to observed behavior compared to models based on EMV. These findings emphasize the importance of accommodating \"irrational\" behaviors within the design of policy instruments, promoting fairness and efficiency. Overall, this research provides insights into the integration of behavior in policy assessments and highlights the benefits of considering PT frameworks alongside traditional rational models. By understanding and accounting for human behavior in decision-making processes, policymakers can design more effective and equitable policy instruments for managing flood risk.

Activity within the ventral medial prefrontal cortex (VMPFC) during encoding of persuasive messages has been shown to predict message-consistent behaviors both within scanner samples and at the population level. This suggests that neuroimaging can aid in the development of better persuasive messages, but little is known about how the brain responds to different message features. Building on past findings, the current study found that gain-framed persuasive messages elicited more VMPFC activation than loss-framed messages, but only when messages addressed outcomes that would be experienced by participants directly. Participants also perceived gain-framed messages as more effective than loss-framed messages, and self-reported perceptions of message effectiveness were positively correlated with VMPFC activation. These results support theories that VMPFC activity during message encoding indexes perceptions of value and self-relevance and demonstrate that established theories of persuasion can improve the understanding of the neural correlates of persuasion.