Robust identification of investor beliefs.

This paper develops a method informed by data and models to recover information about investor beliefs. Our approach uses information embedded in forward-looking asset prices in conjunction with asset pricing models. We step back from presuming rational expectations and entertain potential belief distortions bounded by a statistical measure of discrepancy. Additionally, our method allows for the direct use of sparse survey evidence to make these bounds more informative. Within our framework, market-implied beliefs may differ from those implied by rational expectations due to behavioral/psychological biases of investors, ambiguity aversion, or omitted permanent components to valuation. Formally, we represent evidence about investor beliefs using a nonlinear expectation function deduced using model-implied moment conditions and bounds on statistical divergence. We illustrate our method with a prototypical example from macrofinance using asset market data to infer belief restrictions for macroeconomic growth rates.

Eliciting Beliefs about COVID-19 Prevalence and Mortality: Epidemiological Models Compared with The Street.

Subjective belief elicitation about uncertain events has a long lineage in the economics and statistics literatures. Recent developments in the experimental elicitation and statistical estimation of subjective belief distributions allow inferences about whether these beliefs are biased relative to expert opinion, and the confidence with which they are held. Beliefs about COVID-19 prevalence and mortality interact with risk management efforts, so it is important to understand relationships between these beliefs and publicly disseminated statistics, particularly those based on evolving epidemiological models. The pandemic provides a unique setting over which to bracket the range of possible COVID-19 prevalence and mortality outcomes given the proliferation of estimates from epidemiological models. We rely on the epidemiological model produced by the Institute for Health Metrics and Evaluation together with the set of epidemiological models summarised by FiveThirtyEight to bound prevalence and mortality outcomes for one-month, and December 1, 2020 time horizons. We develop a new method to partition these bounds into intervals, and ask subjects to place bets on these intervals, thereby revealing their beliefs. The intervals are constructed such that if beliefs are consistent with epidemiological models, subjects are best off betting the same amount on every interval. We use an incentivised experiment to elicit beliefs about COVID-19 prevalence and mortality from 598 students at Georgia State University, using six temporally-spaced waves between May and November 2020. We find that beliefs differ markedly from epidemiological models, which has implications for public health communication about the risks posed by the virus.

The importance of individual beliefs in assessing treatment efficacy.

In recent years, there has been debate about the effectiveness of treatments from different fields, such as neurostimulation, neurofeedback, brain training, and pharmacotherapy. This debate has been fuelled by contradictory and nuanced experimental findings. Notably, the effectiveness of a given treatment is commonly evaluated by comparing the effect of the active treatment versus the placebo on human health and/or behaviour. However, this approach neglects the individual's subjective experience of the type of treatment she or he received in establishing treatment efficacy. Here, we show that individual differences in subjective treatment - the thought of receiving the active or placebo condition during an experiment - can explain variability in outcomes better than the actual treatment. We analysed four independent datasets (N = 387 participants), including clinical patients and healthy adults from different age groups who were exposed to different neurostimulation treatments (transcranial magnetic stimulation: Studies 1 and 2; transcranial direct current stimulation: Studies 3 and 4). Our findings show that the inclusion of subjective treatment can provide a better model fit either alone or in interaction with objective treatment (defined as the condition to which participants are assigned in the experiment). These results demonstrate the significant contribution of subjective experience in explaining the variability of clinical, cognitive, and behavioural outcomes. We advocate for existing and future studies in clinical and non-clinical research to start accounting for participants' subjective beliefs and their interplay with objective treatment when assessing the efficacy of treatments. This approach will be crucial in providing a more accurate estimation of the treatment effect and its source, allowing the development of effective and reproducible interventions.

Neuromodulation is a type of intervention that relies on various non-invasive techniques to temporarily stimulate the brain and nervous system. It can be used for the treatment of depression or other medical conditions, as well as the improvement of cognitive abilities such as attention. However, there is conflicting evidence regarding whether this approach has beneficial effects. Most studies aiming to assess the efficiency of a treatment rely on examining the outcomes of people who received the intervention in comparison to participants who undergo a similar procedure with no therapeutic effect (or placebo). However, the influence of other, 'subjective' factors on these results ­ such as the type of intervention participants think they have received ­ remains poorly investigated. To bridge this gap, Fassi and Hochman et al. used statistical modeling to assess how patients' beliefs about their treatment affected the results of four neuromodulation studies on mind wandering, depression and attention deficit hyperactivity disorder symptoms. In two studies, participants' perceptions of their treatment status were more strongly linked to changes in depression scores and mind-wandering than the actual treatment. Results were more nuanced in the other two studies. In one of them, participants who received the real neuromodulation but believed they received the placebo showed the most improvement in depressive symptoms; in the other study, subjective beliefs and objective treatment both explained changes in inattention symptoms. Taken together, the results by Fassi and Hochman et al. suggest that factoring in patients' subjective beliefs about their treatment may be necessary in studies of neuromodulation and other interventions like virtual reality or neurofeedback, where participants are immersed in cutting-edge research settings and might therefore be more susceptible to develop beliefs about treatment efficacy.

Subjective beliefs and economic preferences during the COVID-19 pandemic.

The COVID-19 pandemic presents a remarkable opportunity to put to work all of the research that has been undertaken in past decades on the elicitation and structural estimation of subjective belief distributions as well as preferences over atemporal risk, patience, and intertemporal risk. As contributors to elements of that research in laboratories and the field, we drew together those methods and applied them to an online, incentivized experiment in the United States. We have two major findings. First, the atemporal risk premium during the COVID-19 pandemic appeared to change significantly compared to before the pandemic, consistent with theoretical results of the effect of increased background risk on foreground risk attitudes. Second, subjective beliefs about the cumulative level of deaths evolved dramatically over the period between May and November 2020, a volatile one in terms of the background evolution of the pandemic. Supplementary Information: The online version contains supplementary material available at 10.1007/s10683-021-09738-3.

Futurization of Aging: Subjective Beliefs and Effects.

Aging in the face of an increasing population and growing life expectancy is considered one of the major demographic challenges in modern society. Previous research has revealed that quality of life in aging could significantly differ depending on the resources one possesses. However, little attention has been given to the mechanisms of formation of these resources and the role of intentionality. In the present study, we identified 22 strategies that favor a better life quality in aging and analyzed them from the perspective of subjective beliefs and reported performance. Our sample was adults (n = 72) aged 57-65, living in St. Petersburg, Russia. The results showed that although participants were aware of the strategies that favor aging, their reported performance ranged on a scale from average to infrequent use of these strategies. We found that subjective beliefs about the role of psychological resources for better aging predicted higher scores on subjective beliefs about the role of lifestyle resources and the reported performance of psychological resources. Our results suggest that there is a gap between subjective beliefs about the controllability of aging processes and the transformation of these beliefs into real performance.

Biased health perceptions and risky health behaviors-Theory and evidence.

This paper investigates the role of biased health perceptions as a potential driving force of risky health behaviors. We define absolute and relative health perception biases, illustrate their measurement in surveys and provide evidence on their relevance. Next, we decompose the theoretical effect into its extensive and intensive margin: When the extensive margin dominates, people (wrongly) believe they are healthy enough to "afford" unhealthy behavior. Finally, using three population surveys, we provide robust empirical evidence that respondents who overestimate their health are less likely to exercise and sleep enough, but more likely to eat unhealthily and drink alcohol daily.

Modeling subjective belief states in computational psychiatry: interoceptive inference as a candidate framework.

The nascent field computational psychiatry has undergone exponential growth since its inception. To date, much of the published work has focused on choice behaviors, which are primarily modeled within a reinforcement learning framework. While this initial normative effort represents a milestone in psychiatry research, the reality is that many psychiatric disorders are defined by disturbances in subjective states (e.g., depression, anxiety) and associated beliefs (e.g., dysmorphophobia, paranoid ideation), which are not considered in normative models. In this paper, we present interoceptive inference as a candidate framework for modeling subjective-and associated belief-states in computational psychiatry. We first introduce the notion and significance of modeling subjective states in computational psychiatry. Next, we present the interoceptive inference framework, and in particular focus on the relationship between interoceptive inference (i.e., belief updating) and emotions. Lastly, we will use drug craving as an example of subjective states to demonstrate the feasibility of using interoceptive inference to model the psychopathology of subjective states.

How Well Can We Assess Our Ability to Understand Others' Feelings? Beliefs About Taking Others' Perspectives and Actual Understanding of Others' Emotions.

People vary in their beliefs about their tendency to engage in perspective taking and to understand other's feelings. Often, however, those beliefs are suggested to be poor indicators of actual skills and thus provide an inaccurate reflection of performance. Few studies, however, have examined whether people's beliefs accurately predict their performance on emotion recognition tasks using dynamic or spontaneous emotional expressions. We report six studies (N ranges from 186 to 315; N total = 1,347) testing whether individuals' report of their engagement in perspective taking, as measured by the Interpersonal Reactivity Index (IRI; Davis, 1983), is associated with accurate emotion recognition. In Studies 1-3, emotion recognition performance was assessed using three standard tests of nonverbal emotion recognition. To provide a more naturalistic test, we then assessed performance with a new emotion recognition test in Studies 4-6, using videos of real targets that share their emotional experiences. Participants' multi-scalar ratings of the targets' emotions were compared with the targets' own emotion ratings. Across all studies, we found a modest, yet significant positive relationship: people who believe that they take the other's perspective also perform better in tests of emotion recognition (r = 0.20, p < 0.001). Beliefs about taking others' perspective thus reflect interpersonal reality, but only partially.

Estimating the subjective risks of driving simulator accidents.

We examine the subjective risks of driving behavior using a controlled virtual reality experiment. Use of a driving simulator allows us to observe choices over risky alternatives that are presented to the individual in a naturalistic manner, with many of the cues one would find in the field. However, the use of a simulator allows us the type of controls one expects from a laboratory environment. The subject was tasked with making a left-hand turn into incoming traffic, and the experimenter controlled the headways of oncoming traffic. Subjects were rewarded for making a successful turn, and lost income if they crashed. The experimental design provided opportunities for subjects to develop subjective beliefs about when it would be safe to turn, and it also elicited their attitudes towards risk. A simple structural model explains behavior, and showed evidence of heterogeneity in both the subjective beliefs that subjects formed and their risk attitudes. We find that subjective beliefs change with experience in the task and the driver's skill. A significant difference was observed in the perceived probability to successfully turn among the inexperienced drivers who did and did not crash even though there was no significant difference in drivers' risk attitudes among the two groups. We use experimental economics to design controlled, incentive compatible tasks that provide an opportunity to evaluate the impact on driver safety of subject's subjective beliefs about when it would be safe to turn as well as their attitudes towards risk. This method could be used to help insurance companies determine risk premia associated with risk attitudes or beliefs of crashing, to better incentivize safe driving.

Are People Overoptimistic about the Effects of Heavy Drinking?

We test whether heavy or binge drinkers are overly optimistic about probabilities of adverse consequences from these activities or are relatively accurate about these probabilities. Using data from a survey in eight cities, we evaluate the relationship between subjective beliefs and drinking. We assess accuracy of beliefs about several outcomes of heavy/binge drinking: reduced longevity, liver disease onset, link between alcohol consumption and Driving While Intoxicated (DWI), probability of an accident after drinking, accuracy of beliefs about encountering intoxicated drivers on the road, and legal consequences of DWI-ranging from being stopped to receiving fines and jail terms. Overall, there is no empirical support for the optimism bias hypothesis. We do find that persons consuming a lot of alcohol tend to be more overconfident about their driving abilities and ability to handle alcohol. However, such overconfidence does not translate into over-optimism about consequences of high levels of alcohol consumption.